Massive MIMO上行系统能效资源分配算法

Energy-efficient Resource Allocation for Massive MIMO Uplink Systems

研究了在认知无线电Ad Hoc网络(CRAHNs)中基于大规模多输入多输出(Massive MIMO)上行系统的能效资源分配算法。簇头采用迫零(ZF)接收,且考虑在电路功率消耗、各节点的最小数据速率以及最大发射功率的情况下,建立基于能效下界的非凸优化模型。根据分数规划的性质,将能效最优化问题中分数形式转化为减式形式,从而利用凸优化求解最优接收天线数和各节点发射功率来获得最大能效。仿真结果表明,所提算法在能效上近似最优值,能够满足各节点最小数据速率及最大功率的约束条件,且能以较小的迭代次数收敛到最优能效性能。

An energy-efficient resource allocation algorithm for Massive MIMO uplink system in Cognitive Radio Ad Hoc Networks （CRAHNs） is studied. In the case of a Zero-Forcing （ZF） receiver in cluster,the considered problem is modeled as a non-convex optimization based on energy-efficient lower bound. Furthermore, the optimization takes into account the circuit power consumption, minimum required data rate and maximum required power of each node. According to the properties of fractional programming, the resulting energy-efficient optimization in the fractional form is transformed into subtractive form. Convex optimization is exploited to obtain the numbers of antennas and optimal transmit power of each node which lead to maximum energy efficiency. Simulation shows that the proposed algorithm approximates the optimal value of the energy efficiency, satisfies the minimum data rate and the maximum power constraint, and converges to energy-efficient optimization in a small number of iterations.