Zero-Forcing Based Downlink Virtual MIMO-NOMA Communications in IoT Networks

TL;DR

This paper proposes a downlink virtual MIMO-NOMA scheme for IoT networks that enhances connectivity and spectral efficiency by combining cooperative cluster-based MIMO with power-domain NOMA, using statistical CSI for optimization.

Contribution

It introduces a novel cooperative virtual MIMO-NOMA framework with outage and goodput analysis, and develops closed-form solutions for joint power and rate optimization under statistical CSI.

Findings

Outage probability and goodput are thoroughly analyzed considering channel correlations.

Asymptotic analysis provides insights into system parameters and diversity-multiplexing tradeoff.

Optimal power and rate allocation strategies maximize goodput under various channel conditions.

Abstract

To support massive connectivity and boost spectral efficiency for internet of things (IoT), a downlink scheme combining virtual multiple-input multiple-output (MIMO) and nonorthogonal multiple access (NOMA) is proposed. All the single-antenna IoT devices in each cluster cooperate with each other to establish a virtual MIMO entity, and multiple independent data streams are requested by each cluster. NOMA is employed to superimpose all the requested data streams, and each cluster leverages zero-forcing detection to de-multiplex the input data streams. Only statistical channel state information (CSI) is available at base station to avoid the waste of the energy and bandwidth on frequent CSI estimations. The outage probability and goodput of the virtual MIMO-NOMA system are thoroughly investigated by considering Kronecker model, which embraces both the transmit and receive correlations. Furthermore, the asymptotic results facilitate not only the exploration of physical insights but also the goodput maximization. In particular, the asymptotic outage expressions provide quantitative impacts of various system parameters and enable the investigation of diversity-multiplexing tradeoff (DMT). Moreover, power allocation coefficients and/or transmission rates can be properly chosen to achieve the maximal goodput. By favor of Karush-Kuhn-Tucker conditions, the goodput maximization problems can be solved in closed-form, with which the joint power and rate selection is realized by using alternately iterating optimization.Besides, the optimization algorithms tend to allocate more power to clusters under unfavorable channel conditions and support clusters with higher transmission rate under benign channel conditions.