Understand the fundamentals of wireless and MIMO communication with this accessible and comprehensive text. Viewing the subject through an information theory lens, but also drawing on other perspectives, it provides a sound treatment of the key concepts underpinning contemporary wireless communication and MIMO, all the way to massive MIMO. Authoritative and insightful, it includes over 330 worked examples and 450 homework problems, with solutions and MATLAB code and data available online. Altogether, this is an excellent resource for instructors and graduate students, as well as an outstanding reference for researchers and practicing engineers.

Foundations of MIMO Communication

The thriving of artificial intelligence (AI) applications is driving the further evolution of wireless networks. It has been envisioned that 6G will be transformative and will revolutionize the evolution of wireless from “connected things” to “connected intelligence”. However, state-of-the-art deep learning and big data analytics based AI systems require tremendous computation and communication resources, causing significant latency, energy consumption, network congestion, and privacy leakage in both of the training and inference processes. By embedding model training and inference capabilities into the network edge, edge AI stands out as a disruptive technology for 6G to seamlessly integrate sensing, communication, computation, and intelligence, thereby improving the efficiency, effectiveness, privacy, and security of 6G networks. In this paper, we shall provide our vision for scalable and trustworthy edge AI systems with integrated design of wireless communication strategies and decentralized machine learning models. New design principles of wireless networks, service-driven resource allocation optimization methods, as well as a holistic end-to-end system architecture to support edge AI will be described. Standardization, software and hardware platforms, and application scenarios are also discussed to facilitate the industrialization and commercialization of edge AI systems. 

/pdf/edge-artificial-intelligence-for-6g-vision-enabling-uk3m41ke.pdf

Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications

In 1940s, Claude Shannon developed the information theory focusing on quantifying the maximum data rate that can be supported by a communication channel. Guided by this, the main theme of wireless system design up until 5G was the data rate maximization. In his theory, the semantic aspect and meaning of messages were treated as largely irrelevant to communication. The classic theory started to reveal its limitations in the modern era of machine intelligence, consisting of the synergy between IoT and AI. By broadening the scope of the classic framework, in this article we present a view of semantic communication (SemCom) and conveying meaning through the communication systems. We address three communication modalities, human-to-human (H2H), human-to-machine (H2M), and machine-to-machine (M2M) communications. The latter two, the main theme of the article, represent the paradigm shift in communication and computing. H2M SemCom refers to semantic techniques for conveying meanings understandable by both humans and machines so that they can interact. M2M SemCom refers to effectiveness techniques for efficiently connecting machines such that they can effectively execute a specific computation task in a wireless network. The first part of the article introduces SemCom principles including encoding, system architecture, and layer-coupling and end-to-end design approaches. The second part focuses on specific techniques for application areas of H2M (human and AI symbiosis, recommendation, etc.) and M2M SemCom (distributed learning, split inference, etc.) Finally, we discuss the knowledge graphs approach for designing SemCom systems. We believe that this comprehensive introduction will provide a useful guide into the emerging area of SemCom that is expected to play an important role in 6G featuring connected intelligence and integrated sensing, computing, communication, and control.

What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence.

Device-to-Device (D2D) communication is one of the technologies on the spotlight for enhancing the cellular network performance towards the fifth generation wireless systems. It has diverse potentials to cater for both critical and non-critical applications. For example, timely information dissemination can be achieved during disasters using D2D communication. Also, content sharing and real-time applications can be effectively facilitated. Recently, new applications and technologies are beginning to embrace D2D to further improve their performance in terms of spectral efficiency, latency, and energy efficiency. However, this is not bereft of technical challenges due to the peculiar limitations of traditional D2D communication such as interference. In this paper, we focus on techniques for managing these challenges with regards to mode selection, power control, and resource allocation. As compared with other contemporary works on this subject, we discuss these issues in line with some of the most recent research trends. In addition, we compile pertinent design considerations of D2D discussed in literature while extracting new patterns to familiarize readers with applications, models, methods and metrics studied lately. Furthermore, we highlight and classify some of the key challenges of D2D communication with respect to current and future generation cellular technologies, giving a comprehensive outlook of new research problems recently identified in this area.

/pdf/a-primer-on-design-aspects-recent-advances-and-challenges-in-unw4wwum7x.pdf

A primer on design aspects, recent advances, and challenges in cellular device-to-device communication

This article studies the problem of Sum-secure Degrees of Freedom (SDoF) of the $(M,M,N,N)$ multiple-input–multiple-output (MIMO) interference channel with local output feedback, so as to build an information-theoretic foundation and provide practical transmission schemes for 6G-enabled Vehicles-to-Vehicles (V2V). For this problem, we propose two novel transmission schemes, i.e., the interference decoding scheme and the interference alignment scheme, and thus establish a sum-SDoF lower bound. In particular, to optimize the phase duration, we analyze the security and decoding constraints and formulate a linear-fractional optimization problem. Furthermore, we show that the derived sum-SDoF lower bound is the sum-SDoF for $M \le N/2$ , $N=M$ , and $2N \le M$ antenna configurations, and reveal that for a fixed $N$ , the optimal $M$ to maximize the sum-SDoF is not less than $2N$ . Through simulations, we examine the secure sum-rate performance of proposed transmission schemes and reveal that using local output feedback can lead to a higher secure sum-rate than that by using delayed channel state information at the transmitter (CSIT).

On Secure Degrees of Freedom of the MIMO Interference Channel With Local Output Feedback

In this paper, a novel learning-based rate adaptation mechanism is proposed for a downlink massive multiple-input-multiple-output (MIMO) network with underlaid device-to-device (D2D) links, where the link signal-to-interference-plus-noise ratio (SINR) cannot be accurately predicted before transmission, even its distribution statistics are unknown at the very beginning. Specifically, two coexistence schemes are considered: (1) the D2D links only reuse the downlink subframes; and (2) the D2D receivers also join the uplink channel estimation of the associated cells. For the second scheme, the downlink interference to the D2D receivers is suppressed at the cost of channel training overhead. The geographic distributions of the selected downlink and D2D users in each frame are modeled as two independent stochastic processes with unknown statistics. As a result, the distribution of interference power is unknown to the transmitters. In order to facilitate robust rate allocation, we first derive the asymptotic expressions of downlink and D2D signal-to-interference-plus-noise ratios (SINRs) for sufficiently large antenna number, and show that their distributions can be approximated by Gaussian or exponential random variables. Subsequently, distributive learning algorithms are proposed to evaluate the means and variances of these random variables. This enables the BSs and D2D transmitters to determine the transmission rates under a constraint on packet outage probability.

Rate Adaptation for Downlink Massive MIMO Networks and Underlaid D2D Links: A Learning Approach

T In recent years, the attempts on distilling mobile data into useful knowledge have led to the deployment of machine learning algorithms at the network edge. Principal component analysis (PCA) is a classic technique for extracting the linear structure of a dataset, which is useful for feature extraction and data compression. In this work, we propose the deployment of distributed PCA over a multi-access channel based on the algorithm of stochastic gradient descent to learn the dominant feature space of a distributed dataset at multiple devices. Over- the-air aggregation is adopted to reduce the multi-access latency, giving the name over-the-air PCA. The novelty of this design lies in exploiting channel noise to accelerate the descent in the region around each saddle point encountered by gradient descent, thereby increasing the convergence speed of over-the-air PCA. The idea is materialized by proposing a power-control scheme controlling the level of channel noise accordingly. The scheme is proved to achieve faster convergence than in the case without power control by experiments on real datasets.

Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis

We propose a unified framework to analyze the downlink performance of massive multiple-input-multiple-output networks with soft pilot reuse and soft frequency reuse, respectively. Both schemes have different tradeoffs between interference mitigation and data transmission opportunities. Considering randomness of interference power, we derive the asymptotic closed-form expression of the average goodput, measuring the average number of bits successfully delivered to the users, for both reuse schemes as the performance comparison metric.

Massive MIMO Downlink Goodput Analysis With Soft Pilot or Frequency Reuse

Recently years, the attempts on distilling mobile data into useful knowledge has been led to the deployment of machine learning algorithms at the network edge. Principal component analysis (PCA) is a classic technique for extracting the linear structure of a dataset, which is useful for feature extraction and data compression. In this work, we propose the deployment of distributed PCA over a multi-access channel based on the algorithm of stochastic gradient descent to learn the dominant feature space of a distributed dataset at multiple devices. Over-the-air aggregation is adopted to reduce the multi-access latency, giving the name over-the-air PCA. The novelty of this design lies in exploiting channel noise to accelerate the descent in the region around each saddle point encountered by gradient descent, thereby increasing the convergence speed of over-the-air PCA. The idea is materialized by proposing a power-control scheme which detects the type of descent region and controlling the level of channel noise accordingly. The scheme is proved to achieve a faster convergence rate than in the case without power control.

Zezhong Zhang

Papers

What is Semantic Communication? A View on Conveying Meaning in the Era of Machine Intelligence.

Rate Adaptation for Downlink Massive MIMO Networks and Underlaid D2D Links: A Learning Approach

Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis

Massive MIMO Downlink Goodput Analysis With Soft Pilot or Frequency Reuse

Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis