William M Macdonald

Dynamic optimization in future cellular networks

With multiple air-interface support capabilities and higher cell densities, future cellular networks will offer a diverse spectrum of user services. The resulting dynamics in traffic load and resource demand will challenge present control loop algorithms. In addition, frequent upgrades in the network infrastructure will substantially increase the network operation costs if done using current optimization methodology. This motivates the development of dynamic control algorithms that can automatically adjust the network to changes in both traffic and network conditions and autonomously adapt when new cells are added to the system. Bell Labs is pursuing efforts to realize such algorithms with research on near-term approaches that benefit present third-generation (3G) systems and the development of control features for future networks that perform dynamic parameter adjustment across protocol layers. In this paper, we describe the development of conceptual approaches, algorithms, modeling, simulation, and real-time measurements that provide the foundation for future dynamic network optimization techniques.

Sensitivity analysis of handoff algorithms on CDMA forward link

We analyze the performance of different handoff algorithms on the forward link or downlink of a code-division multiple-access (CDMA) cellular system. Unlike the reverse link, soft handoff on the forward link requires additional resources such as CDMA codes and transmit power and also causes additional interference. If handoff requests can be processed and completed instantaneously, transmission from the base station with the best link to the user would achieve a significant fraction of the macrodiversity gain without utilizing additional resources. However, in practical systems, there is a nonzero handoff completion delay and soft handoff provides the required robustness to delays, although it comes at the expense of additional network resources. Thus, there is a tradeoff between the extent of soft handoff required and the handoff execution delay. We compare the performance of hard and soft handoff schemes and study their sensitivity to the delay in the execution of the handoff. Outage probability and the total average power required are used as performance metrics. We present an analytical framework to study this tradeoff and also discuss simulation results with field data. The results provide insights on the conditions under which soft handoff can be eliminated and on the effect of relevant handoff thresholds on the performance.

Delay sensitivity analysis of CDMA downlink handoff algorithms

We analyze the performance of different handoff algorithms for the forward link of a CDMA cellular system. Unlike the reverse link, soft handoff on the forward link requires additional resources such as CDMA codes and transmit power and also causes additional interference. If handoff requests can be processed instantaneously, transmission from the base station with the best link to the user would achieve a significant fraction of the macro-diversity gain without utilizing additional resources. However, in practical systems there is a non zero execution delay and soft-handoff provides the required robustness to delays although it comes at the expense of additional network resources. There is thus a tradeoff between the extent of soft handoff required and the handoff execution delay. We compare the performance of the hard and soft handoff schemes and study their sensitivity to the execution delay. Outage and the average transmit power required are used as performance metrics. We present an analytical framework to study this tradeoff.

Measurements of the lost call rate as a function of the total forward-link power on the dominant interfering cell-sector in a CDMA2000 network

We have performed extensive measurements upon commercial cdma2000 networks supporting predominantly voice services using a proprietary monitoring tool called Celnet Xplorer. These measurements capture large numbers of user-mobile calls under actual operating conditions. We also capture statistically significant number of user-mobile call failures, in particular lost calls. This massive data set allows us to correlate call failures against the network operating conditions at the same moment in time, in order to infer underlying cause and effect relationships. We observe that the call failure probability has a strong exponential dependence upon the total forward-link power on the dominant interfering cell-sector. We also conclude that the dominant physical source of this increase in lost call rate is a loss of coverage at increased load, and not loading-induced diversity reduction. We note that these results support an important new paradigm where network analyses are based upon real-time measurements of actual user mobiles