Two Birds With One Stone: Enhancing Communication and Sensing via Multi-Functional RIS

In this article, we propose new network architectures that integrate multi-functional reconfigurable intelligent surfaces (MF-RISs) into 6G networks to enhance both communication and sensing capabilities. Firstly, we elaborate how to leverage MF-RISs for improving communication performance in different communication modes including unicast, mulitcast, and broadcast and for different multi-access schemes. Next, we emphasize synergistic benefits of integrating MF-RISs with wireless sensing, enabling more accurate and efficient target detection in 6G networks. Furthermore, we present two schemes that utilize MF-RISs to enhance the performance of integrated sensing and communication (ISAC). We also study multi-objective optimization to achieve the optimal trade-off between communication and sensing performance. Finally, we present numerical results to show the performance improvements offered by MF-RISs compared to conventional RISs in ISAC. We also outline key research directions for MF-RIS under the ambition of 6G.

From Single to Multi-Functional RIS: Architecture, Key Technologies, Challenges, and Applications

Although reconfigurable intelligent surfaces (RISs) have demonstrated the potential to boost network capacity and expand coverage by adjusting their electromagnetic properties, existing RIS architectures have certain limitations, such as double-fading attenuation and restricted half-space coverage. In this article, we delve into the progressive development from single to multi-functional RIS (MF-RIS) that enables simultaneous signal amplification, reflection, and refraction. We begin by detailing the hardware design and signal model that distinguish MF-RIS from traditional RISs. Subsequently, we introduce the key technologies underpinning MF-RIS-aided communications, along with the fundamental issues and challenges inherent to its deployment. We then outline the promising applications of MFRIS in the realm of communication, sensing, and computation systems, highlighting its transformative impact on these domains. Lastly, we present simulation results to demonstrate the superiority of MF-RIS in enhancing network performance in terms of spectral efficiency.

Multi-Objective Optimization-Based Waveform Design for Multi-User and Multi-Target MIMO-ISAC Systems

Integrated sensing and communication (ISAC) opens up new service possibilities for sixth-generation (6G) systems, where both communication and sensing (C&S) functionalities co-exist by sharing the same hardware platform and radio resource. In this paper, we investigate the waveform design problem in a downlink multi-user and multi-target ISAC system under different C&S performance preferences. The multi-user interference (MUI) may critically degrade the communication performance. To eliminate the MUI, we employ the constructive interference mechanism into the ISAC system, which saves the power budget for communication. However, due to the conflict between C&S metrics, it is intractable for the ISAC system to achieve the optimal performance of C&S objective simultaneously. Therefore, it is important to strike a tradeoff between C&S objectives. By virtue of the multi-objective optimization theory, we propose a weighted Tchebycheff-based transformation method to re-frame the C&S trade-off problem as a Pareto-optimal problem, thus effectively tackling the constraints in ISAC systems. Finally, simulation results reveal the trade-off relation between C&S performances, which provides insights for the flexible waveform design under different C&S performance preferences in MIMO-ISAC systems.

Secrecy Performance Analysis of Multi-Functional RIS-Assisted NOMA Networks

Although reconfigurable intelligent surface (RIS) can improve the secrecy communication performance of wireless users, it still faces challenges such as limited coverage and double-fading effect. To address these issues, in this paper, we utilize a novel multi-functional RIS (MF-RIS) to enhance the secrecy performance of wireless users, and investigate the physical layer secrecy problem in non-orthogonal multiple access (NOMA) networks. Specifically, we derive closed-form expressions for the secrecy outage probability (SOP) and secrecy throughput of users in the MF-RIS-assisted NOMA networks with external and internal eavesdroppers. The asymptotic expressions for SOP and secrecy diversity order are also analyzed under high signal-to-noise ratio (SNR) conditions. Additionally, we examine the impact of receiver hardware limitations and error transmission-induced imperfect successive interference cancellation (SIC) on the secrecy performance. Numerical results indicate that: i) under the same power budget, the secrecy performance achieved by MF-RIS significantly outperforms active RIS and simultaneously transmitting and reflecting RIS; ii) with increasing power budget, residual interference caused by imperfect SIC surpasses thermal noise as the primary factor affecting secrecy capacity; and iii) deploying additional elements at the MF-RIS brings significant secrecy enhancements for the external eavesdropping scenario, in contrast to the internal eavesdropping case.

Multi-Objective Optimization-based Transmit Beamforming for Multi-Target and Multi-User MIMO-ISAC Systems

Integrated sensing and communication (ISAC) is an enabling technology for the sixth-generation mobile communications, which equips the wireless communication networks with sensing capabilities. In this paper, we investigate transmit beamforming design for multiple-input and multiple-output (MIMO)-ISAC systems in scenarios with multiple radar targets and communication users. A general form of multi-target sensing mutual information (MI) is derived, along with its upper bound, which can be interpreted as the sum of individual single-target sensing MI. Additionally, this upper bound can be achieved by suppressing the cross-correlation among reflected signals from different targets, which aligns with the principles of adaptive MIMO radar. Then, we propose a multi-objective optimization framework based on the signal-to-interference-plus-noise ratio of each user and the tight upper bound of sensing MI, introducing the Pareto boundary to characterize the achievable communication-sensing performance boundary of the proposed ISAC system. To achieve the Pareto boundary, the max-min system utility function method is employed, while considering the fairness between communication users and radar targets. Subsequently, the bisection search method is employed to find a specific Pareto optimal solution by solving a series of convex feasible problems. Finally, simulation results validate that the proposed method achieves a better tradeoff between multi-user communication and multi-target sensing performance. Additionally, utilizing the tight upper bound of sensing MI as a performance metric can enhance the multi-target resolution capability and angle estimation accuracy.

OFDM-Based Digital Semantic Communication with Importance Awareness

Semantic communication (SemCom) has received considerable attention for its ability to reduce data transmission size while maintaining task performance. However, existing works mainly focus on analog SemCom with simple channel models, which may limit its practical application. To reduce this gap, we propose an orthogonal frequency division multiplexing (OFDM)-based SemCom system that is compatible with existing digital communication infrastructures. In the considered system, the extracted semantics is quantized by scalar quantizers, transformed into OFDM signal, and then transmitted over the frequency-selective channel. Moreover, we propose a semantic importance measurement method to build the relationship between target task and semantic features. Based on semantic importance, we formulate a sub-carrier and bit allocation problem to maximize communication performance. However, the optimization objective function cannot be accurately characterized using a mathematical expression due to the neural network-based semantic codec. Given the complex nature of the problem, we first propose a low-complexity sub-carrier allocation method that assigns sub-carriers with better channel conditions to more critical semantics. Then, we propose a deep reinforcement learning-based bit allocation algorithm with dynamic action space. Simulation results demonstrate that the proposed system achieves 9.7% and 28.7% performance gains compared to analog SemCom and conventional bit-based communication systems, respectively.

Multi-Functional Reconfigurable Intelligent Surface: System Modeling and Performance Optimization

In this paper, we propose and study a multi-functional reconfigurable intelligent surface (MF-RIS) architecture. In contrast to conventional single-functional RIS (SF-RIS) that only reflects signals, the proposed MF-RIS simultaneously supports multiple functions with one surface, including reflection, refraction, amplification, and energy harvesting of wireless signals. As such, the proposed MF-RIS is capable of significantly enhancing RIS signal coverage by amplifying the signal reflected/refracted by the RIS with the energy harvested. We present the signal model of the proposed MF-RIS, and formulate an optimization problem to maximize the sum-rate of multiple users in an MF-RIS-aided non-orthogonal multiple access network. We jointly optimize the transmit beamforming, power allocations as well as the operating modes and parameters for different elements of the MF-RIS and its deployment location, via an efficient iterative algorithm. Simulation results are provided which show significant performance gains of the MF-RIS over SF-RISs with only some of its functions available. Moreover, we demonstrate that there exists a fundamental trade-off between sum-rate maximization and harvested energy maximization. In contrast to SF-RISs which can be deployed near either the transmitter or receiver, the proposed MF-RIS should be deployed closer to the transmitter for maximizing its communication throughput with more energy harvested.

Convergence Analysis and Latency Minimization for Semi-Federated Learning in Massive IoT Networks

As the number of sensors becomes massive in Internet of Things (IoT) networks, the amount of data is humongous. To process data in real-time while protecting user privacy, federated learning (FL) has been regarded as an enabling technique to push edge intelligence into IoT networks with massive devices. However, FL latency increases dramatically due to the increase of the number of parameters in deep neural network and the limited computation and communication capabilities of IoT devices. To address this issue, we propose a semi-federated learning (SemiFL) paradigm in which network pruning and over-the-air computation are efficiently applied. To be specific, each small base station collects the raw data from its served sensors and trains its local pruned model. After that, the global aggregation of local gradients is achieved through over-the-air computation. We first analyze the performance of the proposed SemiFL by deriving its convergence upper bound. To reduce latency, a convergence-constrained SemiFL latency minimization problem is formulated. By decoupling the original problem into several sub-problems, iterative algorithms are designed to solve them efficiently. Finally, numerical simulations are conducted to verify the effectiveness of our proposed scheme in reducing latency and guaranteeing the identification accuracy.

Performance Analysis and Optimization of Reconfigurable Multi-Functional Surface Assisted Wireless Communications

Although reconfigurable intelligent surfaces (RISs) can improve the performance of wireless networks by smartly reconfiguring the radio environment, existing passive RISs face two key challenges, i.e., double-fading attenuation and dependence on grid/battery. To address these challenges, this paper proposes a new RIS architecture, called multi-functional RIS (MF-RIS). Different from conventional reflecting-only RIS, the proposed MF-RIS is capable of supporting multiple functions with one surface, including signal reflection, amplification, and energy harvesting. As such, our MF-RIS is able to overcome the double-fading attenuation by harvesting energy from incident signals. Through theoretical analysis, we derive the achievable capacity of an MF-RIS-aided communication network. Compared to the capacity achieved by the existing self-sustainable RIS, we derive the number of reflective elements required for MF-RIS to outperform self-sustainable RIS. To realize a self-sustainable communication system, we investigate the use of MF-RIS in improving the sum-rate of multi-user wireless networks. Specifically, we solve a non-convex optimization problem by jointly designing the transmit beamforming and MF-RIS coefficients. As an extension, we investigate a resource allocation problem in a practical scenario with imperfect channel state information. By approximating the semi-infinite constraints with the S-procedure and the general sign-definiteness, we propose a robust beamforming scheme to combat the inevitable channel estimation errors. Finally, numerical results show that: 1) compared to the self-sustainable RIS, MF-RIS can strike a better balance between energy self-sustainability and throughput improvement; and 2) unlike reflecting-only RIS which can be deployed near the transmitter or receiver, MF-RIS should be deployed closer to the transmitter for higher spectrum efficiency.

Semi-Federated Learning: Convergence Analysis and Optimization of A Hybrid Learning Framework

Under the organization of the base station (BS), wireless federated learning (FL) enables collaborative model training among multiple devices. However, the BS is merely responsible for aggregating local updates during the training process, which incurs a waste of the computational resource at the BS. To tackle this issue, we propose a semi-federated learning (SemiFL) paradigm to leverage the computing capabilities of both the BS and devices for a hybrid implementation of centralized learning (CL) and FL. Specifically, each device sends both local gradients and data samples to the BS for training a shared global model. To improve communication efficiency over the same time-frequency resources, we integrate over-the-air computation for aggregation and non-orthogonal multiple access for transmission by designing a novel transceiver structure. To gain deep insights, we conduct convergence analysis by deriving a closed-form optimality gap for SemiFL and extend the result to two extra cases. In the first case, the BS uses all accumulated data samples to calculate the CL gradient, while a decreasing learning rate is adopted in the second case. Our analytical results capture the destructive effect of wireless communication and show that both FL and CL are special cases of SemiFL. Then, we formulate a non-convex problem to reduce the optimality gap by jointly optimizing the transmit power and receive beamformers. Accordingly, we propose a two-stage algorithm to solve this intractable problem, in which we provide the closed-form solutions to the beamformers. Extensive simulation results on two real-world datasets corroborate our theoretical analysis, and show that the proposed SemiFL outperforms conventional FL and achieves 3.2% accuracy gain on the MNIST dataset compared to state-of-the-art benchmarks.