Statistical Analysis of Received Signal Strength in Industrial IoT Distributed Massive MIMO Systems

Abstract

The Fifth Generation (5G) of wireless networks introduced native support for Machine-Type Communication (MTC), which is a key enabler for the Internet of Things (IoT) revolution. Current 5G standards are not yet capable of fully satisfying the requirements of critical MTC (cMTC) and massive MTC (mMTC) use cases. This is the main reason why industry and academia have already started working on technical solutions for beyond-5G and Sixth Generation (6G) networks. One technological solution that has been extensively studied is the combination of network densification, massive Multiple-Input Multiple-Output (mMIMO) systems and user-centric design, which is known as distributed mMIMO or Cell-Free (CF) mMIMO. Under this new paradigm, there are no longer cell boundaries: all the Access Points (APs) on the network cooperate to jointly serve all the devices. In this paper, we compare the performance of traditional mMIMO and different distributed mMIMO setups, and quantify the macro diversity and signal spatial diversity performance they provide. Aiming at the uplink in industrial indoor scenarios, we adopt a path loss model based on real measurement campaigns. Monte Carlo simulation results show that the grid deployment of APs provide higher average channel gains, but radio stripes deployments provide lower variability of the received signal strength.

Survey on Resource Allocation for Future 6G Network Architectures: Cell-Free and Radio Stripe Technologies

“…In [120], a novel UL detection scheme named quasi-LMMSE is introduced, utilizing only the MRC fronthaul signals. In [121], the received signal strength in an industrial Internet of things (IoT) context with mMIMO is studied. The authors compared the macro-diversity and signal spatial diversity performance of traditional mMIMO with different D-mMIMO setups, such as RS, and found that D-mIMO can provide higher average channel gains.…”

“…Uplink (UL) NLMMSE-based [115] OSLP-based [116] RZF-based [119] Quasi-LMMSE-based [120] Clustered-based [123] Cluster-MMSE-based [137] MRT-based [118] Spatial Diversity Performance [121] Exploration [126] Integration [127] Outage Probabilities [122] LDPC codification [136] Comparison with CF [16] Comparison with LIS [138] Low-pass [124,125] Joint Positioning and synchronization with ML [133] APS to improve PE [135,139] SO power optimization [128] BO power optimization [129] BO APS to improve SE/LB [131] MO power optimization [130]…”

Energy-Sustainable IoT Connectivity: Vision, Technological Enablers, Challenges, and Future Directions

“…Due to the above, radio stripes technology is attractive for supporting energy-efficient (and sustainable) networks. Applications are numerous, e.g., to: i) facilitate high spatial multiplexing and low EC in indoor communications [209], ii) increase the coverage and end-to-end PTE of RF-WET [22], [210], and iii) support ultra-reliable low-latency communications in industrial IoT networks [211]. In general, although the last few years have witnessed significant advances in this technology (e.g., see [212]- [214] and references therein), more advanced/efficient resource allocation schemes, circuit implementations and prototypes, and distributed processing architectures to avoid costly signaling between the antenna elements, still pose open research challenges.…”