Robust Monotonic Optimization Framework for Multicell MISO Systems
Robust Monotonic Optimization Framework for Multicell MISO Systems
复制标题
DOI：
10.1109/tsp.2012.2184099
复制
发表时间：
2011-04
影响因子：
5.4
通讯作者：
Emil Björnson;G. Zheng;M. Bengtsson;B. Ottersten
中科院分区：
文献类型：
--
作者：
Emil Björnson;G. Zheng;M. Bengtsson;B. Ottersten
多用户系统的性能既难以公平衡量，也难以优化。大多数资源分配问题是非凸的和np困难的，即使在简化的假设下，如完美的信道知识、用户之间的均匀信道属性和简单的功率约束。我们建立了一个通用的优化框架，系统地解决了这些问题，达到全局最优。提出的分支约简定界（BRB）算法处理具有单天线用户、多天线发射机、任意二次功率约束和信道不确定性鲁棒性的一般多小区下行系统。在每次迭代中解决一个鲁棒公平性-轮廓优化问题，该问题是一个拟凸问题，是极大最小公平性的一种新的推广。BRB算法计算量大，但收敛性优于先前提出的外多块近似算法。我们的框架适用于有或没有信道不确定性的一般多单元系统的计算基准。我们通过推导和评估一般问题的零强迫解来说明这一点。
The performance of multiuser systems is both difficult to measure fairly and to optimize. Most resource allocation problems are nonconvex and NP-hard, even under simplifying assumptions such as perfect channel knowledge, homogeneous channel properties among users, and simple power constraints. We establish a general optimization framework that systematically solves these problems to global optimality. The proposed branch-reduce-and-bound (BRB) algorithm handles general multicell downlink systems with single-antenna users, multiantenna transmitters, arbitrary quadratic power constraints, and robust- ness to channel uncertainty. A robust fairness-profile optimization (RFO) problem is solved at each iteration, which is a quasiconvex problem and a novel generalization of max-min fairness. The BRB algorithm is computationally costly, but it shows better convergence than the previously proposed outer polyblock approximation algorithm. Our framework is suitable for computing benchmarks in general multicell systems with or without channel uncertainty. We illustrate this by deriving and evaluating a zero-forcing solution to the general problem.