Abstract
We consider a downlink Heterogeneous Massive Multiple-Input Multiple-Output (HM-MIMO) system with the macro-base station (BS) having hundreds of antennas and each of the micro-BSs having tens of antennas. Two lower bounds and one approximation of the achievable per-user equipment (UE) rate in closed forms under this system are derived and compared in terms of network utility. Using the derived approximation, three BS-UE association approaches are proposed. Firstly, a simple, sub-optimal, and heuristic multiple BS-UE association approach is designed, which achieves around 25% utility performance improvement compared with the conventional maximum received signal strength (Max-RSS) approach. Secondly, based on the results given by this heuristic approach, a learning approach for BS-UE association using convolutional neural network (CNN) is introduced. After being fully trained, the CNN can take any new BS-UE configuration as input and provide a sub-optimal BS-UE association for that configuration directly. It has only a small performance degradation compared with the proposed heuristic approach. Thirdly, realizing that the BS-UE connection probability in the proposed CNN architecture can be considered as a power allocation ratio, a combined power allocation and association approach is proposed. Its performance achieves as high as 60% utility improvement compared with the Max-RSS association and is also comparable to that achieved by the max-min power allocation approach which requires more than $10000\times $ running time. It is remarkable that by using the gradients of the derived achievable per-UE rate approximation with respect to the power control coefficients, accurate target data is in fact not required for training in this approach.
Highlights
Massive multiple-input multiple-output (MIMO) technology [1] has been regarded as one of the ‘‘big three’’ of fifth-generation (5G) wireless technologies due to its high spectral efficiency, good energy efficiency, and simple signal processing characteristics [2], [3]
We focus on low complexity physical layer base station (BS)-user equipment (UE) association schemes and adopt the setting that all served UEs share all allocated resource block (RB) [3] so that the complexity and overhead associated with scheduling are nearly zero
Regarding the connection probability between a BS-UE pair as the transmit power portion allocated to the UE by the BS, we propose a new combined power allocation and BS-UE association approach based on the convolutional neural network (CNN) architecture introduced earlier for BS-UE association
Summary
Massive multiple-input multiple-output (MIMO) technology [1] has been regarded as one of the ‘‘big three’’ of fifth-generation (5G) wireless technologies due to its high spectral efficiency, good energy efficiency, and simple signal processing characteristics [2], [3]. The derived two lower bounds and one approximation include only large-scale fading coefficients and are very easy to compute Based on these closed forms, three low complexity and versatile BS-UE association approaches are proposed. Based on the proposed heuristic approach, we develop a supervised learning approach for BS-UE association using convolutional neural network (CNN) This learning approach can directly output required BS-UE association and uses obtained information as input such as per-antenna power constraint, number of antennas per BS, and large-scale fading coefficients. Var(·) and Cov(·, ·) means variance and covariance operations, respectively. (·)T , (·)∗, and (·)H denote transpose, conjugate, and transposed conjugate operations, respectively. z ∼ CN (0, σ 2) denotes a circularly symmetric complex Gaussian random variable with zero mean and variance σ 2. 1L×K represents an L × K matrix and each of its component is equal to 1, and |U| denotes the cardinality of set U
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