Cell-Free Massive MIMO-Assisted SWIPT for IoT Networks

TL;DR

This work tackles SWIPT in CF-mMIMO IoT networks by introducing a joint AP mode selection framework that assigns some APs to information transmission (I-APs) and others to energy transmission (E-APs), enabling simultaneous DL WIT and WPT. Using long-term CSI, PPZF and PMRT precoding mitigate interference to IUs while enhancing energy harvesting at EUs, and the authors derive closed-form DL SE for IUs and NL-EH energy expressions for EUs. They formulate two mixed-integer nonconvex problems (sum-SE and EE) and solve them via successive convex approximation with continuous relaxation and penalty terms, plus extensions to sum-HE and max-min HE; benchmarks demonstrate substantial gains over random mode selection and orthogonal SWIPT. Numerical results show up to 4- to 5-fold improvements in EE relative to benchmarks, and significant SE and HE gains in dense CF-mMIMO IoT deployments, validating the practicality of joint AP-mode/power-control design. Overall, the proposed framework enables efficient, scalable CF-mMIMO SWIPT for dense IoT networks by intelligently distributing AP roles and managing transmit power and interference across the network.

Abstract

This paper studies cell-free massive multiple-input multiple-output (CF-mMIMO) systems that underpin simultaneous wireless information and power transfer (SWIPT) for separate information users (IUs) and energy users (EUs) in Internet of Things (IoT) networks. We propose a joint access point (AP) operation mode selection and power control design, wherein certain APs are designated for energy transmission to EUs, while others are dedicated to information transmission to IUs. The performance of the system, from both a spectral efficiency (SE) and energy efficiency (EE) perspective, is comprehensively analyzed. Specifically, we formulate two mixed-integer nonconvex optimization problems for maximizing the average sum-SE and EE, under realistic power consumption models and constraints on the minimum individual SE requirements for individual IUs, minimum HE for individual EUs, and maximum transmit power at each AP. The challenging optimization problems are solved using successive convex approximation (SCA) techniques. The proposed framework design is further applied to the average sum-HE maximization and energy harvesting fairness problems. Our numerical results demonstrate that the proposed joint AP operation mode selection and power control algorithm can achieve EE performance gains of up to $4$-fold and $5$-fold over random AP operation mode selection, with and without power control respectively.

Figures (9)

• Figure 1: The proposed CF-mMIMO SWIPT system.
• Figure 2: Convergence behavior of the optimization problem (P3.2) ($M=60$, $N=12$, $\Gamma_{\ell}=250$$\mu$Joule).
• Figure 3: Average sum-SE versus the per-IU SE requirement. The solid lines depict results for $\tilde{\rho}_t = 0.25$ W, while the dashed lines show results for $\tilde{\rho}_t = 0.05$ W ($M=40$, $N=12$, $K_d=L=5$, $\Gamma_{\ell}=250$$\mu$Joule).
• Figure 4: Average sum-SE versus the number of IUs and EUs with $K_d=L$. The dashed lines depict results for $\mathcal{S}_\mathtt{dl}^o=10$ [bit/s/Hz], while the solid lines show results for $\mathcal{S}_\mathtt{dl}^o=15$ [bit/s/Hz]. ($M=40$, $N=12$, $\Gamma_{\ell}=250$$\mu$Joule).
• Figure 5: Average EE versus the per-IU SE requirement ($M=40$, $N=12$, $K_d=L=5$, $\Gamma_{\ell}=250$$\mu$Joule).

Theorems & Definitions (8)

• Theorem 1
• proof
• Theorem 2
• proof
• Remark 1
• Remark 2
• Remark 3
• Remark 4