The complexity of the optimal phase control problem in wireless MIMO systems with scalar feedback quantization and equal-gain transmission is studied. The problem is shown to be NP-hard when the number of receive antennas grows linearly with the number of transmit antennas. For the case where the number of receive antennas is constant, the problem can be solved in polynomial time. An optimal algorithm is explicitly constructed. For practical purposes, a low-complexity algorithm based on local search is presented. Simulation results show that its performance is nearly optimal.

We consider the optimal phase control problem in wireless MIMO systems with quantized phases. We show that the problem is NP-hard when number of receive antennas grows linearly with the number of transmit antennas. For the case where the number of receive antennas is constant, we show that the problem is no longer NP-hard by providing a polynomial time algorithm.

We propose an opportunistic power control (OPC) algorithm with rate adaptation. It exploits channel variation and transmits opportunistically to optimize system performance. We show that it can be used to support delay-sensitive services like video conference applications. It works well when the wireless channel is changing moderately fast. For slowly varying channels, OPC yields good performance when dumb antenna is used. It outperforms the traditional target tracking approach in terms of both user capacity and power consumption.

We investigate synchronous iterative waterfilling power allocation algorithm for parallel Gaussian interference channels. Mobile terminals are allowed to update their powers in a fully distributed manner We show that a Nash equilibrium always exists in such a system. Some sufficient conditions for convergence are also derived.

The problem of power control and signature sequence adaptation in code-division multiple-access (CDMA) systems is studied under a game-theoretic framework. Each user tries to maximize his own utility function, which may be different from others. Sufficient conditions for the existence of an equilibrium point in this multi-objective optimization problem are identified. The methodology for analyzing this class of problems is illustrated by examples.

An opportunistic power control algorithm was proposed in (C. W. Sund and K. K Leung, June 2004). While it provides significant throughput improvement, fairness has been a major challenge. In this paper, we propose an opportunistic power control algorithm with fairness consideration. Alongside the throughput enhancement of the power control algorithm, we prove the uniqueness, convergence and continuity properties of its fixed point

This paper considers the phase control problem in multiple-input single-output antenna system (MISO) with finite number of control bits. We propose an optimal algorithm which requires only O(nTlog nT) of computations, where nTis the number of transmit (input) antennas

Most power control algorithms that aim at hitting a signal-to-interference ratio (SIR) target fall within Yates' framework. However, for delay-tolerable applications, it is unnecessary to maintain the SIR at a certain level all the time. To maximize throughput, one should increase one's power when the interference level is low, and the information transmission rate is adjusted accordingly by adaptive modulation and coding techniques. This approach is called opportunistic communications. In this paper, we generalize Yates' result and establish a new framework, which is applicable to systems supporting opportunistic communications and with heterogeneous service requirements. Simulation results show that our proposed algorithm yields significant improvement in throughput when compared with the conventional target tracking approach.

When considering mobile communication systems we see that for services which can tolerate delay do not need to maintain a minimum signal-to-interference ratio (SIR). In fact, to achieve maximum throughput transmission power should be increased when the interference level is low, and information transmission rate adjusted accordingly through adaptive modulation and coding. This approach is called opportunistic communications. In this paper, we introduce an opportunistic power control algorithm, which exploits the channel fluctuation of wireless channels. The algorithm is distributed and is proven to converge to a unique fixed point. Simulation results show that a significant increase in system capacity can be achieved, when compared with the traditional target tracking approach.

We consider the convergence issues of distributed power-control algorithms for mobile cellular systems. A convergence theorem for power-control algorithms of canonical type is proved. Our result generalizes Yates' framework and provides a new outlook on the problem. The general applicability of the theorem is demonstrated by showing that many well-known distributed algorithms are canonical. Furthermore, by devising some new discrete algorithms, we exemplify how the theorem can be used to aid new design.

We consider the sequence adaptation problem for cellular asynchronous code-division multiple-access (CDMA) systems. A game-theoretic approach is used to investigate the stability issues of distributed adaptation algorithms. It is shown that the Nash equilibrium may not exist for cellular CDMA systems if the traditional interference measure is used. In turn we propose a new interference measure which ensures system stability.

The relation between interference reduction and capacity maximization in DS-CDMA systems is investigated. We show that the solution which minimizes the total weighted squared correlation (TWSC) happens to maximize the sum capacity of the system. Furthermore, we show that a distributed sequence adaptation algorithm based on the MMSE receiver reduces TWSC and increases sum capacity simultaneously at each iteration step. Simulation results show that the algorithm converges to the optimal solution.

We consider the convergence issues of distributed power control algorithms for mobile cellular systems. A convergence theorem for power control algorithms of canonical type is proven. Our result generalizes Yates' (1995) framework and provides a new outlook on the problem. The general applicability of the theorem is demonstrated by showing that all the well-known algorithms are canonical. Furthermore, by devising a new discrete algorithm, we exemplify how the theorem can be used to aid new design.

A game theoretic approach is used to investigate the stability of distributed adaptation algorithms. We show that for cellular CDMA systems, Nash equilibria may not exist. On the other hand, for single-cell systems, there are multiple Nash equilibria. None of them is better than the others in the Pareto sense. Furthermore, the behavior of a distributed algorithm based on an MMSE receiver is studied. Simulation results show that it converges to the Nash equilibrium which maximizes sum capacity.