Performance Analysis for Near-Field MIMO: Discrete and Continuous Aperture Antennas

Performance analysis is carried out in a near-field multiple-input multiple-output (MIMO) system for both discrete and continuous aperture antennas. The effective degrees of freedom (EDoF) is first derived. It is shown that near-field MIMO systems have a higher EDoF than free-space far-field ones. Additionally, the near-field EDoF further depends on the communication distance. Based on the derived EDoF, closed-form expressions of channel capacity with a fixed distance are obtained. As a further advance, with randomly deployed receivers, ergodic capacity is derived. Simulation results reveal that near-field MIMO has an enhanced multiplexing gain even under line-of-sight transmissions. In addition, the performance of discrete MIMO converges to that of continuous-aperture MIMO.

Computation and Privacy Protection for Satellite-Ground Digital Twin Networks

Satellite-ground integrated digital twin networks (SGIDTNs) are regarded as innovative network architectures for reducing network congestion, enabling nearly-instant data mapping from the physical world to digital systems, and offering ubiquitous intelligence services to terrestrial users. However, the challenges, such as the pricing policy, the stochastic task arrivals, the time-varying satellite locations, mutual channel interference, and resource scheduling mechanisms between the users and cloud servers, are critical for improving quality of service in SGIDTNs. Hence, we establish a blockchain-aided Stackelberg game model for maximizing the pricing profits and network throughput in terms of minimizing overhead of privacy protection, thus performing computation offloading, decreasing channel interference, and improving privacy protection. Next, we propose a Lyapunov stability theory-based model-agnostic metalearning aided multi-agent deep federated reinforcement learning (MAML-MADFRL) framework for optimizing the CPU cycle frequency, channel selection, task-offloading decision, block size, and cloud server price, which facilitate the integration of communication, computation, and block resources. Subsequently, the extensive performance analyses show that the proposed MAMLMADFRL algorithm can strengthen the privacy protection via the transaction verification mechanism, approach the optimal time average penalty, and fulfill the long-term average queue size via lower computational complexity. Finally, our simulation results indicate that the proposed MAML-MADFRL learning framework is superior to the existing baseline methods in terms of network throughput, channel interference, cloud server profits, and privacy overhead.

Federated and Meta learning over Non-Wireless and Wireless Networks: A Tutorial

In recent years, various machine learning (ML) solutions have been developed to solve resource management, interference management, autonomy, and decision-making problems in non-wireless and wireless networks. Standard ML approaches require collecting data at a central server for training, which cannot preserve the data privacy of devices. To address this issue, federated learning (FL) is an effective method to allow edge devices to collaboratively train ML models without sharing local datasets for data privacy. Typically, FL focuses on learning a global model for a given task and all devices and hence cannot adapt the model to devices with different data distributions. In such cases, meta learning can be employed to adapt learning models to different data distributions using a few data samples. In this tutorial, we conduct a comprehensive review on FL, meta learning, and federated meta learning (FedMeta). Compared to other tutorial papers, our objective is to leverage how FL/meta-learning/FedMeta can be designed, optimized, and evolved over non-wireless and wireless networks. Furthermore, we analyze not only the relationship among these learning algorithms but also their advantages and disadvantages in real-world applications.

Is the Envelope Beneficial to Non-Orthogonal Multiple Access?

Non-orthogonal multiple access (NOMA) is capable of serving different numbers of users in the same time-frequency resource element, and this feature can be leveraged to carry additional information. In the orthogonal frequency division multiplexing (OFDM) system, we propose a novel enhanced NOMA scheme, called NOMA with informative envelope (NOMA-IE), to explore the flexibility of the envelope of NOMA signals. In this scheme, data bits are conveyed by the quantified signal envelope in addition to classic signal constellations. The subcarrier activation patterns of different users are jointly decided by the envelope former. At the receiver, successive interference cancellation (SIC) is employed, and we also introduce the envelope detection coefficient to eliminate the error floor. Theoretical expressions of spectral efficiency and energy efficiency are provided for the NOMA-IE. Then, considering the binary phase shift keying modulation, we derive the asymptotic bit error rate for the two-subcarrier OFDM subblock. Afterwards, the expressions are extended to the four-subcarrier case. The analytical results reveal that the imperfect SIC and the index error are the main factors degrading the error performance. The numerical results demonstrate the superiority of the NOMA-IE over the OFDM and OFDM-NOMA, especially in the high signal-to-noise ratio (SNR) regime.

Task-Oriented and Semantics-Aware 6G Networks

Upon the arrival of emerging devices, including Extended Reality (XR) and Unmanned Aerial Vehicles (UAVs), the traditional bit-oriented communication framework is approaching Shannon's physical capacity limit and fails to guarantee the massive amount of transmission within latency requirements. By jointly exploiting the context of data and its importance to the task, an emerging communication paradigm shift to semantic level and effectiveness level is envisioned to be a key revolution in Sixth Generation (6G) networks. However, an explicit and systematic communication framework incorporating both semantic level and effectiveness level has not been proposed yet. In this article, we propose a generic task-oriented and semantics-aware (TOSA) communication framework for various tasks with diverse data types, which incorporates both semantic level information and effectiveness level performance metrics. We first analyze the unique characteristics of all data types, and summarise the semantic information, along with corresponding extraction methods. We then propose a detailed TOSA communication framework for different time critical and non-critical tasks. In the TOSA framework, we present the TOSA information, extraction methods, recovery methods, and effectiveness level performance metrics. Last but not least, we present a TOSA framework tailored for Virtual Reality (VR) data with interactive VR tasks to validate the effectiveness of the proposed TOSA communication framework.

Knowledge-aided Federated Learning for Energy-limited Wireless Networks

The conventional model aggregation-based federated learning (FL) approaches require all local models to have the same architecture and fail to support practical scenarios with heterogeneous local models. Moreover, the frequent model exchange is costly for resource-limited wireless networks since modern deep neural networks usually have over-million parameters. To tackle these challenges, we first propose a novel knowledge-aided FL (KFL) framework, which aggregates light high-level data features, namely knowledge, in the per-round learning process. This framework allows devices to design their machine learning models independently, and the KFL also reduces the communication overhead in the training process. We then theoretically analyze the convergence bound of the proposed framework under a non-convex loss function setting, revealing that large data volumes should be scheduled in the early rounds if the total data volumes during the entire learning course are fixed. Inspired by this, we define a new objective function, i.e., the weighted scheduled data sample volume, to transform the inexplicit global loss minimization problem into a tractable one for device scheduling, bandwidth allocation and power control. To deal with the unknown time-varying wireless channels, we transform the problem into a deterministic problem with the assistance of the Lyapunov optimization framework. Then, we also develop an efficient online device scheduling algorithm to achieve an energy-learning trade-off in the learning process. Experimental results on two typical datasets (i.e., MNIST and CIFAR-10) under highly heterogeneous local data distribution show that the proposed KFL is capable of reducing over 99% communication overhead while achieving better learning performance than the conventional model aggregation-based algorithms.

Dynamic Task Software Caching-assisted Computation Offloading for Multi-Access Edge Computing

In multi-access edge computing (MEC), most existing task software caching works focus on statically caching data at the network edge, which may hardly preserve high reusability due to the time-varying user requests in practice. To this end, this work considers dynamic task software caching at the MEC server to assist users' task execution. Specifically, we formulate a joint task software caching update (TSCU) and computation offloading (COMO) problem to minimize users' energy consumption while guaranteeing delay constraints, where the limited cache size and computation capability of the MEC server, as well as the time-varying task demand of users are investigated. This problem is proved to be non-deterministic polynomial-time hard, so we transform it into two sub-problems according to their temporal correlations, i.e., the real-time COMO problem and the Markov decision process-based TSCU problem. We first model the COMO problem as a multi-user game and propose a decentralized algorithm to address its Nash equilibrium solution. We then propose a double deep Q-network (DDQN)-based method to solve the TSCU policy. To reduce the computation complexity and convergence time, we provide a new design for the deep neural network (DNN) in DDQN, named state coding and action aggregation (SCAA). In SCAA-DNN, we introduce a dropout mechanism in the input layer to code users' activity states. Additionally, at the output layer, we devise a two-layer architecture to dynamically aggregate caching actions, which is able to solve the huge state-action space problem. Simulation results show that the proposed solution outperforms existing schemes, saving over 12% energy, and converges with fewer training episodes.

A Novel Resource Allocation for Anti-jamming in Cognitive-UAVs: an Active Inference Approach

This work proposes a novel resource allocation strategy for anti-jamming in Cognitive Radio using Active Inference ($\textit{AIn}$), and a cognitive-UAV is employed as a case study. An Active Generalized Dynamic Bayesian Network (Active-GDBN) is proposed to represent the external environment that jointly encodes the physical signal dynamics and the dynamic interaction between UAV and jammer in the spectrum. We cast the action and planning as a Bayesian inference problem that can be solved by avoiding surprising states (minimizing abnormality) during online learning. Simulation results verify the effectiveness of the proposed $\textit{AIn}$ approach in minimizing abnormalities (maximizing rewards) and has a high convergence speed by comparing it with the conventional Frequency Hopping and Q-learning.

Task Offloading with Multi-Tier Computing Resources in Next Generation Wireless Networks

With the development of next-generation wireless networks, the Internet of Things (IoT) is evolving towards the intelligent IoT (iIoT), where intelligent applications usually have stringent delay and jitter requirements. In order to provide low-latency services to heterogeneous users in the emerging iIoT, multi-tier computing was proposed by effectively combining edge computing and fog computing. More specifically, multi-tier computing systems compensate for cloud computing through task offloading and dispersing computing tasks to multi-tier nodes along the continuum from the cloud to things. In this paper, we investigate key techniques and directions for wireless communications and resource allocation approaches to enable task offloading in multi-tier computing systems. A multi-tier computing model, with its main functionality and optimization methods, is presented in details. We hope that this paper will serve as a valuable reference and guide to the theoretical, algorithmic, and systematic opportunities of multi-tier computing towards next-generation wireless networks.

Federated Learning for Energy-limited Wireless Networks: A Partial Model Aggregation Approach

The limited communication resources, e.g., bandwidth and energy, and data heterogeneity across devices are two of the main bottlenecks for federated learning (FL). To tackle these challenges, we first devise a novel FL framework with partial model aggregation (PMA), which only aggregates the lower layers of neural networks responsible for feature extraction while the upper layers corresponding to complex pattern recognition remain at devices for personalization. The proposed PMA-FL is able to address the data heterogeneity and reduce the transmitted information in wireless channels. We then obtain a convergence bound of the framework under a non-convex loss function setting. With the aid of this bound, we define a new objective function, named the scheduled data sample volume, to transfer the original inexplicit optimization problem into a tractable one for device scheduling, bandwidth allocation, computation and communication time division. Our analysis reveals that the optimal time division is achieved when the communication and computation parts of PMA-FL have the same power. We also develop a bisection method to solve the optimal bandwidth allocation policy and use the set expansion algorithm to address the optimal device scheduling. Compared with the state-of-the-art benchmarks, the proposed PMA-FL improves 2.72% and 11.6% accuracy on two typical heterogeneous datasets, i.e., MINIST and CIFAR-10, respectively. In addition, the proposed joint dynamic device scheduling and resource optimization approach achieve slightly higher accuracy than the considered benchmarks, but they provide a satisfactory energy and time reduction: 29% energy or 20% time reduction on the MNIST; and 25% energy or 12.5% time reduction on the CIFAR-10.