System for optimizing massive MIMO networks

Abstract

System (100) for improving energy efficiency and capacity in a massive multiple-input multiple-output (MMIMO) mobile network, wherein the system (100) comprises: a precoding module (102) configured to perform downlink transmission from a base station with a plurality of antennas and to reduce interference between users; a reduction module (104) configured to model and minimize static and dynamic circuit components of the base station; a selection module (108) configured to activate a subset of antennas that have higher transmission efficiency; where coordinated operation of the precoding module, the reduction module and the selection module improves the overall energy efficiency and network capacity of the MMIMO mobile network.

Description

AREA

[0001] The present disclosure relates generally to a method for improving energy efficiency and capacity in massive MIMO mobile networks, using quadruple optimization. GENERAL STATE OF THE ART

[0002] The rapid growth of mobile data traffic and bandwidth-intensive applications has driven the evolution of mobile networks toward MMIMO (Massive Multiple Input Multiple Output) architectures, where base stations use a large number of antennas to serve multiple users simultaneously. While MMIMO improves spectral efficiency and reduces interference, its practical application faces challenges related to high power consumption, hardware complexity, and resource allocation. Conventional techniques such as linear precoding, user selection, antenna selection, and power modeling have largely been studied in isolation. However, the increased antenna and user density in 5G and beyond networks necessitates an integrated optimization approach to simultaneously improve energy efficiency and network capacity under realistic operating conditions. SUMMARY

[0003] The subject matter of the present invention is defined in the claims. BRIEF DESCRIPTION OF THE FIGURES Fig. Figure 1 illustrates a method for improving energy efficiency and capacity in massive MIMO mobile networks using quad optimization. DETAILED DESCRIPTION OF THE INVENTION

[0004] The deployment of MMIMO (Massive Multiple Input Multiple Output) systems in modern mobile networks presents significant challenges related to high energy consumption, complex hardware, and inefficient resource allocation. Existing techniques such as precoding, user selection, antenna selection, and power modeling are typically applied independently, resulting in suboptimal energy efficiency and network capacity, particularly in dense 5G and beyond deployment scenarios.

[0005] The present disclosure addresses the aforementioned problem by providing a unified optimization procedure for improving both energy efficiency and network capacity in massive multiple-input multiple-output (MMIMO) mobile communication networks. The procedure integrates four coordinated optimization techniques—Zero Forcing Linear Precoding (ZFLP), Circuit Power Consumption Reduction (CPCR), Norm-based User Selection (NUS), and Norm-based Antenna Selection (NAS)—into a single structured framework. ZFLP is applied to downlink transmissions from a base station with a large number of antennas to effectively suppress user interference caused by channel matrix inversion.CPCR is implemented to reduce the overall power consumption of the circuit by modeling and minimizing both static and dynamic components associated with RF chains, converters, amplifiers, and cooling systems. NUS selects users with optimal channel conditions based on the quadratic norm of their channel vectors, thereby improving spectral utilization. NAS activates only those antennas exhibiting the highest transmission efficiency by exceeding predefined gain thresholds. The coordinated execution of these techniques significantly improves energy efficiency, defined as the ratio of total throughput to total power consumption, and increases network capacity, measured by aggregated total rate, particularly in deployments with hundreds of antennas and users in 5G and beyond networks.

[0006] Fig.Figure 1 illustrates a flowchart 100 that depicts a procedure for improving energy efficiency and capacity in a massive multiple-input multiple-output (MMIMO) cellular network. The procedure comprises four sequential operations, each identified by a label. Block 102 shows the application of Zero Forcing Linear Precoding (ZFLP), configured to manage downlink transmissions from a base station equipped with multiple antennas. Block 104 depicts the implementation of Circuit Power Consumption Reduction (CPCR), achieved by modeling and minimizing both static and dynamic power components within the system. Block 106 demonstrates the performance of norm-based user selection (NUS), which identifies a subset of users with optimal channel conditions.Block 108 illustrates standards-based antenna selection (NAS), which activates a subset of antennas with the highest transmission efficiency. The flowchart visually conveys the structured sequence of these four optimization techniques, which together contribute to improved energy efficiency and network capacity in MMIMO systems.

[0007] Although the implementations of the methods and systems for improving energy efficiency and capacity in massive multiple-input multiple-output (MMIMO) mobile networks have been described with respect to certain structural features and process steps, it should be noted that the attached claims are not limited to the specific embodiments disclosed herein. Rather, the disclosed features and methods are provided as examples to illustrate the implementations of the MMIMO network optimization method, and various modifications, substitutions, and equivalents can be made without deviating from the scope of this disclosure.

Claims

[1] System (100) for improving energy efficiency and capacity in a massive multiple input multiple output mobile network (MMIMO), wherein the system (100) comprises: a precoding module (102) configured to perform downlink transmission from a base station with a plurality of antennas and to reduce interference between users; a reduction module (104) configured to model and minimize static and dynamic circuit components of the base station; a selection module (108) configured to activate a subset of antennas that have higher transmission efficiency; where coordinated operation of the precoding module, the reduction module and the selection module improves the overall energy efficiency and network capacity of the MMIMO mobile network. [2] System (100) according to claim 1, wherein the selection module (106) is configured to select users based on a quadratic norm of their channel vectors, and wherein the selection module (108) is configured to activate antennas that have channel gains exceeding a predefined threshold. [3] System (100) according to claim 1, wherein the MMIMO mobile network comprises at least 220 antennas and 120 users and wherein energy efficiency is defined as the ratio of total data throughput to total energy consumption.

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