HYBRID CAC FOR MBMS-ENABLED 3G UMTS NETWORKS

Transcription

1 The 17th Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 06) HYBRID CAC FOR MBMS-ENABLED 3G UMTS NETWORKS Michael Neophytou Andreas Pitsillides Department of Computer Science, University of Cyprus 75 Kallipoleos Str, P.O. Box 20537, CY-1678 Nicosia, Cyprus ABSTRACT In this paper, a novel hybrid Connection Admission Control (CAC) algorithm based on downlink transmission power and aggregate throughput of shared channels is presented ( DPTCAC: Downlink Power/Throughput based CAC algorithm ). The algorithm has special applicability in the case of wireless networks with particular emphasis on 3G UMTS networks [6] enhanced with new multicasting technologies namely in this case Multimedia Broadcast-Multicast Service (MBMS) [7]. The hybrid nature of the algorithm emerges from the combination of both pure power based and conventional throughput computations to determine the outcome of the admission decision in the case of dedicated and shared connection setup request respectively. The motivation for introducing this hybrid CAC approach is twofold: on one hand it is stimulated from the need to use a representative resource metric on which to base CAC decisions and on the other hand on the necessity to take advantage of the particularities of the resource allocation procedure in true multicast environments with connection sharing. We first present the new algorithm which is based on previously conducted work in [1], [2] and augment this presentation with description of the hybrid nature of our proposal. We then compare via simulations the performance of the proposed algorithm against a reference Throughput based CAC algorithm (TCAC) [3]. Simulation results show a beneficial effect of DPTCAC s applicability on cell capacity without observable degradation of offered QoS. Key terms UMTS, Power based Connection Admission Control, Cell capacity. I. INTRODUCTION Connection Admission Control (CAC) belongs to an array of Radio Resource Management (RRM) techniques. The objective of CAC is to manage resource allocation from the early stage of a new connection establishment by focusing on the two primary goals of satisfying QoS requirements of new connections while preserving QoS commitments for ongoing connections at the maximum possible extent. CAC may be viewed as the forefront of a QoS fulfilment and safeguarding strategy since an intelligent and efficient admission policy ensures smooth network operation in advance while maximizing system capacity in terms of number of users. Extensive past research on CAC targeted the extraction of representative resource metrics on which to base the admission decision. Undoubtedly, the key to a successful CAC policy is the consideration of each network s individual attributes and in the case of 3 rd Generation Universal Mobile Telecommunications System (UMTS) networks [6], these attributes include the inherent sensitivity to interference from which the wireless interface suffers. Other signal interference experienced both at mobile terminals and base stations is an intrinsic limitation of the wireless interface and emerges from the nature of the radio link medium and modulation scheme used. In the case of UMTS networks, the applied modulation scheme is the Wideband-Code Division Multiple Access (WCDMA) for which a detailed presentation can be found in [3]. Independently as to the choice of resource metrics to make use of during the CAC process, further performance efficiency can be achieved by considering the possibility of alternative traffic delivery methods such as multicasting/broadcasting supplementary to traditional unicast communication paths. The specification of Multimedia Broadcast-Multicast Service (MBMS) [7] enhancement of UMTS Release 6 introduces multicasting by means of shared connections for traffic delivery among multiple receivers of the same content and thus accomplishes efficient allocation and utilization of the relatively scarce wireless resources. An optimized CAC algorithm should therefore exhibit multicast environment awareness and take advantage of the multicast mechanism in order to realize efficient and productive RRM. The paper is organized as follows: in Section II a brief outline of the reference Throughput based CAC algorithm (TCAC) is given followed by a presentation of our hybrid Downlink Power/Throughput based CAC algorithm (DPTCAC). Section III provides a description of the simulation environment and setup used to conduct a comparative evaluation of the performance of both DPTCAC and TCAC. Simulation results are provided in Section IV. Finally, Section V concludes the paper with an overview of our proposal and future work guidelines. II. CONNECTION ADMISSION CONTROL A. Throughput based CAC (TCAC) With TCAC algorithm, admission decisions are taken based on the capacity required by the requesting call in conjunction with current capacity usage due to ongoing connections. The condition that needs to be met for new connection admission is that aggregate throughput in both directions of the wireless link (uplink and downlink) does not exceed certain respective maximum thresholds and therefore smooth network operation is ensured. Given the QoS requirements of the new connection in terms of data rate and BLER as well as the applied WCDMA encoding type (e.g. convolutional) and rate (e.g. half/third rate), it is This work is partially supported by IST B-Bone: Broadcasting and multicasting over Enhanced UMTS Mobile Broadband Networks project. The authors also thank OPNET Technologies Inc. for providing software license to carry out the simulations of this research.

2 possible to compute the load increase that would occur should the connection be established using (1) and (2) [3]: log10 ( lengthsdu ) log10 ( BLER ) half rate Eb 1.71 (db) = N o log10 ( lengthsdu ) log10 ( BLER ) third rate 1.54 d f, uplink W 1 + Eb No Load increase = 10 SAF RUL Eb No+ 3.0 R 10.0 DL ( df + α ) 10 SAF, downlink W where: E b /N o length SDU BLER d f Α W R UL R DL SAF Signal energy per bit divided by noise spectral density to meet predefined QoS The time length of a Service Data Unit The requested BLock Error Rate for the service Downlink other-cell interference factor computed at the edge of the cell Downlink spreading codes orthogonality factor WCDMA chip rate (3.84 Mcps) Requested service data rate in the uplink direction Requested service data rate in the downlink direction Service activity factor (1.0 for real-time interactive services like voice and video telephony, < 1.0 for data applications) Current load due to ongoing admitted connections and reflects the capacity usage of the wireless link between base stations and mobile terminals. The sum of current load (calculated and maintained by the RNC for each cell under coverage) and anticipated load increase due to the new connection is compared to the loading factor separately in both directions and the call is accepted if the latter are not exceeded. Clearly, by setting loading factor threshold values sufficiently high, it is possible to achieve a specific targeted system capacity in terms of number of users even though from some point on this occurs at the expense of QoS *. The weakness of such strategy is that by increasing loading factors and hence allowing arbitrary number of users into the network, base stations and mobile terminals are forced into transmitting with high power levels; even if base stations and terminals physical limitations on maximum transmission power are ignored, the problem of increased intra- and inter-cell interference still persists. This situation results in a never ending cycle of transmission power amplifications and may lead to instability of network s operation if not eventual collapse. The outlined TCAC approach takes into consideration (to some extent) inter-cell interference through the d f factor and intra-cell interference through the orthogonality factor α and it could be theoretically classified as soft capacity oriented; however, the hard capacity characterisation would be more appropriate instead. The reason is that the algorithm does not consider terminal s mobility property and the consequent constant location change that occurs during the admission process which plays an important role in QoS fulfilment potential; instead it uses the non-volatile property of call load to determine the impact of admitting the request. For example, supposing the sim- * Theoretical loading factor values range between [0, 1) with the value of 1 meaning that a base station has to transmit with infinite power in order for the required SNR level to be met at the receiving end (the mobile terminal). However, simulation tool s implementation allows the specification of loading factor values greater than 1 without noticeable QoS deterioration (for relatively small loading factor values beyond 1). (1) (2) ple case of two users one of which is initially located in the near proximity of the base station and the other on the border of the cell, if both users request connection establishment for identical services, the algorithm ignores the fact that admittance of the latter user will result in the base station originally using more transmission power to meet user s QoS requirements and therefore interference level for other users will increase significantly. Based on simple throughput computations it is possible that both users are accepted but if the admission decision is based on transmission power levels and the consequential interference, the case could be that only the former user is accepted and the latter is rejected, thus ensuring that QoS commitments are being respected and connections are not subjected to quality degradation. As a result of the load-based computations performed by TCAC, system capacity in terms of number of users can be a priori determined, given the QoS requirements of each class of users and loading factor values, hence hard capacity planning may be performed. Thus dynamic aspects, such as user mobility, are ignored. B. Downlink Power and Throughput based CAC (DPTCAC) The proposed DPTCAC algorithm performs admission control based on an estimation of the required downlink transmission power level for the new connection in order to meet its QoS requirements in conjunction with the currently used downlink power levels for ongoing connections and the physical limitation on the maximum transmission power of the base station. Note that currently the algorithm only considers downlink transmission power (from base stations to mobile terminals); since downlink power levels dominate over uplink power levels (emitted from mobile terminals) this simplified consideration is a sufficiently solid first step towards the overall evaluation of the usefulness of the proposed algorithm. The required transmission power level for a new connection is only an estimation and not an exact value due to the Power Control mechanism employed in UMTS to regulate transmission power both of base stations and the mobile terminals. This mechanism regulates transmission power dynamically according to experienced traffic losses and therefore under certain circumstances it is possible for the actual downlink transmission power to surpass or drop below the level estimated during the admission process that is needed to ensure call s QoS requirements. In addition, user mobility affects the level of required transmission power from the base station in order to maintain the agreed SINR. By considering a worst-case scenario in which the user is located near cell s border, we can also derive an upper bound on the estimation of the required transmission power to support the new connection and determine whether this upper limit can be satisfied in the long run. However, the usefulness of this second approximation is tightly connected to the mobility pattern (direction, speed etc) of the user; users moving fast away from the base station put in jeopardy any power calculations performed based on the instantaneous location of the user. On the other hand, power requirements of users moving with slow speed will not vary dramatically in the short-term and it is highly unlikely to cause serious fluctuations on base station transmission power.

3 The estimation of the required base station transmission power level to support the new connection is made using (3); admission decision is then made considering the sum of the estimated required power level and currently used power for ongoing connections against the maximum physical limit of target base station s transmission power (4) [1] [2]. k PDL, j ( rj ) * j = 1 1 α PDL = SINRt PN + + PDL,0 ( 0 r ) N N where: * P DL * max DL DL,0 0 (3) P + P P (4) The estimated required downlink transmission power for the new user (at its current location) SINR t The target SINR that should be met to ensure user s QoS requirements P N The interfering power of background and thermal noise P DL,j (r j ) The total downlink transmission power of base station j (not own base station) perceived at user s location (at distance r j from base station j) N Service spreading factor α Own cell downlink orthogonality factor P DL,0 (r 0 ) The total transmission power of the target base station perceived at user s location (at distance r 0 from target base station) k The number of base stations in the network max P 0 The maximum physical transmission power of the target base station It is worth noting that (4) could be adjusted by introducing a scaling factor for the right-hand side of the inequality which enables the implementation of a guard-power strategy scheme. The purpose served by introducing the scaling factor is to ensure that target base station behaves conservatively regarding new call admission requests by reserving part of its potential transmission power to account for fluctuations that occur due to Power Control operations and/or power fluctuations due to user mobility. Guard-power policy may also serve the purpose of resource reservation (in terms of transmission power) for handoff calls; these calls may be served by the reserved part of base station s transmission power potential and therefore a form of prioritization is enforced between handoff and new connections. The guard-power scheme was not applied during the sequence of simulations conducted and the exploration of the impact of its application is left as future work. While the dependence of intra- and inter-cell interfering power on the distance of the mobile terminal from the respective base stations is quite intuitive (signal attenuation due to multi-path propagation and fast fading), the constant value of interference power due to noise is justified because of the uniformity in the spectral and spatial distribution of noise power density (i.e invariant of frequency and environment type). On the contrary, signal attenuation is taken into consideration for the intra- and inter-cell constituents of the total interference power and this is expressed by the calculation of these terms with respect to the distance of the mobile terminal from base stations upon the time of CAC decision. Although downlink transmission power from base stations is easy to calculate by aggregating power used to transmit signals over dedicated and shared channels, the key point is to correctly account for the attenuation of these signals power due to the type of environment. Signal attenuation is computed considering some well-known pathloss models (Vehicular, Pedestrian, Indoor Office, Hata small to medium city, Hata large city, Free space) [8] implemented in the simulator tool used. Selection of path loss model can be based on assessment of the environment in which the network operates. Expression (3) for the calculation of the required transmission power level states the requirement that the minimum power allocated to support the new user compared to the power the user will perceive due to other ongoing connections should be adequate to ensure its required SINR level. Therefore, it is of critical importance to express satisfactorily this target SINR level given user s QoS requirements in terms of data rate and BLER. This requirements transformation can be made considering the coding scheme used and E b /N o BLER curves [3]. The algorithm can be considered as soft capacity oriented since terminal properties like location (in case of static users) and mobility pattern play an important role in determining the final admission decision. For example, it is possible that a given user s admission request is granted when the admission control process is initiated with the user in the near proximity of the target base station. Supposing the user is moving away from the base station and a future admission request is issued after the previous connection was terminated, then the new outcome of the algorithm might be negative because of the inability to allocate the increased required power level to the connection even though the same ongoing connections as before are still active in the network. We can therefore state that DPTCAC is mobility aware and interference sensitive ; such properties result in soft capacity calculations during network planning. Mobility awareness results from the sensitivity in DPTCAC s computations regarding the location (and the changes incurred by mobility) of the requesting user. Sensitivity to interference derivates from user location change and the consequent different level of perception of other signals. The hybrid nature of DPTCAC comes into play when admission requests involve multicast services. The power computations described previously are performed once upon establishment of the shared channel for multicast traffic delivery. Subsequent requests for the same service are then being subjected to admission control using shared channel s throughput metric in conjunction with shared channel s maximum throughput limitation. Concisely, downlink transmission power is taken into account for admission control computations for the initial establishment of a shared channel (Forward Access Channel FACH or Dedicated Shared Channel DSCH) while connections running over the shared channel are admitted using the sufficiency of channel s remaining capacity as the acceptance criterion. III. SIMULATION SETUP The comparative performance of DPTCAC over the reference TCAC was explored using OPNET Modeler Release 11.0.A [4]. A baseline scenario comprised of a single-cell environment was setup (Figure 1); the homogenous cell was of 1 Km radius and the pathloss model used

4 was set to Outdoor and Indoor Pedestrian. The modelled traffic mixture involved three representative applications: Voice (VOI), Video Telephony (VTE) and Multimedia Web Browsing (MWB) [5]. Application specific parameters are summarized in Tables 1 and 2. The series of conducted simulations was focused on traditional dedicated resources allocation performance (unicast mode) and multicasting traffic configuration was left as future work. During the series of simulations using both CAC algorithms, a number of connections (6 VOI, 4 VTE) are being established in the cell thus allocating a certain amount of radio resources. The number of MWB users is then gradually increased until call blocks are observed. Consequently, new call blocking will occur only for users of the specific QoS class (i.e. MWB). The choice of service class of which the population is augmented at each step clearly results in different marginal system capacities at which call blocks will occur. The choice of the MWB class users as the class to which additional users belong is based on the reasoning that since this QoS class is the most demanding compared to the other two, the resulting system capacity in terms of number of users will constitute a lower limit on system capacity that would be achieved should users belong to less demanding QoS classes. Two critical parameters of DPTCAC and TCAC algorithms are maximum base station transmission power and uplink/downlink loading factors respectively. These parameters essentially regulate cell capacity in terms of number of users than can be potentially supported. As far as loading factors are concerned, normal network planning values range from 0.50 to 0.80 with the value of 1.0 being a theoretical upper limit at which the required transmission power of the base station goes to infinity [3]. However, using loading factor values above 1.0 in executed simulations did not actually increase base station transmission power infinitely; increased level of intra- and inter-cell interference was experienced due to increased transmission power from base stations and from some point on connections QoS was deteriorated. For the sequence of simulations conducted, values of loading factors (both uplink and downlink) were set to 6.0 in order to enable admission of several connections into the network and be able to observe TCAC algorithm s performance in more detail. As far as DPTCAC algorithm is concerned, loading factors are not taken into consideration since they have no impact on algorithm s execution; the applied value for the crucial parameter of maximum base station transmission power was kept at its default value of 20 Watts. User mobility was not modelled; stationary mobile terminals were distributed over the cell coverage area. The motivation behind not modelling terminal mobility was to compare achieved cell capacity between the two CAC algorithms in a reliable manner; since TCAC disregards user location change due to mobility, introducing the latter would indirectly favour DPTCAC over the former. Application Table 1: Application settings. Data rate (kbps) Session interarrival time (h -1 ) Avg. session duration (sec) Symmetry (UL/DL) VOI ~N(180, 1) 1.00 VTE ~N(180, 1) 1.00 MWB ~N(900, 1) 0.25 Table 2: Application ON/OFF modelling. Application Active state (ON) duration (sec) Inactive state (OFF) duration (sec) VOI ~N(1.4, 10-2 ) ~N(1.7, 10-2 ) VTE ~N(180, 1.0) 0 MWB ~N(5, ) ~N(13, 10-4 ) IV. SIMULATION RESULTS A. Admission Control Figures 2a, 2b show the performance of reference TCAC regarding new connection requests and admissions respectively over time (minutes) for simulated scenarios with 7, 8 and 9 MWB users (retaining a constant number of 6 VOI and 4 VTE users). The plots illustrate that with TCAC, cell capacity is reached with 8 MWB users; attempt to accommodate additional users results in blocking. Fig. 1: Baseline cell topology. Fig. 2: TCAC performance regarding MWB users. New call events, New call admissions The constantly increasing number of new call setup requests depicted in the last plot of Figure 2a results from the continuous re-scheduling of connection requests from the blocked user until admission is granted. Since not enough resources are available at any time point into the simulation, the user is always blocked. Figures 3a, 3b illustrate the same statistics when DPTCAC is applied. It is clear that connection drops oc-

5 cur at higher user densities (18 MWB users) compared to TCAC. The number of requests appears greater than the number of users (18 MWB) and this is again due to request re-scheduling (this in a real scenario would not be allowed as the CAC would only allow 17 MWB users. We allow 18 to illustrate the behaviour at the capacity limit). In addition, the final number of admissions is also greater to the number of users requesting admission and this is attributed to the fact that call drops occur due to excessive experienced errors in traffic reception. Dropped users issue new connection requests and when the conditions are favourable (in terms of available transmission power and interference levels) access to the network may be granted. This highlights the dynamic nature of DPTCAC compared to TCAC and its ability to support higher number of users in the network with respect to existing load and interference condition. In simulated scenarios with 19 and 20 MWB users, call blocks were experienced immediately upon issuing of requests when no power was available to support the connections. These results are not shown because of the limitation in the length of this paper. The 18 MWB users scenario was chosen instead for presentation as the critical point from which on QoS deterioration occurs (even if blocks are not experienced yet). Normal operation with acceptable QoS offered to users is therefore limited up to the configuration with 17 MWB users. The QoS offered to these users is shown in Figures 4 to 15 that follow. B. End-to-end performance Recorded end-to-end performance metrics include experienced end-to-end delay, jitter and traffic loss for each application used in the simulated scenarios. The latter is being assessed in terms of traffic delivery rate compared to traffic generation rate. The metrics altogether offer a comprehensive outlook of the impact of both algorithms on ongoing connections which is part of the twofold objective of all CAC policies. Figures 4-9 reveal that DPTCAC does not affect performance negatively on experienced end-to-end delay and jitter even though higher cell capacity is achieved and hence, more load is being offered to the network. It should be noted that the recorded values for VOI application endto-end delay appear above commonly accepted thresholds [5]; however, this behaviour is attributed to simulator s implementation. This supposition is supported by the observation that during simulations with notably increased traffic load, no significant deviation from the values recorded for the baseline scenario was observed. The important point of interest is the decrease in VOI and MWB traffic reception rate outlined in Figures 13 and 15 for the simulation with 6 VOI, 4 VTE and 17 MWB users when applying our DPTCAC algorithm. This fact denotes traffic loss and consequently, QoS degradation of the ongoing connections; it also signals the fact that marginal cell capacity is reached. A similar observation was not made with TCAC since the specific algorithm restricts cell capacity to lower values and no congestion phenomena are experienced. Fig. 4: VOI application End-to-end delay. TCAC, DPTCAC Fig. 3: DPTCAC performance regarding MWB users. New call events, New call admissions Fig. 5: VTE application End-to-end delay. TCAC, DPTCAC Fig. 6: MWB application End-to-end delay. TCAC, DPTCAC

6 Fig. 7: VOI application Jitter. TCAC, DPTCAC. Fig. 11: VTE application Traffic (TCAC). Sent, Received. Fig. 8: VTE application Jitter. TCAC, DPTCAC. Fig. 12: MWB application Traffic (TCAC). Sent, Received. Fig. 9: MWB application Jitter. TCAC, DPTCAC. Fig. 13: VOI application Traffic (DPTCAC). Sent, Received. Fig. 10: VOI application Traffic (TCAC). Sent, Received. Fig. 14: VTE application Traffic (DPTCAC). Sent, Received.

7 Fig. 15: MWB application Traffic (DPTCAC) Sent, Received. C. Radio Access Network (RAN) performance Aggregate base station s downlink transmission power for dedicated channels (DCHs) is as expected directly correlated to the number of accepted users. TCAC demonstrates low transmission power levels (about 20% of the maximum base station transmission power) since cell capacity is restricted to low values. On the contrary, DPTCAC improves cell capacity and forces increased transmission power levels at the base station. The important point is that with TCAC, there is much unused potential in terms of transmission power usage while DPTCAC can unleash this potential thus achieving higher cell capacities as a trade-off. Another feature observed with DPTCAC is that during the admission process of an increasing number of users, base station s transmission power does not increase constantly. When high power levels are reached and new connection requests are received, UTRAN s Power Control mechanism is invoked to regulate power levels. This process is made without compromising QoS commitments and in order to provide room for additional connections given that minimum power requirement for all connections can still be met. This behaviour is depicted in Fig. 16 and 17; increased number of users in the cell does not always entail higher transmission power from the base station. With few users in the cell, power allocation is more elastic but when the number of users approaches cell capacity, power allocation becomes more stringent. Cell throughput plots (Figures 18 and 19) reveal the intuitive fact that throughput in the downlink direction is directly correlated to cell capacity; the increased number of users DPTCAC allows is reflected in the increased throughput compared to reference TCAC. Fig. 18: Cell downlink throughput with TCAC. Fig. 16: Base station transmission power with TCAC (scenarios with 7, 8 and 9 MWB users). Fig. 19: Cell downlink throughput with DPTCAC. Fig. 17: Base station transmission power with DPTCAC (scenarios with 7 to 18 MWB users). V. CONCLUSION In this paper, a novel concept for CAC using a hybrid CAC algorithm was presented (DPTCAC). The algorithm bases connection admission decisions on (i) downlink transmission power from base stations to respect requested and ongoing QoS commitments in the case of dedicated connections as well as (ii) transmission channel maximum capacity in terms of throughput in the case of shared connections over a common channel. Preliminary results on the performance of the algorithm were compared against a

8 reference Throughput based CAC algorithm (TCAC) and through simulations it has been shown that DPTCAC achieves higher cell capacities in terms of number of users without significant degradation of QoS metrics. The observed performance is encouraging and further research is underway. In addition, DPTCAC exhibits dynamic capacity behaviour i.e. maximum cell capacity is not inflexible since it is being influenced by dynamic factors like network load and experienced signal interference. Future work includes the comprehensive evaluation of the proposed algorithm, the setting of the power scaling factor, the investigation of DPTCAC extension to consider the uplink direction of the WCDMA modulated radio link, and the application of similar computations to determine the potential of new connection admittance. Furthermore, the exploitation of Multimedia Broadcast-Multicast Service (MBMS) concept with true multicast traffic delivery over shared channels is to be explored with supplementary simulations. VI. REFERENCES [1] Elayoubi S. E. and Chahed T., Admission Control in the Downlink of WCDMA/UMTS, Springer-Verlag Berlin, Heidelberg, [2] Elayoubi S. E, Chahed T. and Hébuterne G., Connection Admission Control in UMTS in the Presence of Shared Channels, Computer Communications, volume 27, issue 11, June [3] Holma H. and Toskala A., WCDMA for UMTS, 3 rd Edition, John Wiley & Sons Ltd, England, [4] OPNET University Program: [5] Ferreira J. and Velez F. J., Enhanced UMTS services and application characterization, Telektronikk, Vol. 101, No. 1, [6] Kaaramen H., Ahtiainen A. et.al., UMTS Networks Architecture, Mobility and Services, John Wiley and Sons Ltd, England [7] 3GPP TS , Technical Specification Group Services and System Aspects: Introduction of the Multimedia Broadcast/Multicast Service (MBMS) in the Radio Access Network (RAN); Stage 2 [8] Digital mobile radio towards future generation systems, COST Action 231, Brussels, Belgium, 1999.