Prioritized Access in a Channel-Hopping Network with Spectrum Sensing

Transcription

1 Prioritized Access in a Channel-Hopping Network with Spectrum Sensing NSysS, January 9, 206

2 Outline The problem: traffic prioritization in cognitive networks, and ways to achieve it Related work Details of the cognitive MAC protocol with different mechanisms for priority-based traffic differentiation Probabilistic analysis of the protocol, including Modeling the MAC algorithm Collisions with primary source transmissions Packet access delay Performance evaluation of the proposed schemes Conclusions and directions for future work

3 Traffic Prioritization in Cognitive Networks Traffic prioritization often used to achieve the desired QoS Cognitive networks pose additional problems since traffic prioritization interferes with spectrum sensing, esp. when nodes are equipped with a single radio and collaborative spectrum sensing is performed by individual nodes Prioritization in such cases may be achieved by scheduling the order of transmissions per klass Using different service disciplines (K-limited), Adjusting the amount of spectrum sensing, or A combination of previous ones In this paper, we investigate the impact of traffic prioritization on the performance of the transmission tax-based cognitive MAC protocol

4 Related Work A cross-layer approach integrates PHY level spectrum sensing with MAC level QoS-aware packet scheduling (Su and Zhang) but two radios are used and MAC needs a dedicated control channel Hamdi et al. schedule uplink transmissions with QoS provisions Negotiated secondary access with centralized resource allocation scheme (Attar et al) uses game theory to meet the QoS requirements for both primary and secondary users Queueing network model based on M/G/ queues with preemptive resume priority (Wang et al.) analyzes the difference between reactive and proactive spectrum decision model

5 Related Work (2) MAC protocol by Chen et al. integrates spectrum sensing and QoS provisioning whilst making use of nodes with a single radio, but only a small subset of working channels is sensed in each transmission CSMA/CA mechanism was modified to differentiate between real time and non-real time used by Wang et al. using both contention-based and contention-free access A similar approach by Alshamrani et al. reserves some channels for exclusive use by real time users, while others can be shared between real-time and non-real time users A distributed MAC protocol by Cai et al. allocates different sensing periods to different classes of multimedia traffic

6 Network Operation leading beacon data exchange other nodes sense all other channels sensing results bandwidth requests trailing beacon leading beacon time channel x nodes that transmit/receive remain on channel x other nodes sense all other channels all nodes channel y Time structured in superframes, each of which may take place on a different channel Collaborative spectrum sensing: nodes sense, send data to the coordinator which makes spectrum decision Transmissions paid for by performing spectrum sensing Sensing may be suspended for reception If the node returns from sensing with an empty buffer, it undertakes additional sensing duty

7 Traffic Classes A number of traffic classes, from (highest) to m (lowest) Each class has m i nodes, for a total of M = m i= M i nodes Transmissions from nodes of a single priority class are scheduled in a round robin fashion Higher priority traffic will get its transmission slots scheduled before lower priority ones Traffic class i serviced using (gated) K i -limited service A different value of the penalty coefficient k p (i) class i allocated to each traffic

8 Modeling the MAC algorithm Superframe length is s f unit slots and the packet size be k d + slots incl. acknowledgment, resulting in the PGF of b(z) = z k d + Packets arrive at the class i node according to a Poisson process with arrival rate λ i, resulting in offered load of ρ i = λ i b Nodes have buffers of infinite capacity Markov points are moment when node applies for bandwidth. Mass probabilities for k packets in the traffic class i node are q (i) j Q (i) (z) denotes the corresponding PGF for the number of packets queued

9 MAC Operation (2) reservation waiting for allocated time (incl. higher priority classes) beacon/trailer frames Tx from node i to j sensing and reporting, reception last sensing report reservation time A2 A3 A4 A Packet transmission cycle includes waiting for the transmission slot, actual transmission, synchronization with becon, sensing and waiting for reservation. Node transmission time is randomly positioned with respect to the beacon.

10 MAC Operation (3) Table : Periods and PGFs for arrival processes within MAC period period pdf arrival process PGF mass probs. waiting for allocated time d 2 (x) A (i) 2 (z) a(i) 2,k data transfer for target node s(x) A (i) 3 (z) a(i) 3,k sensing period v u (x) A (i) f (z) a (i) f,k total sensing period v(x) A (i) s f A (i) 0 sensing for a single superframe synchronization with the beacon d 4 (x) A (i) 4 (z) a(i),k (z) a(i) 0,k (z) a(i) 4,k single packet transmission b(x) A (i) s (z) a (i) s,k

11 MAC Operation (3a) If each node of class i is allowed to transmit up to K i packets in each transmission cycle, balance equations for each traffic class are q (i) k = q (i) 0 a(i) + x=0,k + Ki (λ i x) k k! q (i) j j=k i j= q (i) j e λ i x d (i) 2 (x) b(x)(j) v u(x) (j) d 4(x)dx x=0 (λ i x) k j+k i (k j + K i )! e λ i x d (i) 2 (x) b(x)(k i ) v u(x) (K i ) d 4(x)dx () where q (i) j denotes mass probability of having j packets upon return from sensing for a node in traffic class i; symbol denotes convolution; b(x) (j) denotes j-fold convolution of packet transmission time; and v u (x) (j) denotes j-fold convolution of single packet penalty time.

12 MAC Operation (4) Probability generating function (PGF) for the number of packets queued at node from class i is Q (i) (z) = j=0 q(i) j z j = Qn(i) (z) Qd (i) (z) ; for tractability we need auxilary PGFs: (z) = A (i) 2 (z)a(i) 4 (z)(a(i) s (z)) j, j =... K i β (i) j (z)a (i) f K i and partial PGF of Q (i) kl (z) = q (i) j z j. j= These two are then used to write denominator and numerator polynomials as Qn (i) (z) = z K i q (i) 0 A(i) (z) + z K i K i j= q (i) j β (i) j (z) β (i) K i (z)q (i) kl (z) (2) Qd (i) (z) = z K i β (i) K i (z) (3)

13 Important Periods the PGF for the number of transmitted packets is Φ (i) (z) = the PGF for the service period is Ki k= q(i) k z k + q (i) 0 k=k i q (i) k z K i (4) Sr (i) (z) = K i k=0 q (i) k z k + k=k i q (i) k z K i (5) The PGF for the number of packets that arrive to the node during transmission time is A (i) 3 (z) = Φ(i) (λ i λ i z). (6)

14 Sensing periods Since sensing period is proportinal to the number of transmitted packets in the busy period the PGF for the time node spent in sensing is V (i) (z) = Φ (i) (z k(i) p s f ) (7) The number of packet arrivals during a single sensing period has the PGF of A (i) (z) = V (i) (λ i λ i z) = a (i),k z k (8) After packet transmission, the node needs to wait for the next trailing beacon in order to learn about the next and backup channels. This waiting time is residual time with respect to a random point in superframe, and its LST has the form R (s) = ( e s s f ) /(ss f ); k=0 the corresponding number of packet arrivals has the PGF of A (i) 4 (z) = R (λ i λ i z).

15 Waiting for service A class i node has to wait until all nodes from higher priority classes, and nodes from the same priority but with lower IDs have been serviced The time between two successive transmission opportunities the class i cycle time has the LST of i C (i) (s) = C (j) (s)(sr (i) (s)) M i (9) For target node waiting time for service is elapsed cycle time: j= C (i) (s) = C (i) (s) sc (i) (0) The number of packet arrivals during that time has the PGF of i 2 (z) = C (j) (λ i λ i z)c (i) (λ i λ i z) () A (i) j= By combining these equations we obtain the probability distribution of the number of class i packets in the buffer upon return from sensing

16 Collisions with primary source transmission Type collision is caused by the onset of primary source activity during an ongoing superframe this component depends only on the dynamics of primary source activity Since higher priority traffic classes transmit earlier in the superframe, they may be expected to experience fewer collisions with primary source transmissions Type 2 collisions are caused by errors in the channel table maintained by the coordinator, which occur when the primary source changes the state between two sensing events Type 2 collsions are significant when the number of sensing nodes is small compared to the number of channels

17 Collisions (2) Piconet uses N frequency channels primary source s onset and OFF periods can be described with (pdf-s) t on (x) and t off. pdf for the channel residual idle time is d(y) = z=y t off (z)dz T off The time from the moment of arrival of the piconet to the channel, until the onset of channel activity is residual (channel) idle time, which has the PDF of D(x) = x d(y)dy. 0 Collision probability due to the onset of primary user activity for traffic class i is the probability that residual idle channel time is shorter than the superframe duration: P (i) c = x=0 (D(x + i j= C (j) + C (i) + S (i) ) D(x))d(x)dx (2) The total collision probability is, then, P (i) Col = P (i) c + p s (3)

18 Packet access delay Under K-limited service, up to K i packets that were in the buffer at the time of the bandwidth request will be serviced. the PGF for the number of packets left after n-th 0 < n K i departing packet is A (i) 2 (z)a(i) s (z) n q (i) k z k L (i) k=n n (z) = (4) We can further derive the PGF for the number of packets left after any departing packet as L (i) (z) = A(i) s (z)a (i) 2 (z) [ Q (i) Φ (i) kl (A (i) s (z)) Q (i) (z) ] ( A(i) s (z) z z n k=n q (i) k ) K i (Q (i) (z) Q (i) kl (z)) we derive the LST of the packet access time as T (i) (s) = L (i) ( s λ i ). (5)

19 Performance evaluation To obtain performance parameters we have solved the system of equations using Maple 6 by Maplesoft, Inc We have considered a piconet with the coordinator and two traffic classes with M = M 2 = 5 nodes each Packet arrival rate was varied simultaneously for both traffic classes between λ = λ 2 = 0.00 and packets per node per slot. Penalty coefficient for higher priority traffic (class ) was fixed at k p () = 0.25 while that for lower priority traffic (class 2) was varied between k p () = The piconet uses N = 30 RF channels with independent primary sources that are characterized by exponentially distributed ON and OFF periods with mean values of 900 and 200 slots, respectively Data packets have a constant size of k d = 0 slots, while acknowledgment packets take a single slot, while the superframe size was set to s f = 00 unit slots

20 Impact of scheduling parameter on class traffic class : num. packets served in transmission cycle lambda (a) Mean number of packets per node served in one active transmission cycle mean access time for class lambda (b) Mean packet access time collision probability for class lambda (c) Probability of collision with the primary user transmission. K =, 2, 3, 4, are denoted diamond, box, circle, cross. K 2 = but figs. are the same for K 2 = 2, 3, 4. Scheduling parameter of class 2 does not affect class. increasing the scheduling parameter K above 2, where performance is very good, shows no significant improvement for class traffic. Higher values of K might be considered only when traffic load increases, or higher sensing penalty is required due to the high number of channels.

21 Class 2 performance average number of packtes served in trasnmission cycle av packet access time Collision probability lambda kp 0.8 (d) Mean number of packets per node served in one trans. cycle, K = K 2 = 2. Tav lambda kp 0.8 (e) Mean packet access time, K = K 2 = Pc lambda kp 0.8 (f) Probability of collision with the primary source, K = K 2 = 2. average number of packtes served in transmission cycle av packet access time Collision probability lambda kp 0.8 (g) Mean number of packets per node served in one trans. cycle, K = K 2 = 3. Tav lambda kp 0.8 (h) Mean packet access time, K = K 2 = Pc lambda kp 0.8 (i) Probability of collision with the primary source, K = K 2 = 3.

22 Class 2 performance average number of packtes served in transmission cycle av packet access time Collision probability lambda kp 0.8 (j) Mean number of packets per node served in one trans. cycle, K = K 2 = 4. Tav lambda kp 0.8 (k) Mean packet access time, K = K 2 = Pc lambda kp 0.8 (l) Probability of collision with the primary source, K = K 2 = 4. the value of the penalty parameter is important for the performance: for k p = and K 2 = 2, the CPAN approaches stability limit marked by large access time. When K 2 is increased to 3, access time has a large drop of about 40%, and further increase of scheduling parameter gives no improvement.

23 Conclusions We have described and evaluated priority differentiation in a cognitive PAN using different scheduling parameters and/or variable sensing penalty We used class ordering and duration of sensing for priority differentiation and K-limited scheduling per class as a fairness mitigation parameter. Our results show that larger scheduling parameters for low traffic classes do not affect higher traffic classes. On the other hand they extend stability region of lower class nodes and still ensure accurate spectrum sensing reports.