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Distributed computing frameworks such as MapReduce and Spark are often used to process large-scale data computing jobs. In wireless scenarios, exchanging data among distributed nodes would seriously suffer from the communication bottleneck due to limited communication resources such as bandwidth and power. To address this problem, we propose a coded parallel computing (CPC) scheme for distributed computing systems where distributed nodes exchange information over a half-duplex wireless interference network. The CPC scheme achieves the multicast gain by utilizing coded computing to multicast coded symbols intended to multiple receiver nodes and the cooperative transmission gain by allowing multiple transmitter nodes to jointly deliver messages via interference alignment. To measure communication performance, we apply the widely used latency-oriented metric: normalized delivery time (NDT). It is shown that CPC can significantly reduce the NDT by jointly exploiting the parallel transmission and coded multicasting opportunities. Surprisingly, when the number of computation nodes K tends to infinity and the computation load is fixed, CPC approaches zero NDT while all state-of-the-art schemes achieve positive values of NDT. Finally, we establish an information-theoretic lower bound for the NDT-computation load trade-off over the half-duplex network, and prove our scheme achieves the minimum NDT within a multiplicative gap of 3, i.e., our scheme is order optimal.

Federated edge learning (FL) has become an unprecedented machine learning paradigm that enables distributed training across multiple edge devices without sharing their private data. Nevertheless, recent privacy eavesdropping attacks have raised severe privacy concerns, which make FL untrustworsthy and thus hinder the wide deployment of FL in emerging high-stake applications, such as vehicular networks and healthcare industry. Fortunately, differential privacy (DP) provides a flexible approach by introducing additional randomness to the released model updates so that the eavesdroppers cannot divulge any private information. However, the injected perturbation ensures privacy at the expense of learning accuracy and communication cost, yielding an accuracy-privacy-communication dilemma. In this article, we propose an active reconfigurable intelligent surface (RIS) approach to tackle the dilemma in differentially private FL, which is achieved by exploiting the reconfigurability of active RIS to address the heterogeneous wireless links and privacy concerns, as well as the waveform superposition property with over-the-air computation (AirComp) for low-latency model aggregation. We comprehensively analyze the convergence behavior and systematic privacy guarantee of the active RIS-enabled differentially private FL system, followed by proposing a two-step online power adaptation scheme to minimize the learning optimality gap while satisfying the systematic privacy and power constraints by jointly designing the transmit scalar and artificial noise at the edge devices and the reflection beamforming pattern at the active RIS. Simulation results validate our theoretical achievements and demonstrate the advancements of active RIS in addressing the accuracy-privacy-communication dilemma in differentially private FL.

In this paper, we propose an online federated learning framework with massive random access, aiming to learn a sequence of global models using local data that are sequentially collected by massive edge devices. As only a subset of devices is capable of collecting data and performing local model update at any specific moment, the communication pattern between the edge server and devices is random and sporadic, which is referred to as sporadic local updates. This motivates us to adopt a two-phase grant-free random access scheme that consists of the activity detection and model transmission phases to facilitate efficient communication between the edge server and devices. We first provide the regret analysis for online federated learning, and derive the optimality gap in terms of successful transmission probabilities. Then, we characterize the achievable transmission rate of each active device using random matrix theory and establish the relationship between the pilot length and the outage probability. Furthermore, we propose an optimal pilot length design by minimizing the optimality gap. To validate our scheme, we provide comprehensive experimental results that demonstrate the superiority of the proposed scheme over traditional schemes in various online tasks.

With the ever-increasing user density and quality of service (QoS) demand, 5G networks with limited spectrum resources are facing massive access challenges. To address these challenges, in this paper, we propose a novel discrete semantic feature division multiple access (SFDMA) paradigm for multi-user digital interference networks. Specifically, by utilizing deep learning technology, SFDMA extracts multi-user semantic information into discrete representations in distinguishable semantic subspaces, which enables multiple users to transmit simultaneously over the same time-frequency resources. Furthermore, based on a robust information bottleneck, we design a SFDMA based multi-user digital semantic interference network for inference tasks, which can achieve approximate orthogonal transmission. Moreover, we propose a SFDMA based multi-user digital semantic interference network for image reconstruction tasks, where the discrete outputs of the semantic encoders of the users are approximately orthogonal, which significantly reduces multi-user interference. Furthermore, we propose an Alpha-Beta-Gamma (ABG) formula for semantic communications, which is the first theoretical relationship between inference accuracy and transmission power. Then, we derive adaptive power control methods with closed-form expressions for inference tasks. Extensive simulations verify the effectiveness and superiority of the proposed SFDMA.

Coded distributed computing (CDC) introduced by Li et al. can greatly reduce the communication load for MapReduce computing systems. In the cascaded CDC with $K$ workers, $N$ input files and $Q$ output functions, each input file will be mapped by $r$ workers and each output function will be computed by $s$ workers such that coding techniques can be applied to create multicast opportunities. The main drawback of most existing CDC schemes is that they require the original data to be split into a large number of input files that grows exponentially with $K$ , which would significantly increase the coding complexity and degrade the system performance. In this paper, we first use a classical combinatorial structure $t$ -design, for any integer $t\geq 2$ , to develop a low-complexity and communication-efficient CDC with $r=s$ . Our scheme has much smaller $N$ and $Q$ than the existing schemes under the same parameters $K$ , $r$ , and $s$ ; and achieves smaller communication loads compared with the state-of-the-art schemes when $K$ is relatively large. Remarkably, unlike the previous schemes that realize on large operation fields, our scheme operates in one-shot communication on the minimum binary field $\mathbb {F}_{2}$ . With a derived lower bound on the communication load under one-shot linear delivery, we show that the $t$ -design scheme is asymptotically optimal. Furthermore, we show that our construction method can incorporate the other combinatorial structures that have a similar property to $t$ -design. For instance, we use $t$ -GDD to obtain another one-shot asymptotically optimal CDC scheme over $\mathbb {F}_{2}$ that has different parameters from $t$ -design. Finally, we show that our construction method can also be used to construct CDC schemes with $r\neq s$ that have small file number and output function number.

Distributed multi-task learning (MTL) is a learning paradigm where distributed users simultaneously learn multiple tasks by leveraging the correlations among tasks. However, distributed MTL suffers from a more severe communication bottleneck than single-task learning as more than one models need to be transmitted in the communication phase. To address this issue, we investigate the hierarchical MTL system where distributed users wish to jointly learn different learning models orchestrated by a central server with the help of multiple relays. We propose a coded distributed computing scheme for hierarchical MTL systems that jointly exploits the network topology and relays’ computing capability to create coded multicast opportunities to improve communication efficiency. We theoretically prove that the proposed scheme can significantly reduce the communication loads both in the uplink and downlink transmissions between relays and the server. To further illustrate the optimality of the proposed scheme, we derive information-theoretic lower bounds on the minimum uplink and downlink communication loads and prove that the gaps between achievable upper bounds and lower bounds are within the minimum number of connected users among all relays. In particular, when the network topology can be delicately designed, the proposed scheme can achieve the information-theoretic optimal communication loads. Experiments on real-world datasets show that our proposed scheme can greatly reduce the overall training time compared to the conventional hierarchical MTL scheme.

Distributed learning has become a promising computational parallelism paradigm that enables a wide scope of intelligent applications from the Internet of Things (IoT) to autonomous driving and the healthcare industry. This paper studies distributed learning in wireless data center networks, which contain a central edge server and multiple edge workers to collaboratively train a shared global model and benefit from parallel computing. However, the distributed nature causes the vulnerability of the learning process to faults and adversarial attacks from Byzantine edge workers, as well as the severe communication and computation overhead induced by the periodical information exchange process. To achieve fast and reliable model aggregation in the presence of Byzantine attacks, we develop a signed stochastic gradient descent (SignSGD)-based Hierarchical Vote framework via over-the-air computation (AirComp), where one voting process is performed locally at the wireless edge by taking advantage of Bernoulli coding while the other is operated over-the-air at the central edge server by utilizing the waveform superposition property of the multiple-access channels. We comprehensively analyze the proposed framework on the impacts including Byzantine attacks and the wireless environment (channel fading and receiver noise), followed by characterizing the convergence behavior under non-convex settings. Simulation results validate our theoretical achievements and demonstrate the robustness of our proposed framework in the presence of Byzantine attacks and receiver noise.

Single-user semantic communications have attracted extensive research recently, but multi-user semantic broadcast communication (BC) is still in its infancy. In this paper, we propose a practical robust features-disentangled multi-user semantic BC framework, where the transmitter includes a feature selection module and each user has a feature completion module. Instead of broadcasting all extracted features, the semantic encoder extracts the disentangled semantic features, and then only the users’ intended semantic features are selected for broadcasting, which can further improve the transmission efficiency. Within this framework, we further investigate two information-theoretic metrics, including the ultimate compression rate under both the distortion and perception constraints, and the achievable rate region of the semantic BC. Furthermore, to realize the proposed semantic BC framework, we design a lightweight robust semantic BC network by exploiting a supervised autoencoder (AE), which can controllably disentangle sematic features. Moreover, we design the first hardware proof-of-concept prototype of the semantic BC network, where the proposed semantic BC network can be implemented in real time. Simulations and experiments demonstrate that the proposed robust semantic BC network can significantly improve transmission efficiency.

An $(H,r,M,N)$ combination network is a symmetric relay network that involves a central server equipped with $N$ files that communicates with $K$ users through $H$ cache-less intermediate relays, where each user maintains a local cache of size $M$ files and is connected to a distinct subset of $r$ relays. In this setting, the well-known uniform scheme is proposed by Zewail and Yener via Minimum Distance Separable (MDS) codes. For practical reasons, this paper studies a more general combination network where each distinct subset of $r$ relays is connected to $\Lambda $ users, referred to as $(H,r,\Lambda,M,N)$ dense-user combination network. Although the Zewail-Yener scheme is also feasible for the considered system, it causes high computational complexity since the use of $(H,r)_{q}$ MDS code involves expensive multiplication operations in large finite field. In this paper, we aim to design coded caching schemes not only to minimize the worst-case link-load, but also to be implemented over the minimum operation field, i.e., binary field $\mathbb {F}_{2}$ . First, we propose a construction which can transform any coded caching scheme for the shared-link model to the considered dense-user combination network. By applying the transformation approach based on the seminal work proposed by Maddah-Ali and Niesen, we present the MAN-based scheme that operates in binary field. To further reduce the link-load under small memory regions, we propose a hybrid scheme that can extend any caching scheme for the original $(H,r,M,N)$ combination network to the considered $(H,r,\Lambda,M,N)$ dense-user combination network by an ingenious outer-inner construction. From the theoretical analysis of computation complexity, the proposed schemes can significantly reduce the number of bit operations. From numerical comparisons, the link-loads of proposed schemes are close to or even better than that of Zewail-Yener scheme, while significantly reducing the operation field.

Semantic communications utilize the transceiver computing resources to alleviate scarce transmission resources, such as bandwidth and energy. Although the conventional deep learning (DL) based designs may achieve certain transmission efficiency, the uninterpretability issue of extracted features is the major challenge in the development of semantic communications. In this paper, we propose an explainable and robust semantic communication framework by incorporating the well-established bit-level communication system, which not only extracts and disentangles features into independent and semantically interpretable features, but also only selects task-relevant features for transmission, instead of all extracted features. Based on this framework, we derive the optimal input for rate-distortion-perception theory, and derive both lower and upper bounds on the semantic channel capacity. Furthermore, based on the $\beta $ -variational autoencoder ( $\beta $ -VAE), we propose a practical explainable semantic communication system design, which simultaneously achieves semantic features selection and is robust against semantic channel noise. We further design a real-time wireless mobile semantic communication proof-of-concept prototype. Our simulations and experiments demonstrate that our proposed explainable semantic communications system can significantly improve transmission efficiency, and also verify the effectiveness of our proposed robust semantic transmission scheme.

Coded caching technique is an efficient approach to reduce the transmission load in networks. In this paper, we consider a new widespread caching system called $(K_{1},K_{2},U,r,M,N)$ two-dimensional (2D) caching-aided ultra-dense networks (UDNs) with a server containing $N$ files, $K_{1}K_{2}$ cache nodes arranged neatly on a grid with $K_{1}$ rows and $K_{2}$ columns, and $U$ cache-less users randomly distributed around cache nodes. Each cache node can cache at most $M\leq N$ files and has a certain service region by Euclidean distance. The server connects to users through the error-free shared link and the users in the service region of a cache node can freely retrieve all cached contents of this cache node. We aim to design a coded caching scheme for 2D caching-aided UDN system to reduce the transmission load in the worst case while meeting all possible users’ demands. First, we divide all possible users into four classes according to their geographical locations. Then our first order optimal scheme is proposed based on the Maddah-Ali and Niesen scheme. Furthermore, by compressing the transmitted signals of our first scheme based on Maximum Distance Separable (MDS) code, we obtain an improved order optimal scheme with a smaller transmission load.

In this paper, we investigate an energy efficient waveform design for integrated visible light positioning and communication (VLPC) systems by exploiting the relationship between visible light positioning (VLP) and visible light communication (VLC). We propose that the direct current component and the alternating current component of the VLPC signals are utilized for positioning and communication, respectively. With a single LED-lamp, we propose a received-signal-strength based 3D VLP scheme, and further derive the Cramer-Rao lower bound (CRLB). Then, by exploiting the inherent coupling relationship between VLP and VLC, the positioning results are utilized for channel estimation of VLC, which can significantly reduce the channel estimation pilot overhead. Furthermore, we optimize the waveform design by minimizing the CRLB, while satisfying both the outage probability of communication rate and total transmit power constraints. However, this problem turns to be non-convex and intractable. To address this challenging problem, we utilize the Conditional Value-at-Risk to conservatively transform the outage probability constraint into a deterministic form. By exploiting the block coordinate descent algorithm, the waveform design problem can be efficiently solved by alternately optimizing VLP and VLC convex sub-problems and dual problem. Finally, simulation results verify both the effectiveness and robustness of the proposed waveform design.

Task-oriented communications, mostly using learning-based joint source-channel coding (JSCC), aim to design a communication-efficient edge inference system by transmitting task-relevant information to the receiver. However, only transmitting task-relevant information without introducing any redundancy may cause robustness issues in learning due to the channel variations, and the JSCC which directly maps the source data into continuous channel input symbols poses compatibility issues on existing digital communication systems. In this paper, we address these two issues by first investigating the inherent tradeoff between the informativeness of the encoded representations and the robustness to information distortion in the received representations, and then propose a task-oriented communication scheme with digital modulation, named discrete task-oriented JSCC (DT-JSCC), where the transmitter encodes the features into a discrete representation and transmits it to the receiver with the digital modulation scheme. In the DT-JSCC scheme, we develop a robust encoding framework, named robust information bottleneck (RIB), to improve the communication robustness to the channel variations, and derive a tractable variational upper bound of the RIB objective function using the variational approximation to overcome the computational intractability of mutual information. The experimental results demonstrate that the proposed DT-JSCC achieves better inference performance than the baseline methods with low communication latency, and exhibits robustness to channel variations due to the applied RIB framework.

Integrated visible light positioning and communication (VLPC), capable of combining advantages of visible light communications (VLC) and visible light positioning (VLP), is a promising key technology for the future Internet of Things. In VLPC networks, positioning and communications are inherently coupled, which has not been sufficiently explored in the literature. We propose a robust power allocation scheme for integrated VLPC Networks by exploiting the intrinsic relationship between positioning and communications. Specifically, we derive explicit relationships between random positioning errors, following both a Gaussian distribution and an arbitrary distribution, and channel state information errors. Then, we minimize the Cramer-Rao lower bound (CRLB) of positioning errors, subject to the rate outage constraint and the power constraints, which is a chance-constrained optimization problem and generally computationally intractable. To circumvent the nonconvex challenge, we conservatively transform the chance constraints to deterministic forms by using the Bernstein-type inequality and the conditional value-at-risk for the Gaussian and arbitrary distributed positioning errors, respectively, and then approximate them as convex semidefinite programs. Finally, simulation results verify the robustness and effectiveness of our proposed integrated VLPC design schemes.

In this paper, we propose joint beamforming and photo-detector (PD) orientation (BO) optimization schemes for mobile visible light communication (VLC) with the orientation adjustable receiver (OAR). Since VLC is sensitive to line-of-sight propagation, we first establish the OAR model and the human body blockage model for mobile VLC user equipment (UE). To guarantee the quality of service (QoS) of mobile VLC, we jointly optimize BO with minimal UE the power consumption for both fixed and random UE orientation cases. For the fixed UE orientation case, since the transmit beamforming and the PD orientation are mutually coupled, the joint BO optimization problem is nonconvex and intractable. To address this challenge, we propose an alternating optimization algorithm to obtain the transmit beamforming and the PD orientation. For the random UE orientation case, we further propose a robust alternating BO optimization algorithm to ensure the worst-case QoS requirement of the mobile UE. Finally, the performance of joint BO optimization design schemes are evaluated for mobile VLC through numerical experiments.

We propose covert beamforming design frameworks for integrated radar sensing and communication (IRSC) systems, where the radar can covertly communicate with legitimate users under the cover of the probing waveforms without being detected by the eavesdropper. Specifically, by jointly designing the target detection beamformer and communication beamformer, we aim to maximize the radar detection mutual information (MI) (or the communication rate) subject to the covert constraint, the communication rate constraint (or the radar detection MI constraint), and the total power constraint. For the perfect eavesdropper’s channel state information (CSI) scenario, we transform the covert beamforming design problems into a series of convex subproblems, by exploiting semidefinite relaxation, which can be solved via the bisection search method. Considering the high complexity of iterative optimization, we further propose a single-iterative covert beamformer design scheme based on the zero-forcing criterion. For the imperfect eavesdropper’s CSI scenario, we develop a relaxation and restriction method to tackle the robust covert beamforming design problems. Simulation results demonstrate the effectiveness of the proposed covert beamforming schemes for perfect and imperfect CSI scenarios.

Distributed multi-task learning (MTL) can jointly learn multiple models and achieve better generalization performance by exploiting relevant information between the tasks. However, distributed MTL suffers from communication bottlenecks, in particular for large-scale learning with a massive number of tasks. This paper considers distributed MTL systems where distributed workers wish to learn different models orchestrated by a central server. To mitigate communication bottlenecks both in the uplink and downlink, we propose coded computing schemes for flexible and fixed data placements, respectively. Our schemes can significantly reduce communication loads by exploiting workers’ local information and creating multicast opportunities for both the server and workers. Moreover, we establish information-theoretic lower bounds on the optimal downlink and uplink communication loads, and prove the approximate optimality of the proposed schemes. For flexible data placement, our scheme achieves the optimal downlink communication load, and the order optimal uplink communication load that is smaller than 2 times of the information-theoretic optimum. For fixed data placement, the gaps between our communication load and the optimum are within the minimum computation load among all workers, regardless of the number of workers. Experiments demonstrate that our schemes can significantly speed up the training process compared to the traditional approach.

This work considers the multi-access caching system proposed by Hachem et al., where each user has access to $L$ neighboring caches in a cyclic wrap-around fashion. We first propose a placement strategy called the consecutive cyclic placement, which achieves the maximal local caching gain. Then under the consecutive cyclic placement, we derive an upper bound on the coded caching gain of any PDA, thus obtaining a lower bound on the rate of PDA-based coded caching schemes. Finally, we construct a class of PDAs under the consecutive cyclic placement, leading to a multi-access coded caching scheme with linear subpacketization, which achieves the derived lower bound on the rate for some parameters; while for other parameters, the achieved coded caching gain is only 1 less than the derived upper bound on the coded caching gain. Analytical and numerical comparisons of the proposed scheme with existing schemes are provided to validate the performance.

Driven by the interplay among artificial intelligence, digital twin, and wireless networks, 6G is envisaged to go beyond data-centric services to provide intelligent and immersive experiences. To efficiently support intelligent tasks with customized service requirements, it becomes critical to develop novel information compression and transmission technologies, which typically involve coupled sensing, communication, and computation processes. To this end, task-oriented communication stands out as a disruptive technology for 6G system design by exploiting the task-specific information structures and folding the communication goals into the design of task-level transmission strategies. In this article, by developing task-oriented information extraction and network resource orchestration strategies, we demonstrate the effectiveness of task-oriented communication principles for typical intelligent tasks, including federated learning, edge inference, and semantic communication.

Edge computing has recently garnered significant interest in many Internet of Things (IoT) applications. However, the excessive overhead during data exchange still remains an open challenge, especially for large-scale data processing tasks. This paper considers a master-aided distributed computing system with multiple edge computing nodes and a master node, where the master node helps edge nodes compute output functions. We propose a coded scheme to reduce the communication latency by exploiting computation and communication capabilities of all nodes and creating coded multicast opportunities. More importantly, we prove that the proposed scheme is always optimal, i.e., achieving the minimum communication latency, for arbitrary computing and storage abilities at the master. This extends the previous optimality results in the extreme cases (either the master could compute all input files or compute nothing) to the general case. Finally, numerical results and TeraSort experiments demonstrate that our schemes can greatly reduce the communication latency compared with the existing schemes.

Grid computing is a new computing mode in recent years, which focuses on parallel infrastructure and its comprehensive application ability to network computers and distributed processors. Grid computing has been fully applied in the field of modern information technology and computer. Task scheduling is the core of grid computing. The quality of task scheduling algorithm directly affects the response time of the whole computing system. For heterogeneous tasks on heterogeneous platforms, this paper proposes a task scheduling algorithm with memory function, and introduces the distributed particle swarm optimisation algorithm into this algorithm, which realises the combination of resource processing tasks in grid computing and the behaviour characteristics of intelligent groups, so as to better realise the dynamic and scalable scheduling of heterogeneous tasks on heterogeneous platforms to adapt to grid environment sex. Finally, the grid simulation software GridSim is used to simulate the algorithm proposed in this paper. At the same time, it is compared with the state stochastic scheduling algorithm. Experimental results show that the proposed algorithm has obvious advantages in scheduling quality in grid environment.