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 = 8andE = 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 p
v
(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 p
v
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, p
v
needs to be calculated through Equation (6.7). Unlike the acknowledgements,
p
v
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 D

max
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, D
max
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 Parameter Value
TDMA Frame Duration D
tf
4.615 ms
Time-slots per Frame N 8 (or 64, or 1)
Code-slots per Time-slot E 8 (or 1, or 64)
Dropping Delay Threshold D
max
4.615 ms (no interleaving)
18.462 ms (with interleaving)
Mean Talk Gap Duration D
gap
1.74 s (or 3 s)
Mean Talk Spurt Duration D
spurt
1.41 s (or 3 s)
with a D
max
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 D
spurt
= 1.4sandD
gap
= 1.74 s, is from the
RACE ATDMA project [46], and results in a voice activity factor α
v
of 0.448, which is
slightly higher than that in Chapter 7. As a second set, D
spurt
= D
gap
= 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 P
drop
performance as a function of M. Analogous
to Chapter 7, M
0.01
and M
0.001
stand for the number of conversations which can be

supported at a tolerated P
drop
, (P
drop
)
max
, of 1% and 0.1% respectively. A static scenario
is considered, where P
drop
is established as a function of M,andM remains fixed over the
relevant period of observation. Multiplexing efficiency η
mux
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 P
loss
instead of P
drop
, 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,inwhichcaseP
drop
and
P
loss
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 P
drop
performance of MD PRMA, PRMA, and RCMA respec-
tively, with different fixed p
v
values (in the figures simply referred to as p) on one hand,
and p
v
calculated through the Bayesian algorithm on the other. In all cases, the basic
scheme without interleaving and a very short packet dropping delay threshold D
max
equal
to D
tf
, namely 4.615 ms, was considered.
With MD PRMA (Figure 8.1) and Bayesian Broadcast (BB), M
0.01
= 131 and η
mux
=

0.92, while M
0.001
= 119 (in which case η
mux
= 0.83). With fixed p
v
, M
0.01
lies between
121 (for p
v
= 0.1) and 131 (p
v
= 0.3), and M
0.001
peaks at 118 (with p
v
= 0.5). This
seems to indicate that if M
0.01
(or M
0.001
) 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 p
v
, it is not possible to achieve high
capacity with the same p
v
value which gives low packet dropping at lower load. Further-

more, if p
v
is too large, MD PRMA can become unstable. With the values considered
here for M, this was experienced for p
v
≥ 0.6andM = 140.
In cases in which instability is experienced, P
drop
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,
1.0E-8
1.0E-6
1.0E-4
1.0E-2
1.0E+0
60 70 80 90 100 110 120 130 140 150
Simultaneous conversations
M
Packet dropping ratio
P
drop
p
= 0.1
p
= 0.2
p
= 0.3
p

= 0.4
p
= 0.5
p
= 0.6
p
= 0.7 Bayes
MD PRMA,
N
= 8,
E
= 8
D
max
= 4.615 ms
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 P
drop
measured over a few simulation
runs, which would not deliver statistically relevant results, P
drop
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 p
v
= 0.5 offers the best compromise between capacity (M
0.01
= 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 p
v
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 p
v
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 p
v
, 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, p
v
has to be selected even more carefully. Here, with BB,
M
0.01
= 129 (η

mux
= 0.9), and M
0.001
= 119. With fixed p
v
, M
0.01
lies between 123 (for
p
v
= 0.05) and 129 (p
v
= 0.2). M
0.001
, on the other hand, although assuming 118 for
p
v
= 0.4, is limited to 114 (p
v
= 0.2), if the only values of p
v
considered are those for
which the system remains stable up to M = 140.
1.0E-8
1.0E-6
1.0E-4
1.0E-2
1.0E+0
60 70 80 90 100 110 120 130 140 150
Simultaneous conversations

M
Packet dropping ratio
P
drop
p
= 0.05
p
= 0.07
p
= 0.1
p
= 0.15
p
= 0.2
p
= 0.3
p
= 0.4
Bayes
PRMA,
N
= 64,
E
= 1
D
max
= 4.615 ms
Figure 8.2 Simulated PRMA performance, overview
316
8 MD PRMA ON CODE-TIME-SLOTS

1.0E-6
1.0E-4
1.0E-2
1.0E+0
60 70 80 90 100 110 120 130 140 150
Simultaneous conversations
M
Packet dropping ratio
P
drop
p
= 0.7
p
= 0.8
p
= 0.9
p
= 0.95
p
= 0.98
p
= 1.0
Bayes
RCMA,
N
= 1,
E
= 64
D
max

= 4.615 ms
Figure 8.3 Simulated RCMA performance, overview
Finally, with RCMA, the situation is slightly different, as illustrated in Figure 8.3. Note
first that, since D
max
= D
tf
, there is only one contention opportunity for a terminal until
the first packet in a spurt is dropped, such that there will be significant dropping irrespec-
tive of the load, as soon as p
v
< 1. In fact, for p
v
< 0.98 and M ≤ 100, P
drop
is almost
uniquely determined by the waiting probability 1 − p
v
, which explains the flat segment
of the respective curves. On the other hand, even if there is a temporary accumulation of
contending terminals, they will normally be able to choose between numerous code-slots
available for contention, such that the collision risk is small. Therefore, stability is not
an issue even for p
v
= 1. This in turn means that there is limited benefit in controlling
access dynamically, e.g. through Bayesian broadcast, which is also shown in the figure.
This is very much in contrast to pure PRMA (and to a lesser extent to MD PRMA), where
the accumulation of a few contending terminals C, such that C>1/p
v
, can result in a

number of successive collisions. During these collision slots, C will grow further, and
eventually, C  1/p
v
(orinthecaseofMDPRMA,C  E/p
v
), such that the system
is bound to become unstable. To complete the discussion of the results for RCMA, with
the p
v
values considered, M
0.01
is between 128 and 129, while with BB, M
0.01
= 130.
M
0.001
assumes a value of 118 for both BB and p
v
= 1.
8.2.2 Performance Comparison and Impact of Interleaving
In Reference [35], it is claimed that ‘RCMA is superior to PRMA in terms of system
capacity even when a median size of code set is used’. To come to this conclusion, the
authors of Reference [35] applied a frequency reuse factor of seven to PRMA, which
may be considered conservative, but is probably not completely unrealistic. At the same
time however, and curiously enough, the authors spent not a single word on where the
‘median number of codes’ should come from and what kind of bandwidth or spreading
factor would be required to support the corresponding number of simultaneous users.
For reasons outlined in detail in Sections 3.2 and 5.1, we have no intention of stepping
onto a field full of mines by trying to assess the spectral efficiency of TDMA, hybrid