2023•Unpublished venueRequires access

Performance Evaluation of Multi-user Massive MIMO Systems with Hybrid Precoding

Neeraj Sharma, Bhasker Gupta

Open publisher page 4 citations

Abstract

Unlike conventional multiple-input multiple-output (MIMO), massive MIMO communication is a scalable technology offering benefits corresponding to the deployed base station (BS) antennas. It implies equipping BS with huge number of antennas compared to user terminals. It offers sizable boost in spectral and energy efficiencies along with linear signal processing techniques. Among the various design problems related to massive MIMO, effective precoding is one of the most important signal processing tasks at the BS to enable successful downlink transmission. Digital precoding techniques are employed in conventional MIMO systems, where each antenna is preceded by a dedicated RF chain. These techniques are unsuitable for massive MIMO due to excessive number of power hungry and expensive RF chains. In this paper, we discuss advantages, fundamental properties and design challenges of massive MIMO technology. We also present an energy-efficient, low-cost and low-hardware complexity hybrid precoding technique for massive multi-user MIMO systems under Rayleigh fading and millimeter wave channels. Compared with fully digital zero-forcing precoding, the hybrid precoding technique exhibits very high energy and near-optimal spectral efficiency.

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What this paper is about

Unlike conventional multiple-input multiple-output (MIMO), massive MIMO communication is a scalable technology offering benefits corresponding to the deployed base station (BS) antennas. It implies equipping BS with huge number of antennas compared to user terminals. It offers sizable boost in spectral and energy efficiencies along with linear signal processing techniques. Among the various design problems related to massive MIMO, effective precoding is one of the most important signal processing tasks at the BS to enable successful downlink transmission. Digital precoding techniques are employed in conventional MIMO systems, where each antenna is preceded by a dedicated RF chain. These techniques are unsuitable for massive MIMO due to excessive number of power hungry and expensive RF chains. In this paper, we discuss advantages, fundamental properties and design challenges of massive MIMO technology. We also present an energy-efficient, low-cost and low-hardware complexity hybrid precoding technique for massive multi-user MIMO systems under Rayleigh fading and millimeter wave channels. Compared with fully digital zero-forcing precoding, the hybrid precoding technique exhibits very high energy and near-optimal spectral efficiency.

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Available abstract

Unlike conventional multiple-input multiple-output (MIMO), massive MIMO communication is a scalable technology offering benefits corresponding to the deployed base station (BS) antennas. It implies equipping BS with huge number of antennas compared to user terminals. It offers sizable boost in spectral and energy efficiencies along with linear signal processing techniques. Among the various design problems related to massive MIMO, effective precoding is one of the most important signal processing tasks at the BS to enable successful downlink transmission. Digital precoding techniques are employed in conventional MIMO systems, where each antenna is preceded by a dedicated RF chain. These techniques are unsuitable for massive MIMO due to excessive number of power hungry and expensive RF chains. In this paper, we discuss advantages, fundamental properties and design challenges of massive MIMO technology. We also present an energy-efficient, low-cost and low-hardware complexity hybrid precoding technique for massive multi-user MIMO systems under Rayleigh fading and millimeter wave channels. Compared with fully digital zero-forcing precoding, the hybrid precoding technique exhibits very high energy and near-optimal spectral efficiency.

Key concepts: Precoding, MIMO, 3G MIMO, Spatial multiplexing, Zero-forcing precoding, Multi-user MIMO, Computer science, Electronic engineering

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