arXiv subjects

Mung Chiang

Publications and source records attributed to Mung Chiang.

76 records · Page 5Linked to original sources

arXiv2005

Distributed Algorithms for Optimal Rate-Reliability Tradeoff in Networks

The current framework of network utility maximization for distributed rate allocation assumes fixed channel code rates. However, by adapting the physical layer channel coding, different rate-reliability tradeoffs can be achieved on each link and for each end user. Consider a network where each user has a utility function that depends on both signal quality and data rate, and each link may provide a `fatter' (`thinner') information `pipe' by allowing a higher (lower) decoding error probability. We propose two distributed, pricing-based algorithms to attain optimal rate-reliability tradeoff, with an interpretation that each user provides its willingness to pay for reliability to the network and the network feeds back congestion prices to users. The proposed algorithms converge to a tradeoff point between rate and reliability, which is proved to be globally optimal for codes with sufficiently large codeword lengths and user utilities with sufficiently negative curvatures.

cs.NI↗

arXiv2005

A Genetic Algorithm Based Finger Selection Scheme for UWB MMSE Rake Receivers

Due to a large number of multipath components in a typical ultra wideband (UWB) system, selective Rake (SRake) receivers, which combine energy from a subset of multipath components, are commonly employed. In order to optimize system performance, an optimal selection of multipath components to be employed at fingers of an SRake receiver needs to be considered. In this paper, this finger selection problem is investigated for a minimum mean square error (MMSE) UWB SRake receiver. Since the optimal solution is NP hard, a genetic algorithm (GA) based iterative scheme is proposed, which can achieve near-optimal performance after a reasonable number of iterations. Simulation results are presented to compare the performance of the proposed finger selection algorithm with those of the conventional and optimal schemes.

cs.IT↗

arXiv2005

A Non-Cooperative Power Control Game for Multi-Carrier CDMA Systems

In this work, a non-cooperative power control game for multi-carrier CDMA systems is proposed. In the proposed game, each user needs to decide how much power to transmit over each carrier to maximize its overall utility. The utility function considered here measures the number of reliable bits transmitted per joule of energy consumed. It is shown that the user's utility is maximized when the user transmits only on the carrier with the best "effective channel". The existence and uniqueness of Nash equilibrium for the proposed game are investigated and the properties of equilibrium are studied. Also, an iterative and distributed algorithm for reaching the equilibrium (if it exists) is presented. It is shown that the proposed approach results in a significant improvement in the total utility achieved at equilibrium compared to the case in which each user maximizes its utility over each carrier independently.

cs.IT↗

arXiv2005

Optimal and Suboptimal Finger Selection Algorithms for MMSE Rake Receivers in Impulse Radio Ultra-Wideband Systems

Convex relaxations of the optimal finger selection algorithm are proposed for a minimum mean square error (MMSE) Rake receiver in an impulse radio ultra-wideband system. First, the optimal finger selection problem is formulated as an integer programming problem with a non-convex objective function. Then, the objective function is approximated by a convex function and the integer programming problem is solved by means of constraint relaxation techniques. The proposed algorithms are suboptimal due to the approximate objective function and the constraint relaxation steps. However, they can be used in conjunction with the conventional finger selection algorithm, which is suboptimal on its own since it ignores the correlation between multipath components, to obtain performances reasonably close to that of the optimal scheme that cannot be implemented in practice due to its complexity. The proposed algorithms leverage convexity of the optimization problem formulations, which is the watershed between `easy' and `difficult' optimization problems.

cs.IT↗