This paper investigates the coded caching problem in a multi-access multiple-input single-output (MAMISO) network with the combinatorial topology. The considered system consists of a server containing $N$ files, $Λ$ cache nodes, and $K$ cache-less users, where each user can access a unique subset of $r$ cache nodes. The server is equipped with $L$ transmit antennas. Our objective is to design a caching scheme that simultaneously achieves a high sum Degree of Freedom (sum-DoF) and low subpacketization complexity. To address this challenge, we formulate the design of multi-antenna placement delivery arrays (MAPDA) as a $0$--$1$ knapsack problem to maximize the achievable DoF, thereby transforming the complex combinatorial caching structure into a tractable optimization framework that yields efficient cache placement and flexible delivery strategies. Theoretical and numerical analyses demonstrate that: for networks with combinatorial topologies, the proposed scheme achieves a higher sum-DoF than existing schemes. Under identical cache size constraints, the subpacketization level remains comparable to existing linear subpacketization schemes. Moreover, under specific system conditions, the proposed scheme attains the theoretical maximum sum-DoF of $\min\{L+KM/N, K\}$ while achieving further reductions subpacketization. For particular combinatorial structures, we further derive optimized constructions that achieve even higher sum-DoF with lower subpacketization. ```

Learning-based joint source-channel coding (JSCC) is widely used in task-oriented communication, which aims to extract and transmit only task-relevant information to improve communication efficiency. However, the learning-empowered algorithms in task-oriented communication may lead to information leakage on sensitive attributes and cause discrimination towards specific groups, resulting in fairness issues in social equity. Meanwhile, directly adopting fair representation learning techniques in the source encoder of communication systems poses significant challenges: First, the favorable fairness-utility tradeoff in the encoded feature representations would be deteriorated by channel noise and dynamic variations. Second, the inherent separation of source and channel design precludes the efficiency offered by JSCC for end-to-end transmission. To address these issues, we propose a task-oriented JSCC communication scheme, namely Fair-RIB, that achieves efficient encoding and inference while preserving group fairness. Our approach leverages an information bottleneck-based framework that maximizes the task utility information while limiting the sensitive information leakage to ensure fairness, and adopts a hypernetwork-parametrization mechanism to adapt to varying channel conditions. We also provide theoretical bounds for fairness guarantees by fully exploiting the characteristics of the channel noise, and introduce a selective noise injection mechanism to better manage the fairness-utility tradeoff. To overcome the intractability of the high-dimensional mutual information terms, we adopt variational approximations to derive a tractable upper bound for objective optimization. Experiments on benchmark tabular and image datasets demonstrate the superiority of our framework in achieving a fairness-utility tradeoff and the adaptability to channel variations.

Coded caching is a promising technique to create coded multicast opportunities for cache-aided networks. By splitting each file intoFequal packets (i.e., the subpacketization levelF) and letting each user cache a set of packets, the transmission load can be significantly reduced via coded multicasting. It has been shown that a higher subpacketization level could potentially lead to a lower transmission load, as more packets can be combined for efficient transmission. On the other hand, a largerFindicates a higher coding complexity and is problematic from a practical perspective whenFis extremely large. Despite many works attempting to design coded caching schemes with low subpacketization levels, a fundamental problem remains open: What is the minimum transmission load given any fixed subpacketization level? In this paper, we consider the classical cache-aided networks with identically uncoded placement and one-shot delivery strategy, and investigate the fundamental trade-off between the transmission load and the subpacketization level. We propose agenerallower bound on the transmission load for any fixed subpacketization by reformulating the centralized coded caching schemes via the combinatorial structure of the corresponding placement delivery array. The lower bound also recovers existing optimality results for the bipartite graph scheme (including the well-known Maddah-Ali and Niesen (MN) scheme and the conjugate MN scheme) as well as the grouping bipartite graph scheme. Furthermore, by carefully exploiting the combinatorial structure and computing the union size of sorted sets, we establish a new optimality result, i.e., the partition scheme can achieve the optimal rate-subpacketization trade-off.

This work considers the device-to-device (D2D) multi-access coded caching problem proposed by Wu et al., where each user has access toLneighboring cache nodes in a cyclic wrap-around fashion and the users communicate with each other in the delivery phase. D2D placement delivery array (DPDA) is a combinatorial structure to design D2D coded caching schemes with uncoded placement and one-shot linear delivery. In order to achieve the maximal local caching gain with a linear subpacketization, we adopt the consecutive cyclic placement proposed for the multi-access coded caching system. First, we derive an upper bound on the coded caching gain of D2D coded caching schemes from DPDA under the consecutive cyclic placement, leading to a lower bound on the communication load. Second, we equivalently represent DPDA as a vector set satisfying certain constraints, and design a vector set satisfying the constraints, which generates a new D2D multi-access coded caching scheme with linear subpacketization for arbitrary number of users and cache node memory ratio. The new scheme either achieves the derived lower bound or achieves a coded caching gain that is only 1 less than the derived upper bound. Performance analysis demonstrates that our proposed scheme, maintaining linear subpacketization, achieves a smaller communication load compared to existing schemes with linear subpacketization. Furthermore, compared to the existing schemes employing sub-exponential subpacketization, it significantly reduces subpacketization and even achieves a lower communication load whenLis relatively large.

With the evolution of 6G networks, modern communication systems are facing unprecedented demands for high reliability and low latency. However, conventional transport protocols are designed for bit-level reliability, failing to meet the semantic robustness requirements. To address this limitation, this paper proposes a novel Semantic Information Transport Protocol (SITP), which achieves TCP-level reliability and UDP level latency by verifying only packet headers while retaining potentially corrupted payloads for semantic decoding. Building upon SITP, a cross-layer analytical model is established to quantify packet-loss probability across the physical, data-link, network, transport, and application layers. The model provides a unified probabilistic formulation linking signal noise rate (SNR) and packet-loss rate, offering theoretical foundation into end-to-end semantic transmission. Furthermore, a cross-image feature interleaving mechanism is developed to mitigate consecutive burst losses by redistributing semantic features across multiple correlated images, thereby enhancing robustness in burst-fade channels. Extensive experiments show that SITP offers lower latency than TCP with comparable reliability at low SNRs, while matching UDP-level latency and delivering superior reconstruction quality. In addition, the proposed cross-image semantic interleaving mechanism further demonstrates its effectiveness in mitigating degradation caused by bursty packet losses.

To support efficient and accurate multi-task inference in edge environments, we propose a task-oriented broadcast communication system that enables an edge transmitter to serve multiple edge devices with heterogeneous inference tasks. The proposed system adopts a two-phase design inspired by Marton’s channel coding with rate splitting and the information bottleneck principle. In the first phase, a common feature vector is extracted to capture the shared information across tasks. In the second phase, task-specific private feature vectors are generated conditioned on the common feature to preserve unique task-relevant information. To facilitate interference-robust task execution, our scheme leverages the intrinsic structural alignment between the task correlations and broadcast channel properties; specifically, the common and private features are mapped directly to Marton’s common and private codewords. A variational approximation method is introduced to optimize the feature extraction process in both phases, allowing for compact and informative representations while reducing redundant data transmission. Extensive experiments on a real-world multi-label dataset demonstrate that the proposed method achieves superior inference accuracy and robustness over wireless networks, compared to traditional digital compression and deep learning-based joint source-channel coding schemes. These results confirm the potential of task-oriented design for scalable and reliable edge intelligence.

The Information Bottleneck (IB) principle has emerged as a promising approach for enhancing the generalization, robustness, and interpretability of deep neural networks, demonstrating efficacy across image segmentation, document clustering, and semantic communication. Among IB implementations, the IB Lagrangian method, employing Lagrangian multipliers, is widely adopted. While numerous methods for the optimizations of IB Lagrangian based on variational bounds and neural estimators are feasible, their performance is highly dependent on the quality of their design, which is inherently prone to errors. To address this limitation, we introduce Structured IB, a framework for investigating potential structured features. By incorporating auxiliary encoders to extract missing informative features, we generate more informative representations. Our experiments demonstrate superior prediction accuracy and task-relevant information preservation compared to the original IB Lagrangian method, even with reduced network size.

Learning-based joint source-channel coding (JSCC) is widely used in task-oriented communication, which aims to extract and transmit only task-relevant information to improve communication efficiency. However, the learning-empowered algorithms in task-oriented communication may bring potential bias towards sensitive groups, and the adaptability to dynamic channel conditions still remains a challenge. To address these issues, we propose a task-oriented communication scheme that achieves efficient encoding and inference while preserving group fairness. Our approach leverages an information bottleneckbased framework that maximizes the task utility information while limiting the dependence of the inference result on the sensitive attribute and adopts a hypernetwork-parametrization mechanism to adapt to varying channel conditions. We also provide a theoretical bound for fairness guarantee and design a noise injection module to control the fairness-utility tradeoff. Experiments on benchmark datasets demonstrate the superiority of our framework in achieving a fairness-utility tradeoff and the adaptability to channel variations.

Spiking neural networks (SNNs) have emerged as an energy-efficient alternative to the traditional artificial neural networks (ANNs) which are compute-intensive. This paper proposes a novel MIMO over-the-air federated learning scheme trained on SNNs using lattice code. Based on the lattice structure, we design a reliable transceiver with lattice quantizer that can combat the noise and interference from the devices. We further derive a convergence analysis of the proposed method considering the nondifferentiable spikes of SNNs. The experimental results verify that the proposed method is effective by showing that the proposed method can achieve comparable accuracy to the ideal benchmarks and outperform the existing approach by employing a small number of antennas at the server and devices. We also show that SNNs are$23.08 \times$more energy-efficient than ANNs.

In this paper, we study satellite-to-ground taskoriented communication for edge inference tasks, where a satellite extracts, fuses and encodes multimodal feature vectors and then sends them to a ground server under inevitable channel noise conditions for downstream processing. However, the multispectral and multi-resolution characteristics of multimodal satellite remote sensing data render traditional multimodal methods inapplicable. To reduce the data redundancy caused by the high-dimensional and complex multimodal vectors generated onboard while retaining key information and enhancing robustness against channel noise. We propose a Robust Multimodal Information Bottleneck (RMIB) framework which considers channel noise and communication bandwidth and introduces a new information bottleneck optimization objective. By applying this objective through end-to-end training, we optimize the feature extraction, fusion and encodes multimodal data into robust and effective feature vector in noisy communication environments by reducing redundancy and enhancing feature discrimination. To tackle the RMIB objective function, we derive a tractable variational upper bound using the Variational Information Bottleneck technique to overcome the computational intractability of mutual information. Experimental results demonstrate that our method not only outperforms baseline techniques in classification accuracy on three datasets but also enhances robustness against channel noise and reduces communication overhead.

We consider an all-to-all communication problem over a ring network, where N nodes are arranged in a ring topology, and each one wishes to send data to every other node by communicating with its neighbors within a fixed distance. To reduce the communication load, we propose a new coded broadcast scheme that exploits both storage redundancy (i.e., some messages can be stored repeatedly across nodes) and multicast opportunities (i.e., each encoded packet carries multiple messages intended by different nodes). In our schemes, each node transmits each encoded packet based on bits from two messages traveling in opposite directions, and decodes the desired messages based on the local files and priorly recovered messages. Theoretical converse proof shows that our scheme achieves the optimal trade-off between communication load, cache size, and communication distance when N is sufficiently large. The optimality results indicate that in ring-based broadcast, the redundant storage only leads to an additive gain in reducing communication load while the communication distance contributes to a multiplicative gain.

Coded distributed computing (CDC), introduced by Li et al., effectively reduces communication load in MapReduce systems. In cascaded CDC with$K$nodes,$N$input files, and$Q$output functions, each input file is mapped by$r \geq 1$nodes, and each output function is computed by$s>1$nodes, enabling coding for multicast opportunities. However, existing CDC schemes often require splitting data into exponentially many files or functions as$K$grows, increasing complexity and degrading performance. This paper addresses the case of$K / s \in \mathbb{N}$, proposing a low-complexity CDC scheme through carefully designing the strategies of data placement and output function assignment. The proposed scheme offers key advantages:$\mathbf{1}$) multicast gains of$(r+s-1)(1-1 / s)$and approximately$r+s-1$for large$s$, with better communication load than the well-known Li et al.'s scheme; 2) reduce input and output file requirements; and 3) binary field$\mathbb{F}_{2}$operations implement in a one-shot manner, enabling immediate decoding. We also derive a new information-theoretic bound under the proposed strategies, showing that the communication load is order-optimal within a factor of 2 and approximately optimal when$K$is sufficiently large for a given$r$.

This paper presents a novel Joint Source and Channel Coding (JSCC) method for semantic communication to enhance the communication efficiency for transmitting high-resolution multi-modal data from Low Earth Orbit (LEO) satellites to ground stations. On the satellite, a JSCC encoder consisting of neural networks (NNs) is utilized to map the input multi-modal data into a common signal, while an NN-based JSCC decoder at the ground station reconstructs the original input from the received signal. Throughout this process, the common semantic information among each modality is learned to optimize coding space, and a robust coding scheme specific to the satellite downlink channel is developed between the encoder and decoder. Experiments have demonstrated that the proposed method outperforms existing signal modality JSCC approaches, achieving multi-modal transmission with significantly smaller coding space and thereby reducing communication overhead.

In this article, we propose a digital semantic feature division multiple access (SFDMA) paradigm in multiuser broadcast (broadcast communication (BC)) networks for the inference and the image reconstruction tasks. In this SFDMA scheme, the multiuser semantic information is encoded into discrete approximately orthogonal representations, and the encoded semantic features of multiple users can be simultaneously transmitted in the same time-frequency resource. Specifically, for inference tasks, we design a SFDMA digital BC network based on robust information bottleneck (RIB), which can achieve a tradeoff between inference performance, data compression and multiuser interference. Moreover, for image reconstruction tasks, we develop a SFDMA digital BC network by utilizing a Swin Transformer, which significantly reduces multiuser interference. More importantly, SFDMA can protect the privacy of users’ semantic information, in which each receiver can only decode its own semantic information. Furthermore, we establish a relationship between performance and signal to interference plus noise ratio (SINR), which is fitted by an Alpha-Beta–Gamma (ABG) function. Furthermore, an optimal power allocation method is developed for the inference and reconstruction tasks. Extensive simulations verify the effectiveness and superiority of our proposed SFDMA scheme.

By extracting task-relevant information while maximally compressing the input, the information bottleneck (IB) principle has provided a guideline for learning effective and robust representations of the target inference. However, extending the idea to the multi-task learning scenario with joint consideration of generative tasks and traditional reconstruction tasks remains unexplored. This paper addresses this gap by reconsidering the lossy compression problem with diverse constraints on data reconstruction, perceptual quality, and classification accuracy. Firstly, we study two ternary relationships, namely, therate-distortion-classification (RDC)andrate-perception-classification (RPC). For both RDC and RPC functions, we derive the closed-form expressions of the optimal rate for binary and Gaussian sources. These new results complement the IB principle and provide insights into effectively extracting task-oriented information to fulfill diverse objectives. Secondly, unlike prior research demonstrating a tradeoff between classification and perception in signal restoration problems, we prove that such a tradeoff does not exist in the RPC function and reveal that the source noise plays a decisive role in the classification-perception tradeoff. Finally, we implement a deep-learning-based image compression framework, incorporating multiple tasks related to distortion, perception, and classification. The experimental results coincide with the theoretical analysis and verify the effectiveness of our generalized IB in balancing various task objectives.

In complex-valued neural network (CVNN) applications, complex number calculations require high performance rather than high precision. However, most previous studies focused on high-precision approaches, which have low speed and high hardware costs. This paper proposes a universal methodology of complex number computation for low-complexity and high-speed implementation. The proposed methodology is based on the piecewise linear (PWL) method and can be used for different types of complex number computations. Considering that multiplication operations consume considerable resources, multiplication, fused square-add (FSA) and fused multiply-add (FMA) operations are the focus of optimization. The partial products of the square operation are reduced by folding and merging techniques because of their symmetry in the FSA operation. The partial products of the multiplication and FMA operations are reduced via Booth encoding. In addition, the partial products are further reduced by the proposed step-by-step truncation method. The proposed segmenter, which simulates the hardware implementation, automatically divides the nonlinear functions in the complex number computations into the smallest number of segments according to the required precision. The results show that the proposed approach improves performance and reduces hardware costs compared with the state-of-the-art methods for complex number calculations involving square roots, reciprocals and logarithms.

The distributed linearly separable computation problem finds extensive applications across domains such as distributed gradient coding, distributed linear transform, real-time rendering, etc. This paper investigates this problem in a decentralized network, where N workers, connected through a shared device-to-device (D2D) link, collaboratively perform the computation task without a central master. Each worker aims to compute a linearly separable function that can be manifested as Kclinear combinations of K messages, where each message is a function of a distinct dataset. The system is designed to tolerate up to N − Nrstragglers, ensuring that each worker can successfully complete the task based on transmissions from any Nrworkers. Our goal is to minimize the communication cost (the number of symbols transmitted by the fastest Nrworkers), under arbitrary computation cost (the number of uncoded datasets assigned to each worker). For the scenario where the computation cost is minimal, we propose a novel distributed computing scheme that is optimal under the widely used cyclic data assignment. Interestingly, we demonstrate that the side information at each worker is ineffective in the decoding phase when Kc≤ KNr/N, while it becomes beneficial as Kcincreases. Additionally, we extend the proposed scheme to scenarios where the computation cost is not necessarily minimum, and derive the optimal computation-communication costs tradeoff under the cyclic assignment when Kcis comparatively large.

Due to the black-box characteristics of deep learning based semantic encoders and decoders, finding a tractable method for the performance analysis of semantic communications is a challenging problem. In this paper, we propose an Alpha-Beta-Gamma (ABG) formula to model the relationship between the end-to-end measurement and SNR, which can be applied for both image reconstruction tasks and inference tasks. Specifically, for image reconstruction tasks, the proposed ABG formula can well fit the commonly used DL networks, such as SCUNet, and Vision Transformer, for semantic encoding with the multi scale-structural similarity index measure (MS-SSIM) measurement. Furthermore, we find that the upper bound of the MS-SSIM depends on the number of quantized output bits of semantic encoders, and we also propose a closed-form expression to fit the relationship between the MS-SSIM and quantized output bits. To the best of our knowledge, this is the first theoretical expression between end-to-end performance metrics and SNR for semantic communications. Based on the proposed ABG formula, we investigate an adaptive power control scheme for semantic communications over random fading channels, which can effectively guarantee quality of service (QoS) for semantic communications, and then design the optimal power allocation scheme to maximize the energy efficiency of the semantic communication system. Furthermore, by exploiting the bisection algorithm, we develop the power allocation scheme to maximize the minimum QoS of multiple users for OFDMA downlink semantic communication Extensive simulations verify the effectiveness and superiority of the proposed ABG formula and power allocation schemes.

In this paper, we investigate the fundamental tradeoff between computation and communication for the full-duplex (FD) wireless MapReduce distributed computing network. Specifically, a coded interference alignment and neutralization (CIAN) scheme is proposed to significantly reduce the achievable normalized delivery time (NDT) for any given computation load, which jointly exploits both the coding and interference management technologies. In particular, a novel coding strategy is designed to create the coded message desired by multiple nodes, thereby providing the coded multicasting gain. Furthermore, the Shuffle phase is molded as a special cooperative X-multicast network. For this network, a novel IAN scheme is proposed to improve the achievable sum degree of freedom (SDoF), thereby providing the IAN gain. In the proposed CIAN scheme, the fundamental tradeoff between the coded multicasting gain and IAN gain is characterized, and the achievable NDT is minimized by carefully optimizing these two gains. Furthermore, a tight information-theoretic lower bound on the NDT is derived, demonstrating the optimality of the CIAN scheme in some cases. In other cases, the achievable NDT of the CIAN scheme and the lower bound are within a multiplicative gap of 2. Theoretical analysis and numerical results indicate the superior performance of the CIAN scheme compared to existing schemes, particularly by providing additional coded multicasting gain and improved IAN gain.

Distributed computing, which leverages distributed storage and computing resources, is a promising paradigm for handling large-scale computational tasks. However, its potential is often hindered by high communication latency due to limited network bandwidth. In this paper, we study the computation-communication tradeoff of multi-cluster MapReduce systems where a central server connects to multiple clusters, each comprising a set of workers that jointly perform a MapReduce task. Workers can exchange information directly within their cluster (inner-cluster communication) or indirectly through the central server (cross-cluster communication). To reduce the communication load, we propose a nested coded distributed computing (CDC) scheme that is feasible for the heterogeneous scenario where different clusters could have arbitrary numbers of workers and computation loads. It is shown that our scheme can greatly reduce communication load compared to all existing schemes, and could achieve the optimal cross-cluster communication load. In addition, the proposed scheme can significantly reduce the computational complexity of the conventional CDC schemes, whose computational complexity exponentially increases with the computation load.

Federated Learning (FL) has emerged as an unparalleled machine learning paradigm where multiple edge clients jointly train a global model without sharing the raw data. However, sharing local models or gradients still compromises clients’ privacy and could be susceptible to delivery failures due to unreliable communication links. To address these issues, this paper considers a multi-server FL where E edge clients wish to jointly train the global model with the help of H servers while guaranteeing data privacy and meanwhile combating$s\leq H$unreliable links per client. We first propose a hybrid coding scheme based on repetition coding and MDS Coding, such that any$T_{s}$colluding servers cannot deduce any client data besides the aggregated model, and any$T_{e}$colluding clients remain unaware of honest clients’ data. Furthermore, we propose a Lagrange coding with mask (LCM) to ensure more stringent privacy protection that additionally demands that colluding servers possess no knowledge about either the local or global models. Furthermore, we establish lower bounds for both the uplink and downlink communication loads and theoretically prove that the hybrid scheme and LCM scheme can achieve the optimal uplink communication loads under the first and second threat models, respectively. For the second threat model with no straggling link, the LCM scheme is optimal. These demonstrate the communication efficiency, robustness, and privacy guarantee of our schemes.

Coded distributed computing can reduce the communication load for distributed computing systems by introducing redundant computation and creating multicasting opportunities. However, the existing schemes require delicate data placement and output function assignment, which is not feasible when distributed nodes fetch data without the orchestration of a master node. In this paper, we consider the general systems where the data placement and output function assignment are arbitrary but pre-set. We propose two coded computing schemes, One-shot Coded Transmission (OSCT) and Few-shot Coded Transmission (FSCT), to reduce the communication load. Both schemes first group the nodes into clusters and divide the transmission of each cluster into multiple rounds, and then design coded transmission in each round to maximize the multicast gain. The key difference between OSCT and FSCT is that the former uses a one-shot transmission where each encoded message can be decoded independently by the intended nodes, while the latter allows each node to jointly decode multiple received symbols to achieve potentially larger multicast gains. Furthermore, based on the lower bound proposed by Yuet al., we derive sufficient conditions for the optimality of OSCT and FSCT, respectively. This not only recovers the existing optimality results but also includes some cases where our schemes are optimal while others are not.

Effective information sharing is essential for multi-agent systems to execute cooperative tasks successfully. Typically, agents within such systems are either stationary or possess unrestricted communication ranges. However, in more complex scenarios where agent mobility is introduced, the communication network’s topology becomes dynamic over time. This dynamism can result in partial communication unreachability among certain agents. Consequently, striking a balance between minimizing overall communication overhead and optimizing task performance becomes a formidable challenge. In this paper, we address the issue of dynamic communication in multi-agent systems. We propose a novel approach that leverages the principle of information bottleneck theory to develop a multi-mean field multi-agent reinforcement learning algorithm called MMIB. Through a series of experiments, we demonstrate the effectiveness of our proposed algorithm in reducing communication overhead while maintaining task performance at a level comparable to other state-of-the-art multi-agent reinforcement learning algorithms.

The distributed linearly separable computation problem finds extensive applications across domains such as dis-tributed gradient coding, distributed linear transform, real-time rendering, etc. In this paper, we investigate this problem in a fully decentralized scenario, where$\mathrm{N}$workers collaboratively perform the computation task without a central master. Each worker aims to compute a linearly separable computation that can be manifested as$\mathrm{K}_{\mathrm{c}}$linear combinations of$\mathrm{K}$messages, where each message is a function of a distinct dataset. We require that each worker successfully fulfill the task based on the transmissions from any$\mathrm{N}_{\mathrm{r}}$workers, such that the system can tolerate any$\mathrm{N}-\mathrm{N}_{\mathrm{r}}$stragglers. We focus on the scenario where the computation cost (the number of uncoded datasets assigned to each worker) is minimum, and aim to minimize the communication cost (the number of symbols the fastest$\mathrm{N}_{\mathrm{r}}$workers transmit). We propose a novel distributed computing scheme that is optimal under the widely used cyclic data assignment. Interestingly, we demonstrate that the side information at each worker is ineffective in reducing the communication cost when$\mathrm{K}_{\mathrm{c}}\leq \text{KN}_{\mathrm{r}}/\mathrm{N}$, while it helps reduce the communication cost as$\mathrm{K}_{\mathrm{c}}$increases.

Balancing diverse task objectives under limited rate is crucial for developing robust multitask deep learning (DL) models and improving performance across various domains. In this paper, we consider the lossy compression problem with human-centric and task-oriented metrics, such as perceptual quality and classification accuracy. We investigate two ternary relationships, namely, the rate-distortion-classification (RDC) and rate-perception-classification (RPC). For both RDC and RPC functions, we derive the closed-form expressions of the optimal rate for both binary and Gaussian sources. Notably, both RDC and RPC relationships exhibit distinct characteristics compared to the previous RDP tradeoff proposed by Blau et al. Then, we conduct experiments by implementing a DL-based image compression framework, incorporating rate, distortion, perception, and classification constraints. The experimental results verify the theoretical characteristics of RDC and RPC tradeoffs, providing information-theoretical insights into the design of loss functions to balance diverse task objectives in deep learning.

In this article, we study the latency minimization problem for a wireless federated learning (FL) system with heterogeneous computation capability, where different edge devices perform different numbers of local model updates in each communication round. We formulate a total latency minimization problem with probabilistic device selection, taking into account both the communication and computation latency in the whole FL procedure. However, it is highly challenging to optimally solve this problem due to the coupling issues of model convergence and latency minimization problem caused by the heterogeneity of local model updates. Through convergence analysis, we reveal that decoupling the resource allocation variables from the model convergence is essential to reduce the problem to a single-round latency minimization problem. To solve this simplified problem, we propose an alternating optimization scheme to jointly consider communication and computation resource allocation and mitigate the straggler effect. We prove that the resulting subproblems, i.e., bandwidth and computation capacity allocation, are both convex and can be optimally solved in closed form, respectively. Simulation results show that compared with the baseline scheme that allocates the communication and computation resources equally across edge devices, the proposed scheme can achieve up to 47.04% single-round latency reduction.

In this paper, we study the coded caching scheme for the$(K,L,M_{\text {T}},M_{\text {U}},N)$partially connected linear network, where there are N files each of which has an equal size,$K+L-1$transmitters, and K users; each user and transmitter caches at most$M_{\text {U}}$and$M_{\text {T}}$files, respectively; each user locally communicates with L nearby transmitters. The goal is to design caching and delivery schemes to reduce the transmission latency measured by the metric named normalized delivery time (NDT). By delicately designing the data placement of the transmitters and users according to the topology, we show that a combinatorial structure called multiple-antenna placement delivery array (MAPDA), which was originally proposed for the multiple-input single-output broadcast channels, can be also helpful in designing schemes for the partially connected linear network. Then, based on existing MAPDAs and our constructing approach, we propose new schemes that achieve the optimal NDT when$ {M_{\text {T}}}+ {M_{\text {U}}}\geq N$and smaller NDT than that of the existing schemes when (${M_{\text {T}}}+ {M_{\text {U}}}\leq N$,$\frac {M_{\text {U}}}{N}+\frac {M_{\text {T}}}{N} \frac {L}{K}\left \lceil {{\frac {K}{L} }}\right \rceil \geq 1$) or ($ {M_{\text {U}}}+ {M_{\text {T}}}\lt N, \frac {K}{L}\notin \mathbb {Z}^{+}$). Moreover, our schemes operate in one-shot linear delivery and significantly reduce the subpacketizations compared to the existing scheme, which implies that our schemes have a wider range of applications and lower complexity of implementation.

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.

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.

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 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.

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.

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.

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 assporadic 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.

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.

Federated learning (FL) is a novel training paradigm that allows clients to jointly train models locally without sharing their data. However, it encounters various challenges, such as limited client communication resources, unreliable client-server connections, and the privacy leakage of client information. In this paper, we consider multi-server secure FL with unreliable communication links. We first define a threat model using Shannon's information-theoretic security framework, and propose a novel scheme called Lagrange Coding with Mask (LCM), which introduces client-side masking to reduce the disclosure of client information and injects appropriate coding redundancy to counter the effects of unreliable links while ensuring security. Furthermore, we derive the lower bounds of the uplink and downlink communication loads, respectively, and prove that LCM achieves the optimal uplink communication load, which is unrelated to the number of collusion clients.

In this paper, we consider a task-oriented communication paradigm over multi-user broadcast channels for multi-task edge AI inference, where an edge transmitter performs feature extraction and broadcasts the encoded features to multiple edge devices, each of which conducts a specific inference task based on the received signals. However, due to the heterogeneity of communication links, it still remains an open problem to balance the trade-off between inference accuracy and robustness to channel noise over broadcast channels. To address this issue, we propose a task-oriented broadcast framework guided by information bottleneck (IB) for feature extraction and broadcasting, yielding a two-phase network training strategy, where the former aims to extract task-relevant features for each task, and the latter is utilized to compress the extracted features and perform robust broadcasting. Simulation results demonstrate that the proposed task-oriented broadcast framework can achieve a better trade-off between inference accuracy and robustness under various channel conditions than the benchmarks.

Recent work has explored federated clustering and developed an efficient k-means based method. However, it is well known that k-means clustering underperforms in high-dimensional space due to the so-called "curse of dimensionality". In addition, high-dimensional data (e.g., generated from healthcare, medical, and biological sectors) are pervasive in the big data era, which poses critical challenges to federated clustering in terms of, but not limited to, clustering effectiveness and communication efficiency. To fill this significant gap in federated clustering, we propose a one-shot federated subspace clustering scheme Fed-SC that can achieve remarkable clustering effectiveness on high-dimensional data while keeping communication cost low using only one round of communication for each local device. We further establish theoretical guarantees on the clustering effectiveness of one-shot Fed-SC and exploit the benefits of statistical heterogeneity across distributed data. Extensive experiments on synthetic and real-world datasets demonstrate significant effectiveness gains of Fed-SC compared with both subspace clustering and one-shot federated clustering methods.

In this paper, we consider a two-dimensional caching-aided ultra-dense network caching system, which consists of a server containing N files, K1K2cache nodes that are arranged neatly on the grid with K1rows and K2columns, and U cacheless users randomly distributed around cache nodes. The server connects to users through an error-free shared-link, and the users can be served by nearby cache nodes and freely retrieve the cache content from it. Our goal is to minimize the transmission load in the worst case while meeting all possible users’ demands. We propose a coded caching scheme based on Maddah-Ali and Niesen scheme (MN scheme), which uses the placement strategy of MN scheme to the cache nodes and uses the delivery strategy of MN scheme multiple rounds for different types of users according to their geometrical locations. We prove that our scheme is order optimal and can greatly improve the transmission performance compared with conventional uncoded caching schemes.

In this paper, we investigate the secure gradient aggregation problem for K-server wireless Federated learning (FL) systems. We propose a coded aggregation scheme such that any set of up to T colluding servers cannot infer any information about the local updates, including the aggregation value. In our scheme, each user encodes its local update to K confidential messages using Lagrange Coding, and then sends one confidential message to each server using an artificial noise alignment approach. In the downlink, each server delivers the summation of the confidential messages, by which every user can recover the aggregation of local updates. For the proposed scheme, we characterize the uplink and downlink communication latency, and show that the communication latency monotonically decreases with the total number of servers K while increasing with the number of colluding servers T.

In this paper, we consider a hierarchical distributed multi-task learning (MTL) system where distributed users wish to jointly learn different models orchestrated by a central server with the help of a layer of multiple relays. Since the users need to download different learning models in the downlink transmission, the distributed MTL suffers more severely from the communication bottleneck compared to the single-task learning system. To address this issue, we propose a coded hierarchical MTL scheme that exploits the connection topology and introduces coding techniques to reduce communication loads. It is shown that the proposed scheme can significantly reduce the communication loads both in the uplink and downlink transmissions between relays and the server. Moreover, we provide informationtheoretic lower bounds on the optimal 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.

This paper considers a hierarchical caching system where a server connects with multiple mirror sites, each connecting with a distinct set of users, and both mirror sites and users are equipped with caching memories. Although there already exist works studying this setup and proposing coded caching schemes to reduce transmission loads, two main problems are remained to address: 1) the optimal communication load R1under the uncoded placement for the first layer is still unknown. 2) the previous schemes are based on Maddah-Ali and Niesen’s data placement and delivery, which require high subpacketization level. How to achieve a good tradeoff between transmission loads and subpacketization level for the hierarchical caching system is unclear. In this paper, we aim to address these two problems. We first propose a new combination structure named hierarchical placement delivery array (HPDA), which characterizes the data placement and delivery for a hierarchical caching system. Then we construct two classes of HPDAs, where the first class leads to a scheme achieving the optimal R1for some cases, and the second class requires a smaller subpacketization level at the cost of slight increase in transmission loads.

In this paper, we study the latency minimization problem for a wireless federated learning (FL) system with heterogeneous computation capability, where different edge devices perform different numbers of local updates in each communication round. We formulate a total latency minimization problem, taking into account both the communication and computation latency in the whole FL procedure. We reveal that decoupling the resource allocation variables from the model convergence is essential to reduce the problem to a single-round latency minimization problem. To solve this simplified problem, we propose an alternating optimization scheme to jointly consider communication and computation resource allocation and mitigate the straggler effect. We prove that the resulting sub-problems, i.e., bandwidth and computation capacity allocation, are both convex and can be optimally solved in closed form, respectively. Simulations show that compared with the baseline scheme that allocates the communication and computation resources equally across edge devices, the proposed scheme can achieve single-round latency reduction.

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.

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.

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.

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 theoptimaldownlink communication load, and theorder optimaluplink 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.

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.

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.

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.

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.

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.

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.

In this paper, we consider the general heterogeneous MapReduce system, where the file placement and Reduce function assignment are arbitrary but pre-set among all nodes (i.e., can not be designed by schemes). The storage and the computational capabilities for different nodes are not necessarily equal. We propose a universal CDC scheme, namely One-Shot Coded Transmission (OSCT), and establish the upper bound of the optimal communication load. The OSCT scheme encodes intermediate values into message blocks, each of which can be immediately and independently decoded by multiple intended nodes. We carefully design the bit-length of each message block to increase the multicasting gain. Furthermore, we provide a sufficient condition under which our scheme is optimal. To the best of our knowledge, this is the first work to investigate the general MapReduce problem with fixed data placement and Reduce function assignment.

This paper tackles the distributed computing problem in a joint cellular and device-to-device (D2D) network, where edge users can exchange information both through an access point (e.g., base station) and D2D communication links. The D2D network consists of multiple disjoint clusters, where multiple users can transmit signals simultaneously to their neighbouring users within the same cluster, due to disjoint connections between clusters. A nested coded distributed computation (CDC) scheme is proposed to reduce the communication load both in the uplink and downlink communication, by leveraging coding techniques and parallel transmission opportunities in the D2D network. It is shown that this scheme is optimal in the sense that it achieves the minimum communication load both in the uplink and downlink. The nested CDC scheme can also reduce the coding complexity, compared with the conventional wireless CDC scheme proposed by Li et al., whose computational complexity grows exponentially with the computation load.

This paper proposes a coded parallel computing scheme (CPC) for the wireless MapReduce system where multiple nodes simultaneously exchange information via a wireless interference network. The CPC scheme is based on the coded distributed computing scheme proposed by Li et al., followed by interference alignment to cancel interference caused by the concurrent transmission. It is shown that CPC can significantly reduce the communication latency by jointly exploiting the parallel transmission and coded multicasting opportunities. Different from the previous coded computing schemes whose communication latency increase with the total number of computing nodes K, the communication latency of CPC decreases with K when K is relatively large. As K tends to infinity, all previous schemes achieve constant communication latency, while CPC approaches zero communication latency.

In order to achieve high spectrum utilization, it is necessary to consider the employment of the Integrated Sensing and Communication for the future wireless communication systems. On the other hand, since the radio signal for wireless communication can carry energy at the same time, simultaneous wireless information and power transfer (SWIPT) becomes a popular and helpful technology to enhance the energy transmission efficiency. In order to further improve the spectrum efficiency, we propose to design an integrated sensing and SWIPT (iS2WIPT) system, where a base station transmits combined signals to perform downlink multiuser communication, multiuser energy harvesting and radar target sensing simultaneously. We further formulate the problem as designing the transmit beamforming to minimize the beampattern matching error subject to the given total transmit power budget and the quality-of-service (QoS) constraints at all users, i.e., the individual signal-to-interference-plus-noise ratio (SINR) requirements at information users (IUs) and energy harvesting requirements at energy users (EUs). So as to solve the intractable nonconvex problem, we provide a difference-of-convex-functions (DC) representation for the problem and further solve it with global convergence guarantees. Numerical results clearly show that our proposed DC algorithm outperforms the classical semidefinite relaxation algorithm significantly under different situations. What is more, the results show that there is a tradeoff between the radar sensing performance and the QoS requirements of downlink users (both IUs and EUs).

Coded caching is a technique to create coded multicast opportunities for cache-aided networks. In coded caching problem, a fundamental but open question is: what is the minimum transmission load given any fixed subpacketization level? In this paper, we propose a lower bound on the transmission load for any fixed subpacketization by studying the combinatorial structure of corresponding placement delivery array, which was introduced by Yan et al. to reformulate the centralized coded caching schemes. Then we show that some schemes generated by the well known scheme proposed by Maddah-Ali and Niesen (MN), and some scheme generated by Packing (a classic concept of combinatorial design theory), can achieve our lower bound. This implies that our lower bound is tight for some cases.

Federated learning (FL), as a disruptive machine learning (ML) paradigm, enables the collaborative training of a global model over decentralized local data sets without sharing them. It spans a wide scope of applications from the Internet of Things (IoT) to biomedical engineering and drug discovery. To support low-latency and high-privacy FL over wireless networks, in this article, we propose a reconfigurable intelligent surface (RIS)-empowered over-the-air FL system to alleviate the dilemma between learning accuracy and privacy. This is achieved by simultaneously exploiting the channel propagation reconfigurability with RIS for boosting the received signal power, as well as the waveform superposition property with over-the-air computation (AirComp) for fast model aggregation. By considering a practical scenario, where high-dimensional local model updates are transmitted across multiple communication blocks, we characterize the convergence behaviors of the differentially private federated optimization algorithm. We further formulate a system optimization problem to optimize the learning accuracy while satisfying privacy and power constraints via the joint design of transmit power, artificial noise, and phase shifts at RIS, for which a two-step alternating minimization framework is developed. Simulation results validate our systematic, theoretical, and algorithmic achievements and demonstrate that RIS can achieve a better tradeoff between privacy and accuracy for over-the-air FL systems.

In this paper, we investigate an integrated visible light positioning and communication (VLPC) system with a single LED-lamp. First, by leveraging the fact that the VLC channel model is a function of the receiver’s location, we propose a system model that estimates the channel state information (CSI) based on the positioning information without transmitting pilot sequences. Second, we derive the Cramer-Rao lower bound (CRLB) on the positioning error variance and a lower bound on the achievable rate with on-off keying modulation. Third, based on the derived performance metrics, we optimize the power allocation to minimize the CRLB, while satisfying the rate outage probability constraint. To tackle this non-convex optimization problem, we apply the worst-case distribution of the Conditional Value-at-Risk (CVaR) and the block coordinate descent (BCD) methods to obtain the feasible solutions. Finally, the effects of critical system parameters, such as outage probability, rate threshold, total power threshold, are revealed by numerical results.

In this paper, we investigate the optimal probabilistic constellation shaping design for covert communication systems from a practical view. Different from conventional covert communications with equiprobable constellations modulation, we propose non-equiprobable constellations modulation schemes to further enhance the covert rate. Specifically, we derive covert rate expressions for practical discrete constellation inputs for the first time. Then, we study the covert rate maximization problem by jointly optimizing the constellation distribution and power allocation. In particular, an approximate gradient descent method is proposed for obtaining the optimal probabilistic constellation shaping. To strike a balance between the computational complexity and the transmission performance, we further develop a framework that maximizes a lower bound on the achievable rate where the optimal probabilistic constellation shaping problem can be solved efficiently using the Frank-Wolfe method. Extensive numerical results show that the optimized probabilistic constellation shaping strategies provide significant gains in the achievable covert rate over the state-of-the-art schemes.

A central issue of distributed computing systems is how to optimally allocate computing and storage resources and design data shuffling strategies such that the total execution time for computing and data shuffling is minimized. This is extremely critical when the computation, storage and communication resources are limited. In this paper, we study the resource allocation and coding scheme for the MapReduce-type framework with limited resources. In particular, we focus on the coded distributed computing (CDC) approach proposed by Li et al.. We first extend the asymmetric CDC (ACDC) scheme proposed by Yu et al. to the cascade case where each output function is computed by multiple servers. Then we demonstrate that whether CDC or ACDC is better depends on system parameters (e.g., number of computing servers) and task parameters (e.g., number of input files), implying that neither CDC nor ACDC is optimal. By merging the ideas of CDC and ACDC, we propose a hybrid scheme and show that it can strictly outperform CDC and ACDC. Furthermore, we derive an information-theoretic converse showing that for the MapReduce task using a type of weakly symmetric Reduce assignment, which includes the Reduce assignments of CDC and ACDC as special cases, the hybrid scheme with a corresponding resource allocation strategy is optimal, i.e., achieves the minimum execution time, for arbitrary amount of computing servers and storage memories.

Consider a distributed multi-task learning (MTL) framework where the distributed users first train their own models based on the local data and then send the local updates to the server, and the server sends back helpful information by which each user can learn its task independently. Compared to the single-task case, the distributed MTL suffers more severely from the communication bottleneck, because the users wish to learn multiple models, causing the downlink communication load to linearly increase with the total number of tasks. In this paper, we propose a novel scheme named coded distributed multi-task learning, to reduce the communication loads both in the uplink and downlink. The key idea is to exploit the local information stored at the users during the local training, and utilize a particular repetitive placement and computation on the publicly shared dataset such that coded multicasting opportunities can be created at the server and users. Our method for the first time applies coding strategy to reduce the communication cost for distributed MTL framework. Experiments on real-world dataset show that the proposed scheme can substantially reduce the communication load compared to the traditional uncoded approach.

This paper establishes the convergence rates for fitting a polynomial neural network with quadratic activation function via the mini-batch Stochastic Gradient Descent (SGD) algorithm. Specifically, we focus on the parallel implementation of calculating mini-batch gradients on a distributed computing platform. We first illustrate that the SGD converges at a linear rate to the optimal solution, and the convergence rate can be characterized as a function of mini-batch sizes. Next, we deploy the SGD with a distributed approach across multiple processors, where the partial mini-batch gradient is calculated and quantized to send to a master processor in each iteration, yielding a Quantized Stochastic Gradient Descent (QSGD) algorithm. This scheme can effectively reduce the communication overhead by the quantization strategy. Furthermore, we reveal that QSGD provably maintains a similar convergence rate of SGD to a globally optimal solution while significantly reduces the communication cost. In particular, the number of bits required for quantization and the mini-batch size affect the convergence rate of QSGD.

In this paper, we consider the distributed mean estimation problem where the server has access to some side information, e.g., its local computed mean estimation or the received information sent by the distributed clients at the previous iterations. We propose a practical and efficient estimator based on an r-bit Wynzer-Ziv estimator proposed by Mayekar et al., which requires no probabilistic assumption on the data. Unlike Mayekar's work which only utilizes side information at the server, our scheme jointly exploits the correlation between clients' data and server's side information, and also between data of different clients. We derive an upper bound of the estimation error of the proposed estimator. Based on this upper bound, we provide two algorithms on how to choose input parameters for the estimator. Finally, parameter regions in which our estimator is better than the previous one are characterized.

Relay is a traditional key technology to improve the communication reliability and enlarge the covering range of service. Recently, coded caching schemes that reduce traffic congestion through coding and injecting duplicate data among users have attracted wide interests. This paper studies a relay network where all nodes including the central server, relay nodes and users are equipped with cache memories. Each user demands a file from the server’s library, and is connected to the server through a specific relay node. We define the communication delay for this model and propose new coded caching schemes for the deterministic and random caching setups, respectively. The proposed schemes exploit the spared transmission time resource and can greatly reduce the transmission delay compared to the previously known caching schemes. Surprisingly, we show that even when relay nodes do not cooperate with each other, using a small amount of caching memories at each relay node is sufficient to achieve the same communication delay as if each relay had access to the full library. To our best knowledge, this is the first result showing that even the caching size is strictly smaller than the library’s size, increasing the caching size is wasteful in reducing the transmission latency.

We consider a MapReduce-type task running in a distributed computing model which consists of K edge computing nodes distributed across the edge of the network and a Master node that assists the edge nodes to compute output functions. The Master node and the edge nodes, both equipped with some storage memories and computing capabilities, are connected through a multicast network. We define the communication time spent during the transmission for the sequential implementation (all nodes send symbols sequentially) and parallel implementation (the Master node can send symbols during the edge nodes’ transmission), respectively. We propose a mixed coded distributed computing scheme that divides the system into two subsystems where the coded distributed computing (CDC) strategy proposed by Songze Li et al. is applied into the first subsystem and a novel master-aided CDC strategy is applied into the second subsystem. We prove that this scheme is optimal, i.e., achieves the minimum communication time for both the sequential and parallel implementation, and establish an optimal information-theoretic tradeoff between the overall communication time, computation load, and the Master node’s storage capacity. It demonstrates that incorporating a Master node with storage and computing capabilities can further reduce the communication time. For the sequential implementation, we deduce the approximately optimal file allocation between the two subsystems, which shows that the Master node should map as many files as possible in order to achieve smaller communication time. For the parallel implementation, if the Master node’s storage and computing capabilities are sufficiently large (not necessary to store and map all files), then the proposed scheme requires at most 1/2 of the minimum communication time of system without the help of the Master node.

In many distributed learning setups such as federated learning, client nodes at the edge use individually collected data to compute the local gradients and send them to a central master server, and the master aggregates the received gradients and broadcasts the aggregation to all clients with which the clients can update the global model. As straggling communication links could severely affect the performance of distributed learning system, Prakash et al. proposed to utilize helper nodes and coding strategy to achieve resiliency against straggling client-to-helpers links. In this paper, we propose two coding schemes: repetition coding (RC) and MDS coding both of which enable the clients to update the global model in the presence of only helpers but without the master. Moreover, we characterize the uplink and downlink communication loads, and prove the tightness of uplink communication load. Theoretical tradeoff between uplink and downlink communication loads is established indicating that larger uplink communication load could reduce downlink communication load. Compared to Prakash's schemes which require a master to connect with helpers though noiseless links, our scheme can even reduce the communication load in the absence of master when the number of clients and helpers is relatively large compared to the number of straggling links.

In this paper, we consider a two-layer caching-aided network, where a single server consisting of a library of N files connects with multiple relays, each equipped with a cache memory of M1files and each relay connects with a distinct set of users, each equipped with a cache memory of M2files. We design a caching scheme that exploits the spared transmission time resource by constructing a concurrent transmission between the two layers. It is shown that the caching scheme is order optimal and achieves an additive parallel gain compared to the previously known caching scheme. Also, we show that for the two-relay case, if each relay's caching size M1equals to 0.382N, our scheme achieves the optimal delay as M1= N, implying that increasing the relay's cache size will not always reduce the transmission delay.

In this paper, we consider the coded-caching broadcast network with user cooperation, where a server connects with multiple users and the users can cooperate with each other through a cooperation network. We propose a centralized coded caching scheme based on a new deterministic placement strategy and a parallel delivery strategy. It is shown that the new scheme optimally allocate the communication loads on the server and users, obtaining cooperation gain and parallel gain that greatly reduces the transmission delay. Furthermore, we show that the number of users who parallelly send information should decrease when the users' caching size increases. In other words, letting more users parallelly send information could be harmful. Finally, we derive a constant multiplicative gap between the lower bound and upper bound on the transmission delay, which proves that our scheme is order optimal.

The relay broadcast channel (RBC) is considered, in which a transmitter communicates with two receivers with the assistance of a relay. Based on different degradation orders among the relay and the receivers' outputs, three types of physically degraded RBCs (PDRBCs) are introduced. Inner and outer bounds are derived on the capacity region of the presented three types. The bounds are tight for two types of PDRBCs: 1) one receiver's output is a degraded form of the other receiver's output, and the relay's output is a degraded form of the weaker receiver's output; 2) one receiver's output is a degraded form of the relay's output, and the other receiver's output is a degraded form of the relay's output. For the Gaussian PDRBC, the bounds match for all three types.

Discrete memoryless multicast network (DM-MN) is considered in this paper. We analyze the lower bounds of noisy network coding (NNC) and distributed decode-forward (DDF) for DM-MN, and show that both NNC and DDF ignore the channel output observed at the transmitter. Motivated by this observation, new coding schemes are proposed to improve NNC and DDF by exploiting the transmitter's observation and applying hybrid relaying strategies. We first study a special case when the transmitter's observation is rate-limited feedback signals, and propose a scheme that strictly improves NNC when feedback rates are sufficiently large. For the relay channel with perfect relay-transmitter feedback, our achievable rate reduces to Gabbai and Bross's rate, which is strictly larger than NNC, DDF, and all known lower bounds on the achievable rates proposed for the setup without feedback. In our scheme, both relays and receivers compress their received signals like NNC, and the relays decode independent “common” and “private” parts of the source message. The generated compression indices are sent to the transmitter through feedback, from which the transmitter reconstructs the receivers' and relays' inputs and can thus cooperate with the receivers and relays. We then extend our idea to DM-MN without feedback. For this case, although the transmitter observes channel output, both NNC and DDF simply ignore it, while our new scheme has the transmitter utilize its channel output to decode a set of relays' and receivers' compression indices, which achieves some cooperation levels between the transmitter and the receivers and relays. An enhanced relay channel is introduced to show that our scheme strictly outperforms NNC and DDF.

Achievable rate regions for cooperative relay broadcast channels with rate-limited feedback are proposed. Specifically, we consider two-receiver memoryless broadcast channels where each receiver sends feedback signals to the transmitter through a noiseless and rate-limited feedback link, meanwhile, acts as a relay to transmit cooperative information to the other receiver. The proposed rate regions improve on the known regions that consider either relaying cooperation or feedback communication, but not both.

We propose two coding schemes for the two-receiver discrete memoryless broadcast channel (BC) with rate-limited feedback from one or both receivers. They improve over the no feedback capacity region for a large class of channels, including the class of strictly essentially less-noisy BCs that we introduce in this paper. Examples of strictly essentially less-noisy BCs are the binary symmetric BC or the binary erasure BC with unequal crossover or erasure probabilities at the two receivers. When the feedback rates are sufficiently large, our schemes recover all previously known capacity results for discrete memoryless BCs with feedback. In both our schemes, we let the receivers feedback quantization messages about their receive signals. In the first scheme, the transmitter simply relays the quantization information obtained from Receiver 1 to Receiver 2, and vice versa. This provides each receiver with a second observation of the input signal and can thus improve its decoding performance unless the BC is physically degraded. Moreover, each receiver uses its knowledge of the quantization message describing its own outputs so as to attain the same performance as if this message had not been transmitted at all. In our second scheme, the transmitter first reconstructs and processes the quantized output signals, and then sends the outcome as a common update information to both receivers. A special case of our second scheme also applies to memoryless BCs without feedback but with strictly causal state-information at the transmitter and causal state-information at the receivers. It recovers all previous achievable regions also for this setup with state-information.

We propose two coding schemes for discrete memoryless broadcast channels (DMBCs) with rate-limited feedback. In our first scheme, the encoder does not process the feedback information that it receives, but simply relays it to the other receiver. This first scheme shows that arbitrary small, but positive, feedback rate suffices to improve over the nofeedback capacity for many DMBCs such as: any binary erasure BC (BEBC) with unequal erasure probability at the two receivers, any binary symmetric BC (BSBC) with unequal crossover probability at the receivers, and any binary erasure/binary symmetric BC (BEC/BSC-BC) with nonequal single-user capacity to the receivers. The scheme also improves the entire nofeedback capacity region for any strictly essentially less-noisy BC-a new class of BCs introduced in this paper-that is not physically degraded. In our second scheme, the encoder decodes all the feedback information and processes it with some local information before sending the result to the receivers. For some setups, this second scheme performs better than our first scheme. In the limit, as the available feedback-rates tend to infinity, our second scheme coincides with a special case of the Shayevitz and Wigger (SW) scheme for DMBCs with generalized feedback. The mentioned special case of the SW-scheme includes several other schemes as further special cases, e.g, the schemes by Dueck and by MaddahAli and Tse which achieve capacity or the degrees of freedom on the respectively studied channels. All our results hold also with noisy feedback when the receivers can code over the feedback links.

We consider the\(K\geq 2\)-user memoryless Gaussian broadcast channel (BC) with feedback and common message only. We show that linear-feedback schemes with a message point, in the spirit of Schalkwijk and Kailath’s scheme for point-to-point channels or Ozarow and Leung’s scheme for BCs with private messages, are strictly suboptimal for this setup. Even with perfect feedback, the largest rate achieved by these schemes is strictly smaller than capacity\(C\)(which is the same with and without feedback). In the extreme case where the number of receivers\(K\to \infty \), the largest rate achieved by linear-feedback schemes with a message point tends to 0. To contrast this negative result, we describe a scheme for rate-limited feedback that uses the feedback in an intermittent way, i.e., the receivers send feedback signals only in few channel uses. This scheme achieves all rates\(R\)up to capacity\(C\)with an\(L\)th order exponential decay of the probability of error if the feedback rate\(R_{\text {fb}}\)is at least\((L-1)R\)for some positive integer\(L\).

We propose two coding schemes for discrete memoryless broadcast channels (DMBCs) with rate-limited feedback from only one receiver. For any positive feedback rate and for the class of strictly less-noisy DMBCs, our schemes strictly improve over the no-feedback capacity region.