Ieee Transactions on Communications, Accepted for Publication 1 Exploiting Sparse User Activity in Multiuser Detection
Ieee Transactions on Communications, Accepted for Publication 1 Exploiting Sparse User Activity in Multiuser Detection
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通讯作者：
Hao Zhu;G. Giannakis
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作者：
Hao Zhu;G. Giannakis
— 码分多址 (CDMA) 系统中的活动用户数量通常远低于扩频增益。本文有效地利用了这种先验信息来提高多用户检测器的性能。低活动因素表现为稀疏符号向量，其中的条目取自有限字母表，并通过零符号进行扩充以捕获用户不活动情况。增强字母表的非等概率符号激发了稀疏性利用最大后验概率 (S-MAP) 标准，该标准被证明会产生包含 ℓ2 最小二乘误差的成本，该误差受到所需符号向量第 í µí± 范数的惩罚 (í µí± = 0, 1, 2)。相关的优化问题出现在为线性回归开发的变量选择（收缩）方案中，以及新兴的压缩采样（CS）领域中。这项工作对这种稀疏 CDMA 系统的贡献是一系列利用稀疏性的多用户检测器，在性能和复杂性要求之间进行权衡。从 CS 和最小绝对收缩选择算子 (Lasso) 应用范围的角度来看，当所需信号向量的条目遵守有限字母表约束时，贡献相当于稀疏性利用算法。
—The number of active users in code-division multiple access (CDMA) systems is often much lower than the spreading gain. The present paper exploits fruitfully this a priori information to improve performance of multiuser detectors. A low-activity factor manifests itself in a sparse symbol vector with entries drawn from a finite alphabet that is augmented by the zero symbol to capture user inactivity. The non-equiprobable symbols of the augmented alphabet motivate a sparsity-exploiting maximum a posteriori probability (S-MAP) criterion, which is shown to yield a cost comprising the ℓ2 least-squares error penalized by the í µí±-th norm of the wanted symbol vector (í µí± = 0, 1, 2). Related optimization problems appear in variable selection (shrinkage) schemes developed for linear regression, as well as in the emerging field of compressive sampling (CS). The contribution of this work to such sparse CDMA systems is a gamut of sparsity-exploiting multiuser detectors trading off performance for complexity requirements. From the vantage point of CS and the least-absolute shrinkage selection operator (Lasso) spectrum of applications, the contribution amounts to sparsity-exploiting algorithms when the entries of the wanted signal vector adhere to finite-alphabet constraints.