SLNR Based Hybrid Precoding for HAP Massive MIMO Systems with Limited RF Chains

Zhang, Jian; Jiang, Lingge; Ji, Pingping; He, Chen; He, Di; Wu, Wenjun

doi:10.1109/iscas45731.2020.9180891

Cited by 1 publication

(2 citation statements)

References 13 publications

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“…In this section, we assume a uniform linear array arrangement with spacing d = 𝜆∕2 and N = 256 antennas with K ∈ [10,50] devices. We define one LoS component and L = 2 NLoS components are assumed for all users' channels, 𝛼…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Hence, huge amount of RFCs are required in massive MIMO systems, which may consume the majority of the energy consumption. To solve this problem, some research has focused on reducing energy consumption and hardware complexity in massive antenna 5G networks [6][7][8][9][10]. Recently, to significantly reduce the amount of RFCs in massive MIMO systems, beamformed MIMO structures have been presented [11].…”

Section: Introductionmentioning

confidence: 99%

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The dynamic power allocation to maximize the achievable sum rate for massive MIMO‐NOMA systems

Amirifar

Abouei

2022

IET Communications

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The demand of mobile networks and quality of service have recently increased to higher Spectral Efficiency (SE) or Energy Efficiency (EE) and massive connectivity for 5G wireless communications. The concept of beamforming Multiple‐Input Multiple‐Output (MIMO) is capable of significantly reducing the amount of required Radio Frequency Chains (RFCs) used by massive MIMO systems without remarkable performance loss. However, in existing beamformed MIMO, the amount of supported devices cannot be higher than the amount of RFCs using the same time‐frequency resources, and it is the basic limit for these systems. To address this issue, non‐orthogonal multiple access (NOMA) has been recently recommended, which can accommodate higher covered using via non‐orthogonal resource allocation. Nevertheless, power allocation is a core factor of the NOMA scheme. However, maximizing the sum rate problem based on power allocation in Massive MIMO‐NOMA scenarios is non‐convex and non‐linear, which creates a very challenging situation to acquire the closed‐form approach. Thus, in this paper, an approach to this difficulty is outlined for Massive MIMO‐NOMA systems to maximize the sum rate as a convex and linear problem. Simulation results of the suggested Beamformed MIMO‐NOMA (BMN) algorithm show that a higher achievable sum rate is achieved compared with the usual beamformed MIMO.

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Section: Simulation Resultsmentioning

confidence: 99%