Cramér-Rao Lower Bounds for Hybrid Localization of Mobile Terminals
|
|
- Gilbert Townsend
- 5 years ago
- Views:
Transcription
1 Workshop on Positioning, avigation Communication. Mar 28, Hannover, Germany Cramér-Rao Lower Bounds for Hybrid Localization of Mobile Terminals Carsten Fritsche Anja Klein Technische Universität Darmstadt, Communications Engineering Lab, Merckstr. 25, Darmstadt, Germany Abstract While in outdoor scenarios the Global Positioning System (GPS) provides accurate mobile station (MS) location estimates in the majority of cases, in dense urban indoor scenarios GPS often cannot provide reliable MS location estimates, due to the attenuation or complete shadowing of the satellite signals. The existing cellular radio network (CR)-based localization methods, however, provide MS location estimates in almost every scenario, but they do not reach the accuracy of the MS location estimates provided by GPS. Hybrid localization methods combine MS location information available from measured values of the CR with MS location information provided by the measured values of GPS. In this paper, a hybrid localization method is proposed that combines received signal level timing advance measured values from the Global System for Mobile Communication (GSM) time of arrival measured values from GPS. The best achievable localization accuracy of the proposed hybrid localization method is evaluated in terms of the Cramér- Rao Lower Bound. It is shown that the hybrid localization method significantly improves the localization accuracy compared to existing CR-based localization methods. I. ITRODUCTIO The dem for localization methods offering precise mobile station (MS) location estimates has increased in the last few years. On the one h, this is due to the increasing interest in location based services (LBS) such as, e.g., fleet management, location sensitive billing, navigation other promising applications that are based on accurate knowledge of the MS location that will play a key role in future wireless systems []. On the other h, the requirement of the United States Federal Communications Commission (FCC) that all wireless service providers have to report the location of all enhanced 9 (E-9) callers with specified accuracy, has pushed research stardization activities in the field of MS localization [2]. Several localization methods have been proposed to solve the problem of locating a MS [3], [4]. Global navigation satellite system (GSS)-based localization methods, e.g., such as the Global Positioning System (GPS), utilize the time of arrival (ToA) principle, i.e., the MS location is determined from the propagation time of the satellite (SAT) signals. If the MS receives SAT signals from at least four SATs, a three dimensional (3-D) MS location estimate can be found, where the fourth SAT signal is needed to resolve the unknown clock bias between the SAT the MS clock [5]. GSS-based localization methods have the disadvantage that especially in indoor dense urban scenarios the number of SATs in view is often insufficient to determine a 3-D or even two dimensional (2-D) MS location estimate, due to the attenuation or complete shadowing of the SAT signals. Cellular radio network (CR)-based localization methods are based on, e.g., the received signal strength (), round trip delay time (), time difference of arrival (TDoA), angle of arrival (AoA) or ToA principle (an overview is given in [3], [4]). Although CR-based localization methods have the advantage that the MS location estimates are almost everywhere available, they do not reach the accuracy of MS location estimates of the GSS-based localization methods. In scenarios, where MS location estimates from the GSS are not available the MS location estimates from the CR do not reach the desired localization accuracy, it is a promising approach to combine the information provided by the measured values of the CR the GSS [6], [7]. The resulting hybrid localization methods are expected to enhance the accuracy availability of MS location estimates. The best achievable localization accuracy of hybrid localization methods can be evaluated in terms of the Cramér-Rao lower bound (CRLB). In [8], the CRLB for hybrid /ToA /TDoA for wireless sensor networks is presented. The impact of quantizing measured values on the CRLB for wireless sensor networks is presented in [9]. The localization accuracy of hybrid ToA/TDoA for cellular radio networks is investigated in []. In this paper, the CRLB of a hybrid localization method is evaluated that combines measured values from the Global System for Mobile Communication (GSM) ToA measured values from GPS. In existing GSM networks, only quantized (RXLEV) quantized (TA) measured values are available. Thus, the corresponding CRLB that combines quantized measured values is additionally investigated compared to CRLB of unquantized measured values. Simulation results show that the hybrid localization method will significantly improve the localization accuracy compared to existing CR-based localization methods. The remainder of this paper is organized as follows: In Section II, the well known general expressions for statistical models for the measured values, the Fisher information matrix their relationship to the CRLB are introduced. In Section III, the statistical models for the RXLEV TA are introduced the well known statistical model for the ToA measured value is presented. In Section IV, the CRLB for the hybrid localization method is determined. In Section V, the achievable localization accuracy of hybrid localization method is evalu /8/$ IEEE 57
2 Workshop on Positioning, avigation Communication. Mar 28, Hannover, Germany ated by means of simulations. Section VI concludes the work. II. CRAMÉR-RAO LOWER BOUD The Cramér-Rao Lower Bound (CRLB) gives a lower bound for the covariance matrix of any unbiased estimate of the unknown parameters []. Let ˆθ be an estimate of the n MS location vector θ, where n 2 for MS localization in 2-D scenarios n 3 in 3-D scenarios. Let further m [m,m 2,..., m ] T denote the vector of measured values that are available from the different localization methods, where [ ] T denotes the transpose of a vector. The covariance matrix of any unbiased estimator ˆθ satisfies the following inequality Cov(ˆθ) I(θ) () [], where Cov(ˆθ) E θ (ˆθ θ)(ˆθ θ) T }, E θ } denotes the expectation conditioned on θ, I(θ) denotes the Fisher information matrix (FIM), [ ] denotes the inverse of a matrix. The matrix inequality A B should be interpreted as the matrix (A B) is positive semidefinite. The CRLB matrix is defined as the inverse of the FIM I(θ). The(i, j)-th element of the FIM I(θ) is given by 2 } log p(m θ) [I(θ)] i,j E θ i, j, 2,..., n (2) θ i θ j [], where p(m θ) denotes the probability density function (pdf) of the measured values m conditioned on θ. In the following, the measured values m k m l, k,...,, l,...,, k l are assumed to be statistically independent of each other. This assumption is considered to be a good approximation if the measured values result from different localization methods, each suffering from different errors. The conditional pdf p(m θ) can thus be determined from p(m θ) p(m k θ), (3) where p(m k θ) denotes the likelihood function of each measured value m k. In order to evaluate the CRLB, p(m k θ) has to be known. The likelihood function p(m k θ) can be determined from a statistical model of the measured value that is introduced in the following. Let us assume that each measured value m k provides information about the MS location θ. Each measured value is additionally affected by errors that are assumed to be additive that can be statistically described by a rom variable n k with known probability density function p nk (n k ). The statistical model for the measured value is, thus, given by m k f k (θ)n k (4) [7], where f k (θ) models the functional relationship between the MS s location θ the error-free measured value m k, i.e., m k f k (θ). The likelihood function p(m k θ) of the measured value m k can be determined from (4), yielding p(m k θ) p ni (m k f k (θ)). (5) III. EXAMPLES FOR STATISTICAL MODELS OF MEASURED VALUES A. Introduction In the following, the statistical models of the measured values that can be obtained from the CR, namely, from the GSS, namely ToA, are presented. Due to the fact that in GSM the measured values are quantized to finite precision, the corresponding relationships between quantized unquantized measured values are additionally determined. The SAT location is given by x SAT [x SAT,y SAT,z SAT ] T. The base station (BS) location x BS [x BS,y BS ] T, as well as the MS s location θ [x, y] T to be estimated, are assumed to lie in the xy-plane. For the case of 3-D BS MS locations thus an estimation of the 3-D MS location vector, the statistical models can be obtained in a similar way. B. Round Trip Time In CRs, the is the time the radio signal requires to travel from the BS to the MS back. It is a parameter that is used to synchronize the transmitted bursts of the MSs to the frame of the receiving BS [2]. Let d BS (θ) denote the BS to MS distance given by d BS (θ) (x BS x) 2 (y BS y) 2, (6) c 3 8 m/s the speed of light. Then, the model for the functional relationship between the error-free measured value m the MS location θ is given by m f (θ) 2 d BS(θ) c. (7) The measured values are affected by errors resulting form the propagation conditions - line-of-sight (LOS) or non-line-of-sight (LOS) situation - inaccuracies of the measurement equipment. It is assumed that the errors can be modelled as a Gaussian rom variable n with mean μ accounting for the error due to LOS situations stard deviation σ [3]. Thus, the statistical model of the measured value m is given by m f (θ)n. (8) In GSM, the measured values m are rounded to the nearest integer bit period T b 48/3 μs, also known as Timing Advance (TA) measured values m TA [2]. The statistical model for the TA measured value m TA is given by m T m TA b m 2, T b < 62 m 63, (9) T b 62, where denotes the nearest integer smaller than or equal to the argument. 58
3 Workshop on Positioning, avigation Communication. Mar 28, Hannover, Germany C. Received Signal Strength In CRs, the value is an averaged value of the strength of a radio signal received by the MS from the BS. It is well known that the attenuation of the signal strength through a mobile radio channel is caused by three factors, namely path loss, fast fading slow fading [3]. Let A denote the reference path loss at a BS to MS distance of km B the path loss exponent. Then, the path loss PL in db is given by PL A B log ( dbs (θ) km ) () [3], where both parameters A B strongly depend on the propagation conditions the antenna settings of the transmitter. In a real system, the BS may be equipped with directional antennas in order to enhance the cell s capacity. For a fixed MS to BS distance this means that the MS located in the direction of maximum antenna gain receives a larger value than a MS having the same propagation conditions but which is located in the direction of minimum antenna gain. In the following, it is assumed that normalized antenna gain models are a-priori available. As long as the 2-D MS location is determined, it is a reasonable assumption to assume 2-D normalized antenna gain models. Let ϕ BS (θ) denote the MS to BS angle given by ( ) ybs y ϕ BS (θ) arctan. () x BS x Let further A m denote the maximum attenuation, ϕ the BS antenna s boresight direction ϕ 3dB the half-power beamwidth of the BS antenna in degrees. Then, the model for the normalized antenna gain in db scale is given by ( ) 2 ϕbs(θ) ϕ 2, ϕ BS(θ) ϕ g(ϕ BS (θ)) ϕ 3dB ϕ 3dB A m 2 A m, else (2) [4]. The model for the functional relationship between the error-free measured value m the MS location θ is given by m f (θ) P t PL g(ϕ BS (θ))}, (3) where P t denotes the BS s equivalent isotropic radiated power. The measured value m is further affected by errors due to fast fading slow fading. As the measured value is averaged over several time-consecutive measurements, the error due to fast fading can be removed, thus, has not to be taken into account in the statistical model for the errors. It is well known that the error in db due to slow fading can be modelled as an additive zero-mean Gaussian rom variable n with stard deviation σ [3]. Thus, the statistical model of the mean measured value m in db scale is given by m f (θ)n. (4) In GSM, the measured values m, measured in dbm, are quantized to finite precision, also known as received signal level (RXLEV) measured values m RXLEV [2]. The statistical model for the RXLEV measured value m RXLEV is given by m, dbm m RXLEV m dbm, < m dbm < 62 m 63, dbm 62, (5) where denotes the nearest integer larger than or equal to the argument. D. Time of Arrival The GSS-based localization methods utilize the concept of ToA, i.e., the MS is measuring the time the SAT signal requires to travel from the SAT to the MS [5]. In the following, the statistical model of the ToA measured value m ToA is determined. Let d SAT (θ) denote the SAT to MS distance given by d SAT (θ) (x SAT x) 2 (y SAT y) 2 zsat 2. (6) Then, the model for the functional relationship f ToA (θ) between the error-free measured value m ToA the MS s location θ is given by m ToA f ToA (θ) d SAT(θ). (7) c The measured value m ToA is affected by errors resulting from delays as the signal propagates through the atmosphere, propagation conditions - LOS or LOS situation - as well as resolution errors receiver noise. These errors, given by n ToA, are assumed to be Gaussian distributed with mean μ ToA accounting for the error due to LOS situations stard deviation σ ToA [3]. In general, the MS s clock is not synchronized to the clocks of the GPS SATs, resulting in an unknown receiver clock error [5]. The unknown receiver clock error can be compensated, e.g., by using timing information provided by the radio ressource protocol [5]. In the following, it is assumed that the MS s clock is synchronized to the clocks of the GPS SATs. Consequently, the statistical model of the measured value m ToA is given by m ToA f ToA (θ)n ToA. (8) IV. EVALUATIO OF THE CRAMÉR-RAO LOWER BOUD FOR HYBRID LOCALIZATIO A. Introduction In the following, the CRLB for the hybrid localization method is evaluated. Since the best achievable localization accuracy of the estimated MS location ˆθ [ˆx, ŷ] T is of main interest, it is reasonable to introduce the minimum mean square error (MMSE) of the MS location estimate ˆθ [3]. Let min( ) denote the minimum of the argument, v 2 the L 2 norm of the vector v tra} the trace of a matrix A. The MMSE of the MS location estimate is then given by MMSE min(e θ ˆθ θ 2 }) min(trcov(ˆθ)}) tri(θ) } [I(θ)], [I(θ)] 2,2, (9) Det I(θ)} 59
4 Workshop on Positioning, avigation Communication. Mar 28, Hannover, Germany where Det I(θ)} denotes the determinant of the symmetric 2 2 FIM I(θ). Further, let I (θ) I Q (θ) denote the FIMs of the unquantized quantized measured value, I (θ) I Q (θ) the FIMs of the unquantized quantized measured value I ToA (θ) the FIM of the unquantized ToA measured value. As long as the measured values are assumed to be statistically independent, the corresponding FIMs can be added up [3]. Thus, the FIM of the hybrid localization method for unquantized, ToA measured values can be determined from I(θ) I (θ)i (θ)i ToA (θ), (2) for quantized unquantized ToA measured values from I(θ) I Q (θ)i Q (θ)i ToA (θ). (2) In the following, the FIM of unquantized, ToA measured values quantized measured values are derived, from which the corresponding CRLBs for the hybrid localization method the MMSE of the MS location estimate can be determined. B. Cramér-Rao Lower Bound for unquantized measured values As long as the measured values are unquantized corrupted by additive errors that are zero-mean Gaussian distributed, the (i, j)-th element of I(θ) can be found from the well known expression [I(θ)] i,j σ 2 k f k(θ) θ i f k(θ) θ j (22) []. The FIM of measured values can be found from inserting (7) into (22). Let denote the number of available measured values u BS,k the unit vector originating at the MS location directed towards the k-th BS given by u BS,k u BSx,k u x u BSy,k u y (23) [], where u x, u y are the unit vectors in the x y directions. Let further a k be defined as ( ) 2 2 a k. (24) c σ,k Then, the elements of the FIM of measured values are given by: [I (θ)], [I (θ)],2 [I (θ)] 2,2 a k u 2 BS x,k, (25) a k u BSx,k u BSy,k, (26) a k u 2 BS y,k. (27) The FIM of measured values can be found from inserting (3) into (22). Let denote the number of available measured values. Let further b k w k (θ) be defined as b k B k σ,k log e () (28) w k (θ) g k(ϕ BS,k (θ)). (29) σ,k ϕ BS,k (θ) Then, the elements of the FIM of measured values are given by [ ] 2 bk u BSx,k w k (θ) u BSy,k [I (θ)],,(3) [( ) bk u BSx,k w k (θ) u BSy,k [I (θ)],2 ( )] bk u BSy,k w k (θ) u BSx,k, (3) [ ] 2 bk u BSy,k w k (θ) u BSx,k [I (θ)] 2,2.(32) The FIM of ToA measured values can be found from inserting (7) into (22). Let ToA denote the number of available ToA measured values u SAT,k the unit vector originating at the MS location directed towards the k-th SAT, given by u SAT,k u SATx,k u x u SATy,k u y u SATz,k u z, (33) where u z is the unit vector in the z direction. The projection of the unit vector u SAT,k into the xy-plane is given by p SAT,k u SATx,k u x u SATy,k u y. (34) Let further c k be defined as ( ) 2 c k. (35) c σ ToA,k The elements of the FIM of ToA measured values are given by [I ToA (θ)], [I ToA (θ)],2 [I ToA (θ)] 2,2 ToA ToA c k u 2 SAT x,k, (36) c k u SATx,k u SATy,k, (37) ToA c k u 2 SAT y,k. (38) [8]. The CRLB for the hybrid localization method of unquantized measured values can then be found from (2) (). In the following, a closed form expression of the MMSE of the MS location estimate is determined. For the sake of simplicity, it is assumed that the BS antennas have a uniform normalized antenna gain g k (ϕ BS,k (θ)). From this it follows 6
5 Workshop on Positioning, avigation Communication. Mar 28, Hannover, Germany that w k (θ).leta k,i denote the area of the parallelogram determined by u BS,k u BS,i [], given by A k,i u BS,k u BS,i 2, (39) B k,i denote the area of the parallelogram determined by u BS,k p SAT,i, given by B k,i u BS,k p SAT,i 2 (4) C k,i denote the area of the parallelogram determined by p SAT,k p SAT,i, given by C k,i p SAT,k p SAT,i 2, (4) where a b denotes the cross product between two vectors a b. Then, the MMSE of the MS location estimate for unquantized measured values is given by MMSE a k b 2 k d 2 BS,k where the determinant is given by Det I(θ)} i i>k i ToA i ToA i ToA ToA (θ) Det I (θ)} a k a i A k,i a k b 2 i d 2 BS,i (θ) A k,i a k c i B k,i i i>k ToA i i>k b 2 k c i d 2 BS,k (θ) B k,i c k p SAT,k 2, (42) b 2 k b2 i d 2 BS,k (θ) d2 BS,i (θ) A k,i c k c i C k,i. (43) C. Cramér-Rao Bound for quantized measured values In GSM, the measured values are quantized to K 64 levels, i,..., 63, i Z. Each level i is composed of a lower bound Q,lb (i), denoting the minimum measured in level i an upper bound Q,ub (i), representing the maximum measured in level i. According to (9), the lower upper bounds of the corresponding TA measured values m TA i are given by, i Q,lb (i) (44) (2i ) Tb 2, i,..., 63 Q,ub (i) (2i ) T b 2, i,..., 62, i 63. (45) The FIM of quantized measured values can be found, following the derivation given in [9]. Let Q denote the number of available TA measured values Φ( ) the stard normal cumulative distribution function of the argument. The conditional pdf p k ( θ) is given by p k ( θ) σ,k 2π exp [f,k(θ)] 2 2 σ 2,k }, (46) h k,i is defined as h k,i [ 63 exp 2 (r ) 2} exp 2 (r 2) 2}] 2, (47) 2π Φ(r 2 ) Φ(r ) i with the following functions r Q,lb(i) f,k (θ) σ,k (48) r 2 Q,ub(i) f,k (θ) σ,k. (49) Then, the elements of the FIM of quantized measured values are given by [I Q (θ)], [I Q (θ)],2 [I Q (θ)] 2,2 Q [( f,k (θ) p k ( θ) a k Q Q ) ] d 2 BS,k (θ) u 2 BS x,k d 2 BS,k (θ) a k u 2 BS x,k h k,i, (5) f,k (θ) p k ( θ) ) d 2 BS,k (θ) Q Q [( [( u BSx,ku BSy,k a k ] d 2 BS,k (θ) a k u BSx,k u BSy,k h k,i, (5) f,k (θ) p k ( θ) a k Q ) ] d 2 BS,k (θ) u 2 BS y,k d 2 BS,k (θ) a k u 2 BS y,k h k,i, (52) In GSM, the measured values are quantized to K 64 levels, i,..., 63, i Z. Again, each level i is composed of a lower bound Q,lb (i), denoting the minimum measuredinleveli an upper bound Q,ub (i), representing the maximum measured in level i. According to (5), 6
6 Workshop on Positioning, avigation Communication. Mar 28, Hannover, Germany the lower upper bounds of the corresponding RXLEV measured values m RXLEV i are given by, i Q,lb (i) (53) (i ), i,..., 63 Q,ub (i) i, i,..., 62, i 63. (54) The elements of the FIM of quantized measured values are given by Q [ ] bk u BSx,k w k (θ) u BSy,k [I Q (θ)], 2 h k,i, (55) Q [( ) bk u BSx,k w k (θ) u BSy,k [I Q (θ)],2 ( )] bk u BSy,k w k (θ) u BSx,k h k,i, (56) Q [ ] bk u BSy,k w k (θ) u BSx,k [I Q (θ)] 2,2 2 h k,i, (57) where Q is the number of available RXLEV measured values h k,i is defined in (47) with the exception that r r 2 have to be replaced by r Q,lb(i) f,k (θ) σ,k (58) r 2 Q,ub(i) f,k (θ) σ,k. (59) The CRLB for the hybrid localization method of quantized measured values unquantized ToA measured values can be then found from (2) (). The MMSE of the MS location estimate can be found from (9) (2). For the sake of clarity, the closed form expression of the MMSE of the MS location estimate is omitted. V. SIMULATIO SCEARIO AD RESULTS In the following, the simulation scenario is explained simulation results are presented. It is assumed that the BSs are organized in an ideal 2-D CR with hexagonal cell structure cell radius R cell. The BS locations are assumed to be fixed known, each BS is equipped with an omnidirectional BS antenna. In order to evaluate the CRLB of the hybrid localization method, only the BS nearest BSs are considered for MS localization as shown in Fig., where the nearest BS is assumed to be the serving BS. The GPS SAT locations are assumed to be known since they can be calculated from the navigation message that is transmitted by each SAT. The simulation parameters are given in Table I are assumed to be equal for all BSs all SATs for the sake of y 2R cell 2R cell 2R cell x 2R cell Fig.. CR with the MS ( ) BS 3BSs ( ) that are involved in the MS localization process. The BSs that are not involved in the MS localization process are marked as squares ( ). simplification. The following combinations of measured values are investigated: One TA RXLEV measured value from the serving BS one RXLEV measured value from BS neighbouring BSs (GSM method) Measured values of GSM method, in addition, one ToA measured value from one GPS SAT (Hybrid method) Measured values of GSM method, in addition, one ToA measured value from each of a total of two GPS SATs (Hybrid 2 method) Two ToA measured values provided by two different GPS SATs (2 Satellite method) The hybrid localization method is evaluated in terms of the average root mean square error (RMSE), which gives the localization accuracy for all possible MS locations in the CR is given by } RMSE E θ tr I(θ) }. (6) In Fig. 2, the simulation results for the RMSE in dependence of the cell radius for the GSM, Hybrid Hybrid 2 method for (dense) urban scenarios are shown. For comparison, the results for the RMSE assuming unquantized are given. The GSM method provides the worst results in terms TABLE I SIMULATIO PARAMETERS Parameter Value Parameter Value A in db 32.8 P t in dbm 33 B in db 3.8 σ in μs σ in db 8 σ ToA in ns of localization accuracy, which can be explained by the fact that the investigated GSM measured values cannot provide the same level of accuracy as the GPS measured values. The 62
7 Workshop on Positioning, avigation Communication. Mar 28, Hannover, Germany 5 4 quantized unquantized GSM 5 4 BS 3 BS 4 BS 5 GSM RMSE in m 3 2 Hybrid RMSE in m 3 2 Hybrid 2 Hybrid 2 Hybrid R cell in m Fig. 2. RMSE vs. R cell for the GSM, Hybrid Hybrid 2 method for quantized unquantized measured values for BS R cell in m Fig. 3. RMSE vs. R cell for the GSM, Hybrid Hybrid 2 method for quantized measured values for different BS 3, 4, 5}. localization accuracy can be significantly improved with the Hybrid method, by additionally taking into account one ToA measured value from one GPS SAT. In contrast to the Hybrid method, the Hybrid 2 method takes into account two ToA measured values from two different GPS SATs, leading to a further improvement of the localization accuracy. The impact of the quantization on the achievable localization accuracy can also be seen from Fig. 2. For the GSM Hybrid method the RMSE degrades only for large cell radii, whereas for the Hybrid 2 method, the quantization does not significantly affect the RMSE for almost the entire range of investigated cell radii. In Fig. 3, the RMSE in dependence of the cell radius for the GSM, Hybrid Hybrid 2 method for different values of BS is shown. If BS, thus, the number of available RXLEV measured values is increased, the RMSE can be improved. For the GSM method the improvement is significant, for the Hybrid method moderate for the Hybrid 2 method marginal. The marginal accuracy improvement of the Hybrid 2 method can be explained by the fact that the two ToA measured values from the GPS SATs have a higher level of accuracy than the RXLEV measured values from the CR, so that the improvement of the localization accuracy due to additionally taking into account RXLEV measured values from the CR is marginal. Moreover, the achievable localization accuracy of the Hybrid 2 method strongly depends on the geometric constellation of the SATs BSs relative to the MS. For the 2 Satellite method the geometric constellation of the SATs relative to the MS can be expressed by the geometric dilution of precision (GDOP) value for 2-D scenarios [6]. If the GPS satellites are close together in the sky, the geometry between the SATs the MS is said to be weak the corresponding GDOP value is large. If the GPS SATs are far away from each other, the geometry between the SATs the MS is said to be strong the corresponding GDOP value is small. In Fig. 4, the localization accuracy for the Hybrid 2 method the 2 Satellite method for different average GDOP values BS 3is presented. It can be clearly seen that the larger the GDOP value, the better is the performance of the Hybrid 2 method compared to the 2 Satellite method. Thus, in scenarios where the GDOP value tends to be large as, e.g., in urban street canyons, the localization accuracy of the Hybrid 2 method is expected to be significantly better than the localization accuracy of the 2 Satellite method. RMSE in m Hybrid 2, GDOP5 2 Satellite, GDOP5 Hybrid 2, GDOP 2 Satellite, GDOP Hybrid 2, GDOP2 2 Satellite, GDOP2 Hybrid 2, GDOP5 2 Satellite, GDOP R cell in m Fig. 4. RMSE vs. R cell for the Hybrid 2 2 Satellite method for different average GDOP values for BS 3. VI. COCLUSIO In this paper, the CRLB for a hybrid localization method is determined that combines RXLEV TA measured values from GSM ToA measured values from GPS. It has been shown by simulations that compared to combining only RXLEV TA measured values from GSM the localization accuracy can be significantly improved by additionally taking 63
8 Workshop on Positioning, avigation Communication. Mar 28, Hannover, Germany into account one or two ToA measured values from GPS. In scenarios with low GDOP values it is sufficient to combine two ToA measured values from GPS, whereas in scenarios with high GDOP values the corresponding hybrid localization method should be used. REFERECES [] A. Küpper, Location-based services, st ed. John Wiley & Sons, 25. [2] FCC : Third report order, Federal Communications Commission, Tech. Rep., Oct [3] F. Gustafsson F. Gunnarsson, Mobile positioning using wireless networks, IEEE Signal Processing Mag., vol. 22, no. 4, pp. 4 53, Jul. 25. [4] G. Sun, J. Chen, W. Guo, K. J. R. Liu, Signal processing techniques in network-aided positioning: a survey of state-of-the-art positioning designs, IEEE Signal Processing Mag., vol. 22, no. 4, pp. 2 23, Jul. 25. [5] E. D. Kaplan, Ed., Understing GPS: principles applications, st ed. Artech-House, 996. [6] S. Soliman, P. Agashe, I. Fernez, A. Vayanos, P. Gaal, M. Oljaca, gpsone tm : a hybrid position location system, in Proc. International Symposium on Spread Spectrum Techniques Applications, vol., Sept. 2, pp [7] C. Fritsche, A. Klein, H. Schmitz, Mobile station localization by combining measurements from different sources including reliability information, in Proc. ITG Fachtagung Innovations for Europe - Mobility, Aachen, Germany, Oct. 26, pp [8] A. Catovic Z. Sahinoglu, The Cramer-Rao bounds of hybrid TOA/ TDOA/ location estimation schemes, IEEE Commun. Lett., vol. 8, no., pp , Oct. 24. [9]. Patwari A. O. Hero, Using proximity quantized for sensor localization in wireless networks, in Proceedings 2nd International ACM Workshop on Wireless Sensor etworks Applications (WSA 3), Sept. 23, pp [] M. A. Spirito, On the accuracy of cellular mobile station location estimation, IEEE Trans. Veh. Technol., vol. 5, no. 3, pp , May 2. [] S. M. Kay, Fundamentals of statistical signal processing: estimation theory, st ed. Prentice-Hall, 993. [2] 3GPP TS 5.8, Technical specification group GSM/EDGE radio access network; Radio subsystem link control, Version 8.23., ov. 25. [3] H. K. Y. Qi H. Suda, Analysis of wireless geolocation in a nonline-of-sight environment, IEEE Trans. Wireless Commun., vol. 5, no. 3, pp , Mar. 26. [4] 3GPP TR , Technical specification group radio access network; Spatial channel model for MIMO simulations, Version 6.., Sept. 27. [5] 3GPP TS 4.3, Location services LCS; Mobile Station (MS) - Serving Mobile Location Centre (SMLC) - Radio Resource LCS Protocol (RRLP), Version 8.8., Jun. 27. [6]. Levanon, Lowest GDOP in 2-D scenarios, in IEE Proc. Radar, Sonar avigation., vol. 47, no. 3, Jun. 2, pp
SIGNAL STRENGTH LOCALIZATION BOUNDS IN AD HOC & SENSOR NETWORKS WHEN TRANSMIT POWERS ARE RANDOM. Neal Patwari and Alfred O.
SIGNAL STRENGTH LOCALIZATION BOUNDS IN AD HOC & SENSOR NETWORKS WHEN TRANSMIT POWERS ARE RANDOM Neal Patwari and Alfred O. Hero III Department of Electrical Engineering & Computer Science University of
More informationSIGNAL STRENGTH LOCALIZATION BOUNDS IN AD HOC & SENSOR NETWORKS WHEN TRANSMIT POWERS ARE RANDOM. Neal Patwari and Alfred O.
SIGNAL STRENGTH LOCALIZATION BOUNDS IN AD HOC & SENSOR NETWORKS WHEN TRANSMIT POWERS ARE RANDOM Neal Patwari and Alfred O. Hero III Department of Electrical Engineering & Computer Science University of
More informationGPS Localization Accuracy Improvement by Fusing Terrestrial TOA Measurements
GPS Localization Accuracy Improvement by Fusing Terrestrial TOA Measurements Robin Wentao Ouyang, Albert Kai-Sun Wong and Kam Tim Woo Department of Electronic and Computer Engineering The Hong Kong University
More informationSelective Hybrid RSS/AOA Approximate Maximum Likelihood Mobile intra cell Localization
Selective Hybrid RSS/AOA Approximate Maximum Likelihood Mobile intra cell Localization Leila Gazzah Leila Najjar and Hichem Besbes Carthage University Lab. COSIM Higher School of Communication of Tunis
More informationSIGNALS OF OPPORTUNITY IN MOBILE RADIO POSITIONING. Armin Dammann, Stephan Sand and Ronald Raulefs
20th European Signal Processing Conference (EUSIPCO 2012) Bucharest, Romania, August 27-31, 2012 SIGNALS OF OPPORTUNITY IN MOBILE RADIO POSITIONING Armin Dammann, Stephan Sand and Ronald Raulefs Institute
More informationOn Data Fusion for Wireless Localization
1 On Data Fusion for Wireless Localization Robin Wentao Ouyang, Albert Kai-Sun Wong, Mung Chiang Abstract This paper presents a data fusion framework for wireless localization via the weighted least square
More informationOPTIMAL SENSOR-TARGET GEOMETRIES FOR DOPPLER-SHIFT TARGET LOCALIZATION. Ngoc Hung Nguyen and Kutluyıl Doğançay
OPTIMAL SENSOR-TARGET GEOMETRIES FOR DOPPLER-SHIFT TARGET LOCALIZATION Ngoc Hung Nguyen and Kutluyıl Doğançay Institute for Telecommunications Research, School of Engineering University of South Australia,
More informationGEOLOCATION BOUNDS FOR RECEIVED SIGNAL STRENGTH (RSS) IN CORRELATED SHADOW FADING
STRATEGIC WHITE PAPER GEOLOCATION BOUNDS FOR RECEIVED SIGNAL STRENGTH (RSS) IN CORRELATED SHADOW FADING Laurence Mailaender, LGS Innovations LLC, Florham Park, NJ Abstract Cramer-Rao bounds for geolocation
More informationSELECTIVE ANGLE MEASUREMENTS FOR A 3D-AOA INSTRUMENTAL VARIABLE TMA ALGORITHM
SELECTIVE ANGLE MEASUREMENTS FOR A 3D-AOA INSTRUMENTAL VARIABLE TMA ALGORITHM Kutluyıl Doğançay Reza Arablouei School of Engineering, University of South Australia, Mawson Lakes, SA 595, Australia ABSTRACT
More informationAN EXACT SOLUTION FOR OUTAGE PROBABILITY IN CELLULAR NETWORKS
1 AN EXACT SOLUTION FOR OUTAGE PROBABILITY IN CELLULAR NETWORKS Shensheng Tang, Brian L. Mark, and Alexe E. Leu Dept. of Electrical and Computer Engineering George Mason University Abstract We apply a
More informationMultiple Antennas. Mats Bengtsson, Björn Ottersten. Channel characterization and modeling 1 September 8, Signal KTH Research Focus
Multiple Antennas Channel Characterization and Modeling Mats Bengtsson, Björn Ottersten Channel characterization and modeling 1 September 8, 2005 Signal Processing @ KTH Research Focus Channel modeling
More informationAerial Anchors Positioning for Reliable RSS-Based Outdoor Localization in Urban Environments
1 Aerial Anchors Positioning for Reliable RSS-Based Outdoor Localization in Urban Environments Hazem Sallouha, Mohammad Mahdi Azari, Alessandro Chiumento, Sofie Pollin arxiv:1711.06014v2 [cs.ni] 5 Dec
More informationImproved Hybrid AML Algorithm without identification of LOS/NLOS nodes
Improved Hybrid AML Algorithm without identification of LOS/NLOS nodes Leïla Gazzah, Leïla Najjar and Hichem Besbes Lab. COSIM, Higher School of Communication of Tunis, Carthage University Tunisia Email:
More informationA Linear Estimator for Joint Synchronization and Localization in Wireless Sensor Networks
2014 IEEE Global Communications Conference A Linear Estimator for Joint Synchronization and Localization in Wireless Sensor Networks Reza Monir Vaghefi and R. Michael Buehrer Wireless @ Virginia Tech,
More informationDISTRIBUTED, CONFIDENCE-BASED LOCALIZATION FOR MOBILE SDRS
DISTRIBUTED, CONFIDENCE-BASED LOCALIZATION FOR MOBILE SDRS David Brady (ECE Dept, brady@ece.neu.edu) Mieczyslaw Kokar (ECE Dept, mkokar@coe.neu.edu) Kenneth Baclawski (College of CS, kenb@ccs.neu.edu)
More informationA Single-bounce Channel Model for Dense Urban Street Microcell
URSI-F JAPAN MEETING NO. 52 1 A Single-bounce Channel Model for Dense Urban Street Microcell Mir Ghoraishi Jun-ichi Takada Tetsuro Imai Department of International Development Engineering R&D Center Tokyo
More informationMaximum Likelihood Diffusive Source Localization Based on Binary Observations
Maximum Lielihood Diffusive Source Localization Based on Binary Observations Yoav Levinboo and an F. Wong Wireless Information Networing Group, University of Florida Gainesville, Florida 32611-6130, USA
More informationAspect Aware UAV Detection and Tracking
Aspect Aware UAV Detection and Tracking Christian R. Berger, Shengli Zhou, and Peter Willett Department of Electrical and Computer Engineering University of Connecticut Storrs, Connecticut 6269, USA email:
More informationEFFECT OF STEERING ERROR VECTOR AND AN- GULAR POWER DISTRIBUTIONS ON BEAMFORMING AND TRANSMIT DIVERSITY SYSTEMS IN CORRE- LATED FADING CHANNEL
Progress In Electromagnetics Research, Vol. 105, 383 40, 010 EFFECT OF STEERING ERROR VECTOR AND AN- GULAR POWER DISTRIBUTIONS ON BEAMFORMING AND TRANSMIT DIVERSITY SYSTEMS IN CORRE- LATED FADING CHANNEL
More informationFOR beamforming and emitter localization applications in
IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 17, NO. 11, NOVEMBER 1999 1829 Angle and Time of Arrival Statistics for Circular and Elliptical Scattering Models Richard B. Ertel and Jeffrey H.
More informationEstimation Theory Fredrik Rusek. Chapters
Estimation Theory Fredrik Rusek Chapters 3.5-3.10 Recap We deal with unbiased estimators of deterministic parameters Performance of an estimator is measured by the variance of the estimate (due to the
More informationLocal Strong Convexity of Maximum-Likelihood TDOA-Based Source Localization and Its Algorithmic Implications
Local Strong Convexity of Maximum-Likelihood TDOA-Based Source Localization and Its Algorithmic Implications Huikang Liu, Yuen-Man Pun, and Anthony Man-Cho So Dept of Syst Eng & Eng Manag, The Chinese
More informationOPTIMAL ADAPTIVE TRANSMIT BEAMFORMING FOR COGNITIVE MIMO SONAR IN A SHALLOW WATER WAVEGUIDE
OPTIMAL ADAPTIVE TRANSMIT BEAMFORMING FOR COGNITIVE MIMO SONAR IN A SHALLOW WATER WAVEGUIDE Nathan Sharaga School of EE Tel-Aviv University Tel-Aviv, Israel natyshr@gmail.com Joseph Tabrikian Dept. of
More informationComparison of DPSK and MSK bit error rates for K and Rayleigh-lognormal fading distributions
Comparison of DPSK and MSK bit error rates for K and Rayleigh-lognormal fading distributions Ali Abdi and Mostafa Kaveh ABSTRACT The composite Rayleigh-lognormal distribution is mathematically intractable
More informationA VECTOR CHANNEL MODEL WITH STOCHASTIC FADING SIMULATION
A VECTOR CHANNEL MODEL WITH STOCHASTIC FADING SIMULATION Jens Jelitto, Matthias Stege, Michael Löhning, Marcus Bronzel, Gerhard Fettweis Mobile Communications Systems Chair, Dresden University of Technology
More informationC/N 0 Estimation: Design Criteria and Reliability Analysis under GNSS Weak Signal Scenarios
C/N 0 Estimation: Design Criteria and Reliability Analysis under GNSS Weak Signal Scenarios Shashank Satyanarayana, Daniele Borio and Gérard Lachapelle ssatyana@ucalgary.ca, daniele.borio@ieee.org, Lachapel@ucalgary.ca
More informationJoint FEC Encoder and Linear Precoder Design for MIMO Systems with Antenna Correlation
Joint FEC Encoder and Linear Precoder Design for MIMO Systems with Antenna Correlation Chongbin Xu, Peng Wang, Zhonghao Zhang, and Li Ping City University of Hong Kong 1 Outline Background Mutual Information
More informationUWB Geolocation Techniques for IEEE a Personal Area Networks
MITSUBISHI ELECTRIC RESEARCH LABORATORIES http://www.merl.com UWB Geolocation Techniques for IEEE 802.15.4a Personal Area Networks Sinan Gezici Zafer Sahinoglu TR-2004-110 August 2004 Abstract A UWB positioning
More informationPERFORMANCE COMPARISON OF DATA-SHARING AND COMPRESSION STRATEGIES FOR CLOUD RADIO ACCESS NETWORKS. Pratik Patil, Binbin Dai, and Wei Yu
PERFORMANCE COMPARISON OF DATA-SHARING AND COMPRESSION STRATEGIES FOR CLOUD RADIO ACCESS NETWORKS Pratik Patil, Binbin Dai, and Wei Yu Department of Electrical and Computer Engineering University of Toronto,
More informationNOMA: An Information Theoretic Perspective
NOMA: An Information Theoretic Perspective Peng Xu, Zhiguo Ding, Member, IEEE, Xuchu Dai and H. Vincent Poor, Fellow, IEEE arxiv:54.775v2 cs.it] 2 May 25 Abstract In this letter, the performance of non-orthogonal
More informationAverage Throughput Analysis of Downlink Cellular Networks with Multi-Antenna Base Stations
Average Throughput Analysis of Downlink Cellular Networks with Multi-Antenna Base Stations Rui Wang, Jun Zhang, S.H. Song and K. B. Letaief, Fellow, IEEE Dept. of ECE, The Hong Kong University of Science
More informationCOMPLEX CONSTRAINED CRB AND ITS APPLICATION TO SEMI-BLIND MIMO AND OFDM CHANNEL ESTIMATION. Aditya K. Jagannatham and Bhaskar D.
COMPLEX CONSTRAINED CRB AND ITS APPLICATION TO SEMI-BLIND MIMO AND OFDM CHANNEL ESTIMATION Aditya K Jagannatham and Bhaskar D Rao University of California, SanDiego 9500 Gilman Drive, La Jolla, CA 92093-0407
More informationPROPAGATION PARAMETER ESTIMATION IN MIMO SYSTEMS USING MIXTURE OF ANGULAR DISTRIBUTIONS MODEL
PROPAGATION PARAMETER ESTIMATION IN MIMO SYSTEMS USING MIXTURE OF ANGULAR DISTRIBUTIONS MODEL Cássio B. Ribeiro, Esa Ollila and Visa Koivunen Signal Processing Laboratory, SMARAD CoE Helsinki University
More informationThese outputs can be written in a more convenient form: with y(i) = Hc m (i) n(i) y(i) = (y(i); ; y K (i)) T ; c m (i) = (c m (i); ; c m K(i)) T and n
Binary Codes for synchronous DS-CDMA Stefan Bruck, Ulrich Sorger Institute for Network- and Signal Theory Darmstadt University of Technology Merckstr. 25, 6428 Darmstadt, Germany Tel.: 49 65 629, Fax:
More informationLocation Determination Technologies for Sensor Networks
Location Determination Technologies for Sensor Networks Moustafa Youssef University of Maryland at College Park UMBC Talk March, 2007 Motivation Location is important: Determining the location of an event
More informationA Systematic Description of Source Significance Information
A Systematic Description of Source Significance Information Norbert Goertz Institute for Digital Communications School of Engineering and Electronics The University of Edinburgh Mayfield Rd., Edinburgh
More informationMobile Radio Communications
Course 3: Radio wave propagation Session 3, page 1 Propagation mechanisms free space propagation reflection diffraction scattering LARGE SCALE: average attenuation SMALL SCALE: short-term variations in
More informationJoint Estimation of the Time Delay and the Clock Drift and Offset Using UWB signals
Joint Estimation of the Time Delay and the Clock Drift and Offset Using UWB signals A. Mallat and L. Vandendorpe Université catholique de Louvain ICTEAM Institute Place du Levant, B-1348 Louvain-la-euve,
More informationTOA-Based Robust Wireless Geolocation and Cramér-Rao Lower Bound Analysis in Harsh LOS/NLOS Environments
TOA-Based Robust Wireless Geolocation and Cramér-Rao Lower Bound Analysis in Harsh LOS/NLOS Environments Feng Yin, Carsten Fritsche, Fredrik Gustafsson and Abdelhak M Zoubir Linköping University Post Print
More informationTarget Localization in Wireless Sensor Networks with Quantized Data in the Presence of Byzantine Attacks
Target Localization in Wireless Sensor Networks with Quantized Data in the Presence of Byzantine Attacks Keshav Agrawal, Aditya Vempaty, Hao Chen and Pramod K. Varshney Electrical Engineering Department,
More informationFAST AND ACCURATE DIRECTION-OF-ARRIVAL ESTIMATION FOR A SINGLE SOURCE
Progress In Electromagnetics Research C, Vol. 6, 13 20, 2009 FAST AND ACCURATE DIRECTION-OF-ARRIVAL ESTIMATION FOR A SINGLE SOURCE Y. Wu School of Computer Science and Engineering Wuhan Institute of Technology
More informationImproved Multiple Feedback Successive Interference Cancellation Algorithm for Near-Optimal MIMO Detection
Improved Multiple Feedback Successive Interference Cancellation Algorithm for Near-Optimal MIMO Detection Manish Mandloi, Mohammed Azahar Hussain and Vimal Bhatia Discipline of Electrical Engineering,
More informationIII. Spherical Waves and Radiation
III. Spherical Waves and Radiation Antennas radiate spherical waves into free space Receiving antennas, reciprocity, path gain and path loss Noise as a limit to reception Ray model for antennas above a
More informationOptimal Fronthaul Quantization for Cloud Radio. Positioning
Optimal Fronthaul Quantization for Cloud Radio 1 Positioning Seongah Jeong, Osvaldo Simeone, Alexander Haimovich and Joonhyuk Kang arxiv:1409.2095v2 [cs.it] 10 Sep 2014 Abstract The design of a wireless
More informationAn Efficient Approach to Multivariate Nakagami-m Distribution Using Green s Matrix Approximation
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL 2, NO 5, SEPTEMBER 2003 883 An Efficient Approach to Multivariate Nakagami-m Distribution Using Green s Matrix Approximation George K Karagiannidis, Member,
More informationThe Optimality of Beamforming: A Unified View
The Optimality of Beamforming: A Unified View Sudhir Srinivasa and Syed Ali Jafar Electrical Engineering and Computer Science University of California Irvine, Irvine, CA 92697-2625 Email: sudhirs@uciedu,
More informationOn the Low-SNR Capacity of Phase-Shift Keying with Hard-Decision Detection
On the Low-SNR Capacity of Phase-Shift Keying with Hard-Decision Detection ustafa Cenk Gursoy Department of Electrical Engineering University of Nebraska-Lincoln, Lincoln, NE 68588 Email: gursoy@engr.unl.edu
More informationMultiple Antennas in Wireless Communications
Multiple Antennas in Wireless Communications Luca Sanguinetti Department of Information Engineering Pisa University luca.sanguinetti@iet.unipi.it April, 2009 Luca Sanguinetti (IET) MIMO April, 2009 1 /
More informationOptimal Time Division Multiplexing Schemes for DOA Estimation of a Moving Target Using a Colocated MIMO Radar
Optimal Division Multiplexing Schemes for DOA Estimation of a Moving Target Using a Colocated MIMO Radar Kilian Rambach, Markus Vogel and Bin Yang Institute of Signal Processing and System Theory University
More informationDynamic System Performance of SISO, MISO and MIMO Alamouti Schemes
Dynamic System Performance of SISO, MISO and MIMO Alamouti Schemes Dorra Ben Cheikh Battikh, Jean-Marc Kelif, Marceau Coupechoux and Philippe Godlewski Orange Labs, Issy-les-moulineaux, France Email: {dorra.bencheikh,
More informationEffective Rate Analysis of MISO Systems over α-µ Fading Channels
Effective Rate Analysis of MISO Systems over α-µ Fading Channels Jiayi Zhang 1,2, Linglong Dai 1, Zhaocheng Wang 1 Derrick Wing Kwan Ng 2,3 and Wolfgang H. Gerstacker 2 1 Tsinghua National Laboratory for
More informationPerfect Modeling and Simulation of Measured Spatio-Temporal Wireless Channels
Perfect Modeling and Simulation of Measured Spatio-Temporal Wireless Channels Matthias Pätzold Agder University College Faculty of Engineering and Science N-4876 Grimstad, Norway matthiaspaetzold@hiano
More informationAbstract. 1 Introduction. 2 Continuous-Time Digital Filters
Continuous-Time Digital Filters for Sample-Rate Conversion in Reconfigurable Radio Terminals Tim Hentschel Gerhard Fettweis, Dresden University of Technology, Mannesmann Mobilfunk Chair for Mobile Communications
More informationOn the Performance of. Golden Space-Time Trellis Coded Modulation over MIMO Block Fading Channels
On the Performance of 1 Golden Space-Time Trellis Coded Modulation over MIMO Block Fading Channels arxiv:0711.1295v1 [cs.it] 8 Nov 2007 Emanuele Viterbo and Yi Hong Abstract The Golden space-time trellis
More informationCall Completion Probability in Heterogeneous Networks with Energy Harvesting Base Stations
Call Completion Probability in Heterogeneous Networks with Energy Harvesting Base Stations Craig Wang, Salman Durrani, Jing Guo and Xiangyun (Sean) Zhou Research School of Engineering, The Australian National
More informationOn the Throughput of Proportional Fair Scheduling with Opportunistic Beamforming for Continuous Fading States
On the hroughput of Proportional Fair Scheduling with Opportunistic Beamforming for Continuous Fading States Andreas Senst, Peter Schulz-Rittich, Gerd Ascheid, and Heinrich Meyr Institute for Integrated
More informationAdaptive Particle Filter for Nonparametric Estimation with Measurement Uncertainty in Wireless Sensor Networks
Article Adaptive Particle Filter for Nonparametric Estimation with Measurement Uncertainty in Wireless Sensor Networks Xiaofan Li 1,2,, Yubin Zhao 3, *, Sha Zhang 1,2, and Xiaopeng Fan 3 1 The State Monitoring
More informationMinimum Feedback Rates for Multi-Carrier Transmission With Correlated Frequency Selective Fading
Minimum Feedback Rates for Multi-Carrier Transmission With Correlated Frequency Selective Fading Yakun Sun and Michael L. Honig Department of ECE orthwestern University Evanston, IL 60208 Abstract We consider
More informationSOURCE LOCALIZATION IN THE PRESENCE OF SENSOR MANIFOLD UNCERTAINTIES AND SYNCHRONIZATION ERROR
SOURCE LOCALIZATION IN THE PRESENCE OF SENSOR MANIFOLD UNCERTAINTIES AND SYNCHRONIZATION ERROR A Thesis Presented to the Faculty of Graduate School University of Missouri Columbia In Partial Fulfillment
More informationOn the estimation of the K parameter for the Rice fading distribution
On the estimation of the K parameter for the Rice fading distribution Ali Abdi, Student Member, IEEE, Cihan Tepedelenlioglu, Student Member, IEEE, Mostafa Kaveh, Fellow, IEEE, and Georgios Giannakis, Fellow,
More informationI. Introduction. Index Terms Multiuser MIMO, feedback, precoding, beamforming, codebook, quantization, OFDM, OFDMA.
Zero-Forcing Beamforming Codebook Design for MU- MIMO OFDM Systems Erdem Bala, Member, IEEE, yle Jung-Lin Pan, Member, IEEE, Robert Olesen, Member, IEEE, Donald Grieco, Senior Member, IEEE InterDigital
More informationComparisons of Performance of Various Transmission Schemes of MIMO System Operating under Rician Channel Conditions
Comparisons of Performance of Various ransmission Schemes of MIMO System Operating under ician Channel Conditions Peerapong Uthansakul and Marek E. Bialkowski School of Information echnology and Electrical
More informationA FEASIBILITY STUDY OF PARTICLE FILTERS FOR MOBILE STATION RECEIVERS. Michael Lunglmayr, Martin Krueger, Mario Huemer
A FEASIBILITY STUDY OF PARTICLE FILTERS FOR MOBILE STATION RECEIVERS Michael Lunglmayr, Martin Krueger, Mario Huemer Michael Lunglmayr and Martin Krueger are with Infineon Technologies AG, Munich email:
More informationDETERMATION of the position of a transmitting mobile
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 55, NO. 1, JANUARY 2006 17 Time-of-Arrival Based Localization Under NLOS Conditions Yiu-Tong Chan, Senior Member, IEEE, Wing-Yue Tsui, Hing-Cheung So, Member,
More informationA Proof of the Converse for the Capacity of Gaussian MIMO Broadcast Channels
A Proof of the Converse for the Capacity of Gaussian MIMO Broadcast Channels Mehdi Mohseni Department of Electrical Engineering Stanford University Stanford, CA 94305, USA Email: mmohseni@stanford.edu
More informationHow long before I regain my signal?
How long before I regain my signal? Tingting Lu, Pei Liu and Shivendra S. Panwar Polytechnic School of Engineering New York University Brooklyn, New York Email: tl984@nyu.edu, peiliu@gmail.com, panwar@catt.poly.edu
More informationTwo-Stage Channel Feedback for Beamforming and Scheduling in Network MIMO Systems
Two-Stage Channel Feedback for Beamforming and Scheduling in Network MIMO Systems Behrouz Khoshnevis and Wei Yu Department of Electrical and Computer Engineering University of Toronto, Toronto, Ontario,
More informationA New Approach for Mobile Localization in Multipath Scenarios
A New Approach for Mobile Localization in Multipath Scenarios Nadir Castañeda, Maurice Charbit and Eric Moulines CNRS LTCI & GET, 46 rue Barrault, 75634 Paris cedex 13, France {castaned, charbit, moulines}@tsi.enst.fr
More informationEmmanouel T. Michailidis Athanasios G. Kanatas
Emmanouel T. Michailidis (emichail@unipi.gr) George Efthymoglou (gefthymo@unipi.gr) gr) Athanasios G. Kanatas (kanatas@unipi.gr) University of Piraeus Department of Digital Systems Wireless Communications
More informationChapter 2 Underwater Acoustic Channel Models
Chapter 2 Underwater Acoustic Channel Models In this chapter, we introduce two prevailing UWA channel models, namely, the empirical UWA channel model and the statistical time-varying UWA channel model,
More informationEIE6207: Maximum-Likelihood and Bayesian Estimation
EIE6207: Maximum-Likelihood and Bayesian Estimation Man-Wai MAK Dept. of Electronic and Information Engineering, The Hong Kong Polytechnic University enmwmak@polyu.edu.hk http://www.eie.polyu.edu.hk/ mwmak
More information2-D SENSOR POSITION PERTURBATION ANALYSIS: EQUIVALENCE TO AWGN ON ARRAY OUTPUTS. Volkan Cevher, James H. McClellan
2-D SENSOR POSITION PERTURBATION ANALYSIS: EQUIVALENCE TO AWGN ON ARRAY OUTPUTS Volkan Cevher, James H McClellan Georgia Institute of Technology Atlanta, GA 30332-0250 cevher@ieeeorg, jimmcclellan@ecegatechedu
More informationIntroduction to Wireless & Mobile Systems. Chapter 4. Channel Coding and Error Control Cengage Learning Engineering. All Rights Reserved.
Introduction to Wireless & Mobile Systems Chapter 4 Channel Coding and Error Control 1 Outline Introduction Block Codes Cyclic Codes CRC (Cyclic Redundancy Check) Convolutional Codes Interleaving Information
More informationLecture 2. Fading Channel
1 Lecture 2. Fading Channel Characteristics of Fading Channels Modeling of Fading Channels Discrete-time Input/Output Model 2 Radio Propagation in Free Space Speed: c = 299,792,458 m/s Isotropic Received
More informationWITH the significant advances in networking, wireless
IEEE TRANSACTIONS ON SIGNAL PROCESSING, VOL. 54, NO. 12, DECEMBER 2006 4519 Target Location Estimation in Sensor Networks With Quantized Data Ruixin Niu, Member, IEEE, and Pramod K. Varshney, Fellow, IEEE
More informationOn capacity of multi-antenna wireless channels: Effects of antenna separation and spatial correlation
3rd AusCTW, Canberra, Australia, Feb. 4 5, 22 On capacity of multi-antenna wireless channels: Effects of antenna separation and spatial correlation Thushara D. Abhayapala 1, Rodney A. Kennedy, and Jaunty
More informationSimultaneous Target and Multipath Positioning. Li Li. Department of Electrical and Computer Engineering Duke University.
Simultaneous Target and Multipath Positioning by Li Li Department of Electrical and Computer Engineering Duke University Date: Approved: Jeffrey L. Krolik, Supervisor Loren W. Nolte Lawrence Carin Ronald
More informationVECTOR QUANTIZATION TECHNIQUES FOR MULTIPLE-ANTENNA CHANNEL INFORMATION FEEDBACK
VECTOR QUANTIZATION TECHNIQUES FOR MULTIPLE-ANTENNA CHANNEL INFORMATION FEEDBACK June Chul Roh and Bhaskar D. Rao Department of Electrical and Computer Engineering University of California, San Diego La
More informationCHANNEL FEEDBACK QUANTIZATION METHODS FOR MISO AND MIMO SYSTEMS
CHANNEL FEEDBACK QUANTIZATION METHODS FOR MISO AND MIMO SYSTEMS June Chul Roh and Bhaskar D Rao Department of Electrical and Computer Engineering University of California, San Diego La Jolla, CA 9293 47,
More informationTwo-Way Training: Optimal Power Allocation for Pilot and Data Transmission
564 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, VOL. 9, NO. 2, FEBRUARY 200 Two-Way Training: Optimal Power Allocation for Pilot and Data Transmission Xiangyun Zhou, Student Member, IEEE, Tharaka A.
More informationInterference and SINR in Dense Terahertz Networks
Interference and SINR in Dense Terahertz Networks V. Petrov, D. Moltchanov, Y. Koucheryavy Nano Communications Center Tampere University of Technology Outline 1. Introduction to THz communications 2. Problem
More informationA NEW BAYESIAN LOWER BOUND ON THE MEAN SQUARE ERROR OF ESTIMATORS. Koby Todros and Joseph Tabrikian
16th European Signal Processing Conference EUSIPCO 008) Lausanne Switzerland August 5-9 008 copyright by EURASIP A NEW BAYESIAN LOWER BOUND ON THE MEAN SQUARE ERROR OF ESTIMATORS Koby Todros and Joseph
More informationRate-Optimum Beamforming Transmission in MIMO Rician Fading Channels
Rate-Optimum Beamforming Transmission in MIMO Rician Fading Channels Dimitrios E. Kontaxis National and Kapodistrian University of Athens Department of Informatics and telecommunications Abstract In this
More informationAPPENDIX 1 NEYMAN PEARSON CRITERIA
54 APPENDIX NEYMAN PEARSON CRITERIA The design approaches for detectors directly follow the theory of hypothesis testing. The primary approaches to hypothesis testing problem are the classical approach
More informationOptimal pilot length for uplink massive MIMO systems with lowresolutions
Optimal pilot length for uplink massive MIMO systems with lowresolutions ADCs Fan, L., Qiao, D., Jin, S., Wen, C-K., & Matthaiou, M. 2016). Optimal pilot length for uplink massive MIMO systems with low-resolutions
More informationMaximum Likelihood-Based Methods for Target Velocity Estimation with Distributed MIMO Radar
electronics Article Maximum Likelihood-Based Methods for Target Velocity Estimation with Distributed MIMO Radar Zhenxin Cao Peng Chen * ID Zhimin Chen 2 ID and Xinyi He 3 State Key Laboratory of Millimeter
More informationON DECREASING THE COMPLEXITY OF LATTICE-REDUCTION-AIDED K-BEST MIMO DETECTORS.
17th European Signal Processing Conference (EUSIPCO 009) Glasgow, Scotland, August 4-8, 009 ON DECREASING THE COMPLEXITY OF LATTICE-REDUCTION-AIDED K-BEST MIMO DETECTORS. Sandra Roger, Alberto Gonzalez,
More informationAn Alternative Proof for the Capacity Region of the Degraded Gaussian MIMO Broadcast Channel
IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 58, NO. 4, APRIL 2012 2427 An Alternative Proof for the Capacity Region of the Degraded Gaussian MIMO Broadcast Channel Ersen Ekrem, Student Member, IEEE,
More informationPracticable MIMO Capacity in Ideal Channels
Practicable MIMO Capacity in Ideal Channels S. Amir Mirtaheri,Rodney G. Vaughan School of Engineering Science, Simon Fraser University, British Columbia, V5A 1S6 Canada Abstract The impact of communications
More informationAnalysis of Urban Millimeter Wave Microcellular Networks
Analysis of Urban Millimeter Wave Microcellular Networks Yuyang Wang, KiranVenugopal, Andreas F. Molisch, and Robert W. Heath Jr. The University of Texas at Austin University of Southern California TheUT
More informationLattice Reduction Aided Precoding for Multiuser MIMO using Seysen s Algorithm
Lattice Reduction Aided Precoding for Multiuser MIMO using Seysen s Algorithm HongSun An Student Member IEEE he Graduate School of I & Incheon Korea ahs3179@gmail.com Manar Mohaisen Student Member IEEE
More informationADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS. Jesus Perez and Ignacio Santamaria
ADAPTIVE CLUSTERING ALGORITHM FOR COOPERATIVE SPECTRUM SENSING IN MOBILE ENVIRONMENTS Jesus Perez and Ignacio Santamaria Advanced Signal Processing Group, University of Cantabria, Spain, https://gtas.unican.es/
More information12.4 Known Channel (Water-Filling Solution)
ECEn 665: Antennas and Propagation for Wireless Communications 54 2.4 Known Channel (Water-Filling Solution) The channel scenarios we have looed at above represent special cases for which the capacity
More informationA Network Shadow Fading Gain Model For. Autonomous Cellular Networks
A Network Shadow Fading Gain Model For 1 Autonomous Cellular Networks Bijan Golkar, Student Member, IEEE and Elvino S. Sousa, Fellow, IEEE Department of Electrical and Computer Engineering, University
More informationCapacity of Memoryless Channels and Block-Fading Channels With Designable Cardinality-Constrained Channel State Feedback
2038 IEEE TRANSACTIONS ON INFORMATION THEORY, VOL. 50, NO. 9, SEPTEMBER 2004 Capacity of Memoryless Channels and Block-Fading Channels With Designable Cardinality-Constrained Channel State Feedback Vincent
More informationRoot-MUSIC Time Delay Estimation Based on Propagator Method Bin Ba, Yun Long Wang, Na E Zheng & Han Ying Hu
International Conference on Automation, Mechanical Control and Computational Engineering (AMCCE 15) Root-MUSIC ime Delay Estimation Based on ropagator Method Bin Ba, Yun Long Wang, Na E Zheng & an Ying
More informationGame Theoretic Approach to Power Control in Cellular CDMA
Game Theoretic Approach to Power Control in Cellular CDMA Sarma Gunturi Texas Instruments(India) Bangalore - 56 7, INDIA Email : gssarma@ticom Fernando Paganini Electrical Engineering Department University
More informationJoint Localiza,on Algorithms for Network Topology Ambiguity Reduc,on
Ss. Cyril and Methodius University in Skopje T Faculty of Electrical Engineering and Information Technologies (FEEIT) Institute of Telecommunications (ITK) Joint Localiza,on Algorithms for Network Topology
More informationMaximum Achievable Diversity for MIMO-OFDM Systems with Arbitrary. Spatial Correlation
Maximum Achievable Diversity for MIMO-OFDM Systems with Arbitrary Spatial Correlation Ahmed K Sadek, Weifeng Su, and K J Ray Liu Department of Electrical and Computer Engineering, and Institute for Systems
More informationSTABILITY OF FINITE-USER SLOTTED ALOHA UNDER PARTIAL INTERFERENCE IN WIRELESS MESH NETWORKS
The 8th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 7) STABILITY OF FINITE-USER SLOTTED ALOHA UNDER PARTIAL INTERFERENCE IN WIRELESS MESH NETWORKS Ka-Hung
More information