Abstract
Bit error rate (BER) minimization and SNR‐gap maximization, two robustness optimization problems, are solved, under average power and bitrate 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, using 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 bitrates 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 the 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 Rayleigh fading channel for multicarrier systems. Simulation results confirm the asymptotic convergence of both resource allocation policies. In nonasymptotic regime the resource allocation policies can be interchanged depending on the robustness measure and on the operating point of the communication system. The low computational effort leads to a good trade‐off between performance and complexity.
Highlights
In transmitter design, a problem often encountered is resource allocation among multiple independent parallel channels
In nonasymptotic regime the resource allocation policies can be interchanged depending on the robustness measure and on the operating point of the communication system
The resource allocation policies are performed under constraints and assumptions, and the independent parallel channels can be encountered in multitone transmission
Summary
A problem often encountered is resource allocation among multiple independent parallel channels. Both approaches have been compared in terms of performance and complexity [7, 12,13,14] All these adaptive resource allocations are possible when channel state information (CSI) is known at both transmitter and receiver sides. The average BER must be computed as weighted arithmetic mean, and the resource allocation has been performed using a greedy-type algorithm [23]. The first main contribution of this paper is the analytical solution of the resource allocation problem in the case of weighted arithmetic mean BER minimization. The bitrates {ri}ni=1 are defined as a number of bits per two dimensions and they are given by a number of bits (undertone per constellation)
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