DiffSG: A Generative Solver for Network Optimization with Diffusion Model

Diffusion generative models, famous for their performance in image generation, are popular in various cross-domain applications. However, their use in the communication community has been mostly limited to auxiliary tasks like data modeling and feature extraction. These models hold greater promise for fundamental problems in network optimization compared to traditional machine learning methods. Discriminative deep learning often falls short due to its single-step input-output mapping and lack of global awareness of the solution space, especially given the complexity of network optimization's objective functions. In contrast, diffusion generative models can consider a broader range of solutions and exhibit stronger generalization by learning parameters that describe the distribution of the underlying solution space, with higher probabilities assigned to better solutions. We propose a new framework Diffusion Model-based Solution Generation (DiffSG), which leverages the intrinsic distribution learning capabilities of diffusion generative models to learn high-quality solution distributions based on given inputs. The optimal solution within this distribution is highly probable, allowing it to be effectively reached through repeated sampling. We validate the performance of DiffSG on several typical network optimization problems, including mixed-integer non-linear programming, convex optimization, and hierarchical non-convex optimization. Our results show that DiffSG outperforms existing baselines. In summary, we demonstrate the potential of diffusion generative models in tackling complex network optimization problems and outline a promising path for their broader application in the communication community.

Filtering Reconfigurable Intelligent Computational Surface for RF Spectrum Purification

The increasing demand for communication is degrading the electromagnetic (EM) transmission environment due to severe EM interference, significantly reducing the efficiency of the radio frequency (RF) spectrum. Metasurfaces, a promising technology for controlling desired EM waves, have recently received significant attention from both academia and industry. However, the potential impact of out-of-band signals has been largely overlooked, leading to RF spectrum pollution and degradation of wireless transmissions. To address this issue, we propose a novel surface structure called the Filtering Reconfigurable Intelligent Computational Surface (FRICS). We introduce two types of FRICS structures: one that dynamically reflects resonance band signals through a tunable spatial filter while absorbing out-of-band signals using metamaterials and the other one that dynamically amplifies in-band signals using computational metamaterials while reflecting out-of-band signals. To evaluate the performance of FRICS, we implement it in device-to-device (D2D) communication and vehicular-to-everything (V2X) scenarios. The experiments demonstrate the superiority of FRICS in signal-to-interference-noise ratio (SINR) and energy efficiency (EE). Finally, we discuss the critical challenges faced and promising techniques for implementing FRICS in future wireless systems.

AI-Empowered Multiple Access for 6G: A Survey of Spectrum Sensing, Protocol Designs, and Optimizations

With the rapidly increasing number of bandwidth-intensive terminals capable of intelligent computing and communication, such as smart devices equipped with shallow neural network models, the complexity of multiple access for these intelligent terminals is increasing due to the dynamic network environment and ubiquitous connectivity in 6G systems. Traditional multiple access (MA) design and optimization methods are gradually losing ground to artificial intelligence (AI) techniques that have proven their superiority in handling complexity. AI-empowered MA and its optimization strategies aimed at achieving high Quality-of-Service (QoS) are attracting more attention, especially in the area of latency-sensitive applications in 6G systems. In this work, we aim to: 1) present the development and comparative evaluation of AI-enabled MA; 2) provide a timely survey focusing on spectrum sensing, protocol design, and optimization for AI-empowered MA; and 3) explore the potential use cases of AI-empowered MA in the typical application scenarios within 6G systems. Specifically, we first present a unified framework of AI-empowered MA for 6G systems by incorporating various promising machine learning techniques in spectrum sensing, resource allocation, MA protocol design, and optimization. We then introduce AI-empowered MA spectrum sensing related to spectrum sharing and spectrum interference management. Next, we discuss the AI-empowered MA protocol designs and implementation methods by reviewing and comparing the state-of-the-art, and we further explore the optimization algorithms related to dynamic resource management, parameter adjustment, and access scheme switching. Finally, we discuss the current challenges, point out open issues, and outline potential future research directions in this field.

Reconfigurable Intelligent Computational Surfaces for MEC-Assisted Autonomous Driving Networks

In this paper, we focus on improving autonomous driving safety via task offloading from cellular vehicles (CVs), using vehicle-to-infrastructure (V2I) links, to an multi-access edge computing (MEC) server. Considering that the frequencies used for V2I links can be reused for vehicle-to-vehicle (V2V) communications to improve spectrum utilization, the receiver of each V2I link may suffer from severe interference, causing outages in the task offloading process. To tackle this issue, we propose the deployment of a reconfigurable intelligent computational surface (RICS) to enable, not only V2I reflective links, but also interference cancellation at the V2V links exploiting the computational capability of its metamaterials. We devise a joint optimization formulation for the task offloading ratio between the CVs and the MEC server, the spectrum sharing strategy between V2V and V2I communications, as well as the RICS reflection and refraction matrices, with the objective to maximize a safety-based autonomous driving task. Due to the non-convexity of the problem and the coupling among its free variables, we transform it into a more tractable equivalent form, which is then decomposed into three sub-problems and solved via an alternate approximation method. Our simulation results demonstrate the effectiveness of the proposed RICS optimization in improving the safety in autonomous driving networks.

Massive Access of Static and Mobile Users via Reconfigurable Intelligent Surfaces: Protocol Design and Performance Analysis

The envisioned wireless networks of the future entail the provisioning of massive numbers of connections, heterogeneous data traffic, ultra-high spectral efficiency, and low latency services. This vision is spurring research activities focused on defining a next generation multiple access (NGMA) protocol that can accommodate massive numbers of users in different resource blocks, thereby, achieving higher spectral efficiency and increased connectivity compared to conventional multiple access schemes. In this article, we present a multiple access scheme for NGMA in wireless communication systems assisted by multiple reconfigurable intelligent surfaces (RISs). In this regard, considering the practical scenario of static users operating together with mobile ones, we first study the interplay of the design of NGMA schemes and RIS phase configuration in terms of efficiency and complexity. Based on this, we then propose a multiple access framework for RIS-assisted communication systems, and we also design a medium access control (MAC) protocol incorporating RISs. In addition, we give a detailed performance analysis of the designed RIS-assisted MAC protocol. Our extensive simulation results demonstrate that the proposed MAC design outperforms the benchmarks in terms of system throughput and access fairness, and also reveal a trade-off relationship between the system throughput and fairness.

A Multi-Head Ensemble Multi-Task Learning Approach for Dynamical Computation Offloading

Computation offloading has become a popular solution to support computationally intensive and latency-sensitive applications by transferring computing tasks to mobile edge servers (MESs) for execution, which is known as mobile/multi-access edge computing (MEC). To improve the MEC performance, it is required to design an optimal offloading strategy that includes offloading decision (i.e., whether offloading or not) and computational resource allocation of MEC. The design can be formulated as a mixed-integer nonlinear programming (MINLP) problem, which is generally NP-hard and its effective solution can be obtained by performing online inference through a well-trained deep neural network (DNN) model. However, when the system environments change dynamically, the DNN model may lose efficacy due to the drift of input parameters, thereby decreasing the generalization ability of the DNN model. To address this unique challenge, in this paper, we propose a multi-head ensemble multi-task learning (MEMTL) approach with a shared backbone and multiple prediction heads (PHs). Specifically, the shared backbone will be invariant during the PHs training and the inferred results will be ensembled, thereby significantly reducing the required training overhead and improving the inference performance. As a result, the joint optimization problem for offloading decision and resource allocation can be efficiently solved even in a time-varying wireless environment. Experimental results show that the proposed MEMTL outperforms benchmark methods in both the inference accuracy and mean square error without requiring additional training data.

Next Generation Reconfigurable MetaSurfaces: When Wave Propagation Control Meets Computing

The envisioned sixth-generation (6G) of wireless networks will involve an intelligent integration of communications and computing, thereby meeting the urgent demands of diverse applications. To realize the concept of the smart radio environment, reconfigurable intelligent surfaces (RISs) are a promising technology for offering programmable propagation of impinging electromagnetic signals via external control. However, the purely reflective nature of conventional RISs induces significant challenges in supporting computation-based applications, e.g., wave-based calculation and signal processing. To fulfil future communication and computing requirements, new materials are needed to complement the existing technologies of metasurfaces, enabling further diversification of electronics and their applications. In this event, we introduce the concept of reconfigurable intelligent computational surface (RICS), which is composed of two reconfigurable multifunctional layers: the `reconfigurable beamforming layer' which is responsible for tunable signal reflection, absorption, and refraction, and the `intelligence computation layer' that concentrates on metamaterials-based computing. By exploring the recent trends on computational metamaterials, RICSs have the potential to make joint communication and computation a reality. We further demonstrate two typical applications of RICSs for performing wireless spectrum sensing and secrecy signal processing. Future research challenges arising from the design and operation of RICSs are finally highlighted.

Spectrum Learning-Aided Reconfigurable Intelligent Surfaces for 'Green' 6G Networks

In the sixth-generation (6G) era, emerging large-scale computing based applications (for example processing enormous amounts of images in real-time in autonomous driving) tend to lead to excessive energy consumption for the end users, whose devices are usually energy-constrained. In this context, energy-efficiency becomes a critical challenge to be solved for harnessing these promising applications to realize 'green' 6G networks. As a remedy, reconfigurable intelligent surfaces (RIS) have been proposed for improving the energy efficiency by beneficially reconfiguring the wireless propagation environment. In conventional RIS solutions, however, the received signal-to-interference-plus-noise ratio (SINR) sometimes may even become degraded. This is because the signals impinging upon an RIS are typically contaminated by interfering signals which are usually dynamic and unknown. To address this issue, `learning' the properties of the surrounding spectral environment is a promising solution, motivating the convergence of artificial intelligence and spectrum sensing, termed here as spectrum learning (SL). Inspired by this, we develop an SL-aided RIS framework for intelligently exploiting the inherent characteristics of the radio frequency (RF) spectrum for green 6G networks. Given the proposed framework, the RIS controller becomes capable of intelligently `{think-and-decide}' whether to reflect or not the incident signals. Therefore, the received SINR can be improved by dynamically configuring the binary ON-OFF status of the RIS elements. The energy-efficiency benefits attained are validated with the aid of a specific case study. Finally, we conclude with a list of promising future research directions.

Converged Reconfigurable Intelligent Surface and Mobile Edge Computing for Space Information Networks

Space information networks (SIN) are facing an ever-increasing thirst for high-speed and high-capacity seamless data transmission due to the integration of ground, air, and space communications. However, this imposes a new paradigm on the architecture design of the integrated SIN. Recently, reconfigurable intelligent surfaces (RISs) and mobile edge computing (MEC) are the most promising techniques, conceived to improve communication and computation capability by reconfiguring the wireless propagation environment and offloading. Hence, converging RISs and MEC in SIN is becoming an effort to reap the double benefits of computation and communication. In this article, we propose an RIS-assisted collaborative MEC architecture for SIN and discuss its implementation. Then we present its potential benefits, major challenges, and feasible applications. Subsequently, we study different cases to evaluate the system data rate and latency. Finally, we conclude with a list of open issues in this research area.

A Joint Energy and Latency Framework for Transfer Learning over 5G Industrial Edge Networks

In this paper, we propose a transfer learning (TL)-enabled edge-CNN framework for 5G industrial edge networks with privacy-preserving characteristic. In particular, the edge server can use the existing image dataset to train the CNN in advance, which is further fine-tuned based on the limited datasets uploaded from the devices. With the aid of TL, the devices that are not participating in the training only need to fine-tune the trained edge-CNN model without training from scratch. Due to the energy budget of the devices and the limited communication bandwidth, a joint energy and latency problem is formulated, which is solved by decomposing the original problem into an uploading decision subproblem and a wireless bandwidth allocation subproblem. Experiments using ImageNet demonstrate that the proposed TL-enabled edge-CNN framework can achieve almost 85% prediction accuracy of the baseline by uploading only about 1% model parameters, for a compression ratio of 32 of the autoencoder.