Optimal QoS tradeoff and power control in CDMA systems

Abstract

Abstract-Dynamic power control and scheduling strategies provide efficient mechanisms for improving performance of wireless communications networks. A common objective is to maximize throughput performance of a network or to minimize the total transmission power while satisfying quality-of-service (QoS) requirements of the users. The achievement of these objectives requires the development of medium access control (MAC) strategies that optimally utilize scarce resources in wireless networks. When developing such strategies, a good understanding of the structure of the feasibility region is essential. The feasibility region is defined as a set of all QoS requirements that can be supported by a network with all users active concurrently. Thus, the structure of this set shows when (if at all) scheduling strategies can improve network performance. In particular, if the feasibility region is a convex set, then concurrent transmission strategies are optimal and the optimal power allocation can be obtained efficiently via a convex optimization. Other important problems are how the total transmission power depends on QoS requirements and what the optimal QoS tradeoff is. In this paper, we address all these problems and solve them completely in some important cases. The purpose of this paper is to explore the interrelationship between QoS requirements and physical quantities such as transmission power. Although the results are obtained in the context of a power-controlled CDMA system, they also apply to some other communications systems. A key assumption is that there is a monotonous relationship between a QoS parameter of interest (such as data rate) and the signal-tointerference ratio at the output of a linear receiver.

Resource allocation in multiantenna systems-achieving max-min fairness by optimizing a sum of inverse SIR

“…Strict convexity could only be shown for fixed beamformers [2]. Nevertheless, it will be shown in the following that we can find a suitable parametrization on beamformers and transmit powers, which allows for an efficient algorithmic solution.…”

“…Suppose that is chosen to be the information-theoretical capacity of the th user in the high-power regime, i.e., SIR SIR . Then the sum capacity of the system (the maximal achievable sum rate) is obtained by the following optimization strategy: SIR subject to a power constraint (2) Note that the optimum is mostly achieved by only a small subset of users. The remaining users are inactive until the channel improves.…”

“…The structure of the problem depends on the choice of the utility functions (see, e.g., [2]) but also on the way the SIR depends on beamformers and transmission powers. The interaction between beamformers and powers makes an analytical treatment difficult; thus most results in the literature (see, e.g., [2] and [3]) are based on the assumption of fixed link gains, which corresponds to a scenario with fixed beamformers.…”

“…The interaction between beamformers and powers makes an analytical treatment difficult; thus most results in the literature (see, e.g., [2] and [3]) are based on the assumption of fixed link gains, which corresponds to a scenario with fixed beamformers.…”

Power control and QoS trade-offs for real-time wireless traffic

“…We refer to this trade-off as the power v/s QoS trade-off for wireless networks. Trade-offs for non-real-time traffic were investigated in [6], [8]. We focus on the real-time traffic case.…”