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International Conference on Telecommunications and Signal Processing (TSP)

New Constrained Quantum Optimization Algorithm for Power Allocation in MIMO

Abdulbasit M. A. Sabaawi, Mohammed Rabeea Almasaoodi, Sara El Gaily, Sándor Imre

Published Jul 13, 2022• 11 citations
Publisher

Abstract

Multiple-input multiple-output (MIMO) has acquired significant interest as the demand for high data rates has increased and, it is regarded as a key technique in 5G wireless technology. Previously, the water filling algorithm (WFA) has been found as an effective method for power allocation channels in (MIMO). However, this solution is computationally intractable owing to the increasing size of channels. To address this problem, this paper applies the constrained quantum optimization algorithm (CQOA) for reducing the overall transmit power and the computational complexity of the MIMO downlink system. An analytical comparison has been performed between the CQOA and water filling algorithm-based binary searching algorithm (WFA-BSA). Numerical results show the efficiency of the CQOA against the WFA-BSA in terms of power and computation.

DOI
10.1109/tsp55681.2022.9851241

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Multiple-input multiple-output (MIMO) has acquired significant interest as the demand for high data rates has increased and, it is regarded as a key technique in 5G wireless technology. Previously, the water filling algorithm (WFA) has been found as an effective method for power allocation channels in (MIMO). However, this solution is computationally intractable owing to the increasing size of channels. To address this problem, this paper applies the constrained quantum optimization algorithm (CQOA) for reducing the overall transmit power and the computational complexity of the MIMO downlink system. An analytical comparison has been performed between the CQOA and water filling algorithm-based binary searching algorithm (WFA-BSA). Numerical results show the efficiency of the CQOA against the WFA-BSA in terms of power and computation.
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.
In the past decades, many quantum algorithms have been developed. The main obstacle that prevents the widespread implementation of these algorithms is the small size of the available quantum computer in terms of qubits. Blind Quantum Computation (BQC) holds the promise of handling this issue by delegating computation to quantum remote devices. Here, we introduce a novel Constrained Quantum Genetic Algorithm (CQGA) that selects the optimum extreme (minimum or maximum) value of a constrained goal function (or a vast unsorted database) with very low computational complexity. Since the convergence speed to the optimal solution for the Constrained Classical Genetic Algorithm (CCGA) is highly dependent on the level of quality of the initially selected potential solutions, the CCGA's heuristic initialization stage is replaced by a quantum one. This is achieved by exploiting the strengths of the Constrained Quantum Optimization Algorithm (CQOA) and the BQC. The proposed CQGA is applied as an embedded computational infrastructure for the uplink multi-cell massive MIMO system. The algorithm maximizes the energy efficiency (EE) of the uplink massive MIMO while considering different users target bit rate classes. Simulation results show that the suggested CQGA maximizes energy efficiency through careful computation of the optimal transmit power for each active user using fewer computational steps than the CCGA. We demonstrated that when the overall transmit power set or the overall number of active users increases, the CQGA keeps executing a smaller number of generation steps compared to the CCGA. For instance, if we consider a scenario where the overall number of active users () is set to 18, the CQGA finds the optimal solution with a smaller number of generation steps equal to 6, while the CCGA takes a larger number of generation steps, reaching 65.
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.
Quantum computing appears as an alternative solution for solving computationally intractable problems. This paper presents a new constrained quantum genetic algorithm designed specifically for identifying the extreme value of a highly constrained optimization problem, where the search space size _database is massive and unsorted_ cannot be handled by the currently available classical or quantum processor, called the highly constrained quantum genetic algorithm (HCQGA). To validate the efficiency of the suggested quantum method, maximizing the energy efficiency with respect to the target user bit rate of an uplink multi-cell massive multiple-input and multiple- output (MIMO) system is considered as an application. Simulation results demonstrate that the proposed HCQGA converges rapidly to the optimum solution compared with its classical benchmark.
Cloud resource demands are rapidly increasing exponentially with the development of cloud computing. For this sake, new modern methods and techniques are needed to improve its quality of service. In this work, the problem of resource distribution management in cloud computing is formulated as an optimisation problem. To this end, we exploit the constrained quantum optimisation algorithm (CQOA) in resource distribution management by considering queueing aspect in order to optimise and improve its performance in terms of computational complexity, accuracy, and energy consumption. Next, we study the implementation and the configuration of the CQOA. After, we investigate the computational complexity of selecting the optimal distribution scenario that corresponds to minimum energy consumption. We also express the lower and upper bounds of the overall possible assignment scenarios within the database. Finally, we validate the efficiency of the proposed implemented CQOA by constructing a simulation environment.
This paper introduces a pioneering method in the realm of signal processing for Massive Multiple-Input Multiple-Output (MIMO) systems, leveraging the synergistic potential of hybrid quantum-classical computing paradigms. The escalating demand for high data rate communications necessitates the advancement of MIMO technologies. However, the computational complexity associated with signal processing in such systems, especially in scenarios involving large array sizes, presents a significant challenge. This study proposes an innovative framework that integrates quantum computing algorithms with traditional classical methods to address this challenge. The quantum algorithms are specifically designed to expedite matrix operations and optimization tasks that are central to MIMO signal processing, such as channel estimation and precoding. A comparative analysis with purely classical approaches demonstrates a substantial improvement in computational efficiency and latency reduction, while maintaining, and in certain aspects enhancing, the accuracy of signal detection and system throughput. Furthermore, this hybrid approach offers scalability and adaptability to varying channel conditions and system configurations, a crucial feature for next-generation wireless communication systems. The theoretical foundation is complemented by simulation results, showcasing the feasibility and effectiveness of this approach in practical scenarios. This research not only paves the way for advanced signal processing techniques in Massive MIMO systems but also opens new horizons in the application of quantum computing in telecommunications.