Energy efficiency enhancement of cell‐free massive multiple‐input multiple‐output network employing threshold‐based beamforming

Abstract

The downlink of cell‐free massive multiple‐input multiple‐output network is considered. It is assumed that both access points (APs) and users are equipped with multiple antennas. A heuristic threshold‐based‐beamforming (TBF) protocol is proposed, which performs an AP selection algorithm to significantly improve the total energy efficiency (EE) of the network. The beamforming method that fits the model is the maximum eigenmode beamforming (MEB). The threshold level is determined in such a way that the strongest channel eigenmodes contribute in the downlink transmissions. Indeed, power control coefficients are chosen in such a way that the number of users served by each AP is limited according to the TBF‐based AP selection scheme. The spectral efficiency of the proposed method is investigated considering both perfect and imperfect knowledge of the channel state information at transmitters. Simulation results show that the proposed TBF protocol greatly enhances the total EE of the network compared to the MEB, conjugate beamforming, and zero‐force precoding techniques. Furthermore, a modification algorithm is proposed to obtain the minimum per‐user quality of service requirement, which prevents the dramatic reduction in the EE caused by high inter‐user interference levels.

Energy‐efficient radio resource allocation for dual‐hop relay‐assisted orthogonal frequency division multiple access systems with quality of service provisioning

“…We begin by stating the main system constraints that are common to the three problems. First, for hop 1, any subcarrier n can only transmit in a unique MCS level m as well as be associated with a unique relay node k as shown in (2), that is,…”

“…In other words, increasing the EE or reducing the energy cost per transmitted bit has become a key requirement in fifth generation and beyond mobile communication networks. Motivated by this requirement, the design of energy-aware architectures and green systems has attracted considerable interest in the recent literature in scenarios with or without relays 2,3 . However, the design of energy-efficient networking techniques must not affect the quality of service (QoS) perceived by users.…”

Cell-Free Massive MIMO : Scalability, Signal Processing and Power Control

“…Uplink processing MMSE [59,124,145,146] ZF [147] Power allocation and load balancing [148] Max-min power control [149] Pilot power control [150] Sequential linear MMSE [120] mmWave Limited fronthaul capacity [151] Power control [152] Channel modeling [153] Energy efficiency [154] Energy Efficiency [116,132,[155][156][157] Imperfect Fronthaul [158][159][160][161][162][163][164] Low Resolution ADCs [163,[165][166][167] Hardware Impairments [164,[168][169][170] Non-orthogonal [171][172][173][174][175] Multiple Access…”

Spatial correlation modeling and ergodic capacity analysis of networks with overlapping conflict

“…The user‐centric architecture is a “disruptive and innovative” design for 5G and even future B5G networks. The user‐centric network (UCN) breaks the traditional cell‐specific architecture and implements user equipment (UE)‐specific design by dynamically constructing the optimal groups for different users, which is recognized as a promising technology for future wireless networks that provides uniformly great services to all users considering no cell boundary 10 …”

“…The user-centric network (UCN) breaks the traditional cell-specific architecture and implements user equipment (UE)-specific design by dynamically constructing the optimal groups for different users, which is recognized as a promising technology for future wireless networks that provides uniformly great services to all users considering no cell boundary. 10 As Zhang et al 8 mentioned, multiple TPs serving the UE at the same time are regarded as a user-centric group called transmission point group (TPG). As a result, UCN is composed of multiple overlapping user-centric groups.…”