This article develops a quantum bacterial foraging optimization (QBFO) algorithm, a quantum intelligence algorithm based on quantum computing and bacterial foraging optimization (BFO), with application in MIMO system optimization designs. In QBFO, a multiqubit is used to represent a bacterium, and a quantum rotation gate is used to mimic chemotaxis. Because the quantum bacterium with multiqubit has the advantage that it can represent a linear superposition of states (binary solutions) in search space probabilistically, the proposed QBFO algorithms shows better performance on solving combinatorial optimization problems than its classical counterpart BFO and Quantum Genetic Algorithm (QGA), especially for parallel non-gradient optimization. A sparse channel estimation scheme based on QBFO with adaptive phase rotation (AQBFO) in 3D MIMO system is proposed, and simulation results show that AQBFO achieved a better performance than existing algorithms including least squares (LS), iteratively reweighted least squares (IRLS), matching pursuit (MP), and orthogonal matching pursuit (OMP). We further improve some critical aspects such as reproduction and dispersal processes of AQBFO, propose an improved IQBFO algorithm, and apply it for interference coordination in 3D multi-cell multi-user MIMO systems, aiming to maximize the spectral efficiency. It considers user fairness and jointly optimizes cell-center and cell-edge user specific antenna downtilts and power to maximize each user's sum rate. This problem is a combinatorial non-convex optimization problem that cannot be solved by the traditional Karush-Kuhn-Tucker Lagrangian algorithm whereas the IQBFO algorithm solves it effectively.
This article develops a quantum bacterial foraging optimization (QBFO) algorithm, a quantum intelligence algorithm based on quantum computing and bacterial foraging optimization (BFO), with application in MIMO system optimization designs. In QBFO, a multiqubit is used to represent a bacterium, and a quantum rotation gate is used to mimic chemotaxis. Because the quantum bacterium with multiqubit has the advantage that it can represent a linear superposition of states (binary solutions) in search space probabilistically, the proposed QBFO algorithms shows better performance on solving combinatorial optimization problems than its classical counterpart BFO and Quantum Genetic Algorithm (QGA), especially for parallel non-gradient optimization. A sparse channel estimation scheme based on QBFO with adaptive phase rotation (AQBFO) in 3D MIMO system is proposed, and simulation results show that AQBFO achieved a better performance than existing algorithms including least squares (LS), iteratively reweighted least squares (IRLS), matching pursuit (MP), and orthogonal matching pursuit (OMP). We further improve some critical aspects such as reproduction and dispersal processes of AQBFO, propose an improved IQBFO algorithm, and apply it for interference coordination in 3D multi-cell multi-user MIMO systems, aiming to maximize the spectral efficiency. It considers user fairness and jointly optimizes cell-center and cell-edge user specific antenna downtilts and power to maximize each user's sum rate. This problem is a combinatorial non-convex optimization problem that cannot be solved by the traditional Karush-Kuhn-Tucker Lagrangian algorithm whereas the IQBFO algorithm solves it effectively.
This paper proposes a novel swarm intelligence optimization method which integrates bacterial foraging optimization (BFO) with quantum computing, called quantum bacterial foraging optimization (QBFO) algorithm. In QBFO, a multi-qubit which can represent a linear superposition of states in search space probabilistically is used to represent a bacterium, so that the quantum bacteria representation has a better characteristic of population diversity. A quantum rotation gate is designed to simulate the chemotactic step to drive the bacteria toward better solutions. Several tests are conducted based on benchmark functions including multi-peak function to evaluate optimization performance of the proposed algorithm. The numeric results show that the proposed QBFO has more powerful properties in convergence rate, stability and the ability of searching for the global optimal solution than the original BFO and quantum genetic algorithm. In addition, we applied our proposed QBFO to solve the traveling salesman problem, which is a well-known NP-hard problem in combinatorial optimization. The results indicate that the proposed QBFO shows better convergence behavior without premature convergence, and has more powerful properties in convergence rate, stability and the ability of searching for the global optimal solution, as compared to ant colony optimization algorithm and quantum genetic algorithm.
Bacterial Foraging Optimization(BFO) algorithm is simple,robust and has global search capability.However,the speed of BFO is slow and it often seems to fall into local optimum.To improve the search capabilities of BFO and avoid its premature convergence,a new type of Quantum Bacterial Foraging Optimization(QBFO) algorithm is proposed by integrating binary code quantum evolutionary algorithm into BFO.Quantum triploid chromosome is used to represent bacteria,and Quantum Rotation Gate(QRG) is used to update bacteria's state.To test the new algorithm's optimization performance,a research based on benchmark functions is conducted.The results indicate that the new type of QBFO shows better results than BFO and quantum genetic algorithm in convergence rate,stability and looking for the global optimal solutions weather common function or multi-peak function.
For many physical systems, the fractional-order system can describe the dynamics more accurately comparing with the integer-order system. However, its parameters, which include not only the identification of model coefficients but also the estimation of fractional orders, are more difficult to be identified. This paper proposes a modified quantum bacterial foraging algorithm (MQBFA) for parameters identification of the fractional-order system, and the MQBFA algorithm is a relatively new issue in recent years with strong engineering backgrounds. Not only does the proposed MQBFA algorithm apply the real number encoding of the single gene to effectively perform encoding, but it also utilizes a continuously varying rotation angle to update the rotation gate. First, a real number encoding of the single gene is designed to improve the searching efficiency of the algorithm. Second, an improved quantum rotation angle is applied to update the rotation angle continuously and adaptively. Third, the modificatory factor of probability amplitude is introduced to enrich the population diversity. In addition, to prevent the generation of invariant solutions in the early stage of quantum evolution, probability of optimal rotation angle is presented. Furthermore, the convergence of the proposed algorithm is analyzed. Based on several benchmark functions and parameters identification of the fractional-order system, simulation results and comparisons demonstrate the effectiveness of the proposed MQBFA algorithm.
This paper considers transmit optimization in multi-input multi-output (MIMO) wiretap channels, wherein we aim at maximizing the secrecy capacity or rate of an MIMO channel overheard by one or multiple eavesdroppers. Such optimization problems are nonconvex, and appear to be difficult especially in the multi-eavesdropper scenario. In this paper, we propose an alternating optimization (AO) approach to tackle these secrecy optimization problems. We first consider the secrecy capacity maximization (SCM) problem in the single eavesdropper scenario. An AO algorithm is derived through a judicious SCM reformulation. The algorithm conducts some kind of reweighting and water-filling in an alternating fashion, and thus is computationally efficient to implement. We also prove that the AO algorithm is guaranteed to converge to a Karush-Kuhn-Tucker (KKT) point of the SCM problem. Then, we turn our attention to the multiple eavesdropper scenario, where the artificial noise (AN)-aided secrecy rate maximization (SRM) problem is considered. Although the AN-aided SRM problem has a more complex problem structure than the previous SCM, we show that AO can be extended to deal with the former, wherein the problem is handled by solving convex problems in an alternating fashion. Again, the resulting AO method is proven to have KKT point convergence guarantee. For fast implementation, a custom-designed AO algorithm based on smoothing and projected gradient is also derived. The secrecy rate performance and computational efficiency of the proposed algorithms are demonstrated by simulations.
In this modern era, wireless networks continue to expand worldwide and play a pronounced role in daily life. However, wireless communication has become vulnerable to intelligent invaders in the network. Hence, the security aspects of wireless communication have been the center of modern-era research. Physical Layer Security (PLS) schemes have been proven to be very effective against potential eavesdroppers thanks to their low-complexity nature compared to encryption techniques. Therefore, this thesis is dedicated to the physical layer security of multiple-input multiple-output (MIMO) systems. More specifically, we provide efficient optimization techniques to maximize the achievable secrecy rate of MIMO Wiretap channels (WTC). First, we present novel numerical approaches to finding the achievable secrecy rate of the MIMO WTC subject to multiple power constraints. There is a challenge in deriving computationally efficient solutions to the secrecy rate maximization problem because the secrecy rate is expressed as a difference of convex functions (DC) of the transmit covariance matrix. However, to deal with it, we provided local optimization methods, such as the accelerated DC algorithm (ADCA) and a numerical method named partial best response algorithm (PBRA) based on the concave-convex equivalent reformulation of the secrecy capacity problem. Along with it, we provide a convex reformulation for degraded channels. Interestingly, our experiments have shown that the local optimization methods achieve global optimality for the secrecy rate maximization problem under power constraints. In this regard, we provide analytical proof with Karush-Kuhn-Tucker (KKT) conditions to validate our observation. Motivated by our finding, we propose an accelerated gradient projection algorithm with adaptive momentum parameters (AGPnc) that directly solves the secrecy rate maximization problem rather than the equivalent convex-concave form. We then extend the scope of our research of physical layer security enhancement by considering the Intelligent reflecting surface (IRS)-aided MIMO WTC system, as the IRS has achieved enormous attention recently for enhancement in spectral efficiency. In this context, we provided a numerical solution for the problem of maximizing the achievable secrecy rate of the considered system by jointly optimizing the input covariance matrix and the IRS phase shifts. The proposed is an iterative method based on block successive maximization (BSM) where each iteration is done in closed form. We then extend our research further toward maximizing the secrecy energy efficiency (SEE) of the (IRS)-aided MIMO WTC system. In relation to the SEE maximization problem, we provide a low-complexity based local optimization method. The challenge of this considered problem is the coupling between design variables in both the objective and the constraint. The main idea of the proposed method is to apply a penalty dual decomposition based alternating gradient projection (PDDAPG) method to obtain an efficient solution. Thanks to the low complexity nature of proposed methods, these methods can scale favourably with large-scale wireless systems, which has been an inherent issue of the existing studies. In this way, the contributions of this thesis will significantly impact future research in the field of physical layer security, especially toward the future generation of wireless communication.
The rapid advancement of quantum computing provides unprecedented opportunities for the optimization of multiple-input, multiple-output (MIMO) systems. This paper investigates the efficacy of harnessing quantum algorithms, particularly the Quantum Approximate Optimization Algorithm (QAOA) and Grover's algorithm, for the optimization of MIMO systems. The paper establishes a theoretical framework that maps MIMO optimization problems onto quantum circuits, ensuring a reduction in computational complexity compared to classical algorithms. The proposed methodology is validated through a series of numerical simulations. Notable improvements are observed in both convergence speed and the quality of the optimized solutions. Furthermore, the synergy of quantum computing and MIMO presents new frontiers for reducing interference, enhancing throughput, and ensuring robustness against environmental uncertainties. These results affirm the potential of integrating quantum computing techniques in MIMO system optimization, heralding a transformative era for wireless communication paradigms.