Control of mobile communications with randomly-varying channels via stability methods

• Published in: 42nd IEEE International Conference on Decision and Control (IEEE Cat. No.03CH37475) Vol. 1; pp. 73 - 79 Vol.1
• Main Authors: Buche, R., Kushner, H.J.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 2003
• Subjects: Communication system control; Joining processes; Mobile communication; Multiaccess communication; Signal generators; Stability; Stochastic processes; Stochastic systems; Time division multiple access; Transmitters
• ISBN: 9780780379244; 0780379241
• ISSN: 0191-2216
• DOI: 10.1109/CDC.2003.1272538

Consider the forward link of a mobile communications system with a single transmitter and connecting to K destinations via randomly varying channels. Data arrives in some random way and is queued until transmitted. Time is divided into small scheduling intervals. The new generation of systems can estimate the channel (e.g, via pilot signals) and use this information for scheduling. The issues are the allocation of transmitter power and time to the various queues in a queue and channel-state dependent way to assure stability and good operation. The decisions are made at the beginning of the scheduling intervals. Stochastic stability methods are used both to assure that the system is stable and to get appropriate time and power allocations, under very weak conditions. The choice of Liapunov function allows a choice of the effective performance criteria. The resulting controls are quite reasonable and allow a range of tradeoffs between fairness and queue lengths. Many schemes of current interest are covered. For example, CDMA with or without control over the bit interval and power per bit, and TDMA with control over the time allocated, power per bit, and bit interval. The channel-state process might be well-estimated or only partially known. All essential factors are incorporated into a "mean rate" function, so that the results cover many different systems. Because of the non-Markovian nature of the problem, we use the perturbed Stochastic Liapunov function method, which is well adapted to such problems. The method is simple, effective, and new.