Unicast Barrage Relay Networks: Outage Analysis and Optimization

Transcription

1 Unicast Barrage Relay Networks: Outage Analysis and Optimization S. Talarico, M. C. Valenti, and T. R. Halford West Virginia University, Morgantown, WV. TrellisWare Technologies, nc., San Diego, CA. Oct. 7 th, 04

2 Outline. ntroduction. Network Model 3. Markov Process 4. terative Method 5. Network Optimization 6. Conclusion /5

3 Outline. ntroduction. Network Model 3. Markov Process 4. terative Method 5. Network Optimization 6. Conclusion 3/5

4 Barrage Relays Networks (BRNs) BRN is a cooperative MANET designed for use at the tactical edge. BRNs use time division multiple access and cooperative communications. Packets propagate outward from the source via a decode-and-forward ap- proach as follows: TDMA Frame Time Slot time 4/5

5 Barrage Relays Networks (BRNs) BRN is a cooperative MANET designed for use at the tactical edge. BRNs use time division multiple access and cooperative communications. Packets propagate outward from the source via a decode-and-forward ap- proach as follows: TDMA Frame Time Slot time 4/5

6 Barrage Relays Networks (BRNs) BRN is a cooperative MANET designed for use at the tactical edge. BRNs use time division multiple access and cooperative communications. Packets propagate outward from the source via a decode-and-forward ap- proach as follows: TDMA Frame Time Slot time 4/5

7 Barrage Relays Networks (BRNs) BRN is a cooperative MANET designed for use at the tactical edge. BRNs use time division multiple access and cooperative communications. Packets propagate outward from the source via a decode-and-forward ap- proach as follows: TDMA Frame Time Slot time 4/5

8 Controlled Barrage Regions (CBRs) silent zone silent zone... S R R D S active zone R R D active zone S R R D... active zone CBR is the union of multiple cooperative paths within some subregion or zone of the overall network. CBRs can be established by specifying a set of buffer nodes (S and D) around a set of N cooperating interior nodes (R,...,RN ), enabling spatial reuse. The nodes operate in a half-duplex mode: during a particular radio frame a CBR can be active or silent. Since each node transmits each packet at most once, the maximum number of transmissions per frame is F = N +. 5/5

9 Outage analysis Objective The link outage probabilities are computed in closed form. The dynamics of how a Barrage relay network evolves over time is modeled as a Markov process. Optimization An iterative method is used in order to account for co-channel interference (CC). Maximize the transport capacity by finding the optimal: - code rate; - number of relays; - placement of the relays; - size of the network. 6/5

10 Outline. ntroduction. Network Model 3. Markov Process 4. terative Method 5. Network Optimization 6. Conclusion 7/5

11 Network Model The BRN comprises: K CBRs; M mobiles radios or nodes X = {X,..., XM }. The instantaneous power of Xi at receiver Xj during time slot t is (t) ρi,j = (t) Pi gi,j f (di,j ) where Pi is the transmit power; (t) gi,j is the power gain due to Rayleigh fading; di,j = Xi Xj is the distance between Xi and Xj ; f ( ) is a path-loss function: α d f (d) = d0 α is the path loss exponent; d d0 ; 8/5 ()

12 SNR The signal-to-interference-and-noise ratio (SNR) for the node Xj during time slot t, when the signals are diversity combined at the receiver, is (cf., []): X (t) α (t) Xj gk,j dk,j (t) (t) γj = k Gj Γ + X i gi,j d α i,j (t) (t) () (t) i6 Gj where Xj(t) X is the set of barraging nodes that transmit identical packets to Xj during the tth time slot; Gj(t) is the set of the indexes of the nodes in Xj(t) ; Γ is the signal-to-noise ratio (SNR); i(t) is a Bernoulli variable with probability P [i(t) = ] = p(t) i ; p(t) is the activity probability for the node Xi during time slot t. i [] V. A. Aalo, and C. Chayawan, Outage probability of cellular radio systems using maximal ratio combining in Rayleigh fading channel with multiple interferers, EEE Electronics Letters, vol. 36, pp , Jul /5

13 Conditional Outage Probability An outage occurs when the SNR is below a threshold β. β depends on the choice of modulation and coding. Conditioning on the network topology X and the set of barraging nodes (t) Xj, the outage probability of mobile Xj during slot t is i h (t) (t) (t) j = P γj β X, Xj ; (3) The outage probability depends on the particular network realization, which has dynamics over timescales that are much slower than the fading; The conditional outage probability is found in closed form []. [] S. Talarico, M. C. Valenti, and D. Torrieri, Analysis of multi-cell downlink cooperation with a constrained spatial model, Proc. EEE Global Telecommun. Conf (GLOBECOM), Atlanta, GA, Dec /5

14 Outline. ntroduction. Network Model 3. Markov Process 4. terative Method 5. Network Optimization 6. Conclusion /5

15 Node and CBR States At the boundary between any two time slots, each node can be in one of the three following node states: Node state 0: The node has not yet successfully decoded the packet. Node state : t has just decoded the packet received during previous slot, and it will transmit on next slot. Node state : t has decoded the packet in an earlier slot and it will no longer transmit or receive that packet. The CBR state is the concatenation of the states of the individual nodes within the CBR. The CBR state is represented by a vector of the form [S, R,..., RN, D] containing the states of the source, relays, and destination. /5

16 Markov States The dynamics of how the CBR states evolve over a radio frame can be described as an absorbing Markov process. The state space of the process is composed of % Markov states si. An absorbing Markov process is characterized by: transient states; absorbing states: CBR outage state and CBR success state. p,5 p, p,3 p,4 p,7. [00] 3. [00] p,7 p,8 7. [000] p3,6 p3,7 8. [00] p,8 8. [0] 7. [00] 8. [0] R 8. [] 4. [0]. [000] 5. [0] p3,8 6. [0] 7. [00] 8. [0] S p6,8 R 8. [] 8. [0] p4,7 8. [] p4,8 Slot Slot 7. [0] 8. [] D p6,7 p5,7 p5,8 Slot 3 3/5

17 Transition Probability The transition probability pi,j is the probability that the process moves from Markov state si to state sj. The transition probability is evaluated as follows: f sj is a transient Markov state, it is the product of the individual transmission probabilities; f sj is an absorbing Markov state, it is equal to the sum of the probabilities of transitioning into the constituent CBR states. p,5 p, p,3 p,4 p,7. [00] 3. [00] p,7 p,8 7. [000] p3,6 p3,7 8. [00] p,8 8. [0] 7. [00] 8. [0] 8. [] 4. [0]. [000] 5. [0] p3,8 6. [0] 7. [00] 8. [0] p6,7 p6,8 8. [] 8. [0] p4,7 8. [] p4,8 Slot Slot 7. [0] 8. [] p5,7 p5,8 Slot 3 4/5

18 Transition Probability The transition probability pi,j is the probability that the process moves from Markov state si to state sj. The transition probability is evaluated as follows: f sj is a transient Markov state, it is the product of the individual transmission probabilities; f sj is an absorbing Markov state, it is equal to the sum of the probabilities of transitioning into the constituent CBR states. p,5 p, p,3 p,4 p,7. [00] 3. [00] p,7 p,8 7. [000] p3,6 p3,7 8. [00] p,8 8. [0] For instance: 7. [00] 8. [0]. [000] 8. [] 4. [0]. [000] 5. [0] p3,8 6. [0] 7. [00] 8. [0] p4,8 Slot Slot 7. [0] 8. [] p, p6,7 = () () () R R D p6,8 (t) where j 8. [0] 8. []. [00] Slot 8. [] p4,7 p, p5,7 p5,8 Slot 3 4/5 is found using (3).

19 CBR Outage and Success Probabilities The state transition matrix P, whose (i, j)th entry is {pi,j }, is P = Q 0 R (4) where Q is the τ τ transient matrix; R is the τ r absorbing matrix; is an r r identity matrix. The absorbing probability bi,j is the probability that the process will be absorbed in the absorbing state sj if it starts in the transient state si. The absorbing probability bi,j is the (i, j)th entry of B, which is: B = ( Q) R. (5) f the absorbing states are indexed so that the first absorbing state corre- sponds to a CBR outage, while the second corresponds to a CBR success: The CBR outage probability is CBR = b, ; The CBR success probability is ˆCBR = b,. 5/5

20 Outline. ntroduction. Network Model 3. Markov Process 4. terative Method 5. Network Optimization 6. Conclusion 6/5

21 nter-cbr nterference nter-cbr interference causes a linkage between the transition probabilities in a given CBR and the transmission probabilities of adjacent CBRs. n order to compute the transition matrix Pk, an iterative method is used: Begin by neglecting CC: p i ( t ) [0] = 0, i, t Evaluate the transmission probabilities Compute the transition matrix Pk(t)[n-] Update the transition matrix Compute the activity probabilities No Pk [n] is the transition matrix at the nth iteration for the kth CBR; {pi(t) [n]} is the set of activity prob- abilities at the nth iteration; F is the Frobenius norm; (t) k (t) k P [n]-p [n-] F<ζ? Yes Evaluate the transmission probabilities Compute the transition matrix Pk(t)[n] STOP 7/5 ξ is a tolerance.

22 Example 0 0 SNR = 0 db CBR outage probability 0 Settings: SNR = 0 db Line network; Two relays (N = ); 0 CC from two closest active zones; nfinite cascade of CBRs; 3 0 α=3 α = 3.5 α=4 3 4 Number iterations 5 All CBRs have identical topology. 6 Figure: CBR outage probability for the kth CBR as function of the number of iterations used. Set of curves at the top: SNR = 0 db. Set of curves at the bottom: SNR = 0 db. 8/5

23 Outline. ntroduction. Network Model 3. Markov Process 4. terative Method 5. Network Optimization 6. Conclusion 9/5

24 Network Optimization The transport capacity (TC) of a typical CBR is Υ=d ˆCBR R. (N + ) (6) where d is the distance between the CBR s source and destination; ˆCBR is the CBR success probability; N is the number of relays within the CBR; R = log ( + β) is the code rate (in bit per channel used [bpcu]). Let X k be a vector containing the position of the N relays in the k th CBR. The goal of the network optimization is to determine the set Θ = (X k, R, N, d) that maximizes the TC. The optimization can be solved through a convex optimization over (R, N, d) combined by a stochastic optimization over X k. 0/5

25 Convex Optimization Surface W/o co channel interference W/ co channel interference ϒopt(d Xk ) 0.5 ϒopt(R d, Xk) W/o co channel interference W/ co channel interference 0.6 SNR = 5 db SNR = 0 db SNR = 5 db SNR = 0 db R d W/o co channel interference W/ co channel interference 0.6 Settings: SNR = 5 db ϒopt(N d, Xk) 0.5 Line network; SNR = 0 db 0.4 Unitary CBR; 0.3 CC from two closest active zones; 0. nfinite cascade of CBRs; All CBRs have identical topology. 3 4 N /5 4

26 Optimization Results SNR CC R N d Optimal nodes location S S R R R3 R4 R D S 3 R R R3 R4 R D S 3 D.7 S 0 D. S 5 Υopt D R R R3 R4 R5.3 D Table: Results of the optimization for a line network. /5

27 Outline. ntroduction. Network Model 3. Markov Process 4. terative Method 5. Network Optimization 6. Conclusion 3/5

28 Conclusions This paper presents a new analysis and optimization for unicast in a BRN. A BRN is analyzed by describing the behavior of each constituent CBR as a Markov process: path loss, Rayleigh fading, and inter-cbr interference are taken into account. The analysis is used to optimize a BRN by finding the optimal number of relays that need to be used in the CBR, the optimal placement of the relays, the optimal size of the CBR, the optimal code rate at which the nodes transmit, which maximize TC. The analysis and the model are general enough that can be extended to different types of cooperative ad hoc networks, for which multiple source transmissions are diversity combined at the receiver. 4/5

29 Thank You 5/5