Novel sparse reconstruction algorithms are proposed for beamspace channel estimation in massive multiple-input multiple-output systems. The proposed algorithms minimize a least-squares objective having a nonconvex regularizer. This regularizer removes the penalties on a few large-magnitude elements from the conventional l1-norm regularizer, and thus it only forces penalties on the remaining elements that are expected to be zeros. Accurate and fast reconstructions can be achieved by performing gradient projection updates within the framework of difference of convex functions (DC) programming. A double-loop algorithm and a single-loop algorithm are proposed via different DC decompositions, and these two algorithms have distinct computation complexities and convergence rates. Then, an extension algorithm is further proposed by designing the step sizes of the single-loop algorithm. The extension algorithm has a faster convergence rate and can achieve approximately the same level of accuracy as the proposed double-loop algorithm. Numerical results show significant advantages of the proposed algorithms over existing reconstruction algorithms in terms of reconstruction accuracies and runtimes. Compared to the benchmark channel estimation techniques, the proposed algorithms also achieve smaller mean squared error and higher achievable spectral efficiency.
翻译:在大型多投入多个输出系统中,提出了用于波束空间频道估算的稀有重建算法。提议的算法将最小平方目标的最小平方目标最小化,并配有非convex 正规化器。这个正规化器取消了常规的 l1- 北向调节器对几个大放大元素的处罚,因此只能对预计为零的剩余元素施加惩罚。在对流函数(DC) 编程差异的框架内进行梯度预测更新可以实现准确性和快速重建。通过不同的DC 拆解配置,提出了双曲线算法和单曲线算法,而这两种算法具有不同的计算复杂性和趋同率。然后,通过设计单曲线算法的阶梯度大小,进一步提出了扩展算法。扩展算法的趋同率更快,可以达到与拟议的双曲线算法大致相同的精确度。数值结果显示,拟议的算法在重建中比现有的重建算法和运行时都有很大的优势。比较了较低的平均缩度和测算法。