研究目的
To study the joint pilot assignment and resource allocation for system energy efficiency (SEE) maximization in the multi-user and multi-cell massive multi-input multi-output network, explicitly considering the pilot contamination effect during the channel estimation.
研究成果
The study proposes efficient algorithms to optimize the number of activated antennas together with power allocation and pilot assignment for SEE and SR maximization in MU-MC massive MIMO networks. Numerical results confirm the convergence of the proposed algorithms and their superior performance in terms of total SR and SEE compared to conventional designs.
研究不足
The study assumes that the problems are feasible, which may not always be the case due to constraints like minimum data rate requirements and maximum transmit power. The feasibility problem is left for future works.
1:Experimental Design and Method Selection:
The study employs an iterative algorithm to solve the transformed SEE maximization problem, optimizing power allocation and number of antennas, followed by pilot assignment optimization in each iteration. The SCA technique is used for the first sub-problem, and a novel iterative low-complexity algorithm based on the Hungarian method is proposed for the pilot assignment sub-problem.
2:Sample Selection and Data Sources:
The study considers the downlink of a cellular network with L cells, each serving K single-antenna users, with each base station supported by a large number of antennas Mmax.
3:List of Experimental Equipment and Materials:
The study involves base stations with a large number of antennas Mmax, single-antenna users, and orthogonal pilot sequences for channel estimation.
4:Experimental Procedures and Operational Workflow:
The procedure includes channel estimation based on pilot transmission in the uplink, optimization of power allocation and number of antennas, and pilot assignment optimization using the Hungarian method.
5:Data Analysis Methods:
The study analyzes the achieved SINR and ergodic achievable rate, employing convex optimization techniques and the Hungarian method for data analysis.
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