TY  - EJOU
AU  - Shahjehan, Waleed 
AU  - Ullah, Abid 
AU  - Shah, Syed Waqar 
AU  - Khan, Imran 
AU  - Sani, Nor Samsiah 
AU  - Kim, Ki-Il 

TI  - A Sparse Optimization Approach for Beyond 5G mmWave Massive MIMO Networks
T2  - Computers, Materials \& Continua

PY  - 2022
VL  - 72
IS  - 2
SN  - 1546-2226

AB  - Millimeter-Wave (mmWave) Massive MIMO is one of the most effective technology for the fifth-generation (5G) wireless networks. It improves both the spectral and energy efficiency by utilizing the 30–300 GHz millimeter-wave bandwidth and a large number of antennas at the base station. However, increasing the number of antennas requires a large number of radio frequency (RF) chains which results in high power consumption. In order to reduce the RF chain's energy, cost and provide desirable quality-of-service (QoS) to the subscribers, this paper proposes an energy-efficient hybrid precoding algorithm for mmWave massive MIMO networks based on the idea of RF chains selection. The sparse digital precoding problem is generated by utilizing the analog precoding codebook. Then, it is jointly solved through iterative fractional programming and successive convex optimization (SCA) techniques. Simulation results show that the proposed scheme outperforms the existing schemes and effectively improves the system performance under different operating conditions.
KW  - 5G; mmwave precoding; massive mimo; complexity

DO  - 10.32604/cmc.2022.026185