Mid-Band Extra Large-Scale MIMO System: Channel Modeling and Performance Analysis

TL;DR

This work tackles the challenge of channel modeling and performance analysis for mid-band XL-MIMO systems by proposing a flexible analytical channel model that captures near-field non-stationarity, cluster sparsity, and spatial correlation. It develops a cluster-based, product-channel representation with a non-diagonal power-coupling matrix and a KL-based statistical description, enabling compatibility with existing analytical models and both far-field and near-field regimes. The authors derive closed-form approximations and bounds for ergodic SE and outage probability under two representative scenarios (SS and DS) and validate insights through simulations across multiple configurations, showing substantial SE and reliability gains from increased array size and bandwidth at mid-band frequencies. The work provides actionable guidance on exploiting near-field effects and eigenstructure in XL-MIMO to enhance throughput and coverage, contributing to theoretical foundations and practical design for 6G mid-band deployments.

Abstract

In pursuit of enhanced quality of service and higher transmission rates, communication within the mid-band spectrum, such as bands in the 6-15 GHz range, combined with extra large-scale multiple-input multiple-output (XL-MIMO), is considered a potential enabler for future communication systems. However, the characteristics introduced by mid-band XL-MIMO systems pose challenges for channel modeling and performance analysis. In this paper, we first analyze the potential characteristics of mid-band MIMO channels. Then, an analytical channel model incorporating novel channel characteristics is proposed, based on a review of classical analytical channel models. This model is convenient for theoretical analysis and compatible with other analytical channel models. Subsequently, based on the proposed channel model, we analyze key metrics of wireless communication, including the ergodic spectral efficiency (SE) and outage probability (OP) of MIMO maximal-ratio combining systems. Specifically, we derive closed-form approximations and performance bounds for two typical scenarios, aiming to illustrate the influence of mid-band XL-MIMO systems. Finally, comparisons between systems under different practical configurations are carried out through simulations. The theoretical analysis and simulations demonstrate that mid-band XL-MIMO systems excel in SE and OP due to the increased array elements, moderate large-scale fading, and enlarged transmission bandwidth.

Figures (10)

• Figure 1: Illustration of mid-band XL-MIMO system.
• Figure 2: Ergodic SE of SS scenario against the transmit power per antenna. Setup 1: $N_\mathrm{R} = 256$, $N_\mathrm{T}=16$, $L_{\mathsf{SS}}=4$; Setup 2: $N_\mathrm{R} = 512$, $N_\mathrm{T}=64$, $L_{\mathsf{SS}}=4$; Setup 3: $N_\mathrm{R} = 512$, $N_\mathrm{T}=64$, $L_{\mathsf{SS}}=6$.
• Figure 3: Ergodic SE of DS scenario against the transmit SNR. Setup 1: $N_\mathrm{R} = 256$, $N_\mathrm{T}=64$, $L_{\mathsf{DS}}=8$, Setup 2: $N_\mathrm{R} = 256$, $N_\mathrm{T}=64$, $L_{\mathsf{DS}}=16$, Setup 3: $N_\mathrm{R} = 512$, $N_\mathrm{T}=64$, $L_{\mathsf{DS}}=16$, Setup 4: $N_\mathrm{R} = 64$, $N_\mathrm{T}=8$, $L_{\mathsf{DS}}=4$.
• Figure 4: OP of SS scenario versus the receive SNR threshold. Setup 1: $N_\mathrm{R} = 256$, $N_\mathrm{T}=16$, $L_{\mathsf{SS}}=4$, Setup 2: $N_\mathrm{R} = 512$, $N_\mathrm{T}=64$, $L_{\mathsf{SS}}=4$, Setup 3: $N_\mathrm{R} = 512$, $N_\mathrm{T}=64$, $L_{\mathsf{SS}}=8$.
• Figure 5: OP of DS scenario versus the receive SNR threshold. Setup 1: $N_\mathrm{R} = 256$, $N_\mathrm{T}=64$, $L_{\mathsf{DS}}=8$, Setup 2: $N_\mathrm{R} = 256$, $N_\mathrm{T}=64$, $L_{\mathsf{DS}}=16$, Setup 3: $N_\mathrm{R} = 512$, $N_\mathrm{T}=64$, $L_{\mathsf{DS}}=16$.

Theorems & Definitions (12)

• Proposition 1
• Remark 1
• Proposition 2
• Proposition 3
• Proposition 4
• Remark 2
• Theorem 1
• Proposition 5
• Proposition 6
• Theorem 2