Operators deploy large-scale array antennas on 5G base stations and set antenna weights to achieve beams with different angles and widths, enhancing 5G network coverage. Traditional antenna weight optimization is based on the user distribution characteristics of the network. Based on the user measurement reports, minimum drive tests, and engineering parameters, the antenna weight optimization determines the relative positions between hotspot areas with centralized user distribution and base station antennas. Then it calculates the antenna weights in the reverse direction. This method can achieve ideal results in optimizing single-cell antenna weights, but it is unsuitable for simultaneous optimization of multiple cells. Information technology is developing rapidly. 5G communication is evolving towards 6G communication, and the growth of network and business scale brings huge challenges to network optimization. Quantum computing is a new computing model that follows the laws of quantum mechanics. It has a computing speed advantage over classical computing and is expected to become a solution to communication network optimization. This paper presents an antenna weight optimization method based on the filter variational quantum matrix solver algorithm. The method utilizes the powerful solution space representation ability of qubits, and it is helpful to obtain better antenna weight optimization results or accelerate the optimization process.
Operators deploy large-scale array antennas on 5G base stations and set antenna weights to achieve beams with different angles and widths, enhancing 5G network coverage. Traditional antenna weight optimization is based on the user distribution characteristics of the network. Based on the user measurement reports, minimum drive tests, and engineering parameters, the antenna weight optimization determines the relative positions between hotspot areas with centralized user distribution and base station antennas. Then it calculates the antenna weights in the reverse direction. This method can achieve ideal results in optimizing single-cell antenna weights, but it is unsuitable for simultaneous optimization of multiple cells. Information technology is developing rapidly. 5G communication is evolving towards 6G communication, and the growth of network and business scale brings huge challenges to network optimization. Quantum computing is a new computing model that follows the laws of quantum mechanics. It has a computing speed advantage over classical computing and is expected to become a solution to communication network optimization. This paper presents an antenna weight optimization method based on the filter variational quantum matrix solver algorithm. The method utilizes the powerful solution space representation ability of qubits, and it is helpful to obtain better antenna weight optimization results or accelerate the optimization process.
In this study, an overloaded large-scale distributed antenna network is considered, for which the number of active users is larger than that of antennas distributed in a base station coverage area (called a cell). To avoid overload, users in each cell are divided into multiple user groups, and, to reduce the computational complexity required for multi-user multiple-input and multiple-output (MU-MIMO), users in each user group are grouped into multiple user clusters so that cluster-wise distributed MU-MIMO can be performed in parallel in each user group. However, as the network size increases, conventional computational methods may not be able to solve combinatorial optimization problems, such as user scheduling and user clustering, which are required for performing cluster-wise distributed MU-MIMO in a finite amount of time. In this study, we apply quantum computing to solve the combinatorial optimization problems of user scheduling and clustering for an overloaded distributed antenna network and propose a quantum computing-based user scheduling and clustering method. The results of computer simulations indicate that as the technology of quantum computers and their related algorithms evolves in the future, the proposed method can realize large-scale dense wireless systems and realize real-time optimization with a short optimization execution cycle.
Massive multiple-input multiple-output (MIMO) systems are a key enabling technology for 5G-Advanced and emerging 6G networks, yet their performance is fundamentally constrained by high-dimensional, non-convex optimization problems arising from beamforming, power allocation, and user scheduling under imperfect channel state information (CSI).This paper proposes a hybrid quantum-classical optimization framework that formulates discrete and continuous massive MIMO resource allocation problems using Quadratic Unconstrained Binary Optimization (QUBO) and variational quantum algorithms.Discrete beam selection is addressed using quantum annealing, while continuous power control and hybrid beamforming are refined using variational quantum circuits within a classical feedback loop.Simulation results demonstrate improvements in aggregate spectral efficiency, runtime scalability, and robustness under imperfect CSI compared to conventional ZF and WMMSE baselines.The benefits of using the proposed solution include scalable runtime performance with respect to larger numbers of antennas; thus, it highlights the potential of optimizing quantum-assisted technologies for the future of wireless networks based on realistic assumptions regarding near-term NISQ-era performance.
Quantum computers can potentially solve large-scale combinatorial problems very efficiently when the problems are first converted into the quadratic unconstrained binary optimization (QUBO) format. Scheduling in fifth generation (5G) base stations is a practical combinatorial problem that cannot be solved optimally in real-time using classical computing. We formulate the downlink (DL) multiple-input multiple-output (MIMO) scheduling at 5G base stations as QUBO and analyze the QUBO formulation scalability with respect to the key system parameters. The single-user MIMO (SU-MIMO) QUBO formulation looks promising because the number of QUBO variables grows linearly while the problem search space grows exponentially with increasing number of users. Based on the simulation results, the suboptimal greedy algorithm for the SU-MIMO performs well with a high number of users and low bandwidth. A hybrid approach, where either a quantum solver or a suboptimal low-complexity classical algorithm is selected based on the system parameters, seems sensible in practice. This work paves the way for future quantum and quantum-inspired implementations of scheduling in cellular systems.
Massive MIMO beamforming for 6G networks faces a fundamental tradeoff between solution quality and computational complexity. Exhaustive search guarantees optimal antenna selection; however, this becomes prohibitively expensive for arrays exceeding 16 elements, while polynomial-time classical heuristics sacrifice 15–25% performance to achieve practical scalability. This paper introduces a quantum-enhanced optimization framework using the Quantum Approximate Optimization Algorithm (QAOA) to address this challenge for IoT-integrated 6G massive MIMO systems. Our approach combines quantum solution exploration with classical parameter optimization, integrating realistic 3GPP TR 38.901 channel models across 28–60 GHz bands and heterogeneous IoT device characteristics (mMTC, URLLC, eMBB). The framework incorporates an adaptive penalty mechanism that achieves constraint satisfaction within five iterations while maintaining polynomial complexity. Statistical validation across 50 independent channel realizations demonstrates significant advantages: 10–20% spectral efficiency improvement over classical heuristics (p<0.001, Cohen’s d=1.24), 35–42% IoT energy reduction, and 90–95% near-optimal solution quality compared to 65–85% for polynomial-time classical methods. Hardware validation on IBM quantum platforms (127–133 qubits) confirms practical feasibility for medium-scale systems with M≤16 antennas, achieving 89.3% of ideal performance with 22% measurement success rate. Current hardware limitations restrict deployment to proof-of-concept demonstrations, with full-scale 6G implementations requiring quantum error correction projected for 2030+.
This work presents a novel framework combining quantum computing and machine learning to optimize base antenna station placement in5G/6Gnetworks. Initially, spectral clustering is employed to identify dense population areas, providing initial antenna locations. The problem is then reformulated as a Maximum Independent Set (MIS) problem in graph theory to address overlapping coverage constraints. Neutral atom representations of the potential antenna sites are constructed, and the Rydberg Blockade property is utilized to optimize spatial configurations. Using the QuTiP quantum simulator, this method effectively solves the MIS problem, achieving 85% household coverage with the minimum possible number of antenna stations.
This paper proposes a hybrid quantum optimization framework for large-scale antenna-array beamforming with jointly optimized discrete phases and continuous amplitudes. The method combines quantum-inspired search with classical gradient refinement to handle mixed discrete-continuous variables efficiently. For phase optimization, a Gray-code and odd-combination encoding scheme is introduced to improve robustness and avoid the complexity explosion of higher-order Ising models. For amplitude optimization, a geometric spin-combination encoding and a two-stage strategy are developed, using quantum-inspired optimization for coarse search and gradient optimization for fine refinement. To enhance solution diversity and quality, a rainbow quantum-inspired algorithm integrates multiple optimizers for parallel exploration, followed by hierarchical-clustering-based candidate refinement. In addition, a double outer-product method and an augmented version are proposed to construct the coupling matrix and bias vector efficiently, improving numerical precision and implementation efficiency. Under the scoring rules of the 7th National Quantum Computing Hackathon, simulations on a 32-element antenna array show that the proposed method achieves a score of 461.58 under constraints on near-main-lobe sidelobes, wide-angle sidelobes, beamwidth, and optimization time, nearly doubling the baseline score. The proposed framework provides an effective reference for beamforming optimization in future wireless communication systems.