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A joint resource optimization and adaptive modulation framework for uplink single-carrier frequency-division multiple access systems

机译:上行单载波频分多址系统的联合资源优化和自适应调制框架

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In this paper, we study joint resource allocation and adaptive modulation in single-carrier frequency-division multiple access systems, which is adopted as the multiple access scheme for the uplink in the 3GPP Long Term Evolution standard. We formulate an adaptive modulation and sum-cost minimization (JAMSCmin) problem. Unlike orthogonal frequency-division multiple access, in addition to the restriction of allocating a subchannel to one user at most, the multiple subchannels allocated to a user in single-carrier frequency-division multiple access systems should be consecutive as well. This renders the resource allocation problem prohibitively difficult and the standard optimization tools (e.g., Lagrange dual approach widely used for orthogonal frequency-division multiple access, etc.) cannot help towards its optimal solution. We propose a novel optimization framework for the solution of this problem that is inspired from the recently developed canonical duality theory. We first formulate the optimization problem as binary-integer programming (BIP) problem and then transform this BIP problem into continuous space canonical dual problem that is the concave maximization problem. Based on the solution of the canonical dual problem, we derive joint resource allocation and adaptive modulation algorithm, which has polynomial time complexity. We provide conditions under which the proposed algorithm is optimal. We compare the proposed algorithm with the existing algorithms in the literature. The results show a tremendous performance gain. Copyright (c) 2013 John Wiley & Sons, Ltd.
机译:在本文中,我们研究了单载波频分多址系统中的联合资源分配和自适应调制,该系统在3GPP长期演进标准中被用作上行链路的多址方案。我们制定了自适应调制和总成本最小化(JAMSCmin)问题。与正交频分多址不同,除了最多只能将一个子信道分配给一个用户的限制外,在单载波频分多址系统中分配给用户的多个子信道也应该是连续的。这使得资源分配问题变得异常困难,标准优化工具(例如,广泛用于正交频分多址等的拉格朗日对偶方法)无助于其最佳解决方案。我们提出了一个新的优化框架来解决这个问题,该框架受到了最近发展的规范对偶理论的启发。我们首先将优化问题表述为二进制整数规划(BIP)问题,然后将其转换为连续空间规范对偶问题,即凹极大化问题。基于典型对偶问题的解,得出联合资源分配和自适应调制算法,具有多项式时间复杂度。我们提供了所提出算法最佳的条件。我们将提出的算法与文献中的现有算法进行比较。结果显示出巨大的性能提升。版权所有(c)2013 John Wiley&Sons,Ltd.

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