Simulation Results

Also, denote by P S_i^B( ) the packet blocking probability of user i, therefore

( ) ( )

3.6 Simulation Results

We consider a CDMA network with randomly generated spreading codes. The packet length, spreading gain, and number of correctable errors in each packet are, respectively, 200, 6, and 2. We assume that there are a total number of M = users in the network, among which users 2, 4, 5, 8 and 7 are nearby the CC and users 1, 3, 6, and 8 are located far away from the CC. The MPR matrix of the considered system scenario can be derived in an analogue way as in [28].

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Fig. 3.5 Throughput performance for different number of users participating in cooperation.

3.6.1 Throughput Enhancement due to Cooperation

Fig. 3.5 compares the throughput performance when the number of the near-end users participating in cooperative communication increases from one to four. The throughput curve when all the eight users are involved for full cooperation is also included. In this example the waiting period is determined to be S =4 (assuming that the delay requirement is

80, 2, 4, 5, 7

r

Di = i = in (3.15)). The figure shows that, as the number of near-end user increases, the throughput performance is improved. This benefits from the increase in the multi-user diversity (or cooperation gain). However, further throughput enhancement is hardly seen if full cooperation is allowed. This is because the inclusion of far-end users can not increase the effective cooperation gain, since they are typically subject to worse channel conditions. We can also see from the figure that cooperation can improve the performance only when the packet generating probability is small (in our case p <0.6). That is because, in a heavy traffic environment (large p ), the channel access phase will be fully reserved for direct data transmission, and idle periods are seldom available for cooperative packet relaying.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Fig. 3.6 Average throughput of near, far and all users.

3.6.2 Throughput Results for Near- and Far-End Users

We go on to investigate the throughput results for near-end and far-end users in both cooperative and non-cooperative environments. The results are depicted in Fig. 3.6. As we can see, due to poor channel conditions the average throughput of the far-end users is almost zero without cooperation.

However, when cooperation with near-end users is allowed, throughput up to about 0.4 for the far-end users can be achieved when the packet generating probability p is not large. Also, there is a significant increase in the overall throughput when compared with the non-cooperative case. For the near-end users, it is important to see that the throughput penalty is almost zero even though a certain portion of the channel access will be dedicated to packet relaying. This is mainly because, in the proposed CMGP protocol, only the idle periods are exploited for the relay phase, and the service priority of the relay users are potentially lower than the direct data transmission links. Fig. 3.7 compares the simulated average throughput gain (per direct link user) with the theoretical lower bound (3.12). As we can see, the analytic result shows close agreement with the simulated outcome

0 0.1 0.2 0.3 0.4 0.5

Fig. 3.7 Lower bound of throughput gain derived from Theorem 3.2.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8

Fig. 3.8 Upper bound of throughput penalty derived from Theorem 3.1.

in a low traffic scenario (p≤0.15). However, there is a large discrepancy as the traffic load becomes heavy. This is reasonable since the lower bound (3.12) is derived specifically for the low traffic environment, in which idle periods are available and can be exploited for packet relaying.

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Fig. 3.9 Average delay of near, far and all users.

Fig. 3.8 further compares the simulated throughput penalty (per direct link user) with the theoretical upper bound (3.7). The result shows that the upper bound (3.7) tends to be conservative. Actually, the throughput loss due to packet-relaying interference is pretty small (<0.02) in the proposed CMGP protocol.

3.6.3 Delay and Packet Blocking Performances

Fig. 3.9 further shows the resultant average delay performance. It can be seen that, without cooperation, even a small packet generating probability (p ≈0.1) results in severe delay penalty.

However, if cooperation is allowed, the delay performance becomes more robust against the increase in p . Finally, Fig. 3.10 depicts the packet blocking probability curves. It can be seen that, for small p (hence small packet blocking probability), the blocking probability associated with the near-end users almost diminishes. This reflects the fact that the near-end users typically enjoy good channel conditions, and the MPR capability of these links is strong so that throughput penalty can be kept very small (as evidenced by the analysis in Sec. 3.4.1).

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Fig. 3.10 Average packet blocking probability of near, far and all users.

0 50 100 150 200 250 300

Fig. 3.11 Average throughput in a dense environment.

3.6.4 Throughput Results in a Dense Environment

Fig. 3.11 further illustrates the throughput performance as the total number of users increases;

the aggregate traffic load is set to be 80% of the channel capacity, i.e., 1.2 packets per slot. The proposed CMGP method is seen to achieve the maximal throughput of 1.18 when the number of users equals 48; this yields about a 140% throughout gain as compared with the MGP. As the number of users increases, both methods are subject to throughput floors, but the CMGP still results in a 34% gain as compared with the MGP.

3.7 Summary

Motivated by [24] this chapter proposes a cooperative MAC protocol for MPR channels. As far as we know, our scheme is the first proposal which integrates the user cooperation facility and the PHY-layer MPR advantage for MAC protocol designs. The proposed method relies on a priority-based scheduling mechanism, and does not need active user identification: It is thus a promising candidate for the low-complexity protocol implementation in dense cooperative networks.

Based on Markov chain models we provide throughput analysis for the proposed protocol. We derive closed-form throughput bounds for the worst case that allow us to investigate the impact of the MPR capability on the system performance. Simulation results confirm the throughput advantage achieved by the proposed method, and validate the presented analytical results.

Chapter 4

Dynamic User Set Based Uplink Throughput