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IEEE Transactions on Emerging Topics in Computing

Resource Allocation Optimization by Quantum Computing for Shared Use of Standalone IRS

Takahiro Ohyama, Yuichi Kawamoto, Nei Kato

Published Jul 11, 2023• 22 citations
Publisher

Abstract

Intelligent reflecting surfaces (IRSs) have attracted attention as a technology that can considerably improve the energy utilization efficiency of sixth-generation (6G) mobile communication systems. IRSs enable control of propagation characteristics by adjusting the phase shift of each reflective element. However, designing the phase shift requires the acquisition of channel information for each reflective element, which is impractical from an overhead perspective. In addition, for multiple wireless network operators to share an IRS for communication, new infrastructure facilities and operational costs are required at each operator's end to control the IRS in a coordinated manner. Herein, we propose a wireless communication system using standalone IRSs to solve these problems. The standalone IRSs cover a wide area by periodically switching phase shifts, and each operator allocates radio resources according to their phase-shift switching. Furthermore, we derive a quadratic unconstrained binary optimization equation for the proposed system to optimize radio resource allocation using quantum computing. The results of computer simulations indicate that the proposed system and method can be used to achieve efficient communication in 6G mobile communication systems.

DOI
10.1109/tetc.2023.3292355

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Intelligent reflecting surfaces (IRSs) have attracted attention as a technology that can considerably improve the energy utilization efficiency of sixth-generation (6G) mobile communication systems. IRSs enable control of propagation characteristics by adjusting the phase shift of each reflective element. However, designing the phase shift requires the acquisition of channel information for each reflective element, which is impractical from an overhead perspective. In addition, for multiple wireless network operators to share an IRS for communication, new infrastructure facilities and operational costs are required at each operator's end to control the IRS in a coordinated manner. Herein, we propose a wireless communication system using standalone IRSs to solve these problems. The standalone IRSs cover a wide area by periodically switching phase shifts, and each operator allocates radio resources according to their phase-shift switching. Furthermore, we derive a quadratic unconstrained binary optimization equation for the proposed system to optimize radio resource allocation using quantum computing. The results of computer simulations indicate that the proposed system and method can be used to achieve efficient communication in 6G mobile communication systems.
Symbiotic radio (SR) has recently emerged as a promising technology to boost spectrum efficiency of wireless communications by allowing reflective communications underlying the active RF communications. In this paper, we leverage SR to boost physical layer security by using an array of passive reflecting elements constituting the intelligent reflecting surface (IRS), which is reconfigurable to induce diverse RF radiation patterns. In particular, by switching the IRS’s phase shifting matrices, we can proactively create dynamic channel conditions, which can be exploited by the transceivers to extract common channel features and thus used to generate secret keys for encrypted data transmissions. As such, we firstly present the design principles for IRS-assisted key generation and verify a performance improvement in terms of the secret key generation rate (KGR). Our analysis reveals that the IRS’s random phase shifting may result in a non-uniform channel distribution that limits the KGR. Therefore, to maximize the KGR, we propose both a heuristic scheme and deep reinforcement learning (DRL) to control the switching of the IRS’s phase shifting matrices. Simulation results show that the DRL approach for IRS-assisted key generation can significantly improve the KGR.
The rapid increase in data consumption by mobile users has created a demand for innovative architectures to address this growing need. Concurrently, classical neural network-based intelligent reflecting surfaces (IRS) have gained traction in 6G networks due to their capacity to enhance spectral efficiency (SE). However, relying solely on classical computing for SE calculations imposes limitations on computational rates, stemming from the exponential increase in complexity. In response to this challenge, this paper proposes the use of quantum convolutional neural networks (QCNNs) to improve the SE of communication systems facilitated by IRS configurations. To optimize IRS phase shifts, we first apply singular value decomposition to the received IRS-optimized data. The resulting information is then incorporated into the quantum data and subsequently processed by the parameterized quantum circuit. A dense layer is introduced after the proposed model to boost training performance. The proposed QCNN model demonstrates a significant enhancement in SE while maintaining lower complexity, leveraging the capabilities of qubits in the current era of quantum computing.
In this letter, we propose a novel encrypted data transmission scheme using an intelligent reflecting surface (IRS) to generate secret keys in wireless communication networks. We show that perfectly secure one-time pad (OTP) communications can be established by using a simple random phase shifting of the IRS elements. To maximize the secure transmission rate, we design an optimal time slot allocation algorithm for the IRS secret key generation and the encrypted data transmission phases. Moreover, a theoretical expression of the key generation rate is derived based on Poisson point process (PPP) for the practical scenario when eavesdroppers’ channel state information (CSI) is unavailable. Simulation results show that employing our IRS-based scheme can significantly improve the encrypted data transmission performance for a wide-range of wireless channel gains and system parameters.
We propose and analyze secret key generation using intelligent reflecting surface (IRS) assisted wireless communication networks. To this end, we first formulate the minimum achievable secret key capacity for an IRS acting as a passive beamformer in the presence of multiple eavesdroppers. Next, we develop an optimization framework for the IRS reflecting coefficients based on the secret key capacity lower bound. To derive a tractable and efficient solution, we design and analyze a semidefinite relaxation (SDR) and successive convex approximation (SCA) based algorithm for the proposed optimization. Simulation results show that employing our IRS-based algorithm can significantly improve the secret key generation capacity for a wide-range of wireless channel parameters.
We propose and analyze secret key generation using intelligent reflecting surface (IRS) assisted wireless communication networks. To this end, we first formulate the minimum achievable secret key capacity for an IRS acting as a passive beamformer in the presence of multiple eavesdroppers. Next, we develop an optimization framework for the IRS reflecting coefficients based on the secret key capacity lower bound. To derive a tractable and efficient solution, we design and analyze a semidefinite relaxation (SDR) and successive convex approximation (SCA) based algorithm for the proposed optimization. Simulation results show that employing our IRS-based algorithm can significantly improve the secret key generation capacity for a wide-range of wireless channel parameters.
To achieve the goal of a seamless-connected world with communication assistance, quantum technology is crucial. However, the key limitations are the limited availability of network resources and regulated radio frequency spectrum for future commitments. Future technology will enable billions of connected devices to communicate smoothly, manage data effectively, and achieve computational accuracy. Designing an intelligently operated system with dynamic networks can only offer expanded network capabilities, which is necessary. Quantum computing is a fascinating use of quantum physics with broad implications. Quantum computing, which processes information using quantum bits (qubits), has fundamentally altered how humans process and send information. This study analyses the integration of an Intelligent Reflecting Surface (IRS) with quantum computing with a hybrid Nonorthogonal Multiple Access (NOMA) technique. It shows an increased sum rate of 28 bps/Hz (an improvement of 33%) and a minimal outage probability. To understand the properties of quantum states, Wigner function plots are analysed for three cat states: even, odd, and compass. Thus, the suggested framework is suitable for the upcoming next-generation wireless technologies.