Nhiều giao thức truy cập đối với truyền thông di động P8

Multiple Access Protocols for Mobile Communications: GPRS, UMTS and Beyond Alex Brand, Hamid Aghvami Copyright  2002 John Wiley & Sons Ltd ISBNs: 0-471-49877-7 (Hardback); 0-470-84622-4 (Electronic) 8 MD PRMA ON CODE-TIME-SLOTS This chapter is concerned with MD PRMA on perfect-collision code-time-slot channels. The simple and abstract channel model used, representative for a blocking-limited system, allows one to consider an arbitrary number of code-slots E per time-slot, without having to worry about the spreading factor required to meet a certain packet erasure performance. In this framework, the scope of investigations can conveniently be extended to two extreme cases, namely only one code-slot per time-slot, but numerous time-slots N per TDMA frame, and only one time-slot per frame carrying numerous code-slots. In the first case, the CDMA feature is relinquished, and MD PRMA degenerates to pure PRMA. In the second case, the TDMA feature is relinquished. While this configuration (and in fact also PRMA itself) can simply be viewed as a special case of MD PRMA, it actually corresponds to the Reservation-Code Multiple Access (RCMA) protocol proposed in Reference [35]. As in Chapter 7, only voice-traffic will be considered. However, the focus shifts from load-based access control to fixed permission probabilities and backlog-based access control (the latter in the shape of Bayesian broadcast). The performances of pure PRMA, MD PRMA and RCMA will be compared, all with the same number of resource units U = N · E. For MD PRMA with N = 8 and E = 8 (i.e. the original UTRA TD/CDMA parameters), the impact of acknowledgement delays and TDD operation on voice dropping performance is also studied. Furthermore, the code-time-slot channel model is enhanced to account for multiple access interference (MAI). In this scenario, unlike the perfect-collision case, load-based access control can make sense. Therefore, on top of ‘conven-tional’ Bayesian broadcast, a scheme combining Bayesian broadcast with a channel access function is considered. 8.1 System Definition and Simulation Approach 8.1.1 System Definition and Choice of Design Parameters The common thread in this chapter is the consideration of code-time-slots based on the TDMA frame duration specified in Section 5.3, namely the 4.615 ms used in GSM and originally proposed for TD/CDMA. However, the focus is not limited to the TD/CDMA scenario with N = 8 time-slots and E = 8 codes per time-slot. Instead, on top of this balanced case, two extreme cases are also considered, namely one with N = 64 time-slots, 312 8 MD PRMA ON CODE-TIME-SLOTS but only one ‘code-slot’ per time-slot, and one with E = 64 codes on a single ‘time-slot’. Effectively, the first case represents pure TDMA, where MD PRMA degenerates to conventional PRMA, and the second case is pure CDMA, for which MD PRMA corresponds to RCMA proposed in Reference [35]. Choosing the same frame duration and the same number of resource units U (namely 64) for all three schemes allows for a fair comparison of their respective performance. For these three cases, MD PRMA for frequency division duplexing as defined in Section 6.2 is investigated, assuming immediate acknowledgement and using either fixed permission probabilities for voice (again the only traffic considered), or backlog-based access control. In the latter case, the voice permission probability pv (or simply p) is calculated according to the Bayesian algorithm adapted for MD PRMA, as outlined in Subsection 6.5.4. Equation (6.9) is used to carry out the estimation of the arrival rate required for this algorithm. Considering an ideal case, the value of pv is broadcast at the end of each time-slot in such a manner that it is available to all mobile stations with full precision before the next time-slot starts. For the scenario with N = 8 time-slots and E = 8 codes per time-slot, the impact of acknowledgement delays is also studied by varying the parameter x introduced in Subsection 6.2.6. This parameter determines how many time-slots a terminal must wait for an acknowledgement following the time-slot in which it sent a packet in contention mode. While waiting, it is not allowed to contend again. In the case of Bayesian broadcast, if x > 0 (i.e. acknowledgement is not immediate), the Bayesian algorithm needs to be modified, that is, pv needs to be calculated through Equation (6.7). Unlike the acknowledgements, pv is assumed to be broadcast immediately at the end of each time-slot. For the same configuration of resource units, the performance of MD FRMA for TDD with a single switching-point per frame, as specified in Subsection 6.3.3, is assessed. From one to eight time-slots per TDMA frame are assumed to be assigned to the uplink direction, where the last case is obviously only of academic interest, since no resources would be available for the downlink in this case. In the following two sections, when more than one code-slot is considered, these slots are assumed to be mutually orthogonal, which means that MAI is ignored. If dedicated channels were used, the system would exhibit hard-blocking, but owing to the PRMA element, it features soft-blocking or soft-capacity. In Section 8.4, on the other hand, MAI is accounted for in the manner specified therein, in order to assess the impact of the loss of orthogonality on access control. In this case, depending on the quality of service requirements, we are dealing with an interference-limited system; that is, excessive packet erasure may prevent all U resource units from being used. In the terminology used in Subsection 7.5.3, ‘U is soft up to an upper limit of N · E’. When interleaving is applied, it is rectangular interleaving over the length of a voice frame, which in turn is carried on four bursts (see Subsection 6.2.4). In this case, request bursts sent in contention mode are dedicated signalling bursts, transmitted on a single code-time-slot. By contrast, when interleaving is not applied, they carry not only signalling, but also user data, namely the same amount as carried by information bursts. For the basic scheme without interleaving, the delay threshold Dmax is normally set to a small value of 4.615 ms, which is equal to the length of a single TDMA frame. In the case of interleaving, Dmax is set to the length of a voice frame, i.e. 18.462 ms. To isolate the impact of interleaving and dedicated request bursts, the basic scheme is also operated 8.1 SYSTEM DEFINITION AND SIMULATION APPROACH 313 Table 8.1 Parameters relevant for the physical layer, protocol operation and traffic models Description Symbol TDMA Frame Duration Dtf Time-slots per Frame N Code-slots per Time-slot E Dropping Delay Threshold Dmax Mean Talk Gap Duration Dgap Mean Talk Spurt Duration Dspurt Parameter Value 4.615 ms 8 (or 64, or 1) 8 (or 1, or 64) 4.615 ms (no interleaving) 18.462 ms (with interleaving) 1.74 s (or 3 s) 1.41 s (or 3 s) with a Dmax of 18.462 ms in one case. Together with the traffic parameters discussed in the next subsection, all parameters mentioned so far are summarised in Table 8.1. 8.1.2 Simulation Approach, Traffic Parameters and Performance Measures As in the previous chapter, the only traffic considered in the following is packet-voice traffic, using the two-state voice model specified in Section 5.5. Two different parameter sets are considered. The first set, namely Dspurt = 1.4 s and Dgap = 1.74 s, is from the RACE ATDMA project [46], and results in a voice activity factor αv of 0.448, which is slightly higher than that in Chapter 7. As a second set, Dspurt = Dgap = 3 s taken from Reference [56] is used. This is to establish a link with Chapter 9, where mixed voice and data traffic is considered, and parameters from Reference [56] are used for both voice and Web browsing traffic. The system load is determined by the number of conversations M simultaneously supported, and we are interested in Pdrop performance as a function of M. Analogous to Chapter 7, M0.01 and M0.001 stand for the number of conversations which can be supported at a tolerated Pdrop, (Pdrop)max, of 1% and 0.1% respectively. A static scenario is considered, where Pdrop is established as a function of M, and M remains fixed over the relevant period of observation. Multiplexing efficiency ηmux relative to perfect statistical multiplexing can easily be calculated using Equation (6.1). In Section 8.4, where MAI is accounted for, the relevant figure of merit is Ploss instead of Pdrop, exactly as in Chapter 7. Each simulation-run with fixed M covers 1000 s conversation time. Where required, several simulation-runs were performed for the same value of M, in which case Pdrop and Ploss reported are the averaged result over these simulation-runs. 8.1.3 Analysis of MD PRMA Pure and modified PRMA systems were analysed for instance in References [135,143,144, 149,150,268,269]. Most of these articles provide a full Markov analysis, some an equi-librium point analysis (EPA). Due to the dimension of the state space with the here considered design parameters, a full Markov analysis is rather challenging. In Refer-ence [61], we provided an EPA for MD PRMA, which expanded on the EPA for PRMA provided in Reference [143] and adopted a few elements of Reference [149]. In certain 314 8 MD PRMA ON CODE-TIME-SLOTS scenarios, we found EPA to be satisfactory, in others not. In the following, we focus on protocol performance assessment through simulation studies. 8.2 Comparison of PRMA, MD PRMA and RCMA Performances 8.2.1 Simulation Results, No Interleaving Figures 8.1 to 8.3 show Pdrop performance of MD PRMA, PRMA, and RCMA respec-tively, with different fixed pv values (in the figures simply referred to as p) on one hand, and pv calculated through the Bayesian algorithm on the other. In all cases, the basic scheme without interleaving and a very short packet dropping delay threshold Dmax equal to Dtf , namely 4.615 ms, was considered. With MD PRMA (Figure 8.1) and Bayesian Broadcast (BB), M0.01 = 131 and ηmux = 0.92, while M0.001 = 119 (in which case ηmux = 0.83). With fixed pv, M0.01 lies between 121 (for pv = 0.1) and 131 (pv = 0.3), and M0.001 peaks at 118 (with pv = 0.5). This seems to indicate that if M0.01 (or M0.001) were the only performance measure of interest, there would not be much benefit in implementing adaptive access control. However, while it is possible to achieve high capacity with a fixed pv, it is not possible to achieve high capacity with the same pv value which gives low packet dropping at lower load. Further-more, if pv is too large, MD PRMA can become unstable. With the values considered here for M, this was experienced for pv ≥ 0.6 and M = 140. In cases in which instability is experienced, Pdrop results established through simula-tions are heavily affected by the instance in time in which the system first experienced congestion. Once caught in a congested equilibrium point, it is almost certain that the system remains in this state for the remainder of the simulation run and, from then on, Simultaneous conversations M 60 70 80 90 100 110 120 130 140 150 1.0E+0 MD PRMA, N = 8, E = 8 Dmax = 4.615 ms 1.0E-2 1.0E-4 1.0E-6 p = 0.1 p = 0.2 p = 0.3 p = 0.4 p = 0.5 p = 0.6 1.0E-8 p = 0.7 Bayes Figure 8.1 Simulated MD PRMA performance, overview 8.2 COMPARISON OF PRMA, MD PRMA AND RCMA PERFORMANCES 315 the dropping probability is close to one. For values of M for which stability problems were experienced, rather than reporting the average Pdrop measured over a few simulation runs, which would not deliver statistically relevant results, Pdrop was simply set to one in Figures 8.1 and 8.2. A better performance measure in such cases would be the so-called First Exit Time (FET) proposed in Reference [194] for slotted ALOHA and applied to PRMA in Reference [149]. The FET is the average first exit time into the unsafe region (i.e. a system state beyond the unstable equilibrium point, see Figure 3.6) starting from an initially empty channel or system. Choosing pv = 0.5 offers the best compromise between capacity (M0.01 = 128) and low dropping at low load, while appearing to allow for stable operation up to M = 140 (that is, the FET is much larger than the duration of an individual simulation-run). BB on the other hand allows for stable operation at high load while ensuring low packet dropping at low load and performs at least as well as the fixed pv approach over the entire range of M considered. One could argue that the performance of BB could be met by choosing a semi-adaptive approach, i.e. selecting pv depending on M. However, such an approach cannot easily be extended to a mixed traffic scenario, possibly with unknown traffic statistics, whereas BB adapts automatically to different traffic mixes. Furthermore, it would also require regular signalling of pv, leaving reduced computational complexity as the only potential argument in its favour. In view of the very small complexity of BB, this advantage is of no relevance in practice, though. Similar considerations apply in the case of pure PRMA. In fact, looking at Figure 8.2, to avoid stability problems, pv has to be selected even more carefully. Here, with BB, M0.01 = 129 (ηmux = 0.9), and M0.001 = 119. With fixed pv, M0.01 lies between 123 (for pv = 0.05) and 129 (pv = 0.2). M0.001, on the other hand, although assuming 118 for pv = 0.4, is limited to 114 (pv = 0.2), if the only values of pv considered are those for which the system remains stable up to M = 140. Simultaneous conversations M 60 70 80 90 100 110 120 130 140 150 1.0E+0 PRMA, N = 64, E = 1 Dmax = 4.615 ms 1.0E-2 1.0E-4 1.0E-6 1.0E-8 Figure 8.2 p = 0.05 p = 0.07 p = 0.1 p = 0.15 p = 0.2 p = 0.3 p = 0.4 Bayes Simulated PRMA performance, overview ... - tailieumienphi.vn