Cognitive Spectrum Access Control Based on Intrinsic Primary ARQ Information

Transcription

1 Cognitive Spectrum Access Control Based on Intrinsic Primary ARQ Information Fabio E. Lapiccirella, Zhi Ding and Xin Liu Electrical and Computer Engineering University of California, Davis, California Abstract We explore a flexible and non-intrusive opportunistic spectrum access control mechanism for cognitive radios that goes beyond the standard Listen-Before-Talk (LBT) strategy. We exploit the bi-directional nature of many primary communication systems to control cognitive radio access that is based on both spectral sensing and primary receiver acknowledgements. In this work, we limit our attention on the ACK/NAK messages in primary data-link-control. We use partially observable Markovdecision processes to devise an optimized admission control policy. Our new method achieves high cognitive network throughput while offering robust protection of primary user signals. I. INTRODUCTION Cognitive radio access delineates a new paradigm for dynamically alleviating the spectrum scarcity problem. Cognitive radios are designed to access a bandwidth already allocated to a primary user network under the constraint that the primary user communication quality of service is not heavily impaired. Cognitive radio access control has been a popular topic of research, for example, the authors of [1] devised distributed spectrum sensing and access strategies under an energy constraint on secondary users. In [2] and [3], the design of sensing policies for tracking spectrum opportunities is explored. The authors of [4] applied a partially observable Markov decision framework to devise an optimal sensing and channel selection policy in a multi-channel opportunistic communication system. The authors of [5] and [6] studied the challenge of SNR threshold below which the sensing outcome is severely impaired regardless of the sensing time. The authors of [7] derived an optimal sensing policy for exponential idle time whereas in [8] they addressed optimal sensing strategies for continuous time systems, exploiting acknowledgement signals from the SU-Rx. Also, the authors of [9] presented a cognitive radio system that varies transmission power according to all the information available to the spectrum sensor. It should be noted that, thus far, listen-before-talk (LBT) is the state-ofthe-art opportunistipectrum access approach to cognitive radio. LBT simply relies on spectrum sensing, thereby enabling a secondary user (SU) to access the spectrum of a primary user (PU) only after sensing the PU spectrum unoccupied band. LBT is natural and practical, not requiring any modification to the existing PU infrastructure. However, LBT only senses primary transmission activities and it is unaware of the actual receiver conditions. More specifically, it neither solves the hidden receiver problem nor utilizes any capacity that robust interference-resistant PU networks may provide. To expand the applications and to improve the efficiency of cognitive radios, we enhance the LBT learning capability by leveraging the feedback information typically available in duplex PU links. Feedback signals for data-link-control are available in many systems such as HSDPA [] and WiMAX [11] in the form of ACK/NAK packets and downlink/uplink profiles. Such information allows the SU to more accurately infer the effect of the SU activity on the PU receivers (PU-Rx). In this work, we emphasize that advanced cognition should require the SUs to learn about primary network characteristics and user interaction. Similar concept of cognition has been discussed in [12] where a SU transmits probing signals to observe the PU transmission power changes in response to better estimate the SU to PU channel gain. Specifically, we utilize the ACK/NAK signals transmitted by the packet receivers for data-link-control. Exploiting the ACK/NAK signals from the PU-Rx allows the SUs to detect the presence of any hidden PU-Rx and to assess the quality of the PU-Rx reception, thereby better providing the necessary PU protection. It also makes it possible for the SU transmitter (SU-Tx) to optimize its access policy by estimating the PU-Rx reception quality under the SU-Tx access control policy, therefore allowing the SU-Tx to better exploit any PU excess capacity. This paper is an extension of our preliminary work [13] where we presented an optimal channel access scheme based PU ACK/NAK information overhearing. The rest of this manuscript is divided into three sections. Section II presents the description of the problem and the basic formulation of the cognitive spectrum access based on acknowledgement information. Section III presents the development of our optimized spectrum access policy. Section IV presents our test results of our access algorithm. Section V summarizes our conclusions and future directions under pursuit. II. SYSTEM MODEL Fig. 1 illustrates a wireless scenario under investigation that involves the co-existence of a primary and a secondary link. The PU access is time-slotted for packet transmissions. The high priority PU may transmit the available packets at the beginning of each slot. We assume that the SU uses that same

2 PU Tx Fig. 1. Forward link Reverse link SU Tx PU Rx SU learning environment. slot length and that the SU actions are in synchronization with the PU time slot. The PU traffic is randomly busy or idle, irrespective of the SU action. The random BUSY/IDLE durations are independent distributed with probability mass functions, respectively, p B (k) = P[D B = k] p I (k) = P[D I = k]. (1) Here D B and D I are discrete random variables denoting the BUSY and IDLE periods, respectively. SUs are aware of the IDLE/BUSY distribution (from prior measurement). We use partially observable Markov decision processes to devise an optimal admission control policy. Our time horizon is infinite. At the start time t of every slot, we define the binary variable s t via: {, when the PU channel is BUSY; s t = (2) 1, when the PU channel is IDLE. The SU-Tx can take one of three possible actions: I, ; a t = S, ; (3) T,. To take action S, the SU-Tx uses an energy detector to detect whether the PU-Tx spectrum is IDLE or BUSY. The detection (sensing) is not perfect and is characterized by a false-alarm probability P F and a missed-detection probability P M. False alarm means declaring an IDLE primary channel to be BUSY, while a missed detection means declaring a BUSY primary channel to be IDLE. Since the state s t is not directly observable to the SU, we define ρ t = P[s t = 1], and p t as the estimate of ρ t by the SU-Tx from its observations up to time t. We will refer to p t as the information state of the SU-Tx. When the PU is BUSY, it corresponds to packet error rates of Q 1 and Q 2, in the presence and the absence of SU-Tx transmissions, respectively. Naturally, Q 1 < Q 2. We let both Q 1 and Q 2 be known to the SUs a priori from measurements before deploying access control. We assume that the SU-Tx can either correctly decode a feedback message from the PU- Rx or cannot decode it at all. We define as the probability that the SU-Tx can correctly decode a feedback signal from the PU-Rx. To optimize the access control, we need to define the perstage reward/cost function. Let p t = q and the action a t = ν, then: r T c T P[NAK], for ν=t; R(q, ν) = c S, for ν=s; (4), for ν=i; where r T is a reward the SU-Tx gets for each packet transmission, c T and c S are the cost of a packet collision with the PUs and the cost of sensing the PU spectrum, respectively. P[NAK] = (1 q) Q 2 is the prior probability of PU-Rx sending a NAK. In what follows, we will define τ j (t) as the estimated transition instant to state j {; 1} from state 1 j based on observation up to time t. τ j (t) is needed in order to tackle the information state update from one time slot to another. Our algorithm development assumes that the SU-Tx knows the starting point of the first PU BUSY cycle. For simplicity and without loss of generality we will assume the first BUSY cycle starts at time. The access control policy we seek consists of a sequence of functions π = {µ, µ 1,...,µ t,...} where each function µ t (.) maps the information state p t into an action s t {I, S, T}. At time t, upon taking action a t, the SU-Tx uses the current and past observations to determine a maximum-a-posteriori (MAP) estimation of the next PU-traffic transition moment. Given a policy π we define the value function of the SU-Tx from time t s = as: + V π (q, t s, τ (t s )) = E π [ α t r(p t, a t ) p ts = q]. (5) t=t s The optimized access policy is an admissible policy π that maximizes the expected value function (5): DATA Feedback π = argmax{v π (q, t s, τ (t s )))}. (6) π III. OPTIMIZED SPECTRUM ACCESS CONTROL time a = S 1 2 O {I, Ia, In, B, Ba, Bn} O2 {I, B } O3 = PU activity SU activity O4 {ACK, NAK, } a1 = I a2 = S a3 = T Fig. 2. a4 = T Sequences of spectrum access. time

3 Fig. 2 shows a possible sequence of PU/SU activities in time. Clearly, no observation is associated to the action ν = I. At time t, for action a t = T, we have: O T t Ω T = {ACK, NAK, }; (7) where ACK stands for the event of receiving an ACK from the PU-Rx, NAK denotes the event of receiving a NAK packet from the PU-Rx, possibly caused by the SU interference, whereas stands for the case of SU-Tx failing to decode any feedback from the PU-Rx. This may be due to either high noise/interference level at the SU-Tx or an idle PU. For action a t = S, the SU-Tx observes O S t. Specifically, O S t ΩS = {I, I A, I N, B, B A, B N }; (8) where I denotes an IDLE observation while the SU-Tx does not decode any feedback message from the PU-Rx, I A denotes IDLE observation while an ACK is correctly decoded at the end of the time slot; I N represents IDLE sensing while a NAK from the PU-Rx is correctly decoded. The symbol B follows the same convention to represent the three cases when the PU channel has been sensed as BUSY. The SU stores the past observations since the latest PU traffic pattern transition in the vector O t. For example, if at time t the latest transition epoch is τ (t), the vector O t will be: [ ] O t = O a τ (t) τ,..., (t) Oat 1. (9) Recall that τ j (t 1) denotes the last estimated transition instant at t 1. After collecting a new O ν t, the SU-Tx estimates the j 1 j transition instant τ 1 j (t) via the MAP principle τ 1 j (t) = MAP(τ 1 j (t) O ν t,o t, τ j (t 1)) () = argmax {ln(p[ot ν,o t τ 1 j (t) = x]) + ln(p[τ 1 j (t) = x)}. x>τ j(t 1) (11) There are two possibilities for τ i (t): 1) τ j (t 1) < τ 1 j (t) < t: the estimated transition from state j to 1 j happened before t. In this case, the traffic distribution used to calculate the information state p t will change from p j (k) = P[D j = k] to p 1 j (k) = P[D 1 j = k]. Moreover, the SU-Tx will erase the stale O t and create a new one associated to τ 1 j (t). 2) τ 1 j (t) t: the MAP estimator does not detect a transition before t. The SU-Tx assumes the PU channel state to remain in state j and will discard the estimate τ 1 j (t). A. Information state update equations At time t, the SU-Tx takes action a t = ν and uses O ν t and O t to determine the next PU traffic transitions via a MAP estimator. Since the MAP estimator is error-prone, the SU- Tx will base its channel admission control on the information state p t to fine-tune the PU channel state. Let τ j (t) be the latest estimated PU traffic transition at t, the information vector is defined t > : p t = P[s t = 1 O at 1 t 1, τ j(t 1)]; (12) The SU-Tx updates p t following two steps: 1) Determining the posterior probability: ˆp t = P[s t = 1 O ν t, τ j (t 1)]. (13) In order to find ˆp t of (13), the SU-Tx determines first the probabilities v 1 = P[O ν t s t = 1], v = P[O ν t s t = ]. (14) It then uses (14) to calculate the observation probability p(o ν t ) = v 1 p t +v (1 p t ). The posterior probability (13) is defined as: ˆp t = v 1 p t v 1 p t + v (1 p t ). (15) 2) Updating the information vector p t+1. Let K j be the number of time slots the PU channel has been in state j {; 1}. Its probability of being still in state j at time t + 1 is: P[s t+1 = j {s t = j,..., s t Kj = j}] = P[D j K j + 1 D j K j ] = P[Dj Kj+1] P[D j K j] ; (16) where D j is a random variable denoting the number of time slots in state j. For computational tractability, we assume that since the last τ j (t), at most one transition may take place. To update the probability estimate that s t+1 = 1, we need to define this probability: K j w = p[d j = k] (1 P[D 1 j K j k + 1] ). P[D 1 j K j k] k=1 (17) Hence, the updated information vector is p t+1 = P[Dj Kj+1] P[D j K j] ˆp t + w (1 ˆp t ). (18) For the sake of simplicity, we will refer this update procedure as a function: p t+1 = UPDATE(O at t, p t, t, τ j (t)). (19) B. Optimal value function and policy calculation We define V (p, t, τ j (t)) as the maximum expected discounted value function that the SU-Tx can get at time slot t, with the information state p assuming that the latest PU traffic transition was to state j. From the Bellman equation: V (p, t, τ j (t)) = max {V a (p, t, τ j (t))}; () a {I, T, S} where V I (p, t, τ j (t)), V S (p, t, τ j (t)), V T (p, t, τ j (t)) are the value function associated with actions,,

4 and, respectively. They are defined as V I (p, t, τ j (t)) = R(p, I) + α V (q = UPDATE({ }, p, t, τ j (t))); V S (p, t, τ j (t)) = R(p, S) + α P[O S ]V (q = UPDATE(O S, p, t, τ j (t))); O S Ω S V T (p, t, τ j (t)) = R(p, T) (21) + α P[O T ]V (q = UPDATE(O T, p, t, τ j (t))); O T Ω T where the set Ω ν is defined in (7) and (8), for action ν = {T,S} respectively; R(p, ν) is the per-stage reward function associated with the action ν, and q = UPDATE(O ν, p, t, τ j (t))) is the information state-update procedure earlier. We omitted the time indices of the current and next state for simplicity. IV. SIMULATION RESULTS We now test our optimized SU access policy through computer simulations. In the test, we let BUSY and IDLE durations be uniformly distributed over [1, ] and [1, ], respectively. Our performance metrics are the PU packet collision probability (P coll ) and the SU throughput T t= SU th = I(a t = T) (22) (T + 1) where I( ) is the indicator function and T is the test duration. In all our tests, we set T =. To examine the effect of cost/reward on the SU behavior, we test 2 different settings of costs and reward shown below. The sensing cost in setting B is much lower than in setting A. We let Q 1 =.1 and Q 2 =.99, implying that a collision between simultaneous PU-SU transmissions will almost certainly lead to a NAK from the PU-Rx. Total Transmission, Sense, Idle time % Total Transmission, Sense, Idle time % Fig. 3. Total percentage of sensing, transmission and idle time as varies: under setting A; under setting B. SU th set A vs. set A % =5 =.5 set A r T = 5 c T = 15 c S = 5 set B r T = 5 c T = c S = Figure 3 compares the SU behavior as a function of under the two different cost/reward settings. The transmission percentage can also be regarded as a throughput metric. As the decoding probability increases, the feedback information becomes more reliable. Therefore, the SU throughput increases and the percentage of time spent in the idle state decreases. Since setting A represents a high sensing cost, the SU-Tx senses the spectrum more frequently compared to setting B. In both cases, as increases which leads to higher ACK/NAK reliability, the transmission time increases. Fig. 4 provides an SU throughput comparison between the two settings, where as Fig. 4 illustrates the corresponding collision rate under the same conditions. When =, then the optimal decision is based solely on the sensing outcome. In this case, SU-Tx shows a higher throughput under setting B, whose lower sensing cost allows the SU-Tx to sense more Fig. 4. Collision prob % =5 =.5 Comparison of: SU th and PU collision rate. and transmit more. When the ACK/NAK decoding is less reliable (.1.35), the SU throughput associated to

5 setting A is higher because its higher sensing cost does not encourage sensing whereas some correctly decoded feedback information from the PU-Rx may encourage transmission in lieu of sensing. On the other hand, =.5 allows the SU- Tx to be more conservative and to transmit only when it is confident of the PU channel state. This is reflected by the P coll shown in Fig. 4 where it is clear that sensing more frequently leads to less collisions with the PUs. When PU- Tx signals become more reliable ( >.35) the SU th in both settings have similar throughput results, as the SU can rely more on the PU-Tx feedback and less on sensing. When =.5 and the sensing cost is low, the P coll varies little between 5% and 8%. In this case, sensing is more desirable than getting ACK/NACK feedback ( > ) as ACK/NAK does little to improve the performance versus no feedback ( = ). More sensing leads to lower of SU th as less time is devoted to transmission. On the other hand, when = 5, reliable feedback information help reduce the P coll from 15% to 5%. This means that when pure sensing is expensive or unreliable, feedback from the PU-Rx constitutes a reliable observation signal for PU protection. Transmission, Sense, Idle time % in a PU Idle cycle Transmission, Sense, Idle time % in a PU Idle cycle Fig. 5. SU sensing, transmission and idle time percentage during a PU Idle period as a function of under: setting A; setting B. Fig. 5 shows the percentage of the three possible actions during a PU idle period for the two test cases of high versus low sensing cost. Since the T action allows the SU-Tx to listen on the reverse channel and update its information state through the observation of the feedback, when the sensing cost is higher, it is preferable for the SU-Tx to either transmit or stay idle, as shown in Fig. 5. Fig. 5 shows that when the sensing cost decreases, the SU-Tx changes its behavior and senses the PU channel more frequently. In both cases, when the SU-Tx is able to decode almost all its feedback messages ( >.99), the data-link-control feedback message represents a very reliable indicator of spectrum opportunity that allows the SU-Tx to reach throughput of nearly 9%. V. CONCLUSIONS AND FUTURE WORKS We investigated means of improving the basic LBT access strategy for cognitive radio systems. Exploiting data-linkcontrol messages that can be overheard by the SU-Tx, our new approach can enhance the traditional spectral sensing and more accurately determine the operating conditions of the primary reception for protection. Based on the simple ACK/NAK signals from the PU-Rx and the prior knowledge of the PU idle-busy probability distribution, we applied partially observable Markov-decision processes to devise an optimal channel access control strategy in order to maximize the secondary user utility. Our future works include investigating means for SU capacity enhancement by detecting the robust mode of the PU-Rx under SU interference as well as developing more versatile ways of SU access such as multi-level power access. REFERENCES [1] Y. Chen; Q. Zhao; A. Swami, Distributed Spectrum and Access in Cognitive Radio Networks With Energy Constraint, IEEE Trans. Signal Processing, 57(2): , Feb. 9. [2] Q. Zhao; L Tong; A Swami and Yunxia Chen, Decentralized cognitive MAC for opportunistipectrum access in ad hoc networks: A POMDP framework, IEEE Journal Selected Areas in Communications,25(3):589-, April 7. [3] Y. Chen; Q. Zhao; A. Swami, Joint Design and Separation Principle for Opportunistic Spectrum Access in the Presence of Errors, IEEE Trans. Info. Theory, 54(5):53-71, May 8. [4] Q. Zhao; B. Krishnamachari; K. Liu, On myopiensing for multichannel opportunistic access: structure, optimality, and performance, IEEE Trans Wireless Comm., 7(12): , Dec. 8. [5] A. Sahai, N. Hoven, and R. 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