This paper describes a new approach to geographic load balancing in mobile cellular networks with fully adaptive antennas. Our previous work using ...
UTILITY-BASED DISTRIBUTED GEOGRAPHIC LOAD BALANCING IN MOBILE CELLULAR NETWORKS L. Du, J. Bigham, L.G. Cuthbert Queen Mary, University of London, United Kingdom ABSTRACT This paper describes a new approach to geographic load balancing in mobile cellular networks with fully adaptive antennas. Our previous work using cooperative negotiation and semi-smart antennas has been shown to be very effective, however, for fully adaptive antenna systems negotiation becomes increasingly complex because users need to be managed individually. A utility-based approach for geographic load balancing with fully adaptive antennas has been proposed and evaluated in this paper. Instead of cooperating by exchanging messages between base stations, it has encapsulated a form of cooperation into the utility function. This reduces the system complexity. A comparison of system performance using different utility functions and with the previous work is made. The results demonstrate the benefits of this approach.
mobile cellular networks has been shown to be effective [1], and global optimisations using genetic algorithms have provided performance benchmark [19]. However, for fully adaptive antenna systems negotiation becomes increasingly complex and for real time control new approaches have been sought. In aspects related to radio resource management fully adaptive antenna systems are easier, but the optimization complexity is greater, because we now need to manage users individually. This paper considers a utility-based approach for the GLoBal in mobile cellular networks with fully adaptive antennas, as illustrated in Figure 1. Soft handover is also exploited to allow the coverage overlapping between cells.
INTRODUCTION Mobile cellular networks are by far the most common of all public wireless communication systems. Previous research on mobile cellular networks has led to many schemes to increase the system capacity. Balancing the traffic load [2] and use of smart antennas [3] are two of the most important ones. Traffic load balancing in mobile cellular network has been well-studied since the first generation of mobile communication systems. Many methods have been proposed to address this problem, such as cell splitting [2], channel borrowing [2], [4], channel sharing [5], dynamical channel allocation [6]–[8], new soft handover schemes [9], [10]. The application of smart antennas in cellular networks has also been widely investigated, e.g. [11]–[13]. However, most work related to traffic load balancing only focuses on different radio channel allocation schemes, and most work on smart antennas only considers the radio propagation channels within one cell. These severely limit their efficiency. Geographic load balancing (GLoBal) is recognised as a new approach for traffic load balancing which provides dynamic load re-distribution in real time according to the current geographic traffic conditions. It can be used to improve the performance for any distributed systems containing non-uniformly distributed traffic, especially for resolving the traffic hot spots. Studies on dynamic sectorisation [14], [15], use of tilted antennas [16], and dynamic cell-size control (cell breathing) [17], [18] have shown that the system performance can be improved by balancing non-uniformly distributed users. Our previous work using cooperative negotiations and semi-smart antenna to provide dynamic GLoBal for
Figure 1. Geographic load balancing in mobile cellular networks with fully adaptive antennas. Realising such a system requires the capability of approximately locating and tracking mobiles in order to adapt the system parameters to meet the traffic requirements. The existing generation of cellular networks has a limited capability of mobile position location, however the next generation of cellular networks is expected to have much better mobile position location capabilities. The position location capabilities of the cellular network can be used to determine a set of beams that point to prospective users. This process need to be performed cooperatively, as the local base stations, even with fully adaptive antennas, have very limited capability of resolving traffic hot spots independently. This paper explores the use of utility functions as a distributed optimisation technique for dynamical GLoBal in mobile cellular networks, and system level simulations have performed to evaluate performance. Simulation results with different utility functions and different traffic scenarios are presented. The performance of using global optimisations and cooperative negotiation with semismart antennas from our previous work is also referred as a comparison.
UTILITY-BASED APPROACH In mobile cellular networks, given that radio power decreases exponentially along the distance, then decisions regarding mobile connections at locations far away will usually have a negligible impact on decisions based on circumstances in the neighbourhood. So it is intuitively reasonable that local optimizations (e.g. negotiation, utility-based methods) should be effective. However, it is important that no “holes”are created in the network coverage; no dramatic changes to previous assignments are made lest (too many) connected calls are disconnected; and that decisions can be made at the speed that the mobile traffic changes. From our previous experience, cooperation between adjacent cells is required to solve these problems. Negotiation has been shown to be very effective for such a GLoBal system using semi-smart antennas [1]. The autonomy allowed gives an increase in flexibility to deal with new situations in traffic load, and to decrease the signalling overhead on the network. The results of local negotiation is shown to give network performance very close to that obtained by global optimisation techniques and so local negotiation is a viable approach to finding previously unusable capacity in the network. The gain in capacity depends on the demand; however, simulations of simple hot spot scenarios have shown that this can be 20%. Negotiation gives only very marginally poorer results that global optimization and the calculations can be performed in real time. However, for fully adaptive antenna systems negotiation becomes increasingly complex. In aspects related to radio resource management fully adaptive antenna systems are easier, but the optimization complexity is greater, because we now need to manage users individually. Therefore, cooperative negotiation is no longer effective for the GLoBal in mobile cellular networks with fully adaptive antennas, and for real time control new approaches have been sought. In this paper, a utility-based approach is described and evaluated for this problem. Utility-base methods have been used for distributed optimisations for many cases due to its efficiency and simplicity, such as decision making [20], [21], distributed power control in wireless networks [22], radio resource management [23], [24], etc. However, from our knowledge, no one has explored the use of utility-based methods for GLoBal in mobile cellular networks so far.
with adjacent cells, cooperation has been embedded into the utility functions, as explained next. System Model In order to have enough cooperative participators and reduce the boundary effect in the cellular network simulations, a 100 diamond-mesh cellular network model is used, as shown in figure 2. 0,0 1,0 2,0 3,0 4,0 5,0 6,0 7,0 8,0 9,0 0,1 1,1 2,1 3,1 4,1 5,1 6,1 7,1 8,1 9,1 0,2 1,2 2,2 3,2 4,2 5,2 6,2 7,2 8,2 9,2 0,3 1,3 2,3 3,3 4,3 5,3 6,3 7,3 8,3 9,3 0,4 1,4 2,4 3,4 4,4 5,4 6,4 7,4 8,4 9,4 0,5 1,5 2,5 3,5 4,5 5,5 6,5 7,5 8,5 9,5 0,6 1,6 2,6 3,6 4,6 5,6 6,6 7,6 8,6 9,6 0,7 1,7 2,7 3,7 4,7 5,7 6,7 7,7 8,7 9,7 0,8 1,8 2,8 3,8 4,8 5,8 6,8 7,8 8,8 9,8 0,9 1,9 2,9 3,9 4,9 5,9 6,9 7,9 8,9 9,9
Figure 2. Cellular network model. One base station is situated in the central of each cell and provides the radio coverage with one fully adaptive antenna. For simplicity reason, we assume that each user will be tracked with one narrow beam (so no interference between users) and the maximum outreach of beams is a fixed constant. Therefore, the cell model can be simplified by several sectors, shown as figure 3. The frontier of each cell is defined by the maximum outreach of beams. Any demand outside the frontier is of no direct interest to the base station, as it cannot service any of it. Within the frontier the area is divided into six sectors, named as S0 to S5 anticlockwise. The hexagonal region represents the default, baseline coverage, and the shadowed part in the middle represents the forbidden zone (no mobiles allowed in it).
S2 S1 S3 S0 S4 S5 S2 S1 S2 S1 S3 S0 S3 S0 S4 S5 S4 S5
Figure 3. Cell models used for utility-based approach. Optimisation Objectives and Process
The cooperation issue of using utility functions (or cost functions) have gained special interests, as many real world problems have certain constraints, which will be easily violated without cooperation between local optimisations [22], [25]. In order to avoid losing users due to drastic changes or creating “holes” in the network coverage, cooperation has to be considered in our work. Instead of exchanging messages to negotiate
Since our main task is to balance the geographic traffic load along different cells in order to resolve traffic hot spots, system capacity improvement for hot spots traffic scenarios should be considered as our first objective. However, other factors, such as power consumption, optimisation time, system complexity, also need to be taken into account.
Before we start to explain the process of utility-based distributed optimisation, some terms are defined here to make the explanation clearer. • Traffic unit: an active user who is currently generating traffic to the network; • Demand: the bandwidth required by a traffic unit; • Served unit: a traffic unit that is currently served by one base station, which represents a normal user that connects to a base station; • Shared unit: a traffic unit that is currently served by two adjacent base stations, which represents a softhandover user located near the cell boundaries1; • Blocked unit: a traffic unit that is not served by any base stations. Whenever a base station receives a connection request from a traffic unit, it then calculates the utility value of serving this unit. If the value is above a certain threshold (which is calculated at run time according to the traffic condition within the cell frontier) and the base station still has enough free capacity, this traffic unit will be served by the base station and becomes a served unit. If another base station also receives this connection request, it will perform the same process and decide if it will serve this traffic unit. If yes, this traffic unit will be served by both of them and becomes a shared unit. Softer-handover is not considered, so no traffic unit will be served by more than two base stations. However, if the base station does not have enough free capacity to serve this unit, it then checks if there are any served units with lower utility value than the new traffic unit. If so, the worst one has to be switched to the adjacent base station. If the adjacent base station is also full, the worst one has to be dropped or blocked. The whole process is very similar to the process of call admission control, and the main difference is the decision-making criterion, viz. utility value. Utility Function The utility values are calculated by a pre-defined utility function. The utility function is designed according to the optimisation objectives and constraints, as described before. Since the first objective is to balance the geographic load, the traffic conditions in local cell and adjacent cells needs to be included. For each traffic units, there are two closest base stations that could potentially provide the service. They need to cooperative with each other to provide the service for traffic units. When one of them is heavily loaded, it has less potential to serve a traffic unit, thus there is more obligation on the other to serve the traffic units. So the utility value for the traffic units to one of the base stations needs to be inversely proportional to the traffic load of the other one. The transmitting power also needs to be minimised to save energy. As it is proportional to 1
Note that cell boundaries are defined by signal strength in our case, so they are not necessarily restricted at the regions between two cells.
the outreach of each beam, the distances of served units should be keep minimum. Therefore we can obtain our first straightforward utility function by the weighted combination of all the components. As shown in figure 4, the utility value U1,0 j for a traffic unit TU1j to a base station BS j can be calculated by, U1,0 j = w0 ⋅ L1,j j − d1,j j j 1, j
where L
(1) j 0
is the total traffic load of the sector S that
contains TU1j , d1,j j is the distance from TU1j to BS j , and
w0 is the weight.
TU8j TU 7j
TU 6j TU
j 5
BS j
TU 4j
TU10k TU k 11
TU9j
S0j
TU 3j
j 1
TU
TU 2j
S
k TU 9k 3
TU8k TU 6k
TU 4l
TU12k
TU BS k TU k 7
TU5k
TU TU 4k
k 1
TU k 2
k 3
k 0
S
S3l
TU 3l TU 2l
BSl
TU1l
Figure 4. Illustration for the definition of utility functions. Instead of using the real value of traffic load from the neighbour BS k , here we use L1,j j as the estimate value of that for sector S3k , since there are lots of overlapping areas between S0j and S3k . This can eliminate the requirement of exchanging traffic condition information between adjacent base stations, which reduces the communication overheads and system complexity at the same time. As shown in figure 4, for example, L1,j j can be expressed as L1,j j = D j1 + Dj 2 + Dk 5 + Dk 6 + Dk 7 + Dk 8 + Dk 9
(2)
where D j1 , ..., Dk 9 are the demands of TU j1 , ..., TU k 9 respectively. From our tests, this simple utility function works quite well, as shown in figure 5 in the next section. However, with limited communication overheads, more information (or cooperation) from adjacent base stations could be used to improve the utility function to improve it, such as the estimate load at the helper’s helper, and the distance from traffic units to their potential helpers. Utility functions with the consideration of these factors are also evaluated, and results are presented in the next section. The first extension of the original utility function is obtained by considering the estimate load at the helper’s helper, the utility value of traffic unit TU1j to BS j is U1,1 j = w0 ⋅ L1,j j + w1 ⋅ L1,j k − d1,j j j 1,k
where L
(3) k 0
is the total traffic load of the sector S in
figure 4. It is used as the estimate value of the traffic
load of S3l of BSl , which is the further helper for BS k . In our case, L1,j k can be obtained by L j1,k = Dk 1 + Dk 2 + Dl 1 + Dl 2 + Dl 3 + Dl 4
(4)
are plotted in figure 5. The weights for all the utility functions are tuned by tests. The tuning process is not included due to the space limit.
where Dk 1 , ..., Dl 4 are the demand of TU k 1 , ..., TU l 4 respectively.
0.07
Conventional Network Utility Function U 0 Utility Function U 1 Utility Function U 2 Cooperative Negotiation Global Optimsation
0.06 0.05
Call-blocking Rate
Another extension is by considering the distance from traffic units to their potential helpers. When two traffic units have the same distance to a particular base station, they may have different distances to their respective nearest other potential helper. If all the other factors are the same for the two traffic units, the one with longer distance to a potential helper should have higher utility value, since it has less priority to get help from others. Therefore, the third utility function for calculating the utility value of traffic unit TU1j to BS j can be expressed
0.04 0.03 0.02 0.01 0.00 0
as where d
= w0 ⋅ L + w1 ⋅ d j 1, j
j 1,k
−d
is the distance from TU
j 1, j
j 1
(5) to its potential
helper BS k . SIMULATION AND RESULTS Simulations are being performed to test the performance of the approach. As fully adaptive antennas are used for all the base stations, the cell capacity is then limited by the number of beams that base station antenna can produce. The results of using utility–based approach are compared with the conventional ones, where base stations provide services only for the closest users with fully adaptive antennas. Simulation Configuration The approach is being evaluated using a sequence of 200 traffic snapshots taking from an imaginary cellular network where four hotspots are forming. The time interval between two traffic snapshots is 60 seconds. The configuration for traffic snapshots is: • Each traffic snapshot contains 50, 000 users, where • 40, 000 users are uniformly distributed in the whole area, and • Between the first snapshot and the last one 10, 000 users gather to four hotspots. The locations of the traffic in hot spots follows a normal distribution (whose mean value, µ , is uniformly distributed over the whole area, and the standard deviation, σ = 0.5 R ). • The active and idle time for each user has a negative exponential distribution. The mean values are 120 sec and 720 sec respectively. • One base station is situated in the centre of each cell, and each base station antenna can generate 120 narrow beams. Simulation Results Simulations are performed for one series of traffic snapshots with the three utility functions, and the results
50
100
150
200
Traffic Snapshots
Figure 5. Simulation results for three utility functions. As shown in figure 5, the utility-based approach has much better performance than the conventional one. However, the performances of using three different utility functions are very similar after proper tuning. Therefore, the simples one, U 0 , will be used for the rest of the paper. The performances of using global optimisation or cooperative negotiation are still better, but the former is very slow and the latter is significantly more complicated for the fully adaptive problem. This approach has also been tested for more traffic scenarios where the number of traffic hot spots ranges from one to eight, and each hot spot contains 1000 users. The results showing in figure 6 is the average call-blocking rates for each 200 snapshots. 0.016
Conventional network Utility-based approach
0.014
Average Call-blocking Rate
U j 1, k
2 1, j
0.012 0.010 0.008 0.006 0.004 0.002 0.000 0
1
2
3
4
5
6
7
8
9
Number of traffic hotspots
Figure 6. Simulation results for traffic scenarios with different numbers of hot spots When there are more hot spots, the call-blocking rates of both conventional network and GLoBal with fully adaptive antennas increase. However, the performance improvement by using utility-based approach is more significant with more hot spots, since there is more optimisation space for it.
CONCLUSIONS In this paper, we propose and evaluate a utility-based approach for geographic load balancing with fully adaptive antennas in mobile cellular networks. The performance is evaluated for different traffic scenarios and compared with our previous work. Instead of cooperating by negotiations as our previous work, cooperation between adjacent cells is realised by utility functions that reuse local information as the estimate values of neighbours. This reduces the system complexity with only a little degradation on performance. The simulation results have shown that this approach can improve the cellular network capacity significantly for traffic scenarios with hot spots. REFERENCES 1. L. Du, J. Bigham, L. Cuthbert, C. Parini and P. Nahi., 2003, “Intelligent cellular network load balancing using a cooperative negotiation approach,” in The Proceedings of IEEE Wireless Communications and Networking Conference, WCNC 2003., New Orleans, USA, March 2003. 2. T. S. Rappaport, 1996, Wireless communications: principles and practice. Upper Saddle River, N.J., London: Prentice Hall PTR. 3. M. Chryssomallis, 2000, “Smart antennas,” IEEE Antennas and Propagation Magazine, vol. 42, no. 3, pp. 129–136, June 2000. 4. T. Yum and W. Wong, 1993, “Hot-spot traffic relief in cellular systems,” IEEE JSAC, vol. 11, no. 6, pp. 934–940, August 1993. 5. Lindsay Stewart, W. C. Y. Lee, M. A. Schulz, and C. Xu, 2001, “Incremental capacity gains for high blocking sites using dynamic channel sharing,” IEEE Trans. on Vehicular Technology, vol. 50, no. 1, pp. 1– 11, January 2001. 6. Y. Argyropoulos, S. Jordan, and S. Kumar, 1999, “Dynamic channel allocation in interference-limited cellular systems with uneven traffic distribution,”IEEE Trans. on Vehicular Technology, vol. 48, no. 1, pp. 224–232, January 1999. 7. F. Delli Priscoli, N. Magnani, V. Palestini, and F. Sestini, 1997, “Application of dynamic channel allocation strategies to the GSM cellular network,” IEEE JSAC, vol. 15, pp. 1558–1567, October 1997. 8. D. Everitt and D. Manfield, 1989, “Performance analysis of cellular mobile communication systems with dynamic channel assignment,” IEEE JSAC, vol. 7, no. 8, pp. 1172–1180, October 1989. 9. Y. Chen and L. Cuthbert, 2002, “Optimum size of soft handover zone in power controlled UMTS downlink systems,” Electronics Letters, vol. 38, no. 2, pp. 89–90, Jan 2002. 10. D. Tcha, S. Kang, and G. Jin, 2001, “Load analysis of the soft handoff scheme in a cdma cellular system,” IEEE JSAC, vol. 19, no. 6, pp. 1147 –1152, June 2001. 11. M. Feuerstein, 1999, “Applications of smart antennas in cellular networks,”in Proceedings of IEEE International Symposium on Antennas and Propagation
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