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) 9 MD PRMA WITH PRIORITISED BAYESIAN BROADCAST This last of the three chapters dealing exclusively with MD PRMA-related research results finally treats scenarios that are not limited to voice traffic only. To assess the performance of the prioritised Bayesian broadcast algorithm proposed in the following, a simulation study for different mixtures of voice and data traffic is conducted, where the latter consists either of Web traffic, email traffic, or a combination of the two. Preceding the discussion of the simulation results, possible approaches to prioritisation at the random access stage are evaluated, and a number of different algorithms which combine Bayesian broadcast with prioritisation are presented. 9.1 Prioritisation at the Random Access Stage The reader questioning the motivation for introducing prioritisation at the random access stage is referred to Section 3.7, where this topic was treated in considerable detail, and to the priority-class specific access control feature of GPRS described in Section 4.11. UMTS provides also random access prioritisation, see Chapter 10. For a mobile terminal to successfully send a channel request message in a given time- slot, three events must occur simultaneously: (1) there must be a random access slot (or C-slot, in the terminology used so far) in this time-slot; (2) the terminal must gain permission to access this slot; and (3) the request packet transmitted in this slot must be successfully received by the base station. If the capture effect and transmission failures due to bad channel conditions are ignored, the last event essentially means that the packet must not collide with another packet transmitted in the same slot. Since the collision probability depends on how access to C-slots is controlled, events (2) and (3) cannot be de-coupled. On the other hand, the first event can be considered separately from the other two for the purposes considered here 1 . If we want to introduce prioritisation at the random access stage to discriminate the 1 Strictly speaking, there is also a dependency between event one and the other two events. At a given arrival rate, with increasing interval between C-slots, the number of newly arriving packets between two such slots increases. Thus, the collision probability increases as well, if access to C-slots is not controlled adaptively. 330 9 MD PRMA WITH PRIORITISED BAYESIAN BROADCAST access delay experienced by different priority classes, we therefore fundamentally have two options. We could define different classes of C-slots, each class only to be accessed by the respective priority class, and schedule these slots according to the QoS requirements of the respective services associated with the different classes. Alternatively, we could let all users access the same C-slots, but control the access to these slots according to the priority class. High-priority users will obtain permission to access a certain C-slot with higher probability than low-priority users. With the first approach, collisions do not occur between users of different priority classes, with the second, they do. Recall that with in-slot protocols such as MD PRMA considered here, every slot that is currently not used for information transfer is a C-slot available for contention. Since C-slots are not explicitly scheduled, the first approach to random access prioritisation does not easily lend itself to MD PRMA. In the following, therefore, the focus is limited to the second approach, namely service-class specific access control to a single class of C-slots. In Sections 3.5 and 4.11, different approaches to access control for S-ALOHA protocols and their derivatives were discussed. These are essentially: • use of fixed permission probabilities; • retransmission backoff schemes; • stack-based schemes such as splitting or collision resolution algorithms; and • (global) probabilistic access control such as Bayesian broadcast. It is possible to introduce prioritisation with all these approaches. In the case of fixed permission probabilities, different values can be chosen for different priority classes. However, whether with or without prioritisation, stability problems are encountered with such a scheme. With backoff schemes, prioritisation can be achieved by choosing priority- class specific initial permission probability values (i.e. those relevant for the first trans- mission attempt), but also class specific backoff rates. It was reported in Section 3.5 that it is possible to exceed the 1/e throughput-limit of S-ALOHA, for instance with tree-based collision resolution algorithms. This throughput advantage is also maintained in prioritised versions of such protocols, as shown for example in Reference [270] (for a selection of other relevant references in this field, the reader is advised to consult Reference [271]). However, these stack-based algorithms have the disadvantage of requiring immediate (although normally only binary) acknowl- edgements to work properly, which, as discussed at length earlier, is difficult to achieve in a real implementation. Our interest in prioritisation was triggered by a submission to the ETSI SMG 2 GPRS ad-hoc group responsible for the standardisation of the GPRS air interface [272]. In this document, a prioritised pseudo-Bayesian broadcast algorithm with proportional priority distribution between two priority classes was proposed for the GPRS random access. This algorithm and subsequent enhancements are described in detail in the next section. In Reference [55], we compared the performance of a four-class pseudo-Bayesian broadcast algorithm with semi-proportional priority distribution with two other four-class algorithms. The first one is based on exponential backoff, where, on top of priority-class specific initial permission probabilities, as in Reference [273], the backoff rates chosen were also class specific. The second one, a stack-based algorithm, was first proposed in Reference [274]. It is an enhancement of a two-class algorithm due to Stavrakakis and Kazakos [275] and 9.2 PRIORITISED BAYESIAN BROADCAST 331 supports an arbitrary number of priority classes. The Stavrakakis–Kazakos algorithm has a maximum stable throughput from 0.32 to 0.357, depending on the traffic composition. We found that the exponential backoff algorithm, while not requiring any feedback other than acknowledgements, clearly performs worst, and exhibits stability problems. The Bayesian algorithm outperforms the stack-based algorithm, and is (unlike the latter) inherently fair, but may, depending on the chosen implementation, require slightly more signalling overhead, since individual permission probabilities need to be conveyed to the terminals. However, they need not necessarily be signalled for every slot (cf. Section 4.11). A further advantage is that the proposed algorithm can easily be adapted for operation with frame-based protocols. With these considerations in mind, and given the successful adaptation of Bayesian broadcast to MD PRMA discussed earlier, it cannot be a surprise that the prioritised algorithm chosen for the following investigations is indeed based on Bayesian broadcast. 9.2 Prioritised Bayesian Broadcast So far, the focus has been restricted to homogeneous voice traffic, thus for access control only a single (access) permission probability value p v needed to be calculated. Consider now the generic (single-class) permission probability value p calculated according to the (pseudo-)Bayesian broadcast algorithm outlined in Subsection 6.5.4. In the following, starting with the initial proposal in Reference [272] for two-class proportional priority distribution, several algorithms will be introduced which, based on p, calculate individual access probability values p i for each priority class i. Class 1 has highest priority, and voice traffic is always assigned to class 1, thus p v = p 1 . Most of these algorithms were initially proposed and investigated for S-ALOHA, but adaptation to MD PRMA is straightforward. 9.2.1 Bayesian Scheme with Two Priority Classes and Proportional Priority Distribution On a perfect collision channel as considered here, the optimum traffic level G 0 ,atwhich the throughput curve of S-ALOHA peaks, is G 0 = 1. Therefore, p should be chosen such that the expected traffic assumes a value of one. If the estimated mean backlog v is equal to the real backlog n, this is achieved by choosing p = min(1,G 0 /v), (9.1) as already pointed out in Section 6.5. For MD PRMA with A[t] C-slots in time-slot t, the only modification required is that G 0 is now A[t]. In Reference [272] it was suggested to extend the Bayesian algorithm to support two priority classes by assigning different transmission probabilities to the users of the high and the low priority class based on p from the single-class scheme as follows: p 1 = min(1,(1 + α) · p), (9.2a) p 2 = α · p, (9.2b) with the proportion of successfully received access bursts of class-two users to all users α = S 2 /S, (9.3) 332 9 MD PRMA WITH PRIORITISED BAYESIAN BROADCAST averaged over a suitable time-window. The degree of prioritisation cannot be chosen, it is determined by α, which is why this approach was termed ‘proportional priority distribution’ in Reference [272]. Provided that the backlog estimation is accurate, i.e. v ≈ n,andthatα reflects the backlog proportion of low priority users to all users, which is n 2 /n (where n = n 1 + n 2 ), the offered traffic G = p 1 · n 1 + p 2 · n 2 assumes the value of the optimum offered traffic G 0 , as desired. However, since p 1 >p 2 , the throughput proportion α is expected to underestimate the backlog proportion n 2 /n. Interestingly, together with David Sanchez, an M.Sc. student at King’s College London in 1995/1996, we found that while this is indeed the case, at the same time also the backlog is underestimated. Taken together, these two effects compensate in a manner which causes the offered traffic to assume its optimum value all the same. 9.2.2 Bayesian Scheme with Two Priority Classes and Non-proportional Priority Distribution In an internal LINK ACS document, Jason Brown pointed out that the algorithm proposed in Reference [272] could easily be extended to allow for non-proportional priority distri- bution. This is achieved by choosing p 1 = min(1,m· p), (9.4a) p 2 = k · p, (9.4b) with m = 1 − α · k 1 − α ,(9.5) and α as above. Either m or k can be chosen arbitrarily and thus used to control the degree of prioritisation of high-priority users. In the following, k will be used as the main prioritisation parameter. 9.2.3 Bayesian Scheme with Four Priority Classes and Semi-proportional Priority Distribution In 1996/1997, Celia Fresco Diez, an exchange student at King’s College London, continued our earlier investigations on the GPRS random access. Some of her findings are summarised in Reference [55]. The four-class semi-proportional algorithm described here is due to her. In this algorithm, the permission probability values of the four classes are set as follows: p 1 = min(1,m· p), (9.6a) p 2 = min  1, 2 · m + k 3 p  ,(9.6b) p 3 = min  1, m + 2 · k 3 p  ,(9.6c) p 4 = k · p, (9.6d) 9.2 PRIORITISED BAYESIAN BROADCAST 333 with p and m as before and α = (S 2 /3) + 2 · (S 3 /3) + S 4 S .(9.7) The parameter k allows the delay spread between priority classes to be chosen, whereas the degree of prioritisation between each priority class cannot be chosen individually, which is why this approach was termed semi-proportional rather than ‘non-proportional’ priority distribution. The lower the value of k, the larger the difference between the transmission probabilities and thus the stronger the degree of prioritisation. For k = 1the algorithm degenerates to single-class Bayesian broadcast control. 9.2.4 Bayesian Scheme with Four Priority Classes and Non-proportional Priority Distribution In some cases, it may be desirable to control the degree of prioritisation for each class indi- vidually. For Reference [52], we extended the above algorithm to a full non-proportional algorithm according to the following: p 1 = min(1,m· p), (9.8a) p 2 = min  1, z 1 m + z 2 k z p  ,(9.8b) p 3 = min  1, z 2 m + z 1 k z p  ,(9.8c) p 4 = k · p, (9.8d) with p and m as before. In this algorithm, k determines the delay spread between classes 1 and 4, while the parameters z 1 and z 2 , with z 1 >z 2 and z = z 1 + z 2 ,(9.9) determine the relative degree of prioritisation of classes 2 and 3. The relevant throughput proportion must now be calculated as α =  z 2 z S 2 + z 1 z S 3 + S 4  1 S .(9.10) Note that the semi-proportional algorithm is simply a special case of the non- proportional algorithm with z 1 = 2andz 2 = 1. Under assumptions equivalent to those made for the two-class proportional algorithm above, the offered traffic G is controlled to the optimum traffic G 0 = 1, as desired. This can be verified through relatively simple arithmetic, as shown in Reference [61, Appendix E]. In Subsection 3.7.4, a case was made for a centralised implementation of such access control schemes, and the resources for downlink signalling required with such an approach were discussed in Section 4.11. It was pointed out that the permission probability p would have to be transmitted regularly (although not in every slot), but that a 4-bit resolution was sufficient. On the other hand, with the structure of the prioritisation algorithm considered here, m (which fluctuates with α, which in turn depends on the chosen time-window 334 9 MD PRMA WITH PRIORITISED BAYESIAN BROADCAST for averaging) can be broadcast less frequently. Finally, only infrequent signalling of values for k, z 1 ,andz 2 is required. Obviously, exploiting the structure of a specific algorithm to reduce signalling load means limited flexibility once the system is deployed. By contrast, if p 1 to p 4 were signalled individually (e.g. by three to four bits each), a network operator could change the algorithm arbitrarily through a software update at the base station, without affecting mobile terminals already in use. 9.2.5 Priority-class-specific Backlog Estimation The algorithms proposed above have the two following things in common. (1) No attempt is made to estimate the backlog of individual priority classes separately, instead, the backlog proportion is estimated based on the total estimated backlog and the throughput proportion. (2) The individual permission probability values are expressed as a function of the single-class permission probability value G 0 /v. While the second feature is deliberate to limit the signalling overhead required on the downlink, as just discussed, the first was initially identified as a shortcoming of these algorithms, which one might wish to overcome. However, since we found that, for S-ALOHA, the average access-delay performance over all classes with these prioritised algorithms exactly matched the performance of the single-class algorithm, we did not invest any further effort in attempting to estimate the backlog of each class individually. In the mean time, Frigon and Leung proposed in Reference [271] a prioritised version of Bayesian broadcast for x priority classes, which relies on such individual backlog estimation. In this algorithm, a prioritisation parameter γ i can be selected for each priority class individually, which in turn determines the access permission probability to be used for each class through p i = min(1,γ i /v i ).Sinceγ i can be viewed as the expected offered traffic for each class, the sum over all xγ i -values must amount to G 0 = 1. If for any class v i <γ i in any given slot, and p i were simply set to min(1,γ i /v i ), the expected total offered traffic in that slot would be less than one, which is not optimum. Frigon and Leung account for this fact, and assign such leftover capacity to other priority classes (in order of priority) by temporarily increasing the traffic fraction assigned to these classes. Doing so ensures that low priority users send their packet immediately, if currently no high priority users are backlogged and, conversely, that no capacity is reserved for low priority users if there are none. Taking it to the extreme, γ x can be set to zero, such that the lowest priority class obtains only ‘leftover capacity’. This improves the average delay performance compared to the single-class algorithm slightly, and thus also compared to the algorithms we proposed above. To exploit this performance advantage, the backlog needs to be estimated for each class individually, an extra effort which could well be justified. As a further attractive feature, this algorithm could also be extended by setting γ 1 adaptively given v 1 , to help meeting a specific desired access delay performance for class 1 (as a result, γ 2 γ x would obviously also need to be readjusted). However, it would not be possible to link p i to p any more as in Equation (9.8). Instead, individual 9.2 PRIORITISED BAYESIAN BROADCAST 335 p i values would have to be signalled for every time-slot carrying C-slots, adding overhead on the downlink. Rivest’s derivation of the pseudo-Bayesian broadcast algorithm in Reference [51] is based on the assumption that the arrival process of newly generated packets is Poisson, and the same also holds true for all prioritised versions of this algorithm. Frigon and Leung therefore looked at the impact of non-Poisson arrivals, by assessing the performance of their algorithm with self-similar traffic in Reference [271]. While self-similar traffic resulted, not surprisingly, in increased total average access delay, they found that their algorithm still reduced effectively the average access delay of the high-priority users. This is consistent with our findings reported later for email and Web traffic, which is similar in nature to the traffic Frigon and Leung considered. 9.2.6 Algorithms for Frame-based Protocols In Reference [271], Frigon and Leung adapted their prioritised algorithm also for frame- based protocols, that is, TDD protocols with a single switching-point between link direc- tions per TDMA frame. In this modified algorithm, every mobile terminal may pick only one C-slot per TDMA frame. This seems also to be the case in the PRMA/TDD protocol proposed by Delli Priscoli in Reference [161], which also features adaptive and priority- class dependent parameter calculation for access control (but the parameter calculation is not based on Bayesian reasoning, instead it relies heavily on the knowledge of traffic statistics). Compared to the frame-based FRMA protocol discussed in Section 6.3, where a terminal is allowed to contend repeatedly in a frame before receiving acknowledgements, this approach increases access delay unnecessarily. We believe therefore that terminals should be allowed to contend more than once per frame. With the semi-proportional and non-proportional prioritisation algorithms discussed above and the adaptation of the single-class Bayesian broadcast algorithm to FRMA described in Subsection 6.5.6, all tools required for prioritisation with FRMA are available. Since every terminal is allowed to contend repeatedly in a frame, it is expected that rather low values will need to be chosen for the prioritisation parameter k in Equation (9.6) or (9.8) to achieve worthwhile access delay discrimination. This is because with FRMA, as with every frame-based protocol, access delay discrimination through access control alone can only be achieved in terms of multiples of the TDMA frame duration. To influence the delay behaviour beyond the resolution of an entire frame, appropriate priority-class dependent resource allocation algorithms could be added (for instance, the time-slots at the start of a frame could be assigned to high-priority users with preference). Note that both the slot-based and frame-based protocols considered in Reference [271] are purely contention-based. By contrast, both the slot-based MD PRMA protocol and the frame-based MD FRMA protocol (the latter not being investigated any further here) are reservation-based, as they belong to the family of in-slot R-ALOHA protocols (cf. Subsection 3.6.4). Finally, the PRMA/TDD protocol in Reference [161] is an out-slot R-ALOHA protocol, which not only features adaptive parameter calculation for the C- slot permission probability, but also adaptive calculation of the required number of C-slots in each frame to guarantee a certain success probability. This in turn affects the position of the switching-point between link directions. 336 9 MD PRMA WITH PRIORITISED BAYESIAN BROADCAST 9.3 System Definition and Simulation Approach 9.3.1 System Definition The system considered here is based on the original TD/CDMA design parameters, as summarised in Tables 5.2 and 5.3. Pro memoria, a TDMA frame lasts 4.615 ms and carries N = 8 time-slots, as in GSM. E = 8 codes are available on each time-slot and, as in most parts of Chapter 8, these code-slots are assumed to be mutually orthogonal, which means that MAI is ignored. Accordingly, considerations provided in Chapter 8 on the system being blocking-limited apply also here. At most one code-time-slot per TDMA frame is allocated to each user, thus limiting the available net data-rate to 8.125 kbit/s. Rectangular interleaving over the length of one RLC-PDU is applied for all traffic types considered. For the transmission of voice frames and the short RLC-PDUs used for Web traffic, which carry 150 user bits each, four bursts are required. The long RLC-PDU used for email traffic carries 3600 user bits, which fit into the payload of 96 bursts. Padding is used to fill the last of a sequence of RLC-PDUs carrying a datagram or message. Hence, on average, 1800 bits per email message are padding bits. As far as voice and Web traffic are concerned, the traffic model parameters considered are those suggested in Reference [56]. For voice traffic, this means D spurt = D gap = 3s. The Web traffic parameters are listed in Table 5.4 together with the email traffic parameters we derived. For access control, prioritised Bayesian broadcast control with four priority classes and either semi-proportional or non-proportional priority distribution according to Equations (9.5) to (9.10) is used. The basic permission probability p is calculated according to the Bayesian algorithm adapted for MD PRMA, as outlined in Subsection 6.5.4. In the implementation chosen, to calculate the throughput proportion with Equation (9.7) or (9.10), the running average from t = 0 is taken for every simulation-run 2 . To carry out the required estimation of the arrival rate, Equations (6.8) and (6.9) are used for data and voice traffic respectively 3 . Immediate acknowledgement and full signalling of the relevant access parameters for every time-slot are assumed. For duplexing, FDD is considered, that is, the FRMA-based version of the protocol is not investigated further. Finally, with regards to the duration of the reservation phase, voice and Web terminals hold their reservations until they have emptied their transmission buffer (i.e. upon comple- tion of the transfer of a talk spurt or an IP datagram). In the case of email traffic, limited allocation cycles are considered as well, in which case the duration of the reservation phase is limited to the transfer of a few long RLC-PDUs, as indicated. In accordance with the discussion on terminology provided in Section 5.8, this could also be viewed in the following terms. In the case of Web traffic, the network layer (NWL) does not perform segmentation, thus every packet delivered to the RLC layer contains an entire datagram. In the case of emails however, if allocation cycles are limited, a message is segmented by the NWL into several packets equivalent to the allocation cycle length. In both cases, the duration of the reservation phase corresponds simply to the length of the NWL packet. In the implementation considered, it is assumed that terminals with non-empty transmis- sion buffers at the end of an allocation cycle need to contend for further cycles, rather 2 To cater for traffic mix fluctuations, it may be better to average over an appropriate time-window instead. 3 Note that Equation (6.8) could also be used for voice traffic, if the traffic statistics required for Equation (6.9) were not known. 9.3 SYSTEM DEFINITION AND SIMULATION APPROACH 337 than send an extension request message in reservation mode. Again, this could be viewed as the lower layers first releasing the reservation and only then notifying the NWL of successful transfer of its packet, in which case the NWL immediately submits the next packet to the RLC. 9.3.2 Simulation Approach As discussed in Section 5.6, due to the intricacies of the data traffic models considered, no attempt is made to analyse the delay and dropping performance for mixed voice and data traffic, thus only simulation results will be provided in this chapter. Performances are investigated using the global mean data session interarrival time µ Dsess as the main simulation parameter, while keeping the number of ongoing voice conversations fixed at a certain value M. The values considered for µ Dsess are between 0.5 and 12 s. Given the relatively few data sessions per 1000 s simulation-run, and the large variance σ 2 Sd of the size of Web datagrams S d , but particularly of email messages, as determined by Equation (5.31), the amount of data generated per simulation-run, even with constant µ Dsess , fluctuates considerably. Results presented as a function of µ Dsess would therefore be meaningless. Instead, results are reported as a function of the normalised throughput S (that is, normalised to the total user channel rate of N · E · 8.125 kbit/s = 520 kbit/s). This has two added benefits: it allows one to relate P drop for voice in the mixed-traffic-scenarios considered to P drop for voice-only traffic. Furthermore, datagrams dropped due to overflow of the queue of data terminals serving WWW traffic do not distort results, as discussed in Subsection 5.6.2 4 . Recall that such dropping due to memory constraints has nothing to do with dropping a voice frame at the MAC level due to delay constraints. From a MAC perspective, for non-real-time traffic, P drop = 0. The throughput includes all bursts received error free by the BS including request bursts and fill-bursts (due to padding) in the last RLC-PDU. For voice traffic, P drop is reported as a function of S. For data traffic, for reasons outlined in Subsection 6.2.8, only access-delay performance is assessed, separately for each priority class, and again as a function of S. Access delay is defined as the time between arrival of a request at the MS MAC entity (hence excluding queuing delay while this entity is busy transmitting previous NWL packets) and the end of the successfully accessed C-slot. The time between successful contention and actual start of user data transmission, which could also be considered as part of the access delay, is ignored, since it is the same for all packets here, namely N − 1 time-slots. Figure 9.1 shows that the results obtained, before appropriate processing, fluctuate considerably (the processed results for the example shown can be found in Subsec- tion 9.6.2). Typically, P drop fluctuations occur predominantly at low traffic, mainly due to limited statistical significance as a result of the low number of dropped frames. On the other hand, data access delay fluctuates most at high load. This is once more due to the intricacies of the data traffic models, in particular due to the high variance of the packet size distribution σ 2 Sd . To increase the reliability of the results and to obtain smooth 4 As indicated there, with the queues considered (holding 50 datagrams irrespective of their size), rarely more than 1% of the generated datagrams are dropped. This may appear significant from a QoS perspective and would call for longer queues, or better still, for the allocation of multiple slots to reduce the transmission time and therefore the dropping probability. However, it is only relevant here in terms of the impact on the traffic model, which is moderate. 338 9 MD PRMA WITH PRIORITISED BAYESIAN BROADCAST 1.0E-6 1.0E-5 1.0E-4 1.0E-3 1.0E-2 1.0E-1 0.74 0.77 0.8 0.83 0.86 0.89 0.92 0.95 Normalised throughput S P drop (voice) 0 0.2 0.4 0.6 0.8 1 1.2 1.4 Data access delay D acc Voice, class 1 Web browsing, class 2 Web browsing, class 3 Email, class 4 Figure 9.1 Illustration of the fluctuations in unprocessed voice dropping and data access delay results graphs, a large number of simulations had to be performed in a very time-consuming exercise. Every point shown in the following figures is a result of averaging individual points obtained in several 1000 s simulation-runs. The grouping of points for averaging was performed manually. Care was taken that the range of individual throughput values spanned by the averaged points did not exceed 1.5% of the average throughput over these points. In retrospective, obviously, it would have been worthwhile to invest some effort in accelerating the simulation process, and in automating the processing of the results. 9.3.3 Traffic Scenarios Considered To investigate the impact of data traffic on the voice dropping performance and of priori- tised Bayesian broadcast on the access delay performance of data assigned to different priority classes, four traffic scenarios are considered. These are two different scenarios with a mixture of voice and Web browsing traffic, a scenario with voice and email traffic and, finally, voice and both data traffic types together. With voice and a single type of data traffic assigned to only one priority class, voice uses priority class 1 and data priority class 4. For the second scenario with mixed voice and Web traffic, half of the Web traffic is associated with priority class 2, and the other half with priority class 4. Every new Web session is assigned to either of these classes, according to the outcome of a Bernoulli experiment with parameter p = 0.5. In scenario 4 with mixed data sources, voice is associated with priority class 1, Web browsing with priority classes 2 and 3, and emails with priority class 4. The fraction of [...]... four, using both semi- and non-proportional priority distribution 9.7 SUMMARY 347 9.7 Summary Recognising the benefit of prioritisation at the random access stage, one is left with two fundamental options on how to achieve such prioritisation Different classes of C-slots can be defined, which are scheduled according to the QoS requirements of the different services considered Alternatively, a single... in both cases Correspondingly, unlike the voice dropping performance, the data access delay remains virtually unaffected by the changing traffic composition and therefore, the equivalent to Figure 9.10 is not shown Instead, for an unequal traffic-split between priority classes 2 to 4 and only for k = 0.1, Figure 9.12 shows the impact of non-proportional priority distribution, according to Equations (9.8)... appropriately and, depending on the service, limiting the length of allocation cycles, one can effectively control the amount of prioritisation, and also trade-off voice dropping probability against data access delay The proposed four-class algorithm features functional relations between the different access permission probability values to enable efficient downlink signalling Where a predictive access delay... generated, prioritisation at the random access stage through varying k has no impact on Pdrop This can be corrected by limiting the allocation cycle length, which is discussed below 2 Again due to the high message size variance σSd , it was rather difficult to obtain smooth Pdrop curves In fact, we surrendered in our attempts to eliminate some unwanted crossovers between the two curves for S < 0.8, after having... email class 4) 1.0E-5 z1 = 3, z2 = 1 (non-prop priority distribution) 1.0E-6 0.73 0.75 0.77 0.79 0.81 0.83 0.85 0.87 0.89 0.91 0.93 0.95 Normalised throughput S Figure 9.11 Voice dropping performance with mixed voice, Web, and email traffic, with an unequal traffic-split between priority classes two to four, using both semi- and non-proportional priority distribution 1 Data access delay Dacc [sec.] 0.8 M... effects also explain the small spread between the two curves shown in Figure 9.9 In Figure 9.10, the effectiveness of the prioritised Bayesian broadcast algorithm in terms of discriminating the access delays experienced by the different classes is illustrated With k = 0.7, a significant spread between all three data classes can only be observed for S > 0.88, while with k = 0.1 class 4 suffers already... it remains roughly the same for k = 0.7, such that the spread between these two curves is even smaller than that in Figure 9.9 With the semi-proportional priority distribution algorithm used until now, the degree of prioritisation between individual access classes depends on the proportion of contention traffic (or access bursts) per class, which determines α in Equation (9.7), thus cannot be chosen In... experienced by the two data classes increases with a decreasing value of k With k = 0.7, below S = 0.86, the access delay performance of the two data classes is very similar, while with k = 0.1, a delay discrimination can already be observed at much lower throughput levels than with k = 0.7 9.5 Simulation Results for Mixed Voice and Email Traffic 9.5.1 Performance with Unlimited Allocation Cycle Length... priority distribution 1 Data access delay Dacc [sec.] 0.8 M = 80 voice conversations (class 1) Variable data traffic (25% WWW class 2, 25% WWW class 3, 50% email class 4) z1 = 3, z2 = 1 (non-prop priority distribution) k = 0.1 0.6 Class 4, non-prop Class 4, semi-prop 0.4 Class Class Class Class 0.2 0 0.74 0.77 3, 3, 2, 2, 0.8 non-prop semi-prop non-prop semi-prop 0.83 0.86 0.89 0.92 0.95 Normalised throughput... experienced until completion of the message transfer, which includes the time spent in the contention state between allocation cycles These accessdelay values relate to an average transfer delay, ignoring padding, of approximately 9.3 s (i.e 9423 bytes mean message size transmitted at 8.125 kbit/s) 9.6 SIMULATION RESULTS FOR MIXED VOICE, WEB AND EMAIL TRAFFIC 343 1.0E-1 M = 90 voice conversations (class 1) . the disadvantage of requiring immediate (although normally only binary) acknowl- edgements to work properly, which, as discussed at length earlier, is difficult. Preceding the discussion of the simulation results, possible approaches to prioritisation at the random access stage are evaluated, and a number of different