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

Read more

Summary

Introduction

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)

Problem Formulation
Interludes
Ji erfc
Optimal Greedy-Type Resource Allocations
Optimal Analytical Resource Allocations
System Margin Maximization
Greedy-Type versus Analytical Resource Allocations
Conclusion
Proof of Lemma 3
Proof of Lemma 4
Range of Convexity of riberi
Proof of Theorem 5
Proof of Theorem 6
Findings
Proof of Theorem 7

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.