Robustness maximization of parallel multichannel systems

TL;DR

This paper develops and compares robust bit-loading algorithms for multichannel systems under various power constraints, demonstrating their convergence, effectiveness, and trade-offs through analytical proofs and simulations.

Contribution

It introduces new bit-loading solutions for robustness optimization under peak-power constraints using analytical and greedy algorithms, with proven convergence and practical performance evaluation.

Findings

Algorithms converge asymptotically for both analytical and greedy approaches.

Peak-power constraints break the equivalence between SNR-gap maximization and power minimization.

Simulation confirms the effectiveness and trade-offs of the proposed bit-loading policies.

Abstract

Bit error rate (BER) minimization and SNR-gap maximization, two robustness optimization problems, are solved, under average power and bit-rate constraints, according to the waterfilling policy. Under peak-power constraint the solutions differ and this paper gives bit-loading solutions of both robustness optimization problems over independent parallel channels. The study is based on analytical approach with generalized Lagrangian relaxation tool and on greedy-type algorithm approach. Tight BER expressions are used for square and rectangular quadrature amplitude modulations. Integer bit solution of analytical continuous bit-rates is performed with a new generalized secant method. The asymptotic convergence of both robustness optimizations is proved for both analytical and algorithmic approaches. We also prove that, in conventional margin maximization problem, the equivalence between SNR-gap maximization and power minimization does not hold with peak-power limitation. Based on a defined dissimilarity measure, bit-loading solutions are compared over power line communication channel for multicarrier systems. Simulation results confirm the asymptotic convergence of both allocation policies. In non asymptotic regime the allocation policies can be interchanged depending on the robustness measure and the operating point of the communication system. The low computational effort of the suboptimal solution based on analytical approach leads to a good trade-off between performance and complexity.