The Wizard-of-Oz (WoZ) method has long been a core prototyping technique in Human-Computer Interaction (HCI), in which users interact with systems that seem autonomous but are actually controlled by hidden human operators. Advances in interactive technologies have expanded the landscape of future system behaviors, broadening both where and how WoZ is used. However, as more envisioned behaviors become technically feasible, the distinction between engineering a system and simulating an interaction becomes blurred, making it essential to clarify when and why to employ wizarding. This paper presents the first systematic review of WoZ in HCI, drawing on 194 papers from SIGCHI venues to identify ten application domains, five wizard control types, eight motivations, and five categories of concerns. Building on these findings, we propose a reciprocal evolution framework that interprets how technology and wizarding shape each other, and derive guidelines for the rigorous application of WoZ. We further illustrate the framework through emerging prototyping practices with Large Language Models (LLMs).

Fine-tuning (FT) large language models (LLMs) is crucial for adapting general-purpose models to specific tasks, enhancing accuracy and relevance with minimal resources. To further enhance generalization ability while reducing training costs, this paper proposes Federated LoRA with Dropout (FedLoDrop), a new framework that applies dropout to the rows and columns of the trainable matrix in Federated LoRA. A generalization error bound and convergence analysis under sparsity regularization are obtained, which elucidate the fundamental trade-off between underfitting and overfitting. The error bound reveals that a higher dropout rate increases model sparsity, thereby lowering the upper bound of pointwise hypothesis stability (PHS). While this reduces the gap between empirical and generalization errors, it also incurs a higher empirical error, which, together with the gap, determines the overall generalization error. On the other hand, though dropout reduces communication costs, deploying FedLoDrop at the network edge still faces challenges due to limited network resources. To address this issue, an optimization problem is formulated to minimize the upper bound of the generalization error, by jointly optimizing the dropout rate and resource allocation subject to the latency and per-device energy consumption constraints. To solve this problem, a branch-and-bound (B&B)-based method is proposed to obtain its globally optimal solution. Moreover, to reduce the high computational complexity of the B&B-based method, a penalized successive convex approximation (P-SCA)-based algorithm is proposed to efficiently obtain its high-quality suboptimal solution. Finally, numerical results demonstrate the effectiveness of the proposed approach in mitigating overfitting and improving the generalization capability.

In this paper, we propose an energy efficient wireless communication system based on fluid antenna relay (FAR) to solve the problem of non-line-of-sight (NLoS) links caused by blockages with considering the physical properties. Driven by the demand for the sixth generation (6G) communication, fluid antenna systems (FASs) have become a key technology due to their flexibility in dynamically adjusting antenna positions. Existing research on FAS primarily focuses on line-of-sight (LoS) communication scenarios, and neglects the situations where only NLoS links exist. To address the issues posted by NLoS communication, we design an FAR-assisted communication system combined with amplify-and-forward (AF) protocol. In order to alleviate the high energy consumption introduced by AF protocol while ensuring communication quality, we formulate an energy efficiency (EE) maximization problem. By optimizing the positions of the fluid antennas (FAs) on both sides of the FAR, we achieve controllable phase shifts of the signals transmitting through the blockage which causes the NLoS link. Besides, we establish a channel model that jointly considers the blockage-through matrix, large-scale fading, and small-scale fading. To maximize the EE of the system, we jointly optimize the FAR position, FA positions, power control, and beamforming design under given constraints, and propose an iterative algorithm to solve this formulated optimization problem. Simulation results show that the proposed algorithm outperforms the traditional schemes in terms of EE, achieving up to 23.39% and 39.94% higher EE than the conventional reconfigurable intelligent surface (RIS) scheme and traditional AF relay scheme, respectively.

One of the key missions of sixth-generation (6G) mobile networks is to deploy large-scale artificial intelligence (AI) models at the network edge to provide remote-inference services for edge devices. The resultant platform, known as edge inference, will support a wide range of Internet-of-Things applications, such as autonomous driving, industrial automation, and augmented reality. Given the mission-critical and time-sensitive nature of these tasks, it is essential to design edge inference systems that are both reliable and capable of meeting stringent end-to-end (E2E) latency constraints. Existing studies, which primarily focus on communication reliability as characterized by channel outage probability, may fail to guarantee E2E performance, specifically in terms of E2E inference accuracy and latency. To address this limitation, we propose a theoretical framework that introduces and mathematically characterizes the inference outage (InfOut) probability, which quantifies the likelihood that the E2E inference accuracy falls below a target threshold. Under an E2E latency constraint, this framework establishes a fundamental tradeoff between communication overhead (i.e., uploading more sensor observations) and inference reliability as quantified by the InfOut probability. To find a tractable way to optimize this tradeoff, we derive accurate surrogate functions for InfOut probability by applying a Gaussian approximation to the distribution of the received discriminant gain. Experimental results demonstrate the superiority of the proposed design over conventional communication-centric approaches in terms of E2E inference reliability.

Federated learning(FL) has emerged as a popular approach for collaborative machine learning insixth-generation(6G) networks, primarily due to its privacy-preserving capabilities. The deployment of FL algorithms is expected to empower a wide range ofInternet-of-Things(IoT) applications, e.g., autonomous driving, augmented reality, and healthcare. The mission-critical and time-sensitive nature of these applications necessitates the design of low-latency FL frameworks that guarantee high learning performance. In practice, achieving low-latency FL faces two challenges: the overhead of computing and transmitting high-dimensional model updates, and the heterogeneity incommunication-and-computation(C2) capabilities across devices. To address these challenges, we propose a novel-aware framework for optimal batch-size control that minimizesend-to-end(E2E) learning latency while ensuring convergence. The framework is designed to balance a fundamental C2tradeoff as revealed through convergence analysis. Specifically, increasing batch sizes improves the accuracy of gradient estimation in FL and thus reduces the number of communication rounds required for convergence, but results in higher per-round latency, and vice versa. The associated problem of latency minimization is intractable; however, we solve it by designing an accurate and tractable surrogate for convergence speed, with parameters fitted to real data. This approach yields two batch-size control strategies tailored to scenarios with slow and fast fading, while also accommodating device heterogeneity. Extensive experiments using real datasets demonstrate that the proposed strategies outperform conventional batch-size adaptation schemes that do not consider the C2tradeoff or device heterogeneity.

rare-events, despite their infrequency, often carry critical information and require immediate attentions in mission-critical applications such as autonomous driving, healthcare, and industrial automation. The data-intensive nature of these tasks and their need for prompt responses, combined with designing edge AI (or edge inference), pose significant challenges in systems and techniques. Existing edge inference approaches often suffer from communication bottlenecks due to high-dimensional data transmission and fail to provide timely responses to rare-events, limiting their effectiveness for mission-critical applications in thesixth-generation(6G) mobile networks. To overcome these challenges, we propose a channel-adaptive, event-triggered edge-inference framework that prioritizes efficient rare-event processing. Central to this framework is a dual-threshold, multi-exit architecture, which enables early local inference for rare-events detected locally while offloading more complex rare-events to edge servers for detailed classification. To further enhance the system’s performance, we developed an online algorithm to dynamically determine the optimal confidence thresholds for controlling offloading decisions. The associated optimization problem is solved by reformulating the original non-convex function into an equivalent strongly convex one. Using deep neural network classifiers and real medical datasets, our experiments demonstrate that the proposed framework not only achieves superior rare-event classification accuracy, but also effectively reduces communication overhead, as opposed to existing edge-inference approaches.

To bridge the digital divide, space-ground integrated networks (SGINs) are expected to deliver artificial intelligence (AI) services to every corner of the world. One key mission of SGINs is to support federated learning (FL) at a global scale. However, existing space-ground integrated FL frameworks involve ground stations or costly inter-satellite links, entailing excessive training latency and communication costs. To overcome these limitations, we propose an infrastructure-freefederated learning framework based on amodeldispersal (FedMeld) strategy, which exploits periodic movement patterns and store-carry-forward capabilities of satellites to enable parameter mixing across large-scale geographical regions. We theoretically show that FedMeld leads to global model convergence and quantify the effects of round interval and mixing ratio between adjacent areas on its learning performance. Based on the theoretical results, we formulate a joint optimization problem to design the staleness control and mixing ratio (SC-MR) for minimizing the training loss. By decomposing the problem into sequential SC and MR subproblems without compromising the optimality, we derive the round interval solution in a closed form and the mixing ratio in a semi-closed form to achieve theoptimallatency-accuracy tradeoff. Experiments using various datasets demonstrate that FedMeld achieves superior model accuracy while significantly reducing communication costs as compared with traditional FL schemes for SGINs.

Mixture-of-Experts (MoE) models improve the scalability of large language models (LLMs) by activating only a small subset of relevant experts per input. However, the sheer number of expert networks in an MoE model introduces a significant storage/memory burden for an edge device. To address this challenge, we consider a scenario where experts are dispersed across an edge network for distributed inference. Based on the popular Top-$K$expert selection strategy, we formulate a latency minimization problem by optimizing expert caching on edge servers under storage constraints. When$K=1$, the problem reduces to a monotone submodular maximization problem with knapsack constraints, for which we design a greedy-based algorithm with a$(1 - 1/e)$-approximation guarantee. For the general case where$K\geq 1$, expert co-activation within the same MoE layer introduces non-submodularity, which renders greedy methods ineffective. To tackle this issue, we propose a successive greedy decomposition method to decompose the original problem into a series of subproblems, with each being solved by a dynamic programming approach. Furthermore, we design an accelerated algorithm based on the max-convolution technique to obtain the approximate solution with a provable guarantee in polynomial time. Simulation results on various MoE models demonstrate that our method significantly reduces inference latency compared to existing baselines.

Existing speech semantic communication systems mainly based on Joint Source-Channel Coding (JSCC) architectures have demonstrated impressive performance, but their effectiveness remains limited by model structures specifically designed for particular tasks and datasets. Recent advances indicate that generative large models pre-trained on massive datasets, can achieve outstanding performance and exhibit exceptional effectiveness across diverse downstream tasks with minimal fine-tuning. To exploit the rich semantic knowledge embedded in large models and enable adaptive transmission over lossy channels, we propose a Large Speech Model enabled Semantic Communication (LargeSC) system. Simultaneously achieving adaptive compression and robust transmission over lossy channels remains challenging, requiring trade-offs among compression efficiency, speech quality, and latency. In this work, we employ the Mimi as a speech codec, converting speech into discrete tokens compatible with existing network architectures. We propose an adaptive controller module that enables adaptive transmission and in-band Unequal Error Protection (UEP), dynamically adjusting to both speech content and packet loss probability under bandwidth constraints. Additionally, we employ Low-Rank Adaptation (LoRA) to fine-tune the Moshi foundation model for generative recovery of lost speech tokens. Simulation results show that the proposed system supports bandwidths ranging from 550 bps to 2.06 kbps, outperforms conventional baselines in speech quality under high packet loss rates and achieves an end-to-end latency of approximately 460 ms, thereby demonstrating its potential for real-time deployment.

Large Language Models (LLMs) are increasingly integrated into mobile services over wireless networks to support complex user requests. This trend has led to longer prompts, which improve LLMs' performance but increase data transmission costs and require more processing time, thereby reducing overall system efficiency and negatively impacting user experience. To address these challenges, we propose Joint Prompt and Power Optimization (JPPO), a framework that jointly optimizes prompt compression and wireless transmission power for mobile LLM services. JPPO leverages a Small Language Model (SLM) deployed at edge devices to perform lightweight prompt compression, reducing communication load before transmission to the cloud-based LLM. A Deep Reinforcement Learning (DRL) agent dynamically adjusts both the compression ratio and transmission power based on network conditions and service constraints, aiming to minimize service time while preserving response fidelity. We further extend the framework to JPPO++, which introduces a denoising-inspired compression scheme. This design performs iterative prompt refinement by progressively removing less informative tokens, allowing for more aggressive yet controlled compression. Experimental results show that JPPO++ reduces service time by 17% compared to the no-compression baseline while maintaining output quality. Under compression-prioritized settings, a reduction of up to$16\times$in prompt length can be achieved with an acceptable loss in accuracy. Specifically, JPPO with a$16\times$ratio reduces total service time by approximately 42.3%, and JPPO++ further improves this reduction to 46.5%.

Next-generation mobile networks are expected to facilitate fast AI model downloading to end users. By caching models on edge servers, mobile networks can deliver models to end users with low latency, resulting in a paradigm of edge model caching. In this paper, we develop a novel model placement framework, called parameter-sharing model caching (TrimCaching). TrimCaching exploits the key observation that a wide range of AI models, such as convolutional neural networks or large language models, can share a significant proportion of parameter blocks containing reusable knowledge, thereby improving storage efficiency. To this end, we formulate a parameter-sharing model placement problem to maximize the cache hit ratio in multi-edge wireless networks by balancing the fundamental tradeoff between storage efficiency and service latency. We show that the formulated problem is a submodular maximization problem with submodular constraints, for which no polynomial-time approximation algorithm exists. To tackle this challenge, we study an important special case, where a small fixed number of parameter blocks are shared across models, which often holds in practice. In such a case, a polynomial-time algorithm with a $\left(1-ε\right)/2$-approximation guarantee is developed. Subsequently, we address the original problem for the general case by developing a greedy algorithm. Simulation results demonstrate that the proposed TrimCaching framework significantly improves the cache hit ratio compared with state-of-the-art content caching without exploiting shared parameters in AI models.

With the increasing demand for artificial intelligence generated content (AIGC) services across diverse applications, AIGC service migration is essential to ensuring continuous service for mobile users in edge intelligence networks. However, AIGC service migration can lead to decreased inference accuracy due to the discarding of contextual memory. Furthermore, migrating large-scale AIGC models incurs high migration costs and latency. In this paper, we propose a context-aware AIGC service migration scheme to address the trade-off among inference accuracy, latency, and migration cost. Specifically, we focus on migrating historical AIGC context rather than large-scale AIGC models to achieve cost-efficient service provisioning. To improve service migration performance, we propose a Value of Context (VoC) metric to quantify the relevance and freshness of historical AIGC context. Based on the VoC, we formulate an optimization problem to jointly optimize inference accuracy, latency, and migration cost. To solve this problem, we develop a TransFormer-based Soft actor-critic algorithm for Context-aware AIGC service Migration (TFSCM) that leverages long-term dependencies in historical decisions for optimizing the migration process. Extensive experiments on real-world datasets demonstrate that the proposed TFSCM algorithm significantly enhances system performance compared to baseline solutions.

Split inference divides a global deep neural network (DNN) into several sub-models and assigns them to different computing nodes, thereby leveraging distributed computational resources at network edge for executing complex artificial intelligence (AI) algorithms. As an emerging technique,over-the-air computation(AirComp) turns a multi-access channel into a processor for distributed computing. Specifically, by exploiting the waveform superposition of the multi-access channel, a receiver directly aggregates signals transmitted by different transmitters to obtain their average (or any nomographic function). In order to accelerate inference speed and reduce communication and computation loads, we propose a novelin-network AIframework that realizes inference through communications among devices. This distinctive feature of the framework is to generalize multiple-input-multiple output (MIMO) AirComp to realize over-the-air matrix-vector multiplications (MVM), the most computation intensive operations of a DNN. As a result, the participating devices act like neurons in the DNN but they are now linked through computation capable wireless channels, which gives the name ofOver-the-Air Neural Link(AirNeuralink). The proposed AirNeuralink techniques are associated with different system topologies including relay, star, and distributed topologies. Their design involves jointly optimizing the precoding and post-equalization to minimize the MVM errors of the estimated feature values at the receiver set with respect to their ground truth under the transmit power constraint for each transmitter. The optimal post-equalization is derived in the form of Wiener filter but with an aggregation matrix (weight matrix). Utilizing the property of Schur-concave function and matrix inequality, the optimal structure of precoding in each topology is designed to enable spatial-channel power allocation by efficiently solving a convex problem with scalar variables. Besides, the asymptotic MVM errors are analyzed to show that the errors can be sufficiently small if the rank of model-weight matrix is no larger than that of channel matrix. Simulations demonstrate the superior performance of the proposed framework in transmission latency reduction while maintaining the inference accuracy as compared with traditional digital transmission.

Harnessing multi-level electron transitions, Rydberg Atomic REceivers (RAREs) can detect wireless signals across a wide range of frequency bands, from Megahertz to Terahertz. This capability enables multi-band wireless communications and sensing (CommunSense). Existing research on multi-band RAREs primarily focuses on experimental demonstrations, lacking a tractable model to mathematically characterize their mechanisms. This issue leaves the multi-band RARE as a black box and poses challenges in its practical applications. To fill in this gap, this paper investigates the underlying mechanism of multi-band RAREs and explores their optimal performance. For the first time, an analytical transfer function with a closed-form expression for multi-band RAREs is derived by solving the quantum response of Rydberg atoms. It shows that a multi-band RARE simultaneously serves as amulti-band atomic mixerfor down-converting multi-band signals and amulti-band atomic amplifierthat reflects its sensitivity to each band. Further analysis of the atomic amplifier unveils that the intrinsic gain at each frequency band can be decoupled into aglobal gainterm and aRabi attentionterm. The former determines the overall sensitivity of a RARE to all frequency bands of wireless signals. The latter influences the allocation of the overall sensitivity to each frequency band, representing a unique attention mechanism of multi-band RAREs. The optimal design of the global gain is provided to maximize the overall sensitivity of multi-band RAREs. Subsequently, the optimal Rabi attentions are also derived to maximize the practical multi-band CommunSense performance. An experiment platform is built to validate the effectiveness of the derived transfer function, and numerical results confirm the superiority of multi-band RAREs.

To facilitate the development of industrial Internet of Things applications, thewireless networked control system(WNCS) is envisioned to support real-time control and communication interactions performed in finite-time manner. A WNCS comprising multiple wirelessly interconnectedsub-systems(SSs) is considered, wherein the sensed state information in each SS is transmitted to the controller via wireless links, thereby enabling timely decision-making processes. Following multiple operation periods of state sensing and transmission, thesystem identification(SI) is performed, leading to the formulation of optimal control policy. To improve the energy efficiency while guaranteeing the SI performance requirement within the allowed decision-making time, the communication and control co-design for WNCS is investigated, where the transmit power, transmission interval length, number of operation periods, coding block-length, and required transmission reliability are jointly optimized. Our investigation demonstrates the interrelationships among effective capacity, energy consumption, and communication parameters. Furthermore, it is found that the optimal communication parameters, such as transmit power and transmission interval length, should be determined by both communication and control requirements. Consequently, it is found that minimizing energy consumption is equivalent to minimize the number of operation periods while guaranteeing the SI performance with defined confidence, which can be effectively addressed by leveraging the non-decreasing property of controllability Gramian. Moreover, the co-design framework is extended to accommodate the scenarios involving link interruptions and overlapping time slots. Simulation results validate the necessity and effectiveness of exploring optimal system operational configurations from the perspective of the proposed co-design. It is also observed that although a 44.2% surge in energy consumption is associated with the proposed relay scheme in the link interruption case, the proposed time scheduling scheme brings a 23.4% reduction in the extra energy expenditure (from 44.2% to 20.8%).

Semantic communication (SemCom) has emerged as a promising paradigm for achieving unprecedented communication efficiency in sixth-generation (6G) networks by leveraging artificial intelligence (AI) to extract and transmit the underlying meanings of source data. However, deploying SemCom over digital systems presents new challenges, particularly in ensuring robustness against transmission errors that may distort semantically critical content. To address this issue, this paper proposes a novel framework, termed generative feature imputing, which comprises three key techniques. First, we introduce a spatial-error-concentration packetization strategy that spatially concentrates feature distortions by encoding feature elements based on their channel mappings—a property crucial for both the effectiveness and reduced complexity of the subsequent techniques. Second, building on this strategy, we propose a generative feature imputing method that utilizes a diffusion model to efficiently reconstruct missing features caused by packet losses. Finally, we develop a semantic-aware power allocation scheme that enables unequal error protection by allocating transmission power according to the semantic importance of each packet. Experimental results demonstrate that the proposed framework outperforms conventional approaches, such as Deep Joint Source-Channel Coding (DJSCC) and JPEG2000, under block fading conditions, achieving higher semantic accuracy and lower Learned Perceptual Image Patch Similarity (LPIPS) scores.

Thesixth-generation(6G) mobile network is envisioned to incorporate sensing and edgeartificial intelligence(AI) as two key functions. Their natural convergence leads to the emergence ofIntegrated Sensing and Edge AI(ISEA), a novel paradigm enabling real-time acquisition and understanding of sensory information at the network edge. However, ISEA faces a communication bottleneck due to the large number of sensors and the high dimensionality of sensory features. Traditional approaches to communication-efficient ISEA lack awareness ofsemantic relevance, i.e., the level of relevance between sensor observations and the downstream task. To fill this gap, this paper presents a novel framework for semantic-relevance-aware sensor selection to achieve optimalend-to-end(E2E) task performance under heterogeneous sensor relevance and channel states. E2E sensing accuracy analysis is provided to characterize the sensing task performance in terms of selected sensors’ relevance scores and channel states. The analysis reveals that the contribution of each selected sensor to the accuracy is quantized by its expected classification margin, which is an increasing function of its relevance score. Building on the results, the sensor-selection problem for accuracy maximization is formulated as a 0-1 fractional programming problem, which is in general NP-hard. To solve this challenging problem, we exploit a problem-specific property of the objective function to develop its tight approximation. This allows to transform the original problem into a series of solvable sub-problems. The optimal solution of each sub-problem is proved to have a priority-based structure, wherein sensors are ranked according to a priority indicator that combines relevance scores and channel states, and the top-ranked sensors are selected. Based on the derived sensor priority, low-complexity algorithms are developed to determine the optimal numbers of selected sensors and features. Experimental results on both synthetic and real datasets show substantial accuracy gain achieved by the proposed selection scheme compared to existing benchmarks.

This paper investigates the delay minimization problem for multi-task federated learning (MTFL) systems at the network edge. We develop a novel MTFL framework, based on which the divergence bounds are derived for both task-related and task-unrelated scenarios, systematically quantifying the effects of user importance, user participation, and inter-task correlations on convergence behavior. Building upon these insights, a long-term joint optimization problem is formulated to minimize the overall training delay under the long-term divergence bounds and energy constraints. To address the coupling in multi-slot user scheduling, the optimization problem is decomposed into a single-slot joint resource allocation and task scheduling subproblem and a cross-slot user scheduling subproblem. The former is solved using block coordinate descent (BCD) combined with Johnson’s rule, while the latter is modeled as a constrained Markov decision process (CMDP) and addressed via a dueling double deep Q-network (D3QN) with cost shaping and prioritized experience replay. Numerical results verify the effectiveness of the proposed framework and convergence analysis, demonstrating its significant improvements over baseline schemes in terms of convergence and delay reduction.

A distinctive function of sixth-generation (6G) networks is the integration of distributed sensing and edge artificial intelligence (AI) to enable intelligent perception of the physical world. This resultant platform, termed integrated sensing and edge AI (ISEA), is envisioned to enable a broad spectrum of Internet-of-Things (IoT) applications, including remote surgery, autonomous driving, and holographic telepresence. Recently, the communication bottleneck confronting the implementation of an ISEA system is overcome by the development of over-the-air computing (AirComp) techniques, which facilitate simultaneous access through over-the-air data feature fusion. Despite its advantages, AirComp with uncoded transmission remains vulnerable to interference. To tackle this challenge, we propose AirBreath sensing, a spectrum-efficient framework that cascades feature compression and spread spectrum to mitigate interference without bandwidth expansion. This work reveals a fundamental tradeoff between these two operations under a fixed bandwidth constraint: increasing the compression ratio may reduce sensing accuracy but allows for more aggressive interference suppression via spread spectrum, and vice versa. This tradeoff is regulated by a key variable called breathing depth, defined as the feature subspace dimension that matches the processing gain in spread spectrum. To optimally control the breathing depth, we mathematically characterize and optimize this aforementioned tradeoff by designing a tractable surrogate for sensing accuracy, measured by classification discriminant gain (DG). Experimental results on real datasets demonstrate that AirBreath sensing effectively mitigates interference in ISEA systems, and the proposed control algorithm achieves near-optimal performance as benchmarked with a brute-force search.

There is a broad consensus that artificial intelligence (AI) will be a defining component of the sixth-generation (6G) networks. As a specific instance, AI-empowered sensing will gather and process environmental perception data at the network edge, giving rise to integrated sensing and edge AI (ISEA). Many applications, such as autonomous driving and industrial manufacturing, are latency-sensitive and require end-to-end (E2E) performance guarantees under stringent deadlines. However, the 5G-style ultra-reliable and low-latency communication (URLLC) techniques designed with communication reliability and agnostic to the data may fall short in achieving the optimal E2E performance of perceptive wireless systems. In this work, we introduce an ultra-low-latency (ultra-LoLa) inference framework for perceptive networks that facilitates the analysis of the E2E sensing accuracy in distributed sensing by jointly considering communication reliability and inference accuracy. By characterizing the tradeoff between packet length and the number of sensing observations, we derive an efficient optimization procedure that closely approximates the optimal tradeoff. We validate the accuracy of the proposed method through experimental results, and show that the proposed ultra-Lola inference framework outperforms conventional reliability-oriented protocols with respect to sensing performance under a latency constraint.

Federated Dropout is an efficient technique to overcome both communication and computation bottlenecks for deploying federated learning at the network edge. In each training round, an edge device only needs to update and transmit a sub-model, which is generated by the typical method of dropout in deep learning, and thus effectively reduces the per-round latency. \textcolor{blue}{However, the theoretical convergence analysis for Federated Dropout is still lacking in the literature, particularly regarding the quantitative influence of dropout rate on convergence}. To address this issue, by using the Taylor expansion method, we mathematically show that the gradient variance increases with a scaling factor of $γ/(1-γ)$, with $γ\in [0, θ)$ denoting the dropout rate and $θ$ being the maximum dropout rate ensuring the loss function reduction. Based on the above approximation, we provide the convergence analysis for Federated Dropout. Specifically, it is shown that a larger dropout rate of each device leads to a slower convergence rate. This provides a theoretical foundation for reducing the convergence latency by making a tradeoff between the per-round latency and the overall rounds till convergence. Moreover, a low-complexity algorithm is proposed to jointly optimize the dropout rate and the bandwidth allocation for minimizing the loss function in all rounds under a given per-round latency and limited network resources. Finally, numerical results are provided to verify the effectiveness of the proposed algorithm.

Federated learning (FL) has emerged as a widely adopted paradigm for enabling edge learning with distributed data while ensuring data privacy. However, the traditional FL with deep neural networks trained via backpropagation can hardly meet the low-latency learning requirements in the sixth generation (6G) mobile networks. This challenge mainly arises from the high-dimensional model parameters to be transmitted and the numerous rounds of communication required for convergence due to the inherent randomness of the training process. To address this issue, we adopt the state-of-the-art principle of maximal coding rate reduction to learn linear discriminative features and extend the resultant white-box neural network into FL, yielding the novel framework of Low-Latency Federated Learning (LoLaFL) via forward-only propagation. LoLaFL enables layer-wise transmissions and aggregation with significantly fewer communication rounds, thereby considerably reducing latency. Additionally, we propose twononlinearaggregation schemes for LoLaFL. The first scheme is based on the proof that the optimal NN parameter aggregation in LoLaFL should be harmonic-mean-like. The second scheme further exploits the low-rank structures of the features and transmits the low-rank-approximated covariance matrices of features to achieve additional latency reduction. Theoretic analysis and experiments are conducted to evaluate the performance of LoLaFL. In comparison with traditional FL, the two nonlinear aggregation schemes for LoLaFL can achieve reductions in latency of over 87% and 97%, respectively, while maintaining comparable accuracies.

In this paper, we study efficientmulti-beamtraining design fornear-fieldcommunications to reduce the beam training overhead of conventional single-beam training methods. In particular, the array-division-based multi-beam training method, which is widely used in far-field communications, cannot be directly applied in the near-field scenario, since different sub-arrays may observe different user angles and there exist coverage holes in the angular domain. To address these issues, we first devise a new near-field multi-beam codebook by sparsely activating a portion of antennas to form an effectivesparse linear array(SLA), hence generating multiple beams simultaneously by exploiting the near-fieldgrating lobes. Next, atwo-stagenear-field beam training method is proposed. In the first stage, several candidate user locations are identified based on multi-beam sweeping over time, followed by the second stage to determine the true user location with a small number of pilots for single-beam sweeping. Finally, numerical results show that our proposed multi-beam training method significantly reduces the beam training overhead as compared to conventional single-beam training methods, while achieving comparable rate performance in data transmissions.

Rydberg Atomic REceivers (RAREs) have shown compelling advantages in precise measurement of radio-frequency signals, empowering quantum wireless sensing. Existing RARE-based sensing systems primarily rely on the heterodyne-sensing technique, which introduces an extra reference source to serve as the atomic mixer. However, this approach entails a bulky transceiver architecture, requiring additional reference sources and transmitter-receiver signal decoupling. To address this problem, we propose a novel concept called selfheterodyne sensing. It utilizes the self-interference generated by the transmitted sensing signal as the reference signal, thus greatly simplifying the transceiver architecture. We derive the transmission model of self-heterodyne sensing and reveal that a self-heterodyne RARE functions as an atomic autocorrelator, where the received signal represents the autocorrelation of the transmitted signal at different delays. This characteristic translates the range of a sensing target into the frequency of the received signal. Inspired by this finding, a two-stage algorithm is devised to estimate the target range via frequency estimation and Newton refinement. Numerical results validate the superiority of the proposed quantum self-heterodyne sensing method.

One mission of sixth-generation (6G) networks is to deploy large-scale artificial intelligence (AI) models at the network edge to enable intelligent services for edge devices. The resultant platform, known as edge inference, will support a wide range of Internet-of-Things applications, such as autonomous driving, industrial automation, and augmented reality. Given the mission-critical and time-sensitive nature of these tasks, existing studies, which primarily focus on channel outage probability, may fail to ensure E2E inference accuracy and latency. To address this limitation, we propose a theoretical framework that introduces the inference outage (InfOut) probability, quantifying the likelihood that E2E inference accuracy falls below a target threshold. Under latency constraints, this framework reveals a fundamental tradeoff between communication overhead and inference reliability. To optimize this tradeoff, we derive tractable surrogate functions for InfOut probability based on a Gaussian approximation of the receive discriminant gain. Experiments demonstrate the superiority of the proposed design over conventional communication-centric approaches.

Leveraging the strong atom-light interaction, Rydberg atomic receivers (RAREs) can measure radio waves with extreme sensitivity. Existing research primarily focuses on improving the signal detection capability of RAREs, while traditional signal processing methods at the transmitter side have remained unchanged, which leaves a large gap from the maximum channel capacity. To address this issue, we exploit the transmitter precoding in atomic multiple-input-multiple-output systems to achieve the channel capacity. To begin with, a strong-reference approximation is proposed to linearize the nonlinear magnitude-detection model of atomic receivers, allowing us to express the channel capacity analytically. Then, a new digital precoding technique, termed In-phase-and-Quadrature (IQ) aware precoding is presented, which features independent processing of I/Q data streams using four real-valued matrices. The design is shown to be capacity-achieving for the atomic MIMO system. For the case of large-scale MIMO, we extend the proposed design into the popular hybrid precoding architecture, which cascades a classical high-dimensional analog precoder with a low-dimensional version of the proposed IQ-aware digital precoder. By alternatively optimizing the digital and analog parts, the hybrid design is able to approach the performance of the optimal IQ-aware fully digital precoding. Simulation results validate the superiority of proposed IQ-aware precoding methods over existing techniques in atomic MIMO communication systems.

Artificial intelligence (AI) is expected to be a defining component in sixth-generation (6G) wireless networks. One specific use is AI-empowered sensing, where sensor data will be processed at the network edge, giving rise to integrated sensing and edge AI (ISEA). Many sensing applications, such as autonomous driving and industrial manufacturing, are latencysensitive and require end-to-end (E2E) performance guarantees under stringent deadlines. However, data-agnostic ultrareliable and low-latency communication (URLLC) techniques designed for 5G fall short in achieving the optimal E2E sensing performance. In this work, we introduce an ultra-low-latency (ultra-LoLa) inference framework for perceptive networks that facilitates the analysis of E2E sensing accuracy in distributed sensing by jointly considering communication reliability and inference accuracy. By characterizing the tradeoff between packet length and the number of sensing observations, we derive an efficient optimization procedure that closely approximates the optimal balance. The experimental results show that the proposed approach outperforms conventional reliability-oriented protocols with respect to sensing performance under a latency constraint.

Large Language Models (LLMs) have demonstrated remarkable capabilities in various tasks, leading to their increasing deployment in wireless networks for a wide variety of user services. However, the growing longer prompt setting highlights the crucial issue of computational resource demands and huge communication load. To address this challenge, we propose Joint Power and Prompt Optimization (JPPO), a framework that combines Small Language Model (SLM)-based prompt compression with wireless power allocation optimization. By deploying SLM at user devices for prompt compression and employing Deep Reinforcement Learning for joint optimization of compression ratio and transmission power, JPPO effectively balances service quality with resource efficiency. Experimental results demonstrate that our framework achieves high service fidelity and low bit error rates while optimizing power usage in wireless LLM services. The system reduces response time by about 17 %, with the improvement varying based on the length of the original prompt.

Superimposed pilot (SIP) schemes face significant challenges in effectively superimposing and separating pilot and data signals, especially in multiuser mobility scenarios with rapidly varying channels. To address these challenges, we propose a novel channel-aware learning framework for SIP schemes, termed CaSIP, that jointly optimizes pilot-data power (PDP) allocation and a receiver network for pilot-data interference (PDI) elimination, by leveraging channel path gain information, a form of large-scale channel state information (CSI). The proposed framework identifies user-specific, resource elementwise PDP factors and develops a deep neural network-based SIP receiver comprising explicit channel estimation and data detection components. To properly leverage path gain data, we devise an embedding generator that projects it into embeddings, which are then fused with intermediate feature maps of the channel estimation network. Simulation results demonstrate that CaSIP efficiently outperforms traditional pilot schemes and state-of-the-art SIP schemes in terms of sum throughput and channel estimation accuracy, particularly under high-mobility and low signal-to-noise ratio (SNR) conditions.

In space-air-ground-sea networks, the conventional data processing designs separately considering sensing, communication and computation processes lead to severe wastes of radio, energy, and computation resources. To overcome this drawback, an integrated sensing-communication-computation design is pro-posed in this paper, which aims at realizing energy efficient data processing by jointly determining the data offloading ratio together with the sensing and offloading rates according to the processor profiles of mobile devices and servers. It is proved that the data offloading ratio is determined by the server's processor profile, while the string-pulling algorithms are designed to obtain the optimal sensing and offloading rates. Simulations are conducted to verify the effectiveness of the proposed design.

Driven by the rapid development of industrial Internet of Things applications, the wireless networked control system (WNCS) is expected to support real-time control-communication interaction performed in finite-time, which is task-oriented. A WNCS composed of multiple wirelessly inter-connected subsystems (SSs) is considered in this paper. The sensed state information in each SS is transmitted to the controller via wireless links for decision-and-control tasks. After multiple operation periods of state sensing and trans-mission, the system identification (SI) is executed and the optimal control (OC) policy is made. The SI requirement for OC is analyzed via system-level synthesis (SLS) based on robust control theory. A communication and control co-design is investigated, aiming to improve the energy efficiency while guaranteeing the SI performance requirement within the allowed decision-making time. The transmit powers at each sensor and controller, transmission interval length as well as the number of operation periods are jointly optimized. Simulations are conducted to validate the performance of the proposed co-design.

The advancement of Rydberg atoms in quantum information technology is driving a paradigm shift from classicalradio-frequency(RF) receivers to Rydberg atomic receivers. Capitalizing on the extreme sensitivity of Rydberg atoms to external electromagnetic fields, Rydberg atomic receivers are capable of realizing more precise radio-wave measurements than RF receivers to support high-performance wireless communication and sensing. Although the atomic receiver is developing rapidly in quantum-physics domain, its integration with wireless communications is at a nascent stage. In particular, systematic methods to enhance communication performance through this integration are yet to be discovered. Motivated by this observation, we propose in this paper to incorporate Rydberg atomic receivers intomultiple-input-multiple-output(MIMO) communication, a prominent 5G technology, as the first attempt on implementing atomic MIMO receivers. To begin with, we provide a comprehensive introduction on the principles of Rydberg atomic receivers and build on them to design the atomic MIMO receivers. Our findings reveal that signal detection of atomic MIMO receivers corresponds to a non-linear biasedphase retrieval(PR) problem, as opposed to the linear Gaussian model adopted in classical MIMO systems. Then, to recover signals from this non-linear model, we modify the Gerchberg-Saxton (GS) algorithm, a typical PR solver, into a biased GS algorithm to solve the biased PR problem. Moreover, we propose a novel Expectation-Maximization GS (EM-GS) algorithm to cope with the unique Rician distribution of the biased PR model. Our EM-GS algorithm introduces a high-pass filter constructed by the ratio of Bessel functions into the iteration procedure of GS, thereby improving the detection accuracy without sacrificing the computational efficiency. Finally, the effectiveness of the devised algorithms and the feasibility of atomic MIMO receivers are demonstrated by theoretical analysis and numerical simulation.

Semantic communications (SemCom) have emerged as a new paradigm for supporting sixth-generation applications, where semantic features of data are transmitted using artificial intelligence algorithms to attain high communication efficiencies. Most existing SemCom techniques utilize deep neural networks (DNNs) to implement analog source-channel mappings, which are incompatible with existing digital communication architectures. To address this issue, this paper proposes a novel framework of digital deep joint source-channel coding (D2-JSCC) targeting image transmission in SemCom. The framework features digital source and channel codings that are jointly optimized to reduce the end-to-end (E2E) distortion. First, deep source coding with an adaptive prior model is designed to encode semantic features according to their distributions. Second, channel coding is employed to protect encoded features against channel distortion. To facilitate their joint design, the E2E distortion is characterized as a function of the source and channel rates via the analysis of the Bayesian model and Lipschitz assumption on the DNNs. Then to minimize the E2E distortion, a two-step algorithm is proposed to control the source-channel rates for a given channel signal-to-noise ratio. Simulation results reveal that the proposed framework outperforms classic deep JSCC and mitigates the cliff and leveling-off effects, which commonly exist for separation-based approaches.

Task-oriented integrated sensing, communication, and computation (ISCC) is a key technology for achieving low-latency edge inference and enabling efficient implementation of artificial intelligence (AI) in industrial cyber-physical systems (ICPS). However, the constrained energy supply at edge devices has emerged as a critical bottleneck. In this paper, we propose a novel energy-efficient ISCC framework for AI inference at resource-constrained edge devices, where adjustable split inference, model pruning, and feature quantization are jointly designed to adapt to diverse task requirements. A joint resource allocation design problem for the proposed ISCC framework is formulated to minimize the energy consumption under stringent inference accuracy and latency constraints. To address the challenge of characterizing inference accuracy, we derive an explicit approximation for it by analyzing the impact of sensing, communication, and computation processes on the inference performance. Building upon the analytical results, we propose an iterative algorithm employing alternating optimization to solve the resource allocation problem. In each subproblem, the optimal solutions are available by respectively applying a golden section search method and checking the Karush-Kuhn-Tucker (KKT) conditions, thereby ensuring the convergence to a local optimum of the original problem. Numerical results demonstrate the effectiveness of the proposed ISCC design, showing a significant reduction in energy consumption of up to 40% compared to existing methods, particularly in low-latency scenarios.

The unpredictability of network environments, limited edge resources, and the high complexity of collaborative policies are significantly hindering the development of the Industrial Internet of Things (IIoT). These challenges are particularly pronounced in healthcare, where high-priority, delay-sensitive medical tasks and large-scale personalized services face substantial obstacles. To address these challenges, this paper proposes the Self-Attention Enhanced QMIX with Multi-Pass Multi-Task Execution (SAE-MT-QMIX) algorithm, aimed at optimizing communication and computing resource allocation as well as task offloading strategies. By leveraging Digital Twin (DT) support, the algorithm achieves collaborative optimization of communication, computing, and control within the Internet of Medical Things (IoMT), significantly enhancing the quality of service for massive personalized applications. The algorithm adopts a distributed execution and centralized training framework: the distributed execution component uses the Multi-Pass Multi-Task Deep Q-Network (MPMT-DQN) algorithm to handle the complexity of parameterized action spaces in multi-task scenarios, while the centralized training component employs the Self-Attention Enhanced QMIX (SAE-QMIX) algorithm to dynamically optimize credit assignment across multiple users. Simulation results demonstrate that SAE-MT-QMIX significantly reduces delay and energy consumption compared to baseline methods. It ensures effective optimization of communication, computing, and control in dynamic IoMT, efficiently addressing diverse demands and tasks while enhancing service quality and system adaptability.

Semantic communication (SemCom) is emerging as a key technology for future sixth-generation (6G) systems. Unlike traditional bit-level communication (BitCom), SemCom directly optimizes performance at the semantic level, leading to superior communication efficiency. Nevertheless, the task-oriented nature of SemCom renders it challenging to completely replace BitCom. Consequently, it is desired to consider a semantic-bit coexisting communication system, where a base station (BS) serves SemCom users (sem-users) and BitCom users (bit-users) simultaneously. Such a system faces severe and heterogeneous inter-user interference. In this context, this paper provides a new semantic-bit coexisting communication framework and proposes a spatial beamforming scheme to accommodate both types of users. Specifically, we consider maximizing the semantic rate for semantic users while ensuring the quality-of-service (QoS) requirements for bit-users. Due to the intractability of obtaining the exact closed-form expression of the semantic rate, a data driven method is first applied to attain an approximated expression via data fitting. With the resulting complex transcendental function, majorization minimization (MM) is adopted to convert the original formulated problem into a multiple-ratio problem, which allows fractional programming (FP) to be used to further transform the problem into an inhomogeneous quadratically constrained quadratic programs (QCQP) problem. Solving the problem leads to a semi-closed form solution with undetermined Lagrangian factors that can be updated by a fixed point algorithm. This method is referred to as the MM-FP algorithm. Additionally, inspired by the semi-closed form solution, we also propose a low-complexity version of the MM-FP algorithm, called the low-complexity MM-FP (LP-MM-FP), which alleviates the need for iterative optimization of beamforming vectors. Extensive simulation results demonstrate that the proposed MM-FP algorithm outperforms conventional beamforming algorithms such as zero-forcing (ZF), maximum ratio transmission (MRT), and weighted minimum mean-square error (WMMSE). Moreover, the proposed LP-MMFP algorithm achieves comparable performance with the WMMSE algorithm but with lower computational complexity.

Efficiently retrieving relevant data from massive Internet of Things (IoT) networks is essential for downstream tasks such as machine learning. This paper addresses this challenge by proposing a novel data sourcing protocol that combines semantic queries and random access. The key idea is that the destination node broadcasts a semantic query describing the desired information, and the sensors that have data matching the query then respond by transmitting their observations over a shared random access channel, for example to perform joint inference at the destination. However, this approach introduces a tradeoff between maximizing the retrieval of relevant data and minimizing data loss due to collisions on the shared channel. We analyze this tradeoff under a tractable Gaussian mixture model and optimize the semantic matching threshold to maximize the number of relevant retrieved observations. The protocol and the analysis are then extended to handle a more realistic neural network-based model for complex sensing. Under both models, experimental results in classification scenarios demonstrate that the proposed protocol is superior to traditional random access, and achieves a near-optimal balance between inference accuracy and the probability of missed detection, highlighting its effectiveness for semantic query-based data sourcing in massive IoT networks.

The vision of 6G mobile networks aims to connect intelligent machines to humans to provide the latter with cooperation, care, and assistance. The mainstream approach for human-to-machine (H2M) semantic communication is to map words into (word) embedding vectors which are clustered according to their semantic similarity to facilitate machines’ interpretation of human languages. The computation-intensive tasks of text-to-embedding mapping are usually delegated to an edge server that senses human commands, maps them into embedding vectors, and then transmits the vectors to a machine over a wireless link. In this work, we propose a quantization framework customized for embedding vectors, called semantic-topology preserving VQ (SemTop-VQ), to overcome the communication bottleneck due to the vectors’ high dimensionality. While traditional VQ focuses on minimizing the distortion of individual vectors, SemTop-VQ aims to minimize the distortion of the topology of embedding matrix, referring to the vectors’ relative positions that represent semantics. To this end, we adopt a topology-distortion metric, termed pointwise-inner-product (PIP) loss, a hierarchical VQ architecture targeting high-dimensional VQ. In this architecture, an embedding vector is decomposed into blocks; the norm and shape (normalized vector) are quantized separately using a scalar and a Grassmannian quantizers, respectively. The main feature of SemTop-VQ lies in deriving from the PIP loss a set of so-called semantic-importance indicators, which reflect the level of influences of individual blocks’ quantization errors on the topology distortion. Then the indicators are applied to optimize quantization-bit allocation for decomposed vector blocks under the criterion of PIP-loss minimization. In practice, the usage probabilities of embedding vectors for a specific machine task are highly skewed and the task is time-varying. We exploit this fact to further develop SemTop-VQ to feature task adaptation that can attain a higher communication efficiency. The task-adaptive VQ is realized via the use of a frequently used (quantization) codebook that is much smaller in size than the original codebook and continuously updated via estimation of embedding-usage distribution. Our experiments using real embedding datasets, namely Word2Vec and Glove, demonstrate the effectiveness of SemTop-VQ as a goal-oriented technique for efficient H2M communications.

Thesixth-generation(6G) mobile networks will feature the widespread deployment ofartificial intelligence(AI) algorithms at the network edge, which provides a platform for supporting robotic edge intelligence systems. In such a system, a large-scaleknowledge graph(KG) is operated at an edge server as a “remote brain” to guide remote robots on environmental exploration or task execution. In this paper, we present a new air-interface framework targeting the said systems, called knowledge-based roboticsemantic communications(SemCom), which consists of a protocol and relevant transmission techniques. First, the proposed robotic SemCom protocol defines a sequence of system operations for executing a given robotic task. They include identification of all task-relevantknowledge paths(KPs) on the KG, semantic matching between KG and object classifier, and uploading of robot’s observations for objects recognition and feasible KP identification. Next, to supportultra-low-latency (observation) feature transmission(ULL-FT), we propose a novel transmission approach that exploits classifier’s robustness, which is measured byclassification margin, to compensate for a highbit error probability(BEP) resulting from ultra-low-latency transmission (e.g., short packet and/or no coding). By utilizing the tractableGaussian mixture(GM) model, we mathematically derive the relation between BEP and classification margin under constraints on classification accuracy and transmission latency. The result sheds light on system requirements to support ULL-FT. Furthermore, for the case where the classification margin is insufficient for coping with channel distortion, we enhance the ULL-FT approach by studying retransmission and multi-view classification for enlarging the margin and further quantifying corresponding requirements. Finally, experiments using deep neural networks as classifier models and real datasets are conducted to demonstrate the effectiveness of ULL-FT in communication latency reduction while providing a guarantee on accurate feasible KP identification.

Semantic communication is envisioned as a key technology for future wireless networks due to its high communication efficiency. However, research combining semantic communication and advanced multiple access techniques, such as rate splitting multiple access (RSMA), is still lacking. In this paper, the problem of joint communication and computation resource allocation for probabilistic semantic communication (PSCom) with RSMA is investigated. In the considered model, the base station (BS) needs to transmit a large amount of data to multiple users with 1-layer RSMA. Due to limited communication resources, the BS is required to utilize semantic communication techniques to compress the original data. In this paper, we utilize knowledge graphs to represent semantic information and employ probabilistic graphs, which are shared between the BS and users, to further compress the knowledge graphs. The BS can use the probabilistic graph to compress the data to be transmitted, while the user can recover the compressed semantic information using the same shared probabilistic graph. The additional computation power required for semantic information compression inevitably results in a reduction in transmission power due to the limited total power budget. Considering the effect of semantic compression ratio, the semantic rate expression for RSMA is first obtained. Then, based on the obtained rate expression, an optimization problem is formulated with the aim of maximizing the sum of semantic rates of all users under total power, semantic compression ratio, and rate allocation constraints. To tackle this problem, an iterative algorithm is proposed, where the semantic compression ratio subproblem is addressed using a greedy algorithm, and the rate allocation and transmit beamforming design subproblem is solved using a successive convex approximation method. Numerical results validate the effectiveness of the proposed scheme.

By deploying a large number of antennas with subhalf- wavelength spacing in a compact space, dense array systems (DASs) can fully unleash the multiplexing and diversity gains of limited apertures. To acquire these gains, accurate channel state information acquisition is necessary but challenging due to the large antenna numbers. To overcome this obstacle, this paper reveals that designing the observation matrix to exploit the high spatial correlation of DAS channels is crucial for realizing near-optimal Bayesian channel estimation. Specifically, we prove that the observation matrix design for channel estimation is equivalent to a time-domain duality of point-to-point multipleinput multiple-output precoding, except for the change in the total power constraint on the precoding matrix to the pilot-wise discrete power constraint on the observation matrix. Inspired by Bayesian regression, a novel ice-filling algorithm is proposed to design amplitude-and-phase controllable observation matrices, and a majorization-minimization algorithm is proposed to address the phase-only controllable case. Particularly, we prove that the ice-filling algorithm can be interpreted as a “quantized” water-filling algorithm, wherein the latter’s continuous power-allocation process is converted into the former’s discrete pilot-assignment process. To support the near-optimality of the proposed designs, we provide comprehensive analyses on the achievable mean square errors and their asymptotic expressions. Finally, numerical results confirm that our proposed designs achieve the near-optimal channel estimation performance and outperform existing approaches significantly.

6G mobile networks aim to realize ubiquitous intelligence at the network edge via distributed learning, sensing, and data analytics. Their common operation is to aggregate high-dimensional data, which causes a communication bottleneck that cannot be resolved using traditional orthogonal multi-access schemes. A promising solution, called over-the-air computation (AirComp), exploits channels’ waveform superposition property to enable simultaneous access, thereby overcoming the bottleneck. Nevertheless, its reliance on uncoded linear analog modulation exposes data to perturbation by noise and interference. Hence, the traditional analog AirComp falls short of meeting the high-reliability requirement for 6G. Overcoming the limitation of analog AirComp motivates this work, which focuses on developing a framework for digital AirComp. The proposed framework features digital modulation of each data value, integrated with the bit-slicing technique to allocate its bits to multiple symbols, thereby increasing the AirComp reliability. To optimally detect the aggregated digital symbols, we derive the optimal maximum a posteriori detector that is shown to outperform the traditional maximum likelihood detector. Furthermore, a comparative performance analysis of digital AirComp with respect to its analog counterpart with repetition coding is conducted to quantify the practical signal-to-noise ratio (SNR) regime favoring the proposed scheme. On the other hand, digital AirComp is enhanced by further development to feature awareness of heterogeneous bit importance levels and its exploitation in channel adaptation. Lastly, simulation results demonstrate the achivability of substantial reliability improvement of digital AirComp over its analog counterpart given the same channel uses.

The sixth-generation (6G) mobile networks feature two new usage scenarios – distributed sensing and edge artificial intelligence (AI). Their natural integration, termed integrated sensing and edge AI (ISEA), promises to create a platform that enables intelligent environment perception for wide-ranging applications. A basic operation in ISEA is for a fusion center to acquire and fuse features of spatial sensing data distributed at many edge devices (known as agents), which is confronted by a communication bottleneck due to multiple access over hostile wireless channels. To address this issue, we propose a novel framework, called Spatial Over-the-Air Fusion (Spatial AirFusion), which exploits radio waveform superposition to aggregate spatially sparse features over the air and thereby enables simultaneous access. The framework supports simultaneous aggregation over multiple voxels, which partition the 3D sensing region, and across multiple subcarriers. It exploits both spatial feature sparsity with channel diversity to pair voxel-level aggregation tasks and subcarriers to maximize the minimum receive signal-to-noise ratio among voxels. Optimally solving the resultant mixed-integer problem of Voxel-Carrier Pairing and Power Allocation (VoCa-PPA) is a focus of this work. The proposed approach hinges on derivations of optimal power allocation as a closed-form function of voxel-carrier pairing and a useful property of VoCa-PPA that allows dramatic solution space reduction. Both a low-complexity greedy algorithm and an optimal tree-search algorithm are then designed for VoCa-PPA. The latter is accelerated with a customised compact search tree, node pruning and agent ordering. Extensive simulations using real datasets demonstrate that Spatial AirFusion significantly reduces computation errors and improves sensing accuracy compared with conventional over-the-air computation without awareness of spatial sparsity.

With the prevalence of real-time intelligence applications, online edge learning (OEL) has gained increasing attentions due to the ability of rapidly accessing environmental data to improve artificial intelligence models by edge computing. However, the performance of OEL is intricately tied to the dynamic nature of incoming data in ever-changing environments, which does not conform to a stationary distribution. In this work, we develop a data importance-aware collection, communication, and computation integration framework to boost the training efficiency by leveraging the varying data usefulness under dynamic network resources. A model convergence metric (MCM) is firstly derived that quantifies the data importance in mini-batch gradient descent (MGD)-based online learning tasks. To expedite model learning at the edge, we optimize training batch configuration and fine-tune the acquisition of important data through coordinated scheduling, encompassing data sampling, transmission and computational resource allocation. To cope with the time discrepancy and complex coupling of decision variables, we design a two-timescale hierarchical reinforcement learning (TTHRL) algorithm decomposing the original problem into two-layer subproblems and separately optimize the subproblems in a mixed timescale pattern. Experiments show that the proposed data integration framework can effectively improve the online learning efficiency while stabilizing caching queues in the system.

The emergence of large-scale foundation models (FoMo’s) that can perform human-like intelligence motivates their deployment at the network edge for devices to access state-of-the-art artificial intelligence (AI). For better user experiences, the pre-trained FoMo’s need to be adapted to specialized downstream tasks through fine-tuning techniques. To transcend a single device’s memory and computation limitations, we advocate multi-device cooperation within the device-edge cooperative fine-tuning (DEFT) paradigm, where edge devices cooperate to simultaneously optimize different parts of fine-tuning parameters within a FoMo. The edge server is responsible for coordination and gradient aggregation. However, the parameter blocks reside at different depths within a FoMo architecture, leading to varied computation latency-and-memory cost due to gradient backpropagation-based calculations. The heterogeneous on-device computation and memory capacities and channel conditions necessitate an integrated communication-and-computation (C2) allocation of local computation loads and uplink communication resources to achieve low-latency (LoLa) DEFT. To this end, we consider the depth-ware DEFT block allocation problem. The involved optimal block-device matching is tackled by the proposed low-complexity Cutting-RecoUNting-CHecking (CRUNCH) algorithm, which is designed by exploiting the monotone-increasing property between block depth and computation latency-and-memory cost. Next, the joint bandwidth-and-block allocation (JBBA) makes the problem more sophisticated, i.e., mathematically NP-hard. We observe a splittable Lagrangian expression through the transformation and analysis of the original problem, where the variables indicating device involvement are introduced to decouple the block and bandwidth allocation. Then, the dual ascent method is employed to tackle the JBBA problem iteratively. Within each iteration, block allocation and bandwidth allocation are optimized concurrently. The optimal block allocation sub-problem is solved efficiently by applying the Hungarian method facilitated by the proposed CRUNCH algorithm. On the other hand, the bandwidth allocation sub-problem is solved in closed form, shedding light on favorable allocations to resource-limited devices. Through extensive experiments conducted on the GLUE benchmark, our results demonstrate significant latency reduction achievable by LoLa DEFT for fine-tuning a RoBERTa model.

Space-air-ground integrated networks (SAGINs) are emerging as a pivotal element in the evolution of future wireless networks. Despite their potential, the joint design of communication and computation within SAGINs remains a formidable challenge. In this paper, the problem of energy efficiency in SAGIN-enabled probabilistic semantic communication (PSCom) system is investigated. In the considered model, a satellite needs to transmit data to multiple ground terminals (GTs) via an unmanned aerial vehicle (UAV) acting as a relay. During transmission, the satellite and the UAV can use PSCom technique to compress the transmitting data, while the GTs can automatically recover the missing information. The PSCom is underpinned by shared probabilistic graphs that serve as a common knowledge base among the transceivers, allowing for resource-saving communication at the expense of increased computation resource. Through analysis, the computation overhead function in PSCom is a piecewise function with respect to the semantic compression ratio. Therefore, it is important to make a balance between communication and computation to achieve optimal energy efficiency. The joint communication and computation problem is formulated as an optimization problem aiming to minimize the total communication and computation energy consumption of the network under latency, power, computation capacity, bandwidth, semantic compression ratio, and UAV location constraints. To solve this non-convex non-smooth problem, we propose an iterative algorithm where the closed-form solutions for computation capacity allocation and UAV altitude are obtained at each iteration. Numerical results show the effectiveness of the proposed algorithm.

The advancement of Rydberg atoms is driving a paradigm shift from classical receivers to atomic receivers. Capitalizing on the extreme sensitivity of Rydberg atoms to external disturbance, atomic receivers can measure radio waves more precisely than classical receivers to support high-performance wireless communication. Although the atomic receiver is developing rapidly in the field of quantum physics, its integration with wireless communications is at a nascent stage. Particularly, systematic methods to enhance communication performance through this integration are largely uncharted. Motivated by this observation, we propose to incorporate atomic receivers into multiple-input multiple-output (MIMO) communications to implement atomic-MIMO receivers. We establish the framework of atomic-MIMO receivers by exploiting the principle of quantum sensing. Our model reveals that the signal detection of atomic-MIMO systems is intrinsically a nonlinear phase-retrieval problem, as opposed to the linear model in classical MIMO systems. To perform atomic-MIMO signal detection, we propose an Expectation-Maximization-Gerchberg-Saxton (EM-GS) algorithm based on the maximum likelihood (ML) criteria. Its novelty lies in treating the unobserved phase information as a latent variable and thereby decoupling the intricate ML problem into a sequence of tractable linear regression problems with analytical solutions. Experimental results validate the effectiveness of detecting atomic-MIMO signals using the EM-GS algorithm.

Federated dropout on the weight is an efficient technique to overcome both communication and computation bottlenecks for deploying federated learning at the network edge. However, the theoretical analysis for Federated Dropout is still lacking in the literature, due to the challenge arising from the gradient bias. To address this issue, by using the Taylor expansion method, we mathematically show that the gradient vector with dropout can be approximated as an unbiased estimation of that without dropout; while its gradient variance increases with a scaling factor of γ/(1 − γ), with γ ∈ [0,θ) denoting the dropout rate and θ being the maximum dropout rate ensuring the loss function reduction. Based on the above approximation, we provide the loss function analysis for Federated Dropout. Specifically, it is shown that a larger dropout rate of each device leads to a slower convergence rate. Finally, numerical results are provided to verify the effects of dropout rate on convergence in both underfitting and overfitting scenarios.

The sixth-generation (6G) mobile networks will feature the widespread deployment of artificial intelligence (AI) algorithms at the network edge, which provides a platform for edge intelligence. In this paper, we propose a new air-interface framework targeting the edge inference systems, called ultra-low-latency (observation) feature transmission (ULL-FT). It consists of a novel transmission approach that exploits classifier’s robustness, which is measured by classification margin, to compensate for a high bit error probability (BEP) resulting from ultra-low-latency transmission (e.g., short packet and/or no coding). By utilizing the tractable Gaussian mixture (GM) model, we mathematically derive the relation between BEP and classification margin under constraints on classification accuracy and transmission latency. The result sheds light on system requirements to support ULL-FT. Finally, experiments using deep neural networks (DNN) as classifier models and real datasets are conducted to demonstrate the effectiveness of ULL-FT in communication latency reduction while providing a guarantee on classification accuracy.

Next-generation mobile networks are expected to facilitate fast AI model downloading to end users. By caching models on edge servers, mobile networks can deliver models to end users with low latency, resulting in a paradigm called edge model caching. In this paper, we develop a novel model placement scheme, called parameter-sharing model caching (TrimCaching). TrimCaching exploits the key observation that a wide range of AI models, such as convolutional neural networks or large language models, can share a significant proportion of parameter blocks containing reusable knowledge, thereby improving storage efficiency. To this end, we formulate a parameter-sharing model placement problem to maximize the cache hit ratio in multi-edge wireless networks by balancing the fundamental tradeoff between storage efficiency and service latency. We show that the formulated problem is a submodular maximization problem with submodular constraints, for which no polynomial-time approximation algorithm exists. To overcome this challenge, we study an important special case, where a small fixed number of parameter blocks are shared across models, which often holds in practice. In such a case, a polynomial-time algorithm with (1 - E) /2-approximation guarantee is developed. Subsequently, we address the original problem for the general case by developing a greedy algorithm. Simulation results demonstrate that the proposed TrimCaching framework significantly improves the cache hit ratio compared with state-of-the-art content caching without exploiting shared parameters in AI models.

Estimating mutual correlations between random variables or data streams is essential for intelligent behavior and decision-making. As a fundamental quantity for measuring statistical relationships, mutual information has been extensively studied and utilized for its generality and equitability. However, existing methods often lack the efficiency needed for real-time applications, such as test-time optimization of a neural network, or the differentiability required for end-to-end learning, like histograms. We introduce a neural network called InfoNet, which directly outputs mutual information estimations of data streams by leveraging the attention mechanism and the computational efficiency of deep learning infrastructures. By maximizing a dual formulation of mutual information through large-scale simulated training, our approach circumvents time-consuming test-time optimization and offers generalization ability. We evaluate the effectiveness and generalization of our proposed mutual information estimation scheme on various families of distributions and applications. Our results demonstrate that InfoNet and its training process provide a graceful efficiency-accuracy trade-off and order-preserving properties. We will make the code and models available as a comprehensive toolbox to facilitate studies in different fields requiring real-time mutual information estimation.

Semantic communications (SemCom) have emerged as a new paradigm for supporting sixth-generation applications with high communication efficiencies. Most existing SemCom techniques utilize deep neural networks (DNNs) to implement analog source-channel mappings, which are incompatible with existing digital communication architectures. To address this issue, this paper proposes a novel framework of digital deep joint source-channel coding ($\mathrm{D}^{2}$-JSCC) targeting image transmission in SemCom. The framework features digital source and channel codings that are jointly optimized to reduce the end-to-end (E2E) distortion. First, deep source coding with an adaptive density model is designed to efficiently extract and encode semantic features according to their different distributions. Second, channel coding is employed to protect encoded features against channel distortion. To facilitate their joint design, the E2E distortion is characterized as a function of the source and channel rates. Then to minimize the E2E distortion, we propose an efficient two-step algorithm to find the optimal trade-off between the source and channel rates for a given channel signal-to-noise ratio (SNR). Via experiments on simulating the $\mathbf{D}^{2}$-JSCC with different channel codes and real datasets, the proposed framework is observed to outperform the classic deep JSCC and separation-based approaches.

Semantic communication (SemCom) has emerged as a promising technology to improve the spectrum efficiency of next-generation wireless networks, by extracting meaningful content from the data and transmitting relevant semantic information only. However, the existing research usually overlooks the limited computing and storage resources on the mobile devices, which may make it unaffordable to implement resource-demanding deep learning (DL)-based semantic encoders/decoders. Moreover, besides the end-to-end SemCom framework, cooperative SemCom has not been well studied in the existing works, which can further enhance the communication performance. To address these issues, we propose a new architecture in this article, called semantic relay (SemRelay), which acts as an edge server to provide DL-enabled SemCom (DeepSC) services for two categories of edge users, called semantic users (SemUsers) with rich computing resources and conventional users (ConUsers) with limited resources. Two new transmission protocols are proposed for enabling text transmissions from the base station to the SemUsers and ConUsers, respectively, via the SemRelay (edge server). Moreover, an optimization problem is formulated to jointly design the SemRelay transmit power allocation and system bandwidth allocation to maximize the weighted sum-rate of all the users. Although this problem is nonconvex and hence difficult to solve, we propose an efficient algorithm to obtain a high-quality suboptimal solution by applying the block coordinate descent and successive convex approximation techniques. Finally, the numerical results demonstrate the effectiveness of our proposed algorithm and the superior performance of the proposed SemRelay as compared to the traditional decode-and-forward relays, especially in the small bandwidth regime.

Federated learning (FL) enables wireless terminals to collaboratively learn a shared parameter model while keeping all the training data on devices per se. Parameter sharing consists of synchronous and asynchronous ways: the former transmits parameters as blocks or frames and waits until all transmissions finish, whereas the latter provides messages about the status of pending and failed parameter transmission requests. Whatever synchronous or asynchronous parameter sharing is applied, the learning model shall adapt to distinct network architectures as an improper learning model will deteriorate learning performance and, even worse, lead to model divergence for the asynchronous transmission in resource-limited large-scale Internet-of-Things (IoT) networks. This paper proposes a decentralized learning model and develops an asynchronous parameter-sharing algorithm for resource-limited distributed IoT networks. This decentralized learning model approaches a convex function as the number of nodes increases, and its learning process converges to a global stationary point with a higher probability than the centralized FL model. Moreover, by jointly accounting for the convergence bound of federated learning and the transmission delay of wireless communications, we develop a node scheduling and bandwidth allocation algorithm to minimize the transmission delay. Extensive simulation results corroborate the effectiveness of the distributed algorithm in terms of fast learning model convergence and low transmission delay.

To support emerging applications ranging from holographic communications to extended reality, next-generation mobile wireless communication systems require ultrafast and energy-efficient baseband processors. Traditional complementary metal-oxide-semiconductor (CMOS)-based baseband processors face two challenges in transistor scaling and the von Neumann bottleneck. To address these challenges, in-memory computing-based baseband processors using resistive random-access memory (RRAM) present an attractive solution. In this article, we propose and demonstrate RRAM-implemented in-memory baseband processing for the widely adopted multiple-input–multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) air interface. Its key feature is to execute the key operations, including discrete Fourier transform (DFT) and MIMO detection, using linear minimum mean square error (L-MMSE) and zero forcing (ZF), in one-step. In addition, RRAM-based channel estimation module is proposed and discussed. By prototyping and simulations, we demonstrate the feasibility of RRAM-based full-fledged communication system in hardware, and reveal it can outperform state-of-the-art baseband processors with a gain of$91.2\times $in latency and$671\times $in energy efficiency by large-scale simulations. Our results pave a potential pathway for RRAM-based in-memory computing to be implemented in the era of the sixth generation (6G) mobile communications.

Sensing and edge artificial intelligence (AI) are two key features of the sixth-generation (6G) mobile networks. Their natural integration, termed Integrated sensing and edge AI (ISEA), is envisioned to automate wide-ranging Internet-of-Ting (IoT) applications. To achieve a high sensing accuracy, features of multiple sensor views are uploaded to an edge server for aggregation and inference using a large-scale AI model. The view aggregation is realized efficiently using over-the-air computing (AirComp), which also aggregates channels to suppress channel noise. As ISEA is at its nascent stage, there still lacks an analytical framework for quantifying the fundamental performance gains from view-and-channel aggregation, which motivates this work. Our framework is based on a well-established distribution model of multi-view sensing data where the classic Gaussian-mixture model is modified by adding sub-spaces matrices to represent individual sensor observation perspectives. Based on the model and linear classification, we study the End-to-End sensing (inference) uncertainty, a popular measure of inference accuracy, of the said ISEA system by a novel, tractable approach involving designing a scaling-tight uncertainty surrogate function, global discriminant gain, distribution of receive Signal-to-Noise Ratio (SNR), and channel induced discriminant loss. As a result, we prove that the E2E sensing uncertainty diminishes at an exponential rate as the number of views/sensors grows, where the rate is proportional to global discriminant gain. Given AirComp and channel distortion, we further show that the exponential scaling remains but the rate is reduced by a linear factor representing the channel induced discriminant loss. Furthermore, in the case of many spatial degrees of freedom, we benchmark AirComp against equally fast, traditional analog orthogonal access. The comparative performance analysis reveals a sensing-accuracy crossing point between the schemes corresponding to equal receive array size and sensor number. This leads to the proposal of a scheme for adaptive access-mode switching to enhance ISEA performance. Last, the insights from our framework are validated by experiments using a convolutional neural network model and real-world dataset.

In the sixth-generation (6G) networks, massive low-power devices are expected to sense environment and deliver tremendous data. To enhance the radio resource efficiency, the integrated sensing and communication (ISAC) technique exploits the sensing and communication functionalities of signals, while the simultaneous wireless information and power transfer (SWIPT) techniques utilizes the same signals as the carriers for both information and power delivery. The further combination of ISAC and SWIPT leads to the advanced technology namely integrated sensing, communication, and power transfer (ISCPT). In this paper, a multi-user multiple-input multiple-output (MIMO) ISCPT system is considered, where a base station equipped with multiple antennas transmits messages to multiple information receivers (IRs), transfers power to multiple energy receivers (ERs), and senses a target simultaneously. The sensing target can be regarded as a point or an extended surface. When the locations of IRs and ERs are separated, the MIMO beamforming designs are optimized to improve the sensing performance while meeting the communication and power transfer requirements. The resultant non-convex optimization problems are solved based on a series of techniques including Schur complement transformation and rank reduction. Moreover, when the IRs and ERs are co-located, the power splitting factors are jointly optimized together with the beamformers to balance the performance of communication and power transfer. To better understand the performance of ISCPT, the target positioning problem is further investigated. Simulations are conducted to verify the effectiveness of our proposed designs, which also reveal a performance tradeoff among sensing, communication, and power transfer.

Artificial intelligence (AI) technologies have emerged as pivotal enablers across a multitude of industries, including consumer electronics, healthcare, and manufacturing, largely due to their significant resurgence over the past decade. The transformative power of AI is primarily derived from the utilization of deep neural networks (DNNs), which require extensive data for training and substantial computational resources for processing. Consequently, DNN models are typically trained and deployed on resource-rich cloud servers. However, due to potential latency issues associated with cloud communications, deep learning (DL) workflows (e.g., DNN training and inference) are increasingly being transitioned to wireless edge networks in proximity to end-user devices (EUDs). This shift is designed to support latency-sensitive applications and has given rise to a new paradigm of edge AI, which will play a critical role in upcoming sixth-generation (6G) networks to support ubiquitous AI applications. Despite its considerable potential, edge AI faces substantial challenges, mostly due to the dichotomy between the resource limitations of wireless edge networks and the resource-intensive nature of DL. Specifically, the acquisition of large-scale data, as well as the training and inference processes of DNNs, can rapidly deplete the battery energy of EUDs. This necessitates an energy-conscious approach to edge AI to ensure both optimal and sustainable performance. In this article, we present a contemporary survey on green edge AI. We commence by analyzing the principal energy consumption components of edge AI systems to identify the fundamental design principles of green edge AI. Guided by these principles, we then explore energy-efficient design methodologies for the three critical tasks in edge AI systems, including training data acquisition, edge training, and edge inference. Finally, we underscore potential future research directions to further enhance the energy efficiency (EE) of edge AI.

The increasingly deeper neural networks hinder the democratization of privacy-enhancing distributed learning, such as federated learning (FL), to resource-constrained devices. To overcome this challenge, in this paper, we advocate the integration of edge computing paradigm and parallel split learning (PSL), allowing multiple edge devices to offload substantial training workloads to an edge server via layer-wise model split. By observing that existing PSL schemes incur excessive training latency and a large volume of data transmissions, we propose an innovative PSL framework, namely, efficient parallel split learning (EPSL), to accelerate model training. To be specific, EPSL parallelizes client-side model training andreduces the dimension of activations' gradientsfor backpropagation (BP) vialast-layer gradient aggregation, leading to a significant reduction in server-side training and communication latency. Moreover, by considering the heterogeneous channel conditions and computing capabilities at edge devices, we jointly optimize subchannel allocation, power control, and cut layer selection to minimize the per-round latency. Simulation results show that the proposed EPSL framework significantly decreases the training latency needed to achieve a target accuracy compared with the state-of-the-art benchmarks, and the tailored resource management and layer split strategy can considerably reduce latency than the counterpart without optimization.

Edgeartificial intelligence(AI) in the sixth-generation networks will provide inference services at the network edge to enrich the capabilities of mobile devices and lengthen their battery lives. As a well-known technique in computing, batching can boost the computation throughput at an edge server by assembling multiple tasks into a batch that is fed into a pre-trained prediction model. This reduces the memory-access frequency and hence accelerates the execution of each task. In this paper, we study joint batching and (task) scheduling to maximise the throughput (i.e., the number of completed tasks) under the practical assumptions of heterogeneous task arrivals and deadlines. The design aims to optimise the number of batches, their starting time instants, and the task-batch association that determines batch sizes. The joint optimisation problem is complex due to multiple coupled variables as mentioned and numerous constraints including heterogeneous tasks arrivals and deadlines, the causality requirements on multi-task execution, and limited radio resources. Our approach of solving the formulated mixed-integer problem is to transform it into a convex problem via integer relaxation method and ℓ0-norm approximation. This results in an efficient alternating optimization algorithm for finding a close-to-optimal solution. Specifically, it iterates between solving two sub-problems, optimal task-batch association and optimal batch starting time. The former is a linear program whose solution can be found using a derived scheme of greedy task selection while that of the latter is derived in closed form. In addition, we also design the optimal algorithm from leveragingspectrum holes, which are caused by fixed bandwidth allocation to devices and their asynchronized multi-batch task execution, to admit unscheduled tasks so as to further enhance throughput. Simulation results demonstrate that the proposed framework of joint batching and resource allocation can substantially enhance the throughput of multiuser edge-AI as opposed to a number of benchmarking schemes.

The proliferation of novel industrial applications at the wireless edge, such as smart grids and vehicle networks, demands the advancement of cyber-physical systems (CPSs). The performance of CPSs is closely linked to the last-mile wireless communication networks, which often become bottlenecks due to their inherent limited resources. Current CPS operations often treat wireless communication networks as unpredictable and uncontrollable variables, ignoring the potential adaptability of wireless networks, which results in inefficient and overly conservative CPS operations. Meanwhile, current wireless communications often focus more on throughput and other transmission-related metrics instead of CPS goals. In this study, we introduce the framework of goal-oriented wireless communication resource allocations, accounting for the semantics and significance of data for CPS operation goals. This guarantees optimal CPS performance from a cybernetic standpoint. We formulate a bandwidth allocation problem aimed at maximizing the information utility gain of transmitted data brought to CPS operation goals. Since the goal-oriented bandwidth allocation problem is a large-scale combinational problem, we propose a divide-and-conquer and greedy solution algorithm. The information utility gain is first approximately decomposed into marginal utility information gains and computed in a parallel manner. Subsequently, the bandwidth allocation problem is reformulated as a knapsack problem, which can be further solved greedily with a guaranteed sub-optimality gap. We further demonstrate how our proposed goal-oriented bandwidth allocation algorithm can be applied in four potential CPS applications, including data-driven decision-making, edge learning, federated learning, and distributed optimization. Through simulations, we confirm the effectiveness of our proposed goal-oriented bandwidth allocation framework in meeting CPS goals.

The unprecedented growth of industrial Internet of Things applications requires the evolution of wireless networked control system (WNCS). WNCSs are becoming the fundamental infrastructure technologies for critical wireless control applications due to the main benefits of the reduced deployment and maintenance cost, as well as the enhanced flexibility and safety. However, independent designs between communication and control without considering their tight interaction in conventional WNCS lead to poor overall system performance and efficiency. Co-designs are expected to achieve the target control performance while improving the wireless resource efficiency. In this paper, by considering how to allocate wireless resource while guaranteeing control performance, a co-design framework is established based on the finite-time wireless system identification (WSI) - a fundamental problem in systems theory and intelligent control. To this end, two design problems are investigated aiming at maximizing the communication throughput or minimizing the power consumption while guaranteeing the WSI performance. In the former design, the joint optimization of power and channel allocations leads to a non-convex integer combinatorial problem, which is iteratively solved by optimizing the power allocation via Lagrangian method and obtaining the optimal channel allocation via Hungarian algorithm. The minimum number of data samples for guaranteeing the WSI accuracy under confidence level is further derived by exploiting the relationship between WSI accuracy and the number of state sampling processes, which leads to the maximum throughput with respect to both the communication and control processes. In the latter design for energy-efficient WSI, by exploiting the relationship between the power consumption and channel allocation given the WSI performance requirement, the optimization problem can be simplified and solved by Hungarian algorithm. Simulations are conducted to verify the performance of the proposed solutions.

The existing multiple-input single-output (MISO) design to transmit data reliably to multiple mobile users (MUs) has become insufficient as MUs require fresh data, not just data. Therefore, in this paper, we consider multi-user MISO networks, where a base station (BS) equipped with multiple antennas serves MUs that want to maintain data freshness. To analyze the data freshness in this network, we first define the age-of-information (AoI) violation ratio, which is the ratio of duration that the AoI is larger than the AoI violation threshold to a sensing period. We then obtain the upper bound on the AoI violation ratio using the smooth maximum/minimum unit. We consider an optimization problem that minimizes the AoI violation ratio of multiple MUs. We propose a generalized power iteration (GPI) precoding algorithm to find a principal precoding vector that satisfies a first-order optimality condition of the optimization problem. Furthermore, for the scenario where the BS has the imperfect channel state information (CSI) of MUs, we provide the upper bound on the AoI violation time using the lower bound on the ergodic spectral efficiency, and also design the GPI precoding algorithm. Simulation results show proposed methods outperform baseline methods and demonstrate the effect of network parameters on the AoI violation probability.

Sensing is envisioned as a key network function of thesixth-generation(6G) mobile networks.Artificial intelligence(AI)-empowered sensing fuses features of multiple sensing views from devices distributed in edge networks for the edge server to perform accurate inference. This process, known asmulti-view pooling, creates a communication bottleneck due to multi-access by many devices. To alleviate this issue, we propose a task-oriented simultaneous access scheme for distributed sensing calledOver-the-Air Pooling(AirPooling). The existingOver-the-Air Computing(AirComp) technique can be directly applied to enable Average-AirPooling, which exploits the waveform superposition property of a multi-access channel to implement fast over-the-air averaging of pooled features. However, despite being most popular in practice, the over-the-air maximization, called Max-AirPooling, is not AirComp realizable given the fact that AirComp addresses only a limited subset of functions. We tackle the challenge by proposing the novel generalized AirPooling framework that can be configured to support both Max- and Average-AirPooling by controlling a configuration parameter and extended to even other pooling functions. The former is realized by adding to AirComp the designed pre-processing at devices and post-processing at the server. To characterize theEnd-to-End(E2E) sensing performance in object recognition, the theory of classification margin is applied to relate the classification accuracy and the AirPooling error, which allows the latter to be a tractable surrogate of the former. Furthermore, the analysis reveals an inherent tradeoff of Max-AirPooling between the accuracy of the pooling-function approximation and the effectiveness of noise suppression. Using the tradeoff, we make an attempt to optimize the configuration parameter of Max-AirPooling, yielding a sub-optimal closed-form method of adaptive parametric control. Experimental results obtained on real-world datasets show that AirPooling provides sensing accuracies close to those achievable by the traditional digital air interface but dramatically reduces the communication latency, by up to an order of magnitude.

Over-the-air computation (AirComp) is considered a transformative enabler for next-generation artificial intelligence (AI) services and wireless data aggregation via the electromagnetic waveform-superposition property of wireless multi-access channels (MAC). However, the conventional distortion metric, minimum square error (MSE), is imperfect and not universally applicable in specific AirComp scenarios in a low-signal-to-noise ratio (SNR) regime and under power budget constraint. Conversely, the average discriminant gain is studied for task-oriented AirComp AI services like classification but with inaccurate performance indication. To solve these problems, this work establishes a novel framework for AirComp systems from the mutual information (MI) perspective. First, we categorize the AirComp model into two distinct classes based on the source (sensing) data independence, namely diverse-targets (DT) AirComp and homogeneous-target (HT) AirComp. Both categories with different inputs like classical Gaussian and classification-based Gaussian mixture model (GMM), can be unified and assessed via MI criterion. Next, for the DT AirComp system, we introduce a novel MI-aided AirComp beamforming scheme employing majorization-minimization (MM) relaxation. As for the HT AirComp, we present a heuristic successive approximation (SA)-based beamforming method considering complex GMM inputs. We also provide the feedback and update protocol for AirComp tracking. Simulations validate the superior performance on AirComp throughput and task-oriented metrics such as classification accuracy with our proposed MI-aided beamforming schemes.

Federated Learning(FL) is a widely embraced paradigm for distilling artificial intelligence from distributed mobile data. However, the deployment of FL in mobile networks can be compromised by exposure to interference from neighboring cells or jammers. Existing interference mitigation techniques require multi-cell cooperation or at least interference channel state information, which is expensive in practice. On the other hand, power control that treats interference as noise may not be effective due to limited power budgets, and also that this mechanism can trigger countermeasures by interference sources. As a practical approach for protecting FL against interference, we proposeSpectrum Breathing, which cascades stochastic-gradient pruning and spread spectrum to suppress interference without bandwidth expansion. The cost is higher learning latency by exploiting the graceful degradation of learning speed due to pruning. We synchronize the two operations such that their levels are controlled by the same parameter,Breathing Depth. To optimally control the parameter, we develop a martingale-based approach to convergence analysis of Over-the-Air FL with spectrum breathing, termed AirBreathing FL. We show a performance tradeoff between gradient-pruning and interference-induced error as regulated by the breathing depth. Given receive SIR and model size, the optimization of the tradeoff yields two schemes for controlling the breathing depth that can be either fixed or adaptive to channels and the learning process. As shown by experiments, in scenarios where traditional Over-the-Air FL fails to converge in the presence of strong interference, AirBreahing FL with either fixed or adaptive breathing depth can ensure convergence where the adaptive scheme achieves close-to-ideal performance.

Edge inference refers to the use of artificial intelligent (AI) models at the network edge to provide mobile devices inference services and thereby enable intelligent services such as auto-driving and Metaverse towards 6G. However, departing from the classic paradigm of data-centric designs, the 6G networks for supporting edge AI features task-oriented techniques that focus on effective and efficient execution of AI task. Targeting end-to-end system performance, such techniques are sophisticated as they aim to seamlessly integrate sensing (data acquisition), communication (data transmission), and computation (data processing). Aligned with the paradigm shift, a task-oriented over-the-air computation (AirComp) scheme is proposed in this paper for multi-device split-inference system. In the considered system, local feature vectors, which are extracted from the real-time noisy sensory data on devices, are aggregated over-the-air by exploiting the waveform superposition in a multiuser channel. Then the aggregated features as received at a server are fed into an inference model with the result used for decision making or control of actuators. To design inference-oriented AirComp, the transmit precoders at edge devices and receive beamforming at edge server are jointly optimized to rein in the aggregation error and maximize the inference accuracy. The problem is made tractable by measuring the inference accuracy using a surrogate metric called discriminant gain, which measures the discernibility of two object classes in the application of object/event classification. It is discovered that the conventional AirComp beamforming design for minimizing the mean square error in generic AirComp with respect to the noiseless case may not lead to the optimal classification accuracy. The reason is due to the overlooking of the fact that feature dimensions have different sensitivity towards aggregation errors and are thus of different importance levels for classification. This issue is addressed in this work via a new task-oriented AirComp scheme designed by directly maximizing the derived discriminant gain. However, the resultant problem of joint transmit precoding and receive beamforming is nonconvex and difficult to solve due to the complicated form of discriminant gain and the coupling between the control variables. We overcome the difficulty using the successive convex approximation. The performance gain of the proposed task-oriented scheme over the conventional schemes is verified by extensive experiments targeting the application of human motion recognition.

For thesixth-generation(6G) mobile networks, in-situ model downloading has emerged as an important use case to enable real-time adaptiveartificial intelligence(AI) on edge devices. However, the simultaneous downloading of diverse and high-dimensional models to multiple devices over wireless links presents a significant communication bottleneck. To overcome the bottleneck, we propose the framework ofmodel broadcasting and assembling(MBA), which represents the first attempt on leveragingreusable knowledge, referring to shared parameters among tasks/models, to enable parameter broadcasting to reduce communication overhead or latency. The MBA framework comprises two key components. The first, the MBA protocol, defines the system operations including parameter selection from an AI library, power control for broadcasting, and model assembling at devices. The protocol features the use ofShapley valueas a metric for measuring parameters’ reusability. The second component is the joint design ofparameter-selection-and-power-control(PS-PC), which provides guarantees on devices’ model performance and aims to minimize the downloading latency. The corresponding optimization problem is simplified by decomposition into the sequential PS and PC sub-problems without compromising its optimality. The PS sub-problem is solved efficiently by designing two efficient algorithms. On one hand, the low-complexity algorithm of greedy parameter selection features the construction of task-oriented candidate model sets and a greedy selection metric for choosing the sets of model blocks for broadcasting, both of which are designed under the criterion of maximum reusable knowledge among tasks. On the other hand, the optimal tree-search algorithm gains its efficiency via the proposed construction of a compact binary tree pruned using model architecture constraints and an intelligent branch-and-bound search on the tree that fathoms nodes via solving a linear program that integer-relaxes the PS sub-problem. Last, given optimal PS, the optimal PC policy is derived in closed form by transforming the PC sub-problem into the conventional problem of energy-efficient transmission. Through extensive experiments conducted on real-world datasets, our results demonstrate the substantial reduction in downloading latency achieved by the proposed MBA design compared to traditional unicasting-based model downloading.

Radio frequency (RF) fingerprinting is a promising device authentication technique for securing the Internet of Things. It exploits the intrinsic and unique hardware impairments of the transmitters for device identification. Recently, due to the superior performance of deep learning (DL)-based classification models on real-world datasets, DL networks have been explored for RF fingerprinting. Most existing DL-based RF fingerprinting models use a single representation of radio signals as the input, while the multi-channel input model can leverage information from different representations of radio signals and improve the identification accuracy of RF fingerprints. In this work, we propose a multi-channel attentive feature fusion (McAFF) method for RF fingerprinting. It utilizes multi-channel neural features extracted from multiple representations of radio signals, including in-phase and quadrature samples, carrier frequency offsets, fast Fourier transform coefficients and short-time Fourier transform coefficients. The features extracted from different channels are fused adaptively using a shared attention module, where the weights of neural features are learned during the model training. In addition, we design a signal identification module using a convolution-based ResNeXt block to map the fused features to device identities. To evaluate the identification performance of the proposed method, we construct a Wi-Fi dataset using commercial Wi-Fi end-devices as the transmitters and a Universal Software Radio Peripheral platform as the receiver. Experimental results show that the proposed McAFF method significantly outperforms the single-channel-based as well as the existing DL-based RF fingerprinting methods in terms of identification accuracy and robustness.

Distributed tensor decomposition (DTD) is a fundamental data-analytics technique that extracts latent important properties from multi-attribute datasets distributed over edge devices. Its conventional one-shot implementation with over-the-air computation (AirComp) is confronted with the issues of limited storage-and-computation capacities and link interruption, which motivates us to propose a framework of on-the-fly communication-and-computing (FlyCom2) in this work. The proposed framework enables streaming computation with low complexity by leveraging a random sketching technique and achieves progressive global aggregation through the integration of progressive uploading and multiple-input-multiple-output (MIMO) AirComp. To develop FlyCom2, an on-the-fly sub-space estimator is designed to take real-time sketches accumulated at the server to generate online estimates for the decomposition. Its performance is evaluated by deriving both deterministic and probabilistic error bounds, which reveal the scaling laws of the decomposition error and inspire a threshold-based scheme to select reliably received sketches. Experimental results validate the performance gain of the proposed selection algorithm and show that compared to its one-shot counterparts, FlyCom2achieves comparable (even better with large eigen-gaps) decomposition accuracy besides dramatically reducing devices' complexity costs.

Federated Learning (FL) is a widely embraced paradigm for distilling artificial intelligence from distributed mobile data. However, the deployment of FL in mobile networks is compromised due to the exposure to interference from neighboring cells, besides a communication bottleneck caused by the uploading of high-dimensional model updates. Existing interference mitigation techniques require multi-cell cooperation or at least interference Channel State Information (CSI), which is expensive in practice. To address these challenges, we propose Spectrum Breathing, which cascades stochastic-gradient pruning and spread spectrum to suppress interference without bandwidth expansion. The cost is higher learning latency by exploiting the graceful degradation of learning speed due to pruning. We synchronize the two operations using a common parameter, Breathing Depth, and develop a martingale-based approach to convergence analysis of the Over-the-Air FL with Spectrum Breathing (AirBreathing FL). Given the receive SIR and model size, the optimization of the tradeoff between pruning and interference-induced error yields the scheme for controlling the breathing depth that can be adaptive to channels and learning process. Experiments show that AirBreathing FL with adaptive breathing depth can obtain close-to-ideal performance in scenarios where traditional over-the-air FL fails to converge in the presence of strong interference.

In this paper, we propose a semi-federated learning (SemiFL) framework that allows computing-limited clients to collaboratively train a shared model with resource-abundant clients. Specifically, by supporting the coexistence of model-updating and data-offloading, the SemiFL framework enables both centralized and federated learning in a hybrid fashion. Due to the decoding error, we consider the practical case with residual interference. To improve uplink throughput for centralized learning while reducing aggregation distortion for federated learning, we formulate a non-convex optimization problem to jointly optimize the transmit power and receive strategy. Then, we propose an efficient algorithm to solve the challenging problem by using successive convex approximation. Simulation results demonstrate the effectiveness of our SemiFL framework for heterogeneous networks, and reveal the impact of imperfect signal decoding on communication rates.

This work considers multiuser edge inference for providing inference services to multiple users at the wireless edge. Multiple tasks are uploaded and grouped into a single batch for parallel processing at the edge server, while a task may exit early from the neural network without traversing the whole model. To efficiently grant users with heterogeneous requirements on accuracy and latency, we study in this paper the joint allocation of communication-and-computation (C2) resources. Two efficient algorithms are designed under the criterion of maximum throughput. First, consider the case with batching but without early exiting. The target problem is optimally solved using a proposed algorithm that nests a threshold-based scheme, which selects users with the best channels and meeting the computation-time constraints, in a sequential search for the maximum batch size. Next, consider the general case with batching and early exiting. A low-complexity sub-optimal algorithm for C2resource allocation is developed by modifying the preceding algorithm to exploit early exiting for latency reduction. Experimental results demonstrate that the proposed C2resource allocation algorithms can leverage batching and early exiting to achieve 1.95x throughput over conventional schemes.

A task-oriented over-the-air computation (AirComp) scheme is proposed in this paper for multi-device edge split inference system. In the considered system, local noise-corrupted feature vectors are aggregated at the server via AirComp to generate a denoised one for the subsequent inference task. By considering classification tasks, the transmit precoders at edge devices and receive beamforming at edge server are jointly designed in an effort to rein in the aggregation error and maximize the inference accuracy, which is approximately measured by a surrogate but more tractable metric called discriminant gain. It is found that the conventional AirComp beamforming design for minimizing the mean square error between the aggregated feature vector by AirComp and the ideally aggregated one may not lead to the optimal classification accuracy, as it fails to respect the fact that some feature dimensions are more sensitive to the aggregation error than the others in terms of the classification accuracy. To tackle this issue, a new task-oriented AirComp scheme is proposed for directly maximizing the derived discriminant gain. The superiority of the proposed scheme over the heuristic benchmarks is verified by extensive experimental results based on a concrete inference task of human motion recognition.

As IoT devices become increasingly advanced and equipped with sensors such as cameras and microphones, the collection of massive data streams, produced in real-time, becomes challenging. In many cases only a small fraction of the collected data might be relevant, e.g., if cameras are used to search for a specific object. In this paper, we introduce and analyze a set of random access protocols in which the transmitting IoT devices are activated by semantic queries. This can be seen as a semantic data sourcing random access: each device computes a matching score that characterizes the relevance of its current observation and, if the matching score exceeds a threshold, the device transmits its observation over a random access collision channel to an edge node. We study two random access transmission policies. The first is the classical slotted ALOHA policy, while the other is able to exploit semantic correlation between the device observations. Furthermore, we show how the protocol can be integrated with machine learning-based query and matching score functions to capture the semantic content of, say, images. The numerical results show that the proposed protocol is able to effectively filter the device observations, such that mostly relevant data is received. Overall, the protocol is promising for collecting data in real-time from massive IoT networks based on the semantic content of sensor observations.

SUMMARY: Limited by spatial resolution and visual contrast, bone scintigraphy interpretation is susceptible to subjective factors, which considerably affects the accuracy and repeatability of lesion detection and anatomical localization. In this work, we design and implement an end-to-end multi-task deep learning model to perform automatic lesion detection and anatomical localization in whole-body bone scintigraphy. A total of 617 whole-body bone scintigraphy cases including anterior and posterior views were retrospectively analyzed. The proposed semi-supervised model consists of two task flows. The first one, the lesion segmentation flow, received image patches and was trained in a supervised way. The other one, skeleton segmentation flow, was trained on as few as five labeled images in conjunction with the multi-atlas approach, in a semi-supervised way. The two flows joint in their encoder layers so each flow can capture more generalized distribution of the sample space and extract more abstract deep features. The experimental results show that the architecture achieved the highest precision in the finest bone segmentation task in both anterior and posterior images of whole-body scintigraphy. Such an end-to-end approach with very few manual annotation requirement would be suitable for algorithm deployment. Moreover, the proposed approach reliably balances unsupervised labels construction and supervised learning, providing useful insight for weakly labeled image analysis. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Federated edge learning (FEEL) is envisioned as a promising paradigm to achieve privacy-preserving distributed learning. However, it consumes excessive learning time due to the existence of straggler devices. In this article, a novel topology-optimized FEEL (TOFEL) scheme is proposed to tackle the heterogeneity issue in federated learning and to improve the communication-and-computation efficiency. Specifically, a problem of jointly optimizing the aggregation topology and computing speed is formulated to minimize the weighted summation of energy consumption and latency. To solve the mixed-integer nonlinear problem, we propose a novel solution method of penalty-based successive convex approximation (SCA), which converges to a stationary point of the primal problem under mild conditions. To facilitate real-time decision making, an imitation-learning-based method is developed, where deep neural networks (DNNs) are trained offline to mimic the penalty-based method, and the trained imitation DNNs are deployed at the edge devices for online inference. Thereby, an efficient imitation-learning-based approach is seamlessly integrated into the TOFEL framework. Simulation results demonstrate that the proposed TOFEL scheme accelerates the federated learning process and achieves a higher energy efficiency. Moreover, we apply the scheme to 3-D object detection with multivehicle point cloud data sets in the CARLA simulator. The results confirm the superior learning performance of the TOFEL scheme over conventional designs with the same resource and deadline constraints.

Distributed optimization finds a wide range of applications ranging from machine learning to vehicle platooning. To overcome the bottleneck caused by the required extensive message exchange, we propose in this work the framework of distributed over-the-air computing (AirComp) to realize a one-step aggregation for distributed optimization. Equivalently, the technique superimposes multiple instances of conventional AirComp processes, giving rise to the challenge of jointly designing multicast beamforming at devices to rein in errors due to interference and channel distortion. We consider two design criteria. One is to minimize the sum AirComp error (i.e., sum mean-squared error (MSE)) with respect to the desired average-functional values. An efficient solution approach is proposed by transforming the non-convex beamforming problem into an equivalent concave-convex fractional program and solving it by nesting convex programming into a bisection search. The other one, called zero-forcing (ZF) multicast beamforming, is to force the received over-the-air aggregated signals at devices to be equal to the desired functional values, where the optimal beamforming admits closed form. Last, the convergence of a classic distributed optimization algorithm is analyzed. The distributed AirComp is found experimentally to accelerate convergence by dramatically reducing communication latency.

The deployment of inference services at the network edge, called edge inference, offloads computation-intensive inference tasks from mobile devices to edge servers, thereby enhancing the former’s capabilities and battery lives. In a multiuser system, the joint allocation of communication-and-computation (C2) resources (i.e., scheduling and bandwidth allocation) is made challenging by adopting efficient inference techniques, batching and early exiting, and further complicated by the heterogeneity in users’ requirements on accuracy and latency. Batching groups multiple tasks into a single batch for parallel processing to reduce time-consuming memory access and thereby boosts the throughput (i.e., completed task per second). On the other hand, early exiting allows a task to exit from a deep-neural network without traversing the whole network, thereby supporting a tradeoff between accuracy and latency. In this work, we study optimal C2 resource allocation with batching and early exiting, which is an NP-complete integer programming problem. A set of efficient algorithms are designed under the criterion of maximum throughput by tackling the challenge. First, consider the case with batching but without early exiting. The target problem is solved optimally using a proposed best-shelf-packing algorithm that nests a threshold-based scheme, which selects users with the best channels and meeting the computation-time constraints, in a sequential search for the maximum batch size. Next, consider the general case with batching and early exiting. A low-complexity sub-optimal algorithm for C2 resource allocation is developed by modifying the preceding algorithm to exploit early exiting for latency reduction. On the other hand, the optimal approach is developed based on nesting a depth-first tree-search with intelligent online pruning into a sequential search for the maximum batch size. The key idea is to derive pruning criteria based on the simple greedy solution for the target problem without a bandwidth constraint and apply the result to designing an intelligent online pruning scheme. Experimental results demonstrate that both optimal and sub-optimal C2 resource allocation algorithms can leverage integrated batching and early exiting to double the inference throughput compared with conventional schemes.

Although considerable progress has been obtained in neural network quantization for efficient inference, existing methods are not scalable to heterogeneous devices as one dedicated model needs to be trained, transmitted, and stored for one specific hardware setting, incurring considerable costs in model training and maintenance. In this paper, we study a new vertical-layered representation of neural network weights for encapsulating all quantized models into a single one. It represents weights as a group of bits (i.e., vertical layers) organized from the most significant bit (also called the basic layer) to less significant bits (i.e., enhance layers). Hence, a neural network with an arbitrary quantization precision can be obtained by adding corresponding enhance layers to the basic layer. However, we empirically find that models obtained with existing quantization methods suffer severe performance degradation if they are adapted to vertical-layered weight representation. To this end, we propose a simple once quantization-aware training (QAT) scheme for obtaining high-performance vertical-layered models. Our design incorporates a cascade downsampling mechanism with the multi-objective optimization employed to train the shared source model weights such that they can be updated simultaneously, considering the performance of all networks. After the model is trained, to construct a vertical-layered network, the lowest bit-width quantized weights become the basic layer, and every bit dropped along the downsampling process act as an enhance layer. Our design is extensively evaluated on CIFAR-100 and ImageNet datasets. Experiments show that the proposed vertical-layered representation and developed once QAT scheme are effective in embodying multiple quantized networks into a single one and allow one-time training, and it delivers comparable performance as that of quantized models tailored to any specific bit-width.

We consider the scenario of inference at the wireless edge, in which devices are connected to an edge server and ask the server to carry out remote classification, that is, classify data samples available at edge devices. This requires the edge devices to upload high-dimensional features of samples over resource-constrained wireless channels, which creates a communication bottleneck. The conventional feature pruning solution would require the device to have access to the inference model, which is not available in the current split inference scenario. To address this issue, we propose the progressive feature transmission (ProgressFTX) protocol, which minimizes the overhead by progressively transmitting features until a target confidence level is reached. A control policy is proposed to accelerate inference, comprising two key operations: importance-aware feature selection at the server and transmission-termination control. For the former, it is shown that selecting the most important features, characterized by the largest discriminant gains of the corresponding feature dimensions, achieves a sub-optimal performance. For the latter, the proposed policy is shown to exhibit a threshold structure. Specifically, the transmission is stopped when the incremental uncertainty reduction by further feature transmission is outweighed by its communication cost. The indices of the selected features and transmission decision are fed back to the device in each slot. The control policy is first derived for the tractable case of linear classification, and then extended to the more complex case of classification using a convolutional neural network. Both Gaussian and fading channels are considered. Experimental results are obtained for both a statistical data model and a real dataset. It is shown that ProgressFTX can substantially reduce the communication latency compared to conventional feature pruning and random feature transmission strategies.

To support the unprecedented growth of the Internet of Things (IoT) applications, tremendous data need to be collected by the IoT devices and delivered to the server for further computation. By utilizing the same signals for both radar sensing and data transmission, theintegrated sensing and communication(ISAC) technique enables simultaneous data collection and delivery in the physical layer. By exploiting the analog-wave addition property in a multi-access channel,over-the-air computation(AirComp) has been proposed as a communication approach that also enables function computation. The promising performances of ISAC and AirComp motivate the current work on developing a framework calledintegrated sensing, communication, and computation over-the-air(ISCCO). Two schemes are designed to supportmultiple-input-multiple-output(MIMO) ISCCO simultaneously, namely theseparated and sharedschemes. The separated scheme splits antenna array for radar sensing and AirComp, while all the antennas transmit a joint waveform for both radar sensing and AirComp in the shared scheme. The performance of radar sensing is evaluated by themean squared error(MSE) of the estimated target response matrix, while the MSE of the estimated function is adopted as the metric to evaluate the performance of the coupled communication and computation in AirComp. The design challenge of MIMO ISCCO lies in the joint optimization of beamformers at both the IoT devices and the server, which results in a non-convex problem. To solve this problem, an algorithmic solution based on the technique of semidefinite relaxation is proposed. The results reveal that the beamformer at each sensor needs to account for supporting dual-functional signals in the shared scheme, while dedicated beamformers for sensing and AirComp are needed to mitigate the mutual interference between the two functionalities in the separated scheme. The application of ISCCO on target location estimation is further demonstrated via simulation.

The unprecedented growth of mobile data traffic has fueled the deployment of artificial intelligence (AI) at the network edge, while distilling the intelligence from raw data by machine learning requires tremendous labelling effort. To overcome this challenge, wireless crowd labelling (WCL) is proposed for efficient data labelling by exploiting billions of available mobile annotators and the multicasting property of wireless channels. A WCL system is considered in this paper where unlabelled data (objects) are multicast via fading channels to different clusters of annotators for repetition labelling to improve the accuracy. Given the desired labelling accuracy, the superposition coding technique together with the repetition labelling scheme give rise to a new tradeoff between radio-and-annotator resource consumption. Building on such tradeoff, the annotator clustering and transmit power control are jointly optimized to maximize the labelling throughput (i.e., the number of labelled objects) or minimize the power consumption, resulting in NP-hard integer programming problems. To solve these problems, the optimal structure of annotator clustering is derived by exploiting the property that the power allocation for multicasting objects tends to compensate for the worst channel among the annotators in each cluster. Based on such structure, the throughput maximization problem can be recognized as a longest-path problem and solved by means of branch-and-bound, while the power minimization problem can be recasted to a shortest-path problem and solved by means of forward dynamic programming. The solution approaches can be further simplified when the channels are symmetric by merging the same nodes and cutting the identical paths in the path graph. In addition, exact polices are derived for the special cases where either the annotators or power are constrained. Last, simulation results are presented to demonstrate the performance of our proposed joint designs.

Driven by the rapid growth of Internet of Things applications, tremendous data need to be collected by sensors and uploaded to the servers for further process. As a promising solution, mobile crowd sensing (MCS) enables controllable sensing and transmission processes of multiple types of data in a single device. Despite the appealing advantages, existing works on MCS have mostly simplified two design issues, namely joint control of sensing and transmission processes and corresponding energy consumption. To address the above issues, a single-user MCS system is considered with a typical MCS device sensing and transmitting data to a server in a given time duration. In particular, there exists a busy time interval when the device is incapable of sensing. To minimize the sensing-and-transmission energy consumption of the device, an optimization problem is formulated, where the sensing and transmission rates are jointly optimized over time subjecting to the constraints on the sensing data sizes, transmission data sizes, data casualty, and busy time of sensing. This problem is highly challenging due to the coupling between the rates as well as the existence of the busy time. To deal with this problem, we first show that it can be equivalently decomposed into two subproblems, corresponding to a search for the amount of data size that needs to be sensed before the busy time (referred to as the height), as well as the control of sensing and transmission rates given the height. Next, we show that the latter problem can be efficiently solved by using the classical string-pulling method, while an efficient algorithm is proposed to progressively find the optimal height without the exhaustive search. Moreover, the solution approach is extended to a more complex scenario where there is a finite-size buffer at the server for receiving data. Last, simulations are conducted to evaluate the performance of the proposed designs.

Mobile edge computing (MEC) is a promising tech-nology to support the latency-critical applications of mobile devices by offloading complex computation tasks to edge servers. However, the mobility of devices yield a great challenge on de-livering reliable continuous services, especially for those latency- critical applications. Motivated by the fact that the user's location changes slower than the task arrivals, we propose a two-timescale mobility management framework by joint service migration and power control. The management design is formulated as a long-term energy minimization problem, subject to the reliability requirement of the latency-critical application. Leveraging the Lyapunov optimization technique, we develop an online two- timescale control algorithm to solve the problem. The simulation results demonstrate that our proposed online algorithm can significantly improve the energy and reliability performance compared to the baselines.

The rapid development of artificial intelligence together with the powerful computation capabilities of the advanced edge servers make it possible to deploy learning tasks at the wireless network edge, which is dubbed as edge intelligence (EI). The communication bottleneck between the data resource and the server results in deteriorated learning performance as well as tremendous energy consumption. To tackle this challenge, we explore a new paradigm called learning-and-energy-efficient (LEE) EI, which simultaneously maximizes the learning accuracies and energy efficiencies of multiple tasks via data partition and rate control. Mathematically, this results in a multi-objective optimization problem. Moreover, the continuous varying rates introduce infinite variables, which further complicates the problem. To solve this complex problem, the number of variables is reduced to a finite level by exploiting the optimality of constant-rate transmission in each epoch, based on which a string-pulling (SP) algorithm is proposed to obtain the numerical values. The performance of the proposed joint data partition and rate control design is examined by experiments based on public datasets.

This paper proposes a novel semi-federated learning (SemiFL) paradigm, which integrates centralized learning (CL) and over-the-air federated learning (AirFL) into a unified framework, with the aid of a simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS). In particular, this SemiFL framework allows computing-scarce users to participant in the learning process by using non-orthogonal multiple access (NOMA) to transmit their local dataset to the base station for model computation on behalf of them. During the uplink communication, scarce spectrum resources are shared among AirFL users and NOMA-based CL users, using a STAR-RIS for interference management and coverage enhancement. To analyze the learning behavior of SemiFL, closed-form expressions are derived to quantify the impact of learning rates and noisy fading channels. Our analysis shows that SemiFL can achieve a lower error floor than the CL or AirFL schemes with partial users. Simulation results show that SemiFL significantly reduces communication overhead and latency compared to CL, while achieving better learning performance than AirFL.

This is Part II of a double-part special issue on distributed learning over wireless edge networks. This two-part special issue features papers dealing with two main research challenges: optimization of wireless network performance for efficient implementation of distributed learning in wireless networks, and distributed learning for solving communication problems and optimizing network performance. The accepted papers in this special issue have been grouped into three topics: 1) network optimization for federated learning (FL), 2) network optimization for other distributed learning methods, and 3) distributed reinforcement learning (RL) for wireless network optimization. In Part I (vol. 39, no. 12, Dec. 2021), the focus is on the first cluster (network optimization for FL). The focus of Part II is on the second and third clusters (network optimization for other distributed learning methods and RL for wireless network optimization). The readers are referred to Part I for an overview paper [A1] by the team of guest editors where a comprehensive study of how distributed learning can be efficiently deployed over wireless edge networks is provided. The contributions made by the papers in Part II are summarized as follows.

With the advent of 5G technology, the notion oflatencygot a prominent role in wireless connectivity, serving as a proxy term for addressing the requirements for real-time communication. As wireless systems evolve toward 6G, the ambition to immerse the digital into physical reality will increase. Besides making the real-time requirements more stringent, this immersion will bring the notions of time, simultaneity, presence, and causality to a new level of complexity. A growing body of research points out that latency is insufficient to parameterize all real-time requirements. Notably, one such requirement that received significant attention is information freshness, defined through the Age of Information (AoI) and its derivatives. In general, the metrics derived from a conventional black-box approach to communication network design are not representative of new distributed paradigms, such as sensing, learning, or distributed consensus. The objective of this article is to investigate the general notion of timing in wireless communication systems and networks, and its relation to effective information generation, processing, transmission, and reconstruction at the senders and receivers. We establish a general statistical framework oftimingrequirements in wireless communication systems, which subsumes both latency and AoI. The framework is made by associating a timing component with the two basic statistical operations: decision and estimation. We first use the framework to present a representative sample of the existing works that deal with timing in wireless communication. Next, it is shown how the framework can be used with different communication models of increasing complexity, starting from the basic Shannon one-way communication model and arriving at communication models for consensus, distributed learning, and inference. Overall, this article fills an important gap in the literature by providing a systematic treatment of various timing measures in wireless communication and sets the basis for design and optimization for the next-generation real-time systems.

In this paper, we propose a novel wireless architecture, mounted on a high-altitude aerial platform, which is enabled by reconfigurable intelligent surface (RIS). By installing RIS on the aerial platform, rich line-of-sight and full-area coverage can be achieved, thereby, overcoming the limitations of the conventional terrestrial RIS. We consider a scenario where a sudden increase in traffic in an urban area triggers authorities to rapidly deploy unmanned-aerial vehicle base stations (UAV-BSs) to serve the ground users. In this scenario, since the direct backhaul link from the ground source can be blocked due to several obstacles from the urban area, we propose reflecting the backhaul signal using aerial-RIS so that it successfully reaches the UAV-BSs. We jointly optimize the placement and array-partition strategies of aerial-RIS and the phases of RIS elements, which leads to an increase in energy-efficiency of every UAV-BS. We show that the complexity of our algorithm can be bounded by the quadratic order, thus implying high computational efficiency. We verify the performance of the proposed algorithm via extensive numerical evaluations and show that our method achieves an outstanding performance in terms of energy-efficiency compared to benchmark schemes.

The rapid development of artificial intelligence together with the powerful computation capabilities of the advanced edge servers make it possible to deploy learning tasks at the wireless network edge, which is dubbed as edge intelligence (EI). The communication bottleneck between the data resource and the server results in deteriorated learning performance as well as tremendous energy consumption. To tackle this challenge, we explore a new paradigm called learning-and-energy-efficient (LEE) EI, which simultaneously maximizes the learning accuracies and energy efficiencies of multiple tasks via data partition and rate control. Mathematically, this results in a multi-objective optimization problem. Moreover, the continuously varying communication rates introduce infinite variables, which further complicates the problem. To solve this complex problem, we consider the case with infinite server buffer capacity and one-shot data arrival at sensor. First, the number of variables is reduced to a finite level by exploiting the optimality of constant-rate transmission in each epoch. Second, the optimal solution of the multi-objective problem is found by applying the stratified sequencing or merging of objectives. By assuming higher priority of learning efficiency in stratified sequencing, the optimal data partition is derived in closed form by the Lagrange method, while the optimal rate control is proved to have the structure of directional water filling (DWF), based on which a string-pulling (SP) algorithm is proposed to obtain the numerical values. The DWF structure of rate control is also proved to be optimal in merging of objectives, which combines different objectives in a weighted manner. By exploiting the optimal rate changing properties, the SP algorithm is further extended to tackle the more challenging cases with limited server buffer capacity or bursty data arrival at sensor. The performance of the proposed joint data partition and rate control design is examined by extensive experiments based on public datasets.

Mobile edge computing (MEC) is a promising technology for enhancing the computation capacities and features of mobile users by offloading complex computation tasks to the edge servers. However, mobility poses great challenges on delivering reliable MEC service required for latency-critical applications. First, mobility management has to tackle the dynamics of both user’s location changes and task arrivals that vary in different timescales. Second, user mobility could induce service migration, leading to reliability loss due to the migration delay. In this paper, we propose a two-timescale mobility management framework by joint control of service migration and transmission power to address the above challenges. Specifically, the service migration operates at a large timescale to support user mobility in the multi-cell network, while the power control is performed at a small timescale for real-time task offloading. Their joint control is formulated as an optimization problem aiming at the long-term mobile energy minimization subject to the reliability requirement of computation offloading. To solve the problem, we propose a Lyapunov-based framework to decompose the problem into different timescales, based on which a low-complexity two-timescale online algorithm is developed by exploiting the problem structure. The proposed online algorithm is shown to be asymptotically optimal via theoretical analysis, and is further developed to accommodate the multiuser management. The simulation results demonstrate that our proposed algorithm can significantly improve the energy and reliability performance.

The deployment of federated learning in a wireless network, calledfederated edge learning(FEEL), exploits low-latency access to distributed mobile data to efficiently train an AI model while preserving data privacy. In this work, we study the spatial (i.e., spatially averaged) learning performance of FEEL deployed in a large-scale cellular network with spatially random distributed devices. Both the schemes of digital and analog transmission are considered, providing support of error-free uploading and over-the-air aggregation of local model updates by devices. The derived spatial convergence rate for digital transmission is found to be constrained by a limited number of active devices regardless of device density and converges to the ground-true rate exponentially fast as the number grows. The population of active devices depends on network parameters such as processing gain and signal-to-interference threshold for decoding. On the other hand, the limit does not exist for uncoded analog transmission. In this case, the spatial convergence rate is slowed down due to the direct exposure of signals to the perturbation of inter-cell interference. Nevertheless, the effect diminishes when devices are dense as interference is averaged out by aggressive over-the-air aggregation. In terms of learning latency (in second), analog transmission is preferred to the digital scheme as the former dramatically reduces multi-access latency by enabling simultaneous access.

Federated edge learning(FEEL) is a widely adopted framework for training anartificial intelligence(AI) model distributively at edge devices to leverage their data while preserving their data privacy. The execution of a power-hungry learning task at energy-constrained devices is a key challenge confronting the implementation of FEEL. To tackle the challenge, we propose the solution of powering devices usingwireless power transfer(WPT). To derive guidelines on deploying the resultantwirelessly powered FEEL(WP-FEEL) system, this work aims at the derivation of the tradeoff between the model convergence and the settings of power sources in two scenarios: 1) the transmission power and density of power-beacons (dedicated charging stations) if they are deployed, or otherwise 2) the transmission power of a server (access-point). The development of the proposed analytical framework relates the accuracy of distributed stochastic-gradient estimation to the WPT settings, the randomness in both communication and WPT links, and devices’ computation capacities. Furthermore, the local-computation at devices (i.e., mini-batch size and processor clock frequency) is optimized to efficiently use the harvested energy for gradient estimation. The resultant learning-WPT tradeoffs reveal the simple scaling laws of the model-convergence rate with respect to the transferred energy as well as the devices’ computational energy efficiencies. The results provide useful guidelines on WPT provisioning to yield a guaranteer on learning performance. They are corroborated by experimental results using a real dataset.

The enormous data distributed at the network edge and ubiquitous connectivity have led to the emergence of the new paradigm of distributed machine learning and large-scale data analytics. Distributed principal component analysis (PCA) concerns finding a low-dimensional subspace that contains the most important information of high-dimensional data distributed over the network edge. The subspace is useful for distributed data compression and feature extraction. This work advocates the application of over-the-air federated learning to efficient implementation of distributed PCA in a wireless network under a data-privacy constraint, termed AirPCA. The design features the exploitation of the waveform-superposition property of a multi-access channel to realize over-the-air aggregation of local subspace updates computed and simultaneously transmitted by devices to a server, thereby reducing the multi-access latency. The original drawback of this class of techniques, namely channel-noise perturbation to uncoded analog modulated signals, is turned into a mechanism for escaping from saddle points during stochastic gradient descent (SGD) in the AirPCA algorithm. As a result, the convergence of the AirPCA algorithm is accelerated. To materialize the idea, descent speeds in different types of descent regions are analyzed mathematically using martingale theory by accounting for wireless propagation and techniques including broadband transmission, over-the-air aggregation, channel fading and noise. The results reveal the accelerating effect of noise in saddle regions and the opposite effect in other types of regions. The insight and results are applied to designing an online scheme for adapting receive signal power to the type of current descent region. Specifically, the scheme amplifies the noise effect in saddle regions by reducing signal power and applies the power savings to suppressing the effect in other regions. From experiments using real datasets, such power control is found to accelerate convergence while achieving the same convergence accuracy as in the ideal case of centralized PCA.

In this paper, we propose a novel wireless backhaul architecture, mounted on a high-altitude aerial platform, which is enabled by reconfigurable intelligent surface (RIS). We assume a sudden increase in traffic in an urban area, and to serve the ground users therein, authorities rapidly deploy unmanned-aerial-vehicle base-stations (UAV-BSs). In this scenario, since the direct backhaul link from the ground source can be blocked due to several obstacles from the urban area, we propose reflecting the backhaul signal using aerial-RIS and the phase of each RIS element, which leads to an increase in energy-efficiency ensuring the reliable backhaul link for every UAV-BS. We optimize the placement and array-partitioning strategy of aerial-RIS and the phase of each RIS element, which leads to an increase of energy-efficiency under guaranteeing the reliable backhaul link for every UAV-BS. We show that the complexity of our algorithm is upper-bounded by the quadratic order, thus implying high computational efficiency. We verify the performance of the proposed algorithm via extensive numerical evaluations and show that our method achieves an outstanding performance in terms of energy-efficiency compared to benchmark schemes.

The ever-growing popularity and rapid improving of artificial intelligence (AI) have raised rethinking on the evolution of wireless networks. Mobile edge computing (MEC) provides a natural platform for AI applications since it provides rich computation resources to train AI models, as well as low-latency access to the data generated by mobile and Internet of Things devices. In this paper, we present an infrastructure to perform machine learning tasks at an MEC server with the assistance of a reconfigurable intelligent surface (RIS). In contrast to conventional communication systems where the principal criteria are to maximize the throughput, we aim at optimizing the learning performance. Specifically, we minimize the maximum learning error of all users by jointly optimizing the beamforming vectors of the base station and the phase-shift matrix of the RIS. An alternating optimization-based framework is proposed to optimize the two terms iteratively, where closed-form expressions of the beamforming vectors are derived, and an alternating direction method of multipliers (ADMM)-based algorithm is designed together with an error level searching framework to effectively solve the nonconvex optimization problem of the phase-shift matrix. Simulation results demonstrate significant gains of deploying an RIS and validate the advantages of our proposed algorithms over various benchmarks.

Analyzing massive amounts of data using complex machine learning models requires significant computational resources. The conventional approach to such problems involves centralizing training data and inference processes in the cloud, i.e., in data centers. However, with the proliferation of mobile devices and increasing application of the Internet-of-Things (IoT) paradigm, very large amounts of data are collected at the edges of wireless networks, and due to privacy constraints and limited communication resources, it is undesirable or impractical to upload this data from mobile devices to the cloud for centralized learning. This problem can be solved by distributed learning at the network edge, by which edge devices collaboratively train a shared learning model using real-time mobile data. The avoidance of raw-data uploading not only helps to preserve privacy but may also alleviate network-traffic congestion and minimize latency. With that said, distributed training still requires a substantial amount of information exchange between devices and edge servers over wireless links. In the process, wireless impairments such as noise, interference, and imperfect knowledge of channel states can significantly slow down distributed learning (e.g., convergence speed) and degrades its performance (e.g., learning accuracy). This makes it crucial to optimize wireless network performance so as to support the efficient deployment of distributed learning algorithms. On the other hand, distributed learning algorithms provide a powerful tool-set for solving complex problems in wireless communication and networking. One important framework, called federated learning (FL), enables users to collaboratively learn a shared model while helping to preserve local data privacy. The application of FL can endow edge devices with capabilities of user behavior prediction, user identification, and wireless environment analysis. As another example, distributed reinforcement learning is capable of leveraging distributed computation power and data to solve complex optimization and control problems that arise in various use cases, such as network control, user clustering, resource management, and interference alignment. To cover this paradigm of distributed learning over wireless networks, this two-part Special Issue features papers dealing with two main research challenges: a) optimization of wireless network performance for efficient implementation of distributed learning in wireless networks, and b) distributed learning for solving communication problems and optimizing network performance.

The next-generation of wireless networks will enable many machine learning (ML) tools and applications to efficiently analyze various types of data collected by edge devices for inference, autonomy, and decision making purposes. However, due to resource constraints, delay limitations, and privacy challenges, edge devices cannot offload their entire collected datasets to a cloud server for centrally training their ML models or inference purposes. To overcome these challenges, distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges, thus reducing the communication overhead and latency as well as improving data privacy. However, deploying distributed learning over wireless networks faces several challenges including the uncertain wireless environment (e.g., dynamic channel and interference), limited wireless resources (e.g., transmit power and radio spectrum), and hardware resources (e.g., computational power). This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks. We present a detailed overview of several emerging distributed learning paradigms, including federated learning, federated distillation, distributed inference, and multi-agent reinforcement learning. For each learning framework, we first introduce the motivation for deploying it over wireless networks. Then, we present a detailed literature review on the use of communication techniques for its efficient deployment. We then introduce an illustrative example to show how to optimize wireless networks to improve its performance. Finally, we introduce future research opportunities. In a nutshell, this paper provides a holistic set of guidelines on how to deploy a broad range of distributed learning frameworks over real-world wireless communication networks.

Remote classification involves offloading complex object-recognition tasks from mobile devices to servers at the network edge. It brings to the mobile device the capability of discerning hundreds of object classes by using the computational and storage capabilities of the infrastructure. Remote classification is challenged by the finite and variable data rate of the wireless channel, which affects the capability to transfer high-dimensional features and thus limits the classification resolution. We introduce a set of metrics under the name of classification capacity that are defined as the maximum number of classes that can be discerned over a given communication channel while meeting a target probability for classification error. We treat both the cases of a channel where the instantaneous rate is known and unknown. The objective is to choose a subset of classes from a class library that offers satisfactory performance over a given channel. We treat two different cases of subset selection. First, a device can select the subset by pruning the class library until arriving at a subset that meets the targeted error probability while maximizing the classification capacity. Adopting a subspace data model, we prove the equivalence of classification capacity maximization to the problem of packing on the Grassmann manifold. The results show that the classification capacity grows exponentially with the instantaneous communication rate, and super-exponentially with the dimensions of each data cluster. This also holds for ergodic and outage capacities with fading if the instantaneous rate is replaced with an average rate and a fixed rate, respectively. In the second case, a device has a unique preference of class subset for every communication rate, which is modeled as an instance of uniformly sampling the library. Without class selection, the classification capacity and its ergodic and outage counterparts are proved to scale linearly with their corresponding communication rates instead of the exponential growth in the last case.

Recently, over-the-air computation (AirComp) has emerged as an efficient solution for access points (APs) to aggregate distributed data from many edge devices (e.g., sensors) by exploiting the waveform superposition property of multiple access (uplink) channels. While prior work focuses on the single-cell setting where inter-cell interference is absent, this article considers a multi-cell AirComp network limited by such interference and investigates the optimal policies for controlling devices' transmit power to minimize the mean squared errors (MSEs) in aggregated signals received at different APs. First, we consider the scenario of centralized multi-cell power control. To quantify the fundamental AirComp performance tradeoff among different cells, we characterize the Pareto boundary of the multi-cell MSE region by minimizing the sum MSE subject to a set of constraints on individual MSEs. Though the sum-MSE minimization problem is non-convex and its direct solution intractable, we show that this problem can be optimally solved via equivalently solving a sequence of convex second-order cone program (SOCP) feasibility problems together with a bisection search. This results in an efficient algorithm for computing the optimal centralized multi-cell power control, which optimally balances the interference-and-noise-induced errors and the signal misalignment errors unique for AirComp. Next, we consider the other scenario of distributed power control, e.g., when there lacks a centralized controller. In this scenario, we introduce a set of interference temperature (IT) constraints, each of which constrains the maximum total inter-cell interference power between a specific pair of cells. Accordingly, each AP only needs to individually control the power of its associated devices for single-cell MSE minimization, but subject to a set of IT constraints on their interference to neighboring cells. By optimizing the IT levels, the distributed power control is shown to provide an alternative method for characterizing the same multi-cell MSE Pareto boundary as the centralized counterpart. Building on this result, we further propose an efficient algorithm for different APs to cooperate in iteratively updating the IT levels to achieve a Pareto-optimal MSE tuple, by pairwise information exchange. Last, simulation results demonstrate that cooperative power control using the proposed algorithms can substantially reduce the sum MSE of AirComp networks compared with the conventional single-cell approaches.

Mobile-edge computing (MEC) enhances the capacities and features of mobile devices by offloading computation-intensive tasks over wireless networks to edge servers. One challenge faced by the deployment of MEC in cellular networks is to support user mobility. As a result, offloaded tasks can be seamlessly migrated between base stations (BSs) without compromising the resource-utilization efficiency and link reliability. In this paper, we tackle the challenge by optimizing the policy for migration/handover between BSs by jointly managing computation-and-radio resources. The objectives are twofold: maximizing the sum offloading rate, quantifying MEC throughput, and minimizing the migration cost. The policy design is formulated as a decision-optimization problem that accounts for virtualization, I/O interference between virtual machines (VMs), and wireless multi-access. To solve the complex combinatorial problem, we develop an efficient relaxation-and-rounding based solution approach. The approach relies on an optimal iterative algorithm for solving the integer-relaxed problem and a novel integer-recovery design. The latter outperforms the traditional rounding method by exploiting the derived problem properties and applying matching theory. In addition, we also consider the design for a special case of “hotspot mitigation”, referring to alleviating an overloaded server/BS by migrating its load to the nearby idle servers/BSs. From simulation results, we observed close-to-optimal performance of the proposed migration policies under various settings. This demonstrates their efficiency in computation-and-radio resource management for joint service migration and BS handover in multi-cell MEC networks.

By deploying machine-learning algorithms at the network edge, edge learning can leverage the enormous real-time data generated by billions of mobile devices to train AI models, which enable intelligent mobile applications. In this emerging research area, one key direction is to efficiently utilize radio resources for wireless data acquisition to minimize the latency of executing a learning task at an edge server. Along this direction, we consider the specific problem of retransmission decision in each communication round to ensure both reliability and quantity of those training data for accelerating model convergence. To solve the problem, a new retransmission protocol called data-importance aware automatic-repeat-request (importance ARQ) is proposed. Unlike the classic ARQ focusing merely on reliability, importance ARQ selectively retransmits a data sample based on its uncertainty which helps learning and can be measured using the model under training. Underpinning the proposed protocol is a derived elegant communication-learning relation between two corresponding metrics, i.e., signal-to-noise ratio (SNR) and data uncertainty. This relation facilitates the design of a simple threshold based policy for importance ARQ. The policy is first derived based on the classic classifier model of support vector machine (SVM), where the uncertainty of a data sample is measured by its distance to the decision boundary. The policy is then extended to the more complex model of convolutional neural networks (CNN) where data uncertainty is measured by entropy. Extensive experiments have been conducted for both the SVM and CNN using real datasets with balanced and imbalanced distributions. Experimental results demonstrate that importance ARQ effectively copes with channel fading and noise in wireless data acquisition to achieve faster model convergence than the conventional channel-aware ARQ. The gain is more significant when the dataset is imbalanced.

In this paper, we considerpartitioned edge learning(PARTEL), which implements parameter-server training, a well known distributed learning method, in a wireless network. Thereby, PARTEL leverages distributed computation resources at edge devices to train a large-scaleartificial intelligence(AI) model by dynamically partitioning the model into parametric blocks for separated updating at devices. Targeting broadband channels, we consider the joint control of parameter allocation, sub-channel allocation, and transmission power to improve the performance of PARTEL. Specifically, the policies for joint SUbcarrier, Parameter, and POweR allocaTion (SUPPORT) are optimized under the criterion of minimum learning latency. Two cases are considered. First, for the case of decomposable models (e.g., logistic regression), the latency-minimization problem is a mixed-integer program and non-convex. Due to its intractability, we develop a practical solution by integer relaxation and transforming it into an equivalent convex problem of model size maximization under a latency constraint. Thereby, a low-complexity algorithm is designed to compute the SUPPORT policy. Second, consider the case ofdeep neural network(DNN) models which can be trained using PARTEL by introducing some auxiliary variables. This, however, introduces constraints on model partitioning reducing the granularity of parameter allocation. The preceding policy is extended to DNN models by applying the proposed techniques of load rounding and proportional adjustment to rein in latency expansion caused by the load granularity constraints.

Edge machine learning involves the deployment of learning algorithms at the network edge to leverage massive distributed data and computation resources to trainartificial intelligence(AI) models. Among others, the framework offederated edge learning(FEEL) is popular for its data-privacy preservation. FEEL coordinates global model training at an edge server and local model training at devices that are connected by wireless links. This work contributes to the energy-efficient implementation of FEEL in wireless networks by designing jointcomputation-and-communication resource management($\mathrm {C}^{2}$RM). The design targets the state-of-the-art heterogeneous mobile architecture where parallel computing using both CPU and GPU, calledheterogeneous computing, can significantly improve both the performance and energy efficiency. To minimize the sum energy consumption of devices, we propose a novel$\mathrm {C}^{2}$RM framework featuring multi-dimensional control including bandwidth allocation, CPU-GPU workload partitioning and speed scaling at each device, and$\mathrm {C}^{2}$time division for each link. The key component of the framework is a set of equilibriums in energy rates with respect to different control variables that are proved to exist among devices or between processing units at each device. The results are applied to designing efficient algorithms for computing the optimal$\mathrm {C}^{2}$RM policies faster than the standard optimization tools. Based on the equilibriums, we further design energy-efficient schemes for device scheduling and greedy spectrum sharing that scavenges “spectrum holes” resulting from heterogeneous$\mathrm {C}^{2}$time divisions among devices. Using a real dataset, experiments are conducted to demonstrate the effectiveness of$\mathrm {C}^{2}$RM on improving the energy efficiency of a FEEL system.

Multi-point vehicular positioning is an essential operation for autonomous vehicles. However, the state-of-the-art positioning technologies, relying on reflected signals from a target (i.e., RADAR and LIDAR), cannot work without line-of-sight (LoS). Besides, it takes significant time for environment scanning and object recognition with potential detection inaccuracy, especially in complex urban situations. Some recent fatal accidents involving autonomous vehicles further expose such limitations. In this article, we aim at overcoming these limitations by proposing a novel relative positioning approach, called Cooperative Multi-point Positioning (COMPOP). The COMPOP establishes cooperation between a target vehicle (TV) and a sensing vehicle (SV) if a LoS path exists, where a TV explicitly lets an SV to know the TV's existence by transmitting positioning waveforms. This cooperation makes it possible to remove the time-consuming scanning and target recognizing processes, facilitating real-time positioning. One prerequisite for the cooperation is a clock synchronization between a pair of TV and SV. To this end, we use a phase-differential-of-arrival (PDoA) based approach to remove the TV-SV clock difference from the received signal. With clock difference correction, the TV's position can be obtained via peak detection over a 3D power spectrum constructed by a Fourier transform (FT) based algorithm. The COMPOP also incorporates nearby vehicles, without knowing their locations, into the above cooperation for the case without a LoS path. Specifically, several strong non-LoS (NLoS) links from the TV to the SV can be generated via mirror-like reflections over the neighboring vehicles' metal surfaces. Following the same procedures in the LoS case, virtual TVs mirrored by nearby vehicles can be detected. By exploiting the geometric relation between the virtual and actual TVs, COMPOP can be achieved by intelligently combining the virtual TVs to position the actual TV. The effectiveness of the COMPOP is verified by several simulations concerning practical channel parameters.

Federated edge learning (FEEL) is a popular framework for model training at an edge server using data distributed at edge devices (e.g., smart-phones and sensors) without compromising their privacy. In the FEEL framework, edge devices periodically transmit high-dimensional stochastic gradients to the edge server, where these gradients are aggregated and used to update a global model. When the edge devices share the same communication medium, the multiple access channel (MAC) from the devices to the edge server induces a communication bottleneck. To overcome this bottleneck, an efficient broadband analog transmission scheme has been recently proposed, featuring the aggregation of analog modulated gradients (or local models) via the waveform-superposition property of the wireless medium. However, the assumed linear analog modulation makes it difficult to deploy this technique in modern wireless systems that exclusively use digital modulation. To address this issue, we propose in this work a novel digital version of broadband over-the-air aggregation, called one-bit broadband digital aggregation (OBDA). The new scheme features one-bit gradient quantization followed by digital quadrature amplitude modulation (QAM) at edge devices and over-the-air majority-voting based decoding at edge server. We provide a comprehensive analysis of the effects of wireless channel hostilities (channel noise, fading, and channel estimation errors) on the convergence rate of the proposed FEEL scheme. The analysis shows that the hostilities slow down the convergence of the learning process by introducing a scaling factor and a bias term into the gradient norm. However, we show that all the negative effects vanish as the number of participating devices grows, but at a different rate for each type of channel hostility.

Mobile-edge computing (MEC) enhances the capacities and features of mobile devices via offloading computation-intensive tasks over wireless networks to the edge servers. One challenge faced by the deployment of MEC in cellular networks is to support user mobility, so that the offloaded tasks can be seamlessly migrated between base stations (BSs) without compromising the resource-utilization efficiency and link reliability. In this paper, we tackle the challenge by optimizing the policy for migration/handover between BSs by jointly managing computation-and-radio resources. The policy design is formulated as a multi-objective optimization problem that maximizes the sum offloading rate, quantifying MEC throughput, and minimizes the migration cost, where the issues of virtualization, I/O interference between virtual machines (VMs), and wireless multi-access are taken into account. To solve the complex combinatorial problem, we develop an efficient relaxation-and-rounding based approach, including an optimal iterative algorithm for solving the integer-relaxed problem and a novel integer-recovery design that exploits the derived problem properties. The simulation results show the close-to-optimal performance of the proposed migration policies under various settings, validating their efficiency in computation-and-radio resource management for joint service migration and BS handover in multi-cell MEC networks.

In this paper, we consider the framework of partitioned edge learning for iteratively training a large-scale model using many resource-constrained devices (called workers). To this end, in each iteration, the model is dynamically partitioned into parametric blocks, which are downloaded to worker groups for updating using their local data. Then, the local updates are uploaded to and cascaded by the server for updating a global model. To reduce resource usage by minimizing the total learning-and-communication latency, this work focuses on the novel joint design of parameter (computation load) and bandwidth allocation (for downloading and uploading). Two design approaches are adopted. First, a practical sequential approach, called partially integrated parameter-and-bandwidth allocation (PABA), yields one scheme, namely parameter aware bandwidth allocation. It allocates the largest bandwidth to the slowest worker. Second, PABA are jointly optimized. Despite its being a nonconvex problem, an efficient and optimal solution algorithm is derived by intelligently nesting a bisection search and solving a convex problem. Experimental results using real data demonstrate that integrating PABA can substantially improve the performance of partitioned edge learning in terms of latency (by e.g., 46%) and accuracy (by e.g., 4%).

To mitigate the multi-access latency in federated edge learning, an efficient broadband analog transmission scheme has been recently proposed, featuring the aggregation of analog modulated gradients via the waveform-superposition property of the wireless medium. However, the assumed linear analog modulation makes it difficult to deploy this technique in modern wireless systems that exclusively use digital modulation. To address this issue, we propose in this work a novel digital version of broadband over-the-air aggregation, called one-bit broadband digital aggregation. The new scheme features one-bit gradient quantization followed by digital modulation at the edge devices and a simple threshold-based decoding at the edge server. We develop a comprehensive analysis framework for quantifying the effects of wireless channel hostilities (channel noise and fading) on the convergence rate. The analysis shows that the hostilities slow down the convergence of the learning process by introducing a scaling factor and a bias term into the gradient norm. However, all the negative effects vanish as the number of devices grows, but at a different rate for each type of channel hostility.

The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), while tremendous labels are required for AI model training via supervised learning. To tackle this challenge, a novel framework of wireless crowd labelling is proposed that downloads data to many imperfect mobile annotators for repetition labelling by exploiting multicasting in wireless networks. The integration of the rate-distortion theory and the principle of repetition labelling gives rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Aiming at maximizing the labelling throughput, this work focuses on optimizing the joint annotator-and-spectrum allocation (JASA). To develop an efficient solution approach, an optimal sequential annotator-clustering scheme is derived. Thereby, the optimal JASA policy can be found by an efficient tree search.

The scheduling of downlink video streaming in a massive multiple-input-multiple-output (MIMO) network is considered in this paper, where active users arrive randomly to request video contents of a finite playback duration via their service base stations. Each video consists of a sequence of segments, which can be transmitted to the requesting users with variable video bitrates. To facilitate adaptive video streaming, a number of physical-layer frames are grouped as a super frame. We formulate the adaptation of transmitted segment number, frame allocation and segment bitrate in all the super frames as an infinite-horizon Markov decision process (MDP), whose objective is a discounted measurement of the average Quality-of-Experience (QoE). A novel approximate MDP method is proposed to obtain a low-complexity scheduling policy. Specifically, a baseline policy is introduced and its asymptotic value function is derived analytically. The low-complexity scheduling policy will be obtained from one-step iteration based on the analytical expression, which becomes a performance lower bound on the derived policy. It is shown by simulations that the proposed low-complexity scheduling policy has significant performance gain over the baseline policy.

In this paper, we consider a relay selection scheme and analyze the corresponding cooperative diversity for over-the-air computation (AirComp) systems, where multiple source nodes transmit their signals over a wireless multi-access channel to achieve fast data aggregation. We first formulate the power control problems to minimize the computation mean square error (MSE) at the fusion center, and introduce the concept of MSE outage probability and diversity order in the context of AirComp. When there are no relays but multiple receive antennas at the fusion center, we propose an antenna selection scheme that selects the best antenna for reception. We then characterize its outage performance and prove that the AirComp diversity order is equal to the number of receive antennas. Motivated by the analogy between multiple-relay systems and multiple-antenna systems, we develop a relay selection scheme in relay-aided AirComp where only the best relay is chosen to amplify and forward its received signal to the fusion center. We show that the relay selection scheme can achieve the full diversity order, which is equal to the number of relays, and an outage performance comparable to AirComp with the same number of receive antennas.

Over-the-air computation (AirComp) of a function (e.g., averaging) has recently emerged as an efficient multiple-access scheme for fast aggregation of distributed data at mobile devices (e.g., sensors) at a fusion center (FC) over wireless channels. To realize reliable AirComp in practice, it is crucial to adaptively control the devices' transmit power for coping with channel distortion to achieve the desired magnitude alignment of simultaneous signals. In this paper, we solve the power control problem. Our objective is to minimize the computation error by jointly optimizing the transmit power at devices and a signal scaling factor (called denoising factor) at the FC, subject to individual average power constraints at devices. The problem is generally non-convex due to the coupling of the transmit powers at devices and denoising factor at the FC. To tackle the challenge, we first consider the special case with static channels, for which we derive the optimal solution in closed form. The derived power control exhibits a threshold-based structure: if the product of the channel quality and power budget for each device, called quality indicator, exceeds an optimized threshold, this device applies channel-inversion power control; otherwise, it performs full power transmission. We proceed to consider the general case with time-varying channels. To solve the more challenging non-convex power control problem, we use the Lagrange-duality method via exploiting its “time-sharing” property. The derived power control exhibits a regularized channel inversion structure, where the regularization balances the tradeoff between the signal-magnitude alignment and noise suppression. Moreover, for the special case with only one device being power limited, we show that the power control for the power-limited device has an interesting channel-inversion water-filling structure, while those for other devices (with sufficiently large power budgets) reduce to channel-inversion power control. Numerical results show that the derived power control significantly reduces the computation error as compared with the conventional designs.

The scheduling of downlink video streaming in a massive multiple-input multiple-output (MIMO) network is considered in this paper, where active users arrive randomly to request video contents of a finite playback duration via their service base stations (BSs). Each video content consisting of a sequence of segments can be transmitted to the requesting users with variable video bitrates. We formulate the joint control of transmitted segment number, frame allocation and segment bitrate in all the super frames (each comprising multiple frames) as an infinite-horizon Markov decision process (MDP). The maximization objective is a discounted measurement of the average Quality of Experience (QoE). Since there is no efficient method for scheduling design with random user arrivals and departures in the existing literature, a novel approximate MDP method is proposed to obtain a low-complexity scheduling policy, where a lower bound on its performance is derived. Specifically, we first introduce a baseline policy and derive its asymptotic value function. One-step policy iteration is then applied to improve this value function, yielding the mentioned low-complexity policy. Finally, we propose a novel and efficient reinforcement learning (RL) algorithm to evaluate the value function when the prior knowledge on user arrival intensity is absent.

The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), automating the operations of our society ranging from transportation to healthcare. The implementation of ubiquitous AI, however, entails labelling of an enormous amount of data prior to the training of AI models via supervised learning. To tackle this challenge, we explore a new direction called wireless crowd labelling, which involves downloading data to many imperfect mobile annotators for repetition labelling with an aim of exploiting multicasting in wireless networks. In this cross-disciplinary area, the rate-distortion theory and the principle of repetition labelling for accuracy improvement together give rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Building on the tradeoff and aiming at maximizing the labelling throughput, this work focuses on the joint optimization of encoding rate, annotator clustering, and sub-channel allocation, which results in an NP-hard integer programming problem. To devise an efficient solution approach, we establish an optimal sequential annotator-clustering scheme based on the order of decreasing signal-to-noise ratios, thereby allowing the optimal solution to be found by an efficient tree search. This solution can be further simplified when the channels are symmetric. Alternatively, the optimization problem can be recognized as a knapsack problem, which can be efficiently solved in pseudo-polynomial time by means of dynamic programming. In addition, the optimal polices are derived for the annotator constrained and spectrum constrained cases. Last, simulation results are presented to demonstrate the significant throughput gains based on the optimal solution compared with decoupled allocation of the two types of resources.

In cellular federated edge learning (FEEL), multiple edge devices holding local data jointly train a neural network by communicating learning updates with an access point without exchanging their data samples. With very limited communication resources, it is beneficial to schedule the most informative local learning updates. This paper focuses on FEEL with gradient averaging over participating devices in each round of communication. A novel scheduling policy is proposed to exploit both diversity in multiuser channels and diversity in the “importance” of the edge devices' learning updates. First, a new probabilistic scheduling framework is developed to yield unbiased update aggregation in FEEL. The importance of a local learning update is measured by its gradient divergence. If one edge device is scheduled in each communication round, the scheduling policy is derived in closed form to achieve the optimal trade-off between channel quality and update importance. The probabilistic scheduling framework is then extended to allow scheduling multiple edge devices in each communication round. Numerical results obtained using popular models and learning datasets demonstrate that the proposed scheduling policy can achieve faster model convergence and higher learning accuracy than conventional scheduling policies that only exploit a single type of diversity.

To leverage data and computation capabilities of mobile devices, machine learning algorithms are deployed at the network edge for training artificial intelligence (AI) models, resulting in the new paradigm of edge learning. In this paper, we consider the framework of partitioned edge learning for iteratively training a large-scale model using many resource-constrained devices (called workers). To this end, in each iteration, the model is dynamically partitioned into parametric blocks, which are downloaded to worker groups for updating using data subsets. Then, the local updates are uploaded to and cascaded by the server for updating a global model. To reduce resource usage by minimizing the total learning-and-communication latency, this work focuses on the novel joint design of parameter (computation load) allocation and bandwidth allocation (for downloading and uploading). Two design approaches are adopted. First, a practical sequential approach, called partially integrated parameter-and-bandwidth allocation (PABA), yields two schemes, namely bandwidth aware parameter allocation and parameter aware bandwidth allocation. The former minimizes the load for the slowest (in computing) of worker groups, each training a same parametric block. The latter allocates the largest bandwidth to the worker being the latency bottleneck. Second, PABA are jointly optimized. Despite it being a nonconvex problem, an efficient and optimal solution algorithm is derived by intelligently nesting a bisection search and solving a convex problem. Experimental results using real data demonstrate that integrating PABA can substantially improve the performance of partitioned edge learning in terms of latency (by e.g., 46%) and accuracy (by e.g., 4% given the latency of 100 seconds).

To leverage rich data distributed at the network edge, a new machine-learning paradigm, called edge learning, has emerged where learning algorithms are deployed at the edge for providing intelligent services to mobile users. While computing speeds are advancing rapidly, the communication latency is becoming the bottleneck of fast edge learning. To address this issue, this work is focused on designing a low-latency multi-access scheme for edge learning. To this end, we consider a popular privacy-preserving framework, federated edge learning (FEEL), where a global AI-model at an edge-server is updated by aggregating (averaging) local models trained at edge devices. It is proposed that the updates simultaneously transmitted by devices over broadband channels should be analog aggregated “over-the-air” by exploiting the waveform-superposition property of a multi-access channel. Such broadband analog aggregation (BAA) results in dramatical communication-latency reduction compared with the conventional orthogonal access (i.e., OFDMA). In this work, the effects of BAA on learning performance are quantified targeting a single-cell random network. First, we derive two tradeoffs between communication-and-learning metrics, which are useful for network planning and optimization. The power control (“truncated channel inversion”) required for BAA results in a tradeoff between the update-reliability [as measured by the receive signal-to-noise ratio (SNR)] and the expected update-truncation ratio. Consider the scheduling of cell-interior devices to constrain path loss. This gives rise to the other tradeoff between the receive SNR and fraction of data exploited in learning. Next, the latency-reduction ratio of the proposed BAA with respect to the traditional OFDMA scheme is proved to scale almost linearly with the device population. Experiments based on a neural network and a real dataset are conducted for corroborating the theoretical results.

Over-the-air computation (AirComp) of a function (e.g., averaging) has recently emerged as an efficient multi-access scheme for fast aggregation of distributed data at devices (e.g., sensors) to fusion centers (FCs) over wireless channels. To realize reliable AirComp in practice, it is crucial to control the devices' transmit power for coping with channel distortion to achieve the desired magnitude alignment of simultaneous signals. % to strike a balance between enforcing signal-magnitude alignment for overcoming heterogenous channel fading and suppressing noise. In this paper, we study the power control problem for AirComp over fading channels. Our objective is to minimize the computation error by jointly optimizing the transmit power at devices and a signal scaling factor at the FC, called denoising factor, subject to the individual average power constraints at devices. The problem is generally non-convex due to the coupling of transmit power over devices and denoising factor. To optimally solve this problem, we apply the Lagrange duality method via exploiting its ''time-sharing'' property. The derived optimal power control exhibits a regularized channel inversion structure where the regularization has the function of balancing the tradeoff between the signal-magnitude alignment and noise suppression. Moreover, for the special case that only one device is power-limited, we show that the optimal power control for the power-limited device has an interesting channel-inversion water- filling structure, while those for other devices (with sufficiently large power budgets) reduce to channel-inversion power control over all fading states. Numerical results show that the optimal power control remarkably reduces the computation error as compared with other heuristic designs.

One basic operation of Internet-of-Things (IoT) networks is to acquire a function of distributed data collected from sensors over wireless channels, called wireless data aggregation (WDA). Targeting dense sensors, low-latency WDA poses a design challenge for high-mobility or mission critical IoT applications. A promising solution is a low- latency multi-access scheme, called over-the-air computing (AirComp), that supports simultaneous transmission such that an access point (AP) can estimate and receive a summation-form function of the distributed data by exploiting the waveform- superposition property of multi-access channels. In this work, we propose a multiple-input-multiple-output (MIMO) AirComp framework for an IoT network with clustered multi-antenna sensors and an AP with large receive arrays. The contributions of this work are two-fold. Define the AirComp error as the error in the functional value received at AP due to channel noise. First, under the criterion of minimum error, the optimal receive beamformer at the AP, called decomposed aggregation beamformer (DAB), is shown to have a decomposed architecture: the inner component focuses on channel-dimension reduction and the outer component focuses on joint equalization of the resultant low-dimensional small-scale fading channels. Second, to provision DAB with the required channel state information (CSI), a low-latency channel feedback scheme is proposed by intelligently leveraging the AirComp principle to support simultaneous channel- feedback by sensors.

In this paper, the uplink adaptation for massive multiple-input-multiple-output (MIMO) networks without the knowledge of user density is considered. Specifically, a novel cooperative uplink transmission and detection scheme is first proposed for massive MIMO networks, where each uplink frame is divided into a number of data blocks with independent coding schemes and the following blocks are decoded based on previously detected data blocks in both service and neighboring cells. The asymptotic signal-to- interference-plus-noise ratio (SINR) of the proposed scheme is then derived, and the distribution of interference power considering the randomness of the users' locations is proved to be Gaussian. By tracking the mean and variance of interference power, an online robust rate adaptation algorithm ensuring a target packet outage probability is proposed for the scenario where the interfering channel and the user density are unknown.

Multi-point detection of the full-scale environment is an important issue in autonomous driving. The state-of- the-art positioning technologies (such as RADAR and LIDAR) are incapable of real-time detection without \emph{line-of-sight} (LoS). To address this issue, this paper presents a novel multi-point vehicular positioning technology via \emph{millimeter-wave} (mmWave) transmission that exploits multi-path reflection from a \emph{target vehicle} (TV) to a \emph{sensing vehicle} (SV), which enables the SV to fast capture both the shape and location information of the TV in \emph{non-LoS} (NLoS) under the assistance of multi-path reflections. A \emph{phase-difference-of- arrival} (PDoA) based hyperbolic positioning algorithm is designed to achieve the synchronization between the TV and SV. The \emph{stepped-frequency-continuous-wave} (SFCW) is utilized as signals for multi-point detection of the TVs. Transceiver separation enables our approach to work in NLoS conditions and achieve much lower latency compared with conventional positioning techniques.

Mobile-edge computing (MEC) is an emerging technology for enhancing the computational capabilities of mobile devices and reducing their energy consumption via offloading complex computation tasks to the nearby servers. Multiuser MEC at servers is widely realized via parallel computing based on virtualization. Due to finite shared I/O resources, interference between virtual machines (VMs), called I/O interference, arises that degrades the computation performance. In this paper, we study the problem of joint radio-and-computation resource allocation (RCRA) in multiuser MEC systems in the presence of I/O interference. Specifically, we formulate a sum offloading rate maximization problem by joint offloading-user scheduling, the offloaded size control, and time allocation for communication (offloading and downloading) and computation. The problem is a non-convex mixed-integer programming problem. An optimal algorithm with low complexity is designed based on a decomposition approach and Dinkelbach method. The simulation results demonstrate considering of I/O interference can endow on an offloading controller robustness against the performancedegradation factor.

This paper proposes content-based control of wakeup receivers for data collection in wireless sensor networks. The wake-up procedure is designed with a goal of waking up only the subset of the sensor nodes which have the relevant data observations. This prevents the sensors with less relevant data from waking up and wasting energy, which is inevitable when employing conventional ID-based wake-up control. We apply the proposed content-based wake-up scheme to top- k query, where the sink attempts to collect information on the set of nodes that own top- k observations from the sensing field. Assuming medium access based on p-persistent CSMA, we design a content-based wake-up control scheme suited for the data collection of top- k query. We analyze the scheme theoretically in terms of data collection delay and energy-efficiency and compare it to the ID-based wake-up. The numerical results confirm the effectiveness of the proposed content-based wake-up control, especially when the number of sensor nodes is large.

In future Internet-of-Things networks, sensors or even access points can be mounted on ground/aerial vehicles for smart-city surveillance or environment monitoring. For such high-mobility sensing, it is impractical to collect data from a large population of sensors using any traditional orthogonal multi-access scheme due to the excessive latency. To tackle the challenge, a technique called over-the-air computation (AirComp) was recently developed to enable a data-fusion center to receive a desired function of sensing data from concurrent sensor transmissions, by exploiting the superposition property of a multi-access channel. This paper aims at further developing multiple-input-multiple output (MIMO) AirComp for enabling high-mobility multimodal sensing. Specifically, we design MIMO-AirComp equalization and channel feedback techniques for spatially multiplexing multifunction computation. Given the objective of minimizing the computation error, a close-to-optimal equalizer is derived in closed-form using differential geometry. The solution can be computed as the weighted centroid of points on a Grassmann manifold, where each point represents the subspace spanned by the channel matrix of a sensor. As a by-product, the problem of MIMO-AirComp equalization is proved to have the same form as the classic problem of multicast beamforming, establishing the AirComp-multicasting duality. Its significance lies in making the said Grassmannian-centroid solution transferable to the latter problem which otherwise is solved using the computation-intensive semidefinite relaxation method. Last, building on the AirComp architecture, an efficient channel-feedback technique is designed for direct acquisition of the equalizer at the access point from simultaneous feedback by all sensors. This overcomes the difficulty of provisioning orthogonal feedback channels for many sensors.

Leveraging massive numbers of sensors in user equipment as well as opportunistic human mobility, mobile crowd sensing (MCS) has emerged as a powerful paradigm, where prolonging battery life of constrained devices and motivating human involvement are two key design challenges. To address these, we envision a novel framework, named wirelessly powered crowd sensing (WPCS), which integrates MCS with wireless power transfer for supplying the involved devices with extra energy and thus facilitating user incentivization. This paper considers a multiuser WPCS system where an access point (AP) transfers energy to multiple mobile sensors (MSs), each of which performing data sensing, compression, and transmission. Assuming lossless (data) compression, an optimization problem is formulated to simultaneously maximize data utility and minimize energy consumption at the operator side, by jointly controlling wireless-power allocation at the AP as well as sensing-data sizes, compression ratios, and sensor-transmission durations at the MSs. Given fixed compression ratios, the proposed optimal power allocation policy has the threshold-based structure with respect to a defined crowd-sensing priority function for each MS depending on both the operator configuration and the MS information. Further, for fixed sensing-data sizes, the optimal compression policy suggests that compression can reduce the total energy consumption at each MS only if the sensing-data size is sufficiently large. Our solution is also extended to the case of lossy compression, while extensive simulations are offered to confirm the efficiency of the contributed mechanisms.

As a revolution in networking, the Internet of Things (IoT) aims at automating the operations of our societies by connecting and leveraging an enormous number of distributed devices (e.g., sensors and actuators). One design challenge is efficient wireless data aggregation (WDA) over the dense IoT devices. This can enable a series of the IoT applications ranging from latency-sensitive high-mobility sensing to data-intensive distributed machine learning. Over-the-air (function) computation (AirComp) has emerged to be a promising solution that merges computing and communication by exploiting analog-wave addition in the air. Another IoT design challenge is battery recharging for dense sensors which can be tackled by wireless power transfer (WPT). The coexisting of AirComp and WPT in the IoT system calls for their integration to enhance the performance and efficiency of WDA. This motivates the current work on developing the wirelessly powered AirComp (WP-AirComp) framework by jointly optimizing wireless power control, energy and (data) aggregation beamforming to minimize the AirComp error. To derive a practical solution, we recast the non-convex joint optimization problem into the equivalent outer and inner sub-problems for (inner) wireless power control and energy beamforming, and (outer) the efficient aggregation beamforming, respectively. The former is solved in closed form while the latter is efficiently solved using the semidefinite relaxation technique. The results reveal that the optimal energy beams point to the dominant Eigen-directions of the WPT channels, and the optimal power allocation tends to equalize the close-loop (down-link WPT and up-link AirComp) effective channels of different sensors. The simulation demonstrates that the controlling WPT provides additional design dimensions for substantially reducing the AirComp error.

Mobile-edge computing (MEC) is an emerging technology for enhancing the computational capabilities of the mobile devices and reducing their energy consumption via offloading complex computation tasks to the nearby servers. Multiuser MEC at servers is widely realized via parallel computing based on virtualization. Due to finite shared I/O resources, interference between virtual machines (VMs), called I/O interference, degrades the computation performance. In this paper, we study the problem of joint radio-and-computation resource allocation (RCRA) in multiuser MEC systems in the presence of I/O interference. Specifically, offloading scheduling algorithms is designed targeting two system performance metrics: sum offloading rate maximization and sum mobile energy consumption minimization. Their designs are formulated as non-convex mixed-integer programming problems, which account for latency due to offloading, result downloading, and parallel computing. A set of low-complexity algorithms are designed based on a decomposition approach and leveraging classic techniques from combinatorial optimization. The resultant algorithms jointly schedule offloading users, control their offloading sizes, and divide time for communication (offloading and downloading) and computation. They are either optimal or can achieve close-to-optimality as shown by simulation. The comprehensive simulation results demonstrate that considering of I/O interference can endow on an offloading controller robustness against the performance-degradation factor.

Due to densification of wireless networks, there exist abundance of idling computation resources at (network) edge helpers (e.g., base stations and handheld computers). These resources can be scavenged by offloading heavy computation tasks from small Internet-of-Things (IoT) devices (e.g., sensors and wearable computing devices) in proximity, thereby overcoming their limitations and lengthening their battery lives. However, unlike dedicated servers, the spare resources offered by edge helpers are random and intermittent. Thus, it is essential to intelligently control a user (IoT device) the amounts of data for offloading and local computing so as to ensure that a computation task can be finished in time-consuming minimum energy. In this paper, we design energy-efficient control policies in a computation offloading system with a random channel and a helper with a dynamically loaded CPU (due to the primary service). Specifically, the policy adopted by the helper aims at determining the sizes of offloaded and locally computed data for a given task in different slots such that the total energy consumption for transmission and local CPU is minimized under a task-deadline constraint. As the result, the polices endow an offloading user robustness against channel-and-helper randomness besides balancing offloading and local computing. By modeling the channel and helper CPU as Markov chains, the problem of offloading control is converted into a Markov decision process. Though dynamic programming (DP) for numerically solving the problem does not yield the optimal policies in closed form, we leverage the procedure to quantify the optimal policy structure and apply the result to design optimal or sub-optimal policies. For three cases ranging from zero, small to large helper buffers, the low complexity of the policies overcomes the “curse of dimensionality” in DP arising from joint consideration of channel, helper CPU, and buffer states.

The ambient backscatter communication is a newly rising paradigm for the Internet-of-Things networks, which enables the connection of low-cost devices. This paper proposes a novel MFSK modulation for the Tag of ambient backscatter communications systems, and the corresponding detectors are designed depending on the capability of the Reader. In the case the Reader is not capable of removing the direct interference from the ambient source, a maximum likelihood detector is proposed. In another case, leveraging on the frequency shift feature of the MFSK modulation, the Reader can remove the direct interference. Then, a simple energy detector is proposed, and the closed-form expressions for the symbol error rate (SER) and outage probability of the system are derived. The findings of this paper suggest that the proposed MFSK modulation outperforms the popular ON-OFF keying modulation, and the impact of modulation order on the SER performance depends heavily on the operating bit signal to noise ratio. Moreover, it is shown that it is desirable to place the Tag close to the Reader in terms of minimizing the outage probability.

One basic operation of Internet-of-Things (IoT) networks is to acquire a function of distributed data collected from sensors over wireless channels, called wireless data aggregation (WDA). In the presence of dense sensors, low-latency WDA poses a design challenge for high-mobility or mission critical IoT applications. A promising solution is a low-latency multi-access scheme, called over-the-air computing (AirComp), that supports simultaneous transmission such that an access point (AP) can estimate and receive a summation-form function of the distributed sensing data by exploiting the waveform-superposition property of a multi-access channel. In this work, we propose a multiple-input-multiple-output (MIMO) AirComp framework for an IoT network with clustered multi-antenna sensors and an AP with large receive arrays. The framework supports low-complexity and low-latency AirComp of a vector-valued function. The contributions of this work are two-fold. Define the AirComp error as the error in the functional value received at AP due to channel noise. First, under the criterion of minimum error, the optimal receive beamformer at the AP, called decomposed aggregation beamformer (DAB), is shown to have a decomposed architecture: the inner component focuses on channel-dimension reduction and the outer component focuses on joint equalization of the resultant low-dimensional small-scale fading channels. In addition, an algorithm is designed to adjust the ranks of individual components of the DAB for a further performance improvement. Second, to provision DAB with the required channel state information (CSI), a low-latency channel feedback scheme is proposed by intelligently leveraging the AirComp principle to support simultaneous channel feedback by sensors. The proposed framework is shown by simulation to substantially reduce AirComp error compared with the existing design without considering channel structures.

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

Recent breakthroughs in machine learning especially artificial intelligence shift the paradigm of wireless communication towards intelligence radios. One of their core operations is automatic modulation recognition (AMR). Existing research focuses on coherent modulation schemes such as QAM, PSK and FSK. The AMR of (non- coherent) space-time modulation remains an uncharted area despite its wide deployment in modern multiple-input-multiple-output (MIMO) systems. The scheme using a so called Grassmann constellation (comprising unitary matrices) enables rate- enhancement using multi-antennas and blind detection. In this work, we propose an AMR approach for Grassmann constellation based on data clustering, which differs from traditional AMR based on classification using a modulation database. The approach allows algorithms for clustering on the Grassmann manifold (or the Grassmannian), such as Grassmann K-means, originally developed for computer vision to be applied to AMR. In this paper, the maximum- likelihood (ML) Grassmann constellation detection is proved to be equivalent to clustering on the Grassmannian. Thereby, a well-known machine-learning result that was originally established only for the Euclidean space is rediscovered for the Grassmannian.

To support future IoT networks with dense sensor connectivity, a technique called over-the-air computation (Air-Comp) was recently developed to enable a data-fusion center to receive a desired function (e.g., mean value) of sensing data from concurrent sensor transmissions. This is made possible by exploiting the superposition property of a multi-access channel. This work aims at further developing AirComp for next-generation multi-antenna multi-modal sensor networks where a multi-modal sensor monitors multiple environmental parameters such as temperature, pollution and humidity. To be specific, we design beamforming techniques for AirComp of multiple functions, each corresponding to a particular sensing-data type. Given the objective of minimizing sum mean-squared error of computed functions, the optimization of receive beamforming for multi-function AirComp is a NP-hard problem. The approximate problem based on tightening transmission-power constraints, however, is shown to be solvable using differential geometry. The solution is proved to be the weighted centroid of points on a Grassmann manifold, where each point represents the subspace spanned by the channel matrix of a sensor. Simulation results demonstrate the effectiveness of the proposed solution.

Next-generation wireless networks will provide users ubiquitous low-latency computing services using devices at the network edge, called mobile edge computing (MEC). The key operation of MEC is to offload computation intensive tasks from users. Since each edge device comprises an access point (AP) and a computer server (CS), a MEC network can be decomposed as a radio access network (RAN) cascaded with a CS network (CSN). Based on the architecture, we investigate network constrained latency performance, namely communication latency (comm- latency) and computation latency (comp-latency) under the constraints of RAN coverage and CSN stability. To this end, a spatial random network is constructed featuring random node distribution, parallel computing, non-orthogonal multiple access, and random computation-task generation. Based on the model and the network constraints, we derive the scaling laws of comm-latency and comp-latency with respect to network-load parameters and network-resource parameters. Essentially, the analysis involves the interplay of stochastic geometry, queueing, and parallel computing. Combining the derived scaling laws quantifies the tradeoffs between the latency, network coverage and network stability.

This paper presents a technology of sensing hidden vehicles by exploiting multi-path vehicle-to-vehicle (V2V) communication. This overcomes the limitation of existing RADAR technologies that requires line-of-sight (LoS), thereby enabling more intelligent manoeuvre in autonomous driving and improving its safety. The proposed technology relies on transmission of orthogonal waveforms over different antennas at the target (hidden) vehicle. Even without LoS, the resultant received signal enables the sensing vehicle to detect the position, shape, and driving direction of the hidden vehicle by jointly analyzing the geometry (AoA/AoD/propagation distance) of individual propagation path. The accuracy of the proposed technique is validated by realistic simulation including both highway and rural scenarios.

The spatial-and-temporal correlation of interference has been well-studied in Poisson networks, where the interfering base stations (BSs) are independent of each other. However, there exists spatial interdependence including attraction and repulsion among the BSs in practical wireless networks, affecting the interference distribution and hence the network performance. In view of this, by modeling the network as a Poisson clustered process, we quantify the effects of spatial interdependence among BSs on the interference correlation and analytically prove that BS clustering increases the level of interference correlation. In particular, it is shown that the level is a monotone-increasing function of the mean number of BSs in each cluster and a monotone-decreasing function of cluster radius, but is independent of the locations of the clusters. Furthermore, we study the effects of spatial interdependence among BSs on network performance with Type-I HARQ retransmission scheme via considering heterogeneous cellular networks in which small-cell BSs exhibit a clustered topology in practice. We derive the numerically integrable expressions and their bounds for the joint success probabilities, defined as the success probability in multiple successive transmissions, for macro-cell users and small-cell users. It is shown that BS clustering improves the performance of macro-cell users. Further, the level is enhanced by the repulsion between the BSs from different tiers.

Millimeter-wave (mm-wave) communication opens up tens of giga-hertz spectrum in the mm-wave band for use by next-generation wireless systems, thereby solving the problem of spectrum scarcity. Maintaining connectivity stands out as a key design challenge for mm-wave networks deployed in urban regions due to the blockage effect characterizing mm-wave propagation. In this paper, we set out to investigate the blockage effect on the connectivity of mm-wave networks in a Manhattan-type urban region modeled using a random regular lattice, while base stations (BSs) are Poisson distributed in the plane. In particular, we analyze the connectivity probability that a typical user is within the transmission range of a BS and connected by a line-of-sight. First, we consider a single-tier network. By jointly applying the random lattice and stochastic geometry theories, a lower bound on the connectivity probability is derived as a function of building parameters (e.g., size and site occupancy probability) and BS parameters (e.g., transmission range and BS density). For the case of dense buildings, the bound is derived in a simpler form. Next, the preceding lower bounds are tightened based on the geometric technique of partitioning the irregular blockage-free region around the typical user. Moreover, the analysis is generalized to mm-wave channels with both LoS and NLoS paths. Finally, the results are extended to a K-tier heterogeneous network (HetNet), where building heights are random, and depending on its height, a building can block the signals transmitted by a subset of BS tiers but not all. The analysis shows that the connectivity probability of the K-tier HetNet increases linearly with the number of tiers. In general, our work quantifies the relation between the coverage of an mmwave network and the parameters of building and BS processes, providing useful guidelines for deploying practical networks in a Manhattan-type region.

Next-generation wireless networks will provide users ubiquitous low-latency computing services using devices at the network edge, called mobile edge computing (MEC). The key operation of MEC is to offload computation intensive tasks from users. Since each edge device comprises an access point (AP) and a computer server (CS), an MEC network can be decomposed as a radio access network cascaded with a CS network. Based on the architecture, we investigate network-constrained latency performance, namely communication latency and computation latency, under the constraints of radio-access connectivity and CS stability. To this end, a spatial random network is modeled featuring random node distribution, parallel computing, non-orthogonal multiple access, and random computation-task generation. Given the model and the said network constraints, we derive the scaling laws of communication latency and computation latency with respect to network-load parameters (density of mobiles and their task-generation rates) and network-resource parameters (bandwidth, density of APs/CSs, and CS computation rate). Essentially, the analysis involves the interplay of the theories of stochastic geometry, queueing, and parallel computing. Combining the derived scaling laws quantifies the tradeoffs between the latencies, network connectivity, and network stability. The results provide useful guidelines for MEC-network provisioning and planning by avoiding either of the cascaded radio access network or CS network being a performance bottleneck.

Multimedia content, especially videos, is expected to dominate data traffic in next-generation mobile networks. Caching popular content at the network edge, namely content helpers (base stations and access points), has emerged as a solution for low-latency content delivery. Compared with traditional wireless communication, content delivery has a key characteristic that many signals coexisting in the air carry identical popular content. However, they can interfere with each other at a receiver if their modulation-and-coding (MAC) schemes are adapted to individual channels following the classic approach. To address this issue, we present a novel idea of content adaptive MAC (CAMAC) where adapting MAC schemes to content ensures that all signals carrying identical content are encoded using an identical MAC scheme to achieve spatial MAC alignment. Consequently, interference can be harnessed as signals to improve the reliability of wireless delivery. In the remaining part of the paper, we focus on quantifying the gain that CAMAC can bring to a content-delivery network by using a stochastic-geometry model. Specifically, content helpers are distributed as a Poisson point process and each of them transmits a file from a content database based on a given popularity distribution. Given a fixed threshold on the signal-to-interference ratio for successful transmission, it is discovered that the successful content-delivery probability is closely related to the distribution of the ratio of two independent shot noise processes, named a shot-noise ratio. The distribution itself is an open mathematical problem that we tackle in this work. Using stable-distribution theory and tools from stochastic geometry, the distribution function is derived in closed form. Extending the result in the context of content-delivery networks with CAMAC yields the content-delivery probability in different closed forms. In addition, the gain in the probability due to CAMAC is shown to grow with the level of skewness in the content popularity distribution.

Scavenging the idling computation resources at the enormous number of mobile devices, ranging from small IoT devices to powerful laptop computers, can provide a powerful platform for local mobile cloud computing. The vision can be realized by peer-to-peer cooperative computing between edge devices, referred to as co-computing. This paper exploits the non-causal helper's CPU-state information to design energy-efficient co-computing policies for scavenging time-varying spare computation resources at peer mobiles. Specifically, we consider a co-computing system where a user offloads computation of input data to a helper. The helper controls the offloading process for the objective of minimizing the user's energy consumption based on a predicted helper's CPU-idling profile that specifies the amount of available computation resource for co-computing. Consider the scenario that the user has one-shot input-data arrival and the helper buffers offloaded bits. The problem for energy-efficient co-computing is formulated as two sub-problems: the slave problem corresponding to adaptive offloading and the master one to data partitioning. Given a fixed offloaded data size, the adaptive offloading aims at minimizing the energy consumption for offloading by controlling the offloading rate under the deadline and buffer constraints. By deriving the necessary and sufficient conditions for the optimal solution, we characterize the structure of the optimal policies and propose algorithms for computing the policies. Furthermore, we show that the problem of optimal data partitioning for offloading and local computing at the user is convex, admitting a simple solution using the sub-gradient method. Finally, the developed design approach for co-computing is extended to the scenario of bursty data arrivals at the user accounting for data causality constraints. Simulation results verify the effectiveness of the proposed algorithms.

Mobile-edge computation offloading (MECO) is an emerging technology for enhancing mobiles' computation capabilities and prolonging their battery lifetime by offloading intensive computation from mobiles to nearby servers, such as base stations. In this paper, we study the energy-efficient resource-management policy for the asynchronous MECO system, where the mobiles have heterogeneous input-data arrival time instants and computation deadlines. First, we consider the general case with arbitrary arrival-deadline orders. Based on the monomial energy-consumption model for data transmission, an optimization problem is formulated to minimize the total mobile-energy consumption under the time-sharing and computation-deadline constraints. The optimal resource-management policy for data partitioning (for offloading and local computing) and time division (for transmissions) is obtained in (semi-)closed-form expression by using the block coordinate decent method. To gain further insight, we study the optimal resource-management design for two special cases. First, consider the case of identical arrival-deadline orders, i.e., a mobile with input data arriving earlier also needs to complete computation earlier. The optimization problem is reduced to two sequential problems corresponding to the optimal scheduling order and joint data-partitioning and time-division given the optimal order. It is found that the optimal time-division policy tends to equalize the defined effective computing power among offloading mobiles via time sharing. Furthermore, this solution approach is extended to the case of reverse arrival-deadline orders. The corresponding time-division policy is derived by a proposed transformation-and-scheduling approach that first determines the total offloading duration and data size for each mobile in the transformation phase and then specifies the offloading intervals for each mobile in the scheduling phase.

Attaining the vision of Smart Cities requires the deployment of an enormous number of sensors for monitoring various conditions of the environment. Backscatter sensors have emerged to be a promising solution due to the uninterruptible energy supply and relative simple hardwares. On the other hand, backscatter sensors with limited signal processing capabilities are unable to support conventional algorithms for multiple access and channel training. Thus, the key challenge in designing backscatter sensor networks is to enable readers to accurately detect sensing values given simple ALOHA random access, primitive transmission schemes, and no knowledge of channel states. We tackle this challenge by proposing the novel framework of backscatter sensing (BackSense) featuring random encoding at sensors and statistical inference at readers. Specifically, assuming the on/off keying for backscatter transmissions, the practical random encoding scheme causes the on/off transmission of a sensor to follow a distribution parameterized by the sensing values. Facilitated by the scheme, statistical inference algorithms are designed to enable a reader to infer sensing values from randomized transmissions by multiple sensors. The specific design procedure involves the construction of Bayesian networks, namely deriving conditional distributions for relating unknown parameters and variables to signals observed by the reader. Then based on the Bayesian networks and the well-known expectation-maximization principle, inference algorithms are derived to recover sensing values. Simulation of the BackSense system demonstrates high accuracy in reader inference despite the mentioned limitations of backscatter sensors, which grows with increasing numbers of received symbols and reader antennas.

The millimeter-wave (mmWave) communication exploits tens-of-GHz of available spectrum in the mmWave band for solving the problem of spectrum scarcity in next-generation wireless networks. Maintaining connectivity in urban mmWave networks is one key design challenge because of the blockage effect characterizing mmWave propagation. Specifically, mmWave signals can be blocked by buildings and other large urban objects. Thus, the type of urban model affects the performance of mmWave networks. In this paper, we make the first attempt to study the connectivity of mmWave networks in a Manhattan-type region modeled using a random lattice while base stations (BSs) are Poisson distributed in the plane. In particular, we define and analyze the connectivity probability that a typical user is within the transmission range of a BS and connected by a line-of-sight. By jointly applying random-lattice and stochastic-geometry theories, different lower bounds on the connectivity probability are derived as functions of the buildings' size and distribution as well as the BSs' transmission range and density. We also investigate the asymptotic connectivity probability for the case of dense buildings. Our study yields closed-form relations between the parameters of the building process and the BS process, providing useful guidelines for practical mmWave network deployment and opening up many directions for future extensions.

As multimedia content is becoming increasingly dominant in mobile data traffic, low-latency content delivery will be a key feature for next-generation radio access networks. For wireless content delivery, many signals over the air carry identical popular content. However, they can interfere with each other at a receiver if their modulation-and-coding (MAC) schemes are adapted to individual channels. To cope with this issue, we present a novel idea of content adaptive MAC (CAMAC) to ensure that all signals carry identical content are encoded using an uniform MAC scheme and thus interference can be harnessed as signals, thereby improving the reliability of wireless delivery. In quantify the resultant performance gain, we consider a model of content delivery network where the content helpers are distributed as a Poisson point process and each of them randomly transmits a file based on a given popularity distribution. It is found that the (successful) content-delivery probability depends on the distribution of a shot-noise ratio, referring to the ratio of two independent shot-noise processes. Its distribution is an open mathematical problem that we tackle in this work using stable distribution theory and tools from stochastic geometry. The results allow the derivation of content-delivery probability in different closed forms. Then the gain in the probability due to CAMAC is quantified and shown to grow with the level of skewness in the content popularity distribution.

Future Internet-of-Things (IoT) is expected to wirelessly connect tens of billions of low- complexity devices. Extending the finite battery life of massive number of IoT devices is a crucial challenge. The ultra-low-power backscatter communications (BackCom) with the inherent feature of RF energy harvesting is a promising technology for tackling this challenge. Moreover, many future IoT applications will require the deployment of dense IoT devices, which induces strong interference for wireless information transfer (IT). To tackle these challenges, in this paper, we propose the design of a novel multiple-access scheme based on time-hopping spread-spectrum (TH-SS) to simultaneously suppress interference and enable both two-way wireless IT and one-way wireless energy transfer (ET) in coexisting backscatter reader-tag links. The performance analysis of the BackCom network is presented, including the bit-error rates for forward and backward IT and the expected energy-transfer rate for forward ET, which account for non-coherent and coherent detection at tags and readers, and energy harvesting at tags, respectively. Our analysis demonstrates a tradeoff between energy harvesting and interference performance. Thus, system parameters need to be chosen carefully to satisfy given BackCom system performance requirement.

The information-theoretic techniques can ensure security in communication regardless of the computational power of the attackers. The requirements for applying such techniques require: 1) an advantage over the eavesdroppers' quality of reception and 2) the location information on the eavesdropper. Traditionally, the performance of a secure communication link is measured using the metrics of secrecy capacity or outage probability, which are both related to the relative quality of the legitimate link compared with that of the eavesdropper link. In this paper, we present a new metric, called secrecy pressure, which measures the security level of the surface/environment where the legitimate link is embedded but is independent of the position of the eavesdropping node. The metric can be also visualized as a secrecy map. The analytical results show how the optimization of the secrecy pressure measure can lead to decide the optimum transmit antenna orientation and/or the position and power of an additional interfering node (friendly jammer).

Caching popular contents at base stations (BSs) of a heterogeneous cellular network (HCN) reduces latency in content delivery and alleviates congestion in backhaul networks. Despite the existence of many sub-optimal strategies for content placement, the optimal ones for HCNs remain largely unknown and are investigated in this paper. To this end, we adopt the popular HCN model where BSs are modeled as K tiers of independent Poisson point processes (PPPs). Further, the random caching scheme is considered where each of a database of M files with corresponding popularity measures is placed at each BS of a particular tier with a corresponding probability, called placement probability. The probabilities are identical for all BSs in the same tier but vary over tiers, giving the name tier- level content placement. The network performance is measured by hit probability, defined as the probability that a file requested by the typical user is delivered successfully to the user. Consider the case of a uniform received signal-to- interference (SIR) threshold for successful transmissions. The optimal policy is derived in a simple form allowing sequential computation of the optimal placement probabilities. The result shows that the probability for a particular file-and-tier combination is a monotone increasing function of the file's popularity and the tier's density, transmis- sion power and storage capacity. Furthermore, for the general case of non-uniform SIR threshold, the optimization problem is non-convex and a sub-optimal placement policy is designed by approximation. The close- to-optimal policy has a similar structure as that in the previous case.

The integration of two complementary technologies, millimeter-wave (mmWave) communications and massive multiple-input multiple-output (MIMO), will play a key role in enabling gigabit access in 5G systems. However, implementing mmWave massive MIMO using the traditional fully digital architecture will lead to prohibitive hardware complexity as it requires a massive number of RF chains matching antennas in number. To address this issue, the hybrid beamforming architecture has been recently proposed for efficient implementation of mmWave massive MIMO. Specifically, large-scale MIMO beamforming is implemented in the analog domain, called analog beamforming, that exploits the sparsity in mmWave channels for dramatic dimension reduction for digital MIMO signal processing. The typical phase-array implementation of analog beamforming introduces the uni-modulus constraints on the beamforming coefficients and renders the classic MIMO techniques unsuitable. This motivates the novel design framework, called Kronecker analog beamforming, proposed in this paper for multi-cell multiuser massive MIMO systems over mmWave channels characterized by sparse propagation paths. The framework relies on the decomposition of analog beamforming vectors and path observation vectors into Kronecker products of factor vectors with uni-modulus elements. Exploiting the properties of Kronecker product, different factors of the analog beamformer are designed for either nulling interference paths or coherently combining data paths. Thereby, Kronecker analog beamforming achieves interference nulling and signal enhancement both in the analog domain as well as dimension reduction for digital beamforming.

Conventional mobile computation offloading relies on offline prefetching that fetches user-specific data to the cloud prior to computing. For computing depending on real-time inputs, the offline operation can result in fetching large volumes of redundant data over wireless channels and unnecessarily consumes mobile-transmission energy. To address this issue, we propose the novel technique of online prefetching for a large-scale program with numerous tasks, which seamlessly integrates task-level computation prediction and real-time prefetching within the program runtime. The technique not only reduces mobile-energy consumption by avoiding excessive fetching but also shortens the program runtime by parallel fetching and computing enabled by prediction. By modeling the sequential task transition in an offloaded program as a Markov chain, stochastic optimization is applied to design the online-fetching policies to minimize mobile-energy consumption for transmitting fetched data over fading channels under a deadline constraint. The optimal policies for slow and fast fading are shown to have a similar threshold-based structure that selects candidates for the next task by applying a threshold on their likelihoods and furthermore uses them controlling the corresponding sizes of prefetched data. In addition, computation prediction for online prefetching is shown theoretically to always achieve energy reduction.

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) seamlessly integrates two wireless technologies, mmWave communications and massive MIMO, which provides spectrums with tens of GHz of total bandwidth and supports aggressive space division multiple access using large-scale arrays. Though it is a promising solution for next-generation systems, the realization of mmWave massive MIMO faces several practical challenges. In particular, implementing massive MIMO in the digital domain requires hundreds to thousands of radio frequency chains and analog-to-digital converters matching the number of antennas. Furthermore, designing these components to operate at the mmWave frequencies is challenging and costly. These motivated the recent development of the hybrid-beamforming architecture, where MIMO signal processing is divided for separate implementation in the analog and digital domains, called the analog and digital beamforming, respectively. Analog beamforming using a phase array introduces uni-modulus constraints on the beamforming coefficients. They render the conventional MIMO techniques unsuitable and call for new designs. In this paper, we present a systematic design framework for hybrid beamforming for multi-cell multiuser massive MIMO systems over mmWave channels characterized by sparse propagation paths. The framework relies on the decomposition of analog beamforming vectors and path observation vectors into Kronecker products of factors being uni-modulus vectors. Exploiting properties of Kronecker mixed products, different factors of the analog beamformer are designed for either nulling interference paths or coherently combining data paths. Furthermore, a channel estimation scheme is designed for enabling the proposed hybrid beamforming. The scheme estimates the angles-of-arrival (AoA) of data and interference paths by analog beam scanning and data-path gains by analog beam steering. The performance of the channel estimation scheme is analyzed. In particular, the AoA spectrum resulting from beam scanning, which displays the magnitude distribution of paths over the AoA range, is derived in closed form. It is shown that the inter-cell interference level diminishes inversely with the array size, the square root of pilot sequence length, and the spatial separation between paths, suggesting different ways of tackling pilot contamination.

With the drastic increase of mobile devices, there are more and more mobile traffic and repeated requests for content. In 5G networks, small cell base stations (SBSs) caching and caching in wireless device-to-device network can effectively decrease the mobile traffic during peak hours. Currently, most of the related work is focused on how to cache content on SBSs and on mobile devices, and it is assumed that the user can download the entire requested content through the connected SBSs and mobile devices. However, few works have taken user mobility and the randomness of contact duration into consideration. How to improve the caching strategy by exploiting user mobility is still a challenging problem. Thus, in this paper, we first investigate the problem of how to conduct caching placement on SBS and on mobile devices leveraging user mobility, aiming to maximize the cache hit ratio. Specifically, the caching placement on SBSs and on mobile devices is formulated as an integer programming problem, and submodular optimization is adopted to solve the formulated problem. Then, we give the optimal transmission power of SBSs and mobile devices to deliver the caching content in order to reduce the energy cost. Simulation results prove that our caching strategy is more efficient than other existing caching strategies in terms of both cache hit ratio and energy efficiency.

Future Internet-of-Things (IoT) will connect billions of small computing devices embedded in the environment and support their device-to-device (D2D) communication. Powering the massive number of embedded devices is a key challenge of designing IoT, since batteries increase the devices' form factors and battery recharging/replacement is difficult. To tackle this challenge, we propose a novel network architecture that enables D2D communication between passive nodes by integrating wireless power transfer and backscatter communication, which is called a wirelessly powered backscatter communication (WP-BackCom) network. In this network, standalone power beacons (PBs) are deployed for wirelessly powering nodes by beaming unmodulated carrier signals to targeted nodes. Provisioned with a backscatter antenna, a node transmits data to an intended receiver by modulating and reflecting a fraction of a carrier signal. Such transmission by backscatter consumes orders-of-magnitude less power than a traditional radio. Thereby, the dense deployment of low-complexity PBs with high transmission power can power a large-scale IoT. In this paper, a WP-BackCom network is modeled as a random Poisson cluster process in the horizontal plane where PBs are Poisson distributed and active ad hoc pairs of backscatter communication nodes with fixed separation distances form random clusters centered at PBs. The backscatter nodes can harvest energy from and backscatter carrier signals transmitted by PBs. Furthermore, the transmission power of each node depends on the distance from the associated PB. Applying stochastic geometry, the network coverage probability and transmission capacity are derived and optimized as functions of backscatter parameters, including backscatter duty cycle, reflection coefficient, and the PB density. The effects of the parameters on network performance are quantified.

Mobile computation offloading refers to techniques for offloading computation intensive tasks from mobile devices to the cloud so as to lengthen the formers' battery lives and enrich their features. The conventional designs fetch (transfer) user-specific data from mobiles to the cloud prior to computing, called offline prefetching. However, this approach can potentially result in excessive fetching of large volumes of data and cause heavy loads on radio-access networks. To solve this problem, the novel technique of live prefetching, which seamlessly integrates the task-level computation prediction and prefetching within the cloud-computing process of a large program with numerous tasks, is proposed in this paper. The technique avoids excessive fetching but retains the feature of leveraging prediction to reduce the program runtime and mobile transmission energy. By modeling the tasks in an offloaded program as a stochastic sequence, stochastic optimization is applied to design fetching policies to minimize mobile energy consumption under a deadline constraint. The policies enable real-time control of the prefetched-data sizes of candidates for future tasks. For slow fading, the optimal policy is derived and shown to have a threshold-based structure, selecting candidate tasks for prefetching and controlling their prefetched data based on their likelihoods. The result is extended to design close-to-optimal prefetching policies to fast fading channels. Compared with fetching without prediction, live prefetching is shown theoretically to always achieve reduction on mobile energy consumption.

Future Internet-of-Things (IoT) is expected to wirelessly connect billions of low-complexity devices. For wireless information transfer (IT) in IoT, high density of IoT devices and their ad hoc communication result in strong interference, which acts as a bottleneck on wireless IT. Furthermore, battery replacement for the massive number of IoT devices is difficult if not infeasible, making wireless energy transfer (ET) desirable. This motivates: 1) the design of full-duplex wireless IT to reduce latency and enable efficient spectrum utilization and 2) the implementation of passive IoT devices using backscatter antennas that enable wireless ET from one device (reader) to another (tag). However, the resultant increase in the density of simultaneous links exacerbates the interference issue. This issue is addressed in this paper by proposing the design of full-duplex backscatter communication (BackCom) networks, where a novel multiple-access scheme based on time-hopping spread-spectrum is designed to enable both one-way wireless ET and two-way wireless IT in coexisting backscatter reader-tag links. Comprehensive performance analysis of BackCom networks is presented in this paper, including forward/backward bit-error rates and wireless ET efficiency and outage probabilities, which accounts for energy harvesting at tags, non-coherent and coherent detection at tags and readers, respectively, and the effects of asynchronous transmissions.

Information-theoretical approaches can ensure security, regardless of the computational power of the attackers. Requirements for the application of this theory are: 1) assuring an advantage over the eavesdropper quality of reception and 2) knowing where the eavesdropper is. The traditional metrics are the secrecy capacity or outage, which are both related to the quality of the legitimate link against the eavesdropper link. Our goal is to define a new metric, which is the characteristic of the security of the surface/environment where the legitimate link is immersed, regardless of the position of the eavesdropping node. The contribution of this paper is twofold: 1) a general framework for the derivation of the secrecy capacity of a surface, which considers all the parameters that influence the secrecy capacity and 2) the definition of a new metric to measure the secrecy of a surface: the secrecy pressure. The metric can be also visualized as a secrecy map, analogously to weather forecast. Different application scenarios are shown: from “forbidden zone” to Gaussian mobility model for the eavesdropper. Moreover, the secrecy outage probability of a surface is derived. This additional metric can measure, which is the secrecy rate supportable by the specific environment.

While two-way communication can improve the spectral efficiency of wireless networks, distances from the relay to the two users are usually asymmetric, leading to excessive wireless energy at the nearby user. To exploit the excessive energy, energy harvesting at user terminals is a viable option. Unfortunately, the exact gain brought by wireless power transfer (WPT) in two-way communication is currently unknown. To fill this gap, in this paper, the achievable rate region of wirelessly powered two-way communication with a fixed relay is derived. Not only this newly established result is shown to enclose the existing achievable rate region of two-way relay channel without energy harvesting but also the gain is precisely quantified. On the other hand, it is well-known that a major obstacle to WPT is the path-loss. By endowing the relay with mobility, the distances between the relay and users can be varied, thus providing a potential solution to combat pathloss at the expense of energy for transmission. To characterize the consequence brought by such a scheme, a pair of inner and outer bounds to the achievable rate region of wirelessly powered two-way communication under a mobile relay is further derived. By comparing the exact achievable rate region for the fixed relay case and the achievable rate bounds for the mobile relay case, it is possible to quantify the relative advantage of spending energy on moving versus on transmission in wirelessly powered two-way communication.

Caching popular contents at base stations (BSs) of a heterogeneous cellular network (HCN) avoids frequent information passage from content providers to the network edge, thereby reducing latency and alleviating traffic congestion in backhaul links. The potential of caching at the network edge for tackling 5G challenges has motivated recent studies on optimal content placement in large-scale HCNs. However, due to the complexity of the network performance analysis, the existing strategies were mostly based on approximation, heuristics, and intuition. In general, optimal strategies for content placement in HCNs remain largely unknown and deriving them forms the theme of this paper. To this end, we adopt the popular random HCN model, where K tiers of BSs are modeled as independent Poisson point processes distributed in the plane with different densities. Furthermore, the random caching scheme is considered, where each of a given set of M files with corresponding popularity measures is placed at each BS of a particular tier with a corresponding probability, called placement probability. The probabilities are identical for all BSs in the same tier but vary over tiers, giving the name tier-level content placement. We consider the network performance metric, hit probability, defined as the probability that a file requested by the typical user is delivered successfully to the user. Leveraging existing results on HCN performance, we maximize the hit probability over content placement probabilities, which yields the optimal tierlevel placement policies. For the case of uniform received signalto-interference (SIR) thresholds for successful transmissions for BSs in different tiers, the policy is in closed-form, where the placement probability for a particular file is proportional to the square-root of the corresponding popularity measure with an offset depending on BS caching capacities. For the general case of non-uniform SIR thresholds, the optimization problem is non-convex and a sub-optimal placement policy is designed by approximation, which has a similar structure as in the case of uniform SIR thresholds and shown by simulation to be close-tooptimal.

Mobile-edge computation offloading (MECO) off-loads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we study resource allocation for a multiuser MECO system based on time-division multiple access (TDMA) and orthogonal frequency-division multiple access (OFDMA). First, for the TDMA MECO system with infinite or finite cloud computation capacity, the optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under the constraint on computation latency. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Moreover, for the cloud with finite capacity, a sub-optimal resource-allocation algorithm is proposed to reduce the computation complexity for computing the threshold. Next, we consider the OFDMA MECO system, for which the optimal resource allocation is formulated as a mixed-integer problem. To solve this challenging problem and characterize its policy structure, a low-complexity sub-optimal algorithm is proposed by transforming the OFDMA problem to its TDMA counterpart. The corresponding resource allocation is derived by defining an average offloading priority function and shown to have close-to-optimal performance in simulation.

Future Internet-of-Things (IoT) will connect billions of small computing devices embedded in the environment and support their device-to-device (D2D) communication. Powering this massive number of embedded devices is a key challenge of designing IoT since batteries increase the devices' form factors and their recharging/replacement is difficult. To tackle this challenge, we propose a novel network architecture that integrates wireless power transfer and backscatter communication, called wirelessly powered backscatter communication (WP-BC) networks. In this architecture, power beacons (PBs) are deployed for wirelessly powering devices; their ad-hoc communication relies on backscattering and modulating incident continuous waves from PBs, which consumes orders-of-magnitude less power than traditional radios. Thereby, the dense deployment of lowcomplexity PBs with high transmission power can power a largescale IoT. In this paper, a WP-BC network is modeled as a random Poisson cluster process in the horizontal plane where PBs are Poisson distributed and active ad-hoc pairs of backscatter communication nodes with fixed separation distances form random clusters centered at PBs. Furthermore, by harvesting energy from and backscattering radio frequency (RF) waves transmitted by PBs, the transmission power of each node depends on the distance from the associated PB. Applying stochastic geometry, the network coverage probability and transmission capacity are derived and optimized as functions of the backscatter reflection coefficient and duty cycle as well as the PB density. The effects of the parameters on network performance are characterized.

The correlation of interference has been well quantified in Poisson networks where the interferers are independent of each other. However, there exists dependence among the base stations (BSs) in wireless networks. In view of this, we study the interference correlation in non-Poisson networks where the interferers are distributed as a Matern cluster process (MCP) and a second-order cluster process (SOCP). We obtain the explicit expressions for the interference correlation coefficients under these two cases and find that they are the same if these two cluster processes have the identical cluster radius and average number of each cluster. We also prove that they are greater than their counterpart for the Poisson networks, which indicates the clustering in interferers increases the interference correlation. It is also shown that the value of the correlation coefficient goes up as the the attraction between the interferers increases. Finally, the numerical results show the relation between the correlation coefficients and system parameters.

Mobile-edge computation offloading (MECO) offloads intensive mobile computation to clouds located at the edges of cellular networks. Thereby, MECO is envisioned as a promising technique for prolonging the battery lives and enhancing the computation capacities of mobiles. In this paper, we consider resource allocation in a MECO system comprising multiple users that time share a single edge cloud and have different computation loads. The optimal resource allocation is formulated as a convex optimization problem for minimizing the weighted sum mobile energy consumption under constraint on computation latency and for both the cases of infinite and finite edge cloud computation capacities. The optimal policy is proved to have a threshold-based structure with respect to a derived offloading priority function, which yields priorities for users according to their channel gains and local computing energy consumption. As a result, users with priorities above and below a given threshold perform complete and minimum offloading, respectively. Computing the threshold requires iterative computation. To reduce the complexity, a sub-optimal resource-allocation algorithm is proposed and shown by simulation to have close-to-optimal performance.

A practical architecture for wirelessly powered communications (WPC) with dedicated power beacons (PBs) deployed in existing cellular networks, called PB-assisted WPC, is considered in this paper. Assuming those PBs can access to backhaul network and perform simultaneous wireless information and power transfer (SWIPT) to the energy constrained users, the near-far problem in the PB-assisted WPC system is first identified. Specifically, the significant difference of the received power of SWIPT signal (from a PB) and information transfer (IT) signal (from a base station) due to different transmission ranges leads to extremely small signal-to-quantization-noise ratio (SQNR) for the IT signal after quantization of the mixed signals. To retrieve the information carried by the SWIPT and IT signals respectively, it is essential to decouple the strong SWIPT and the weak IT signals in analog domain. To this end, a novel technique called analog spatial decoupling using only simple components such as phase shifters and adders is proposed in this paper. In particular, for the single-PB case, the optimal Fourier based and Hadamard based schemes are proposed for implementing the analog spatial decoupling. For the multiple-PB case, the corresponding design problem is more challenging, making it hard to extend the solution for the single-PB counterpart. To tackle this problem, a systematic solution approach is proposed for analog decoupling with multiple PBs.

Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper presents a novel solution that seamlessly integrates two technologies, mobile cloud computing and microwave power transfer (MPT), to enable computation in passive low-complexity devices such as sensors and wearable computing devices. Specifically, considering a single-user system, a base station (BS) either transfers power to or offloads computation from a mobile to the cloud; the mobile uses harvested energy to compute given data either locally or by offloading. A framework for energy efficient computing is proposed that comprises a set of policies for controlling CPU cycles for the mode of local computing, time division between MPT and offloading for the other mode of offloading, and mode selection. Given the CPU-cycle statistics information and channel state information (CSI), the policies aim at maximizing the probability of successfully computing given data, called computing probability, under the energy harvesting and deadline constraints. The policy optimization is translated into the equivalent problems of minimizing the mobile energy consumption for local computing and maximizing the mobile energy savings for offloading which are solved using convex optimization theory. The structures of the resultant policies are characterized in closed form. Furthermore, given non-causal CSI, the said analytical framework is further developed to support computation load allocation over multiple channel realizations, which further increases the computing probability. Last, simulation demonstrates the feasibility of wirelessly powered mobile cloud computing and the gain of its optimal control.

The implementation of wireless power transfer in wireless communication systems opens up a new research area, known as wirelessly powered communications (WPC). In next-generation heterogeneous networks where ultradense small-cell base stations are deployed, simultaneous-wireless-information-and-power-transfer (SWIPT) is feasible over short ranges. One challenge for designing a WPC system is the severe near-far problem where a user attempts to decode an information-transfer (IT) signal in the presence of extremely strong SWIPT signals. Jointly quantizing the mixed signals causes the IT signal to be completely corrupted by quantization noise, and thus the SWIPT signals have to be suppressed in the analog domain. This motivates the design of a framework in this paper for analog spatial cancellation in a multiantenna WPC system. In the framework, an analog circuit consisting of simple phase shifters and adders is adapted to cancel the SWIPT signals by multiplying it with a cancellation matrix having unit-modulus elements and full rank, where the full rank retains the spatial-multiplexing gain of the IT channel. The unit-modulus constraints render the conventional zero-forcing method unsuitable. Therefore, this paper presents a novel systematic approach for constructing cancellation matrices. For the single-SWIPT-interferer case, the matrices are obtained as truncated Fourier/Hadamard matrices after compensating for propagation phase shifts over the SWIPT channel. For the more challenging multiple-SWIPT-interferer case, it is proposed that each row of the cancellation matrix is constructed as a Kronecker product of component vectors, with each component vectors designed to null the signal from a corresponding SWIPT interferer similarly as in the preceding case.

The paper presents a new indoor system where a large-scale array of dense distributed antennas, called a ubiquitous array (UA), is deployed for simultaneous wireless information-and- power transfer (SWIPT) to passive mobile devices. Thereby, the UA not only provides energy-efficient wireless access to mobiles but also eliminate their need for battery recharging via cables. For tractability, a single-user system is considered where the UA is modeled as a continuous spherical array and a K-antenna receiver is located at the UA center. Free-space channels are assumed. Then using the theories of EM-wave propagation and spherical harmonics, a fundamental tradeoff between the information transfer (IT) and power transfer (PT) is discovered and described as follows. As the separation distances between receive antennas uniformly decrease, the PT efficiency, namely the ratio between the transmit and receive powers, is increased by K times but the spatial multiplexing gain for IT reduces from K to one. In addition, with receive antennas fixed, the PT efficiency is shown to be independent with the propagation distance (or equivalently the UA radius), suppressing path loss that is the bottleneck for efficient microwave power transfer.

Achieving long battery lives or even self sustainability has been a long standing challenge for designing mobile devices. This paper seamlessly integrates two promising energy-saving technologies, namely mobile computation offloading (MCO) and microwave power transfer (MPT), and proposes a novel design framework of wirelessly powered MCO. Consider a single-user system where a base station (BS) either transfers power to or offloads computation from a mobile. Two mobile operation modes, namely local computation and offloading, are optimized separately for maximizing the mobile energy savings. For local computation, the non-convex problem of optimizing the CPU-cycle frequencies under the deadline and energy causality constraints is solved via convex relaxation. The optimal CPU-cycle frequencies are shown to have different forms depending on the BS transmission power. For offloading, the time duration before the deadline is divided for separate MPT and offloading and the optimal division is derived in a closed form. By combining above results, the optimal offloading decision is analyzed with respect to the deadline, data-input size and BS transmission power and validated by simulation.

The inefficiency of the cellular-network architecture has prevented the promising theoretic gains of communication technologies such as network MIMO, massive MIMO and distributed antennas from fully materializing in practice. The revolutionary cell-less cloud radio access networks (C-RANs) are under active development to overcome the drawbacks of cellular networks. In C-RANs, centralized cloud signal processing and minimum onsite hardware make it possible to deploy ubiquitous distributed antennas and coordinate them to form a gigantic array, called the ubiquitous array (UA). This paper focuses on designing techniques for UA communications and characterizing their performance. To this end, the UA is modeled as a continuous circular array enclosing target mobiles and free-space propagation is assumed, which allows the use of mathematical tools including Fourier series and Bessel functions in the analysis. First, exploiting the UA's large circular structure, a novel scheme for multiuser channel estimation is proposed to support noiseless channel estimation using only single pilot symbols. Channel estimation errors due to interference are proved to be Bessel functions of inter-user distances normalized by the wavelength. Besides increasing the distances, it is shown that the errors can be also suppressed by using pilot sequences and eliminated if the sequence length is longer than the number of mobiles. Next, for data communication, we first consider channel conjugate transmission that compensates the phase shift in propagation and thereby allows receive coherent combining. The multiuser interference powers are derived as Bessel functions of normalized inter-user distances. Last, we propose the design of multiuser precoders in the form of Fourier series whose coefficients excite different phase modes of the UA. Under the zero-forcing constraints, the precoder coefficients are proved to lie in the null space of a derived matrix with elements being Bessel functions of normalized inter-user distances.

Cloud storage allows users to outsource their data to a storage server. For general security and privacy concerns, users prefer storing encrypted data to pure ones so that servers do not learn anything about privacy. However, there is a natural issue that servers have worked some analyses (i.e. statistics) or routines for encrypted data without losing privacy. In this paper, we address the basic functionality, equality test, over encrypted data, which at least can be applied to specific analyses like private information retrieval. We introduce a new system, called filtered equality test, which is an additional functionality for existing public key encryption schemes. It satisfies the following scenario: a ciphertext-receiver selects several messages as a set and produces its related warrant; then, on receiving this warrant, an user is able to perform equality test on the receiver's ciphertext without decryption when the hidden message belongs to that message set. Similar to the attribute based encryption, ABE. In ABE schemes, those ones who match the settled conditions could get the privilege of decryption. In FET schemes, those ‘messages inside selected set’ can be equality tested. Combining PKE schemes and filtered equality test, we propose a framework of public key encryption scheme with filtered equality test, abbreviated as PKE-FET. Then, taking ElGamal for example, we propose a concrete PKE-FET scheme based on secret sharing and bilinear map. Finally, we prove our proposition with semantic security in the standard model.

In this paper, we propose a new notion of public key encryption scheme with authorized equality test (PKE-AET), which allows authorized users those who have warrants to test the equivalence between two messages, where the messages are encrypted using different public keys. Comparing with the existing researches, our PKE-AET provides two kinds of warrants that are referred to as receiver's warrants and cipher-warrants. The proposed PKE-AET is able to deal with the following complicated scenario: Assume that a receiver authorizes a receiver's warrant to a tester, which makes the tester be able to perform equality test on all of receivers’ ciphertext; on the other hand, if receiver authorizes a cipher-warrant corresponding to a specific ciphertext to the tester, then the tester only acquires the equality test on that particular ciphertext. The equality between two ciphertexts can be verified by the tester without decryption after he or she receives two warrants and varies their validations. Moreover, for security analysis, we define two types of adversaries and security notions for PKE-AET in the multi-user setting. Furthermore, we prove that our PKE-AET is one-way CCA secure against type-I adversaries and IND-CCA secure against type-II adversaries. Finally, the proposed scheme leads better efficiency than most of previous equality test schemes.

The papers in this special issue presents cutting-edge research results in the emerging area of energy harvesting wireless communications and wireless energy transfer. This first issue starts with a review article coauthored by the guest editors that summarizes recent results in the broad area of energy harvesting communications, in particular, in information-theoretic, offline and online schedulingtheoretic, medium access, networking approaches to energy harvesting communications, as well as in energy cooperation and simultaneous wireless energy and information transfer.

This paper summarizes recent contributions in the broad area of energy harvesting wireless communications. In particular, we provide the current state of the art for wireless networks composed of energy harvesting nodes, starting from the information-theoretic performance limits to transmission scheduling policies and resource allocation, medium access, and networking issues. The emerging related area of energy transfer for self-sustaining energy harvesting wireless networks is considered in detail covering both energy cooperation aspects and simultaneous energy and information transfer. Various potential models with energy harvesting nodes at different network scales are reviewed, as well as models for energy consumption at the nodes.

Certificateless public key cryptography CL-PKC is a cryptosystem solving the key escrow problem of identity-based cryptography. One of the applications of CL-PKC is certificateless aggregate signature CLAS that in practice can be used to efficiently verify concealed data aggregation in wireless sensor networks. CLAS is referred to as an extension of certificateless signature, which in particular performs verification for many signatures efficiently. Therefore, not only plenty of CLAS schemes have been proposed but also the security models of CLAS were introduced in the literature. Recently, some CLAS schemes are extended from specific certificateless signature CLS schemes. However, we found that two certificateless signature CLS and their corresponding CLAS schemes are not secure. In this paper, we simplify the relation of security definitions of CLS and CLAS. Then, a new CLAS scheme is proposed, which leads to the advantages of both certificateless cryptography and aggregate signature. Moreover, our scheme only depends on constant pairing operations to verify a large number of signatures per time, because pairing is a complicated operation with high cost in computations. Copyright © 2014 John Wiley & Sons, Ltd.

Powering radio access networks using renewables, such as wind and solar power, promises dramatic reduction in the network operation cost and the network carbon footprints. However, the spatial variation of the energy field can lead to fluctuations in power supplied to the network and thereby affects its coverage. This warrants research on quantifying the aforementioned negative effect and designing countermeasure techniques, motivating the current work. First, a novel energy field model is presented, in which fixed maximum energy intensity γ occurs at Poisson distributed locations, called energy centers. The intensities fall off from the centers following an exponential decay function of squared distance and the energy intensity at an arbitrary location is given by the decayed intensity from the nearest energy center. The product between the energy center density and the exponential rate of the decay function, denoted as ψ, is shown to determine the energy field distribution. Next, the paper considers a cellular downlink network powered by harvesting energy from the energy field and analyzes its network coverage. For the case of harvesters deployed at the same sites as base stations (BSs), as γ increases, the mobile outage probability is shown to scale as (cγ-πψ+ p), where p is the outage probability corresponding to a flat energy field and c is a constant. Subsequently, a simple scheme is proposed for counteracting the energy randomness by spatial averaging. Specifically, distributed harvesters are deployed in clusters and the generated energy from the same cluster is aggregated and then redistributed to BSs. As the cluster size increases, the power supplied to each BS is shown to converge to a constant proportional to the number of harvesters per BS. Several additional issues are investigated in this paper, including regulation of the power transmission loss in energy aggregation and extensions of the energy field model.

Wireless recharging can be realized by microwave power transfer (MPT) that delivers energy wirelessly from stations called power beacons (PBs) to mobile devices by microwave radiation. To implement mobile charging by MPT, this paper proposes a new network architecture that overlays an uplink cellular network with randomly deployed PBs for powering mobiles, called a hybrid network. We investigate the deployment of the hybrid network under an outage constraint on data links by developing a stochastic-geometry network model where single-antenna base stations (BSs) and PBs form independent homogeneous Poisson point processes with densities λband λp, respectively, and single-antenna passive mobiles are uniformly distributed in Voronoi cells generated by BSs. In this model, the transmission powers of mobiles and PBs are fixed to be constants p and q, respectively. Moreover, a PB either radiates isotropically, called isotropic MPT, or directs energy towards target mobiles by beamforming, called directed MPT. The model is used to derive the tradeoffs between the network parameters {p, λb, q, λp) under the outage constraint and assuming infinite energy storage at mobiles. It is shown that for isotropic MPT, the product qλpλbα/2has to be above a given threshold so that PBs are sufficiently dense; for directed MPT, zmqλpλbα/2with zmdenoting the array gain should exceed a different threshold to ensure short distances between PBs and their target mobiles. In addition, similar results are derived for the case of mobiles having small energy storage.

The performance of wireless networks is fundamentally limited by interference [or, equivalently, the signal-to-interference ratio (SIR)]. In an attempt to characterize the interference as a direction-selective quantity and motivated by the useful analogy between classical propagation channels and wireless networks, we propose a novel network description framework, namely the interference azimuth spectrum (IAS). The IAS represents the distribution of interference in the angular domain and is parallel to the conventional power azimuth spectrum (PAS) used in propagation channels. We also extend this concept to the directional characterization of average achievable rate, assuming that interference is treated as noise. Provided with this analytical framework, we present the notion of local area outage, defined as the probability that a receiver is in the state of outage within a local area where both interference and desired signal are assumed to be wide-sense stationary (WSS). We further propose the geometry-based stochastic models (GBSMs) as a part of the IAS framework, where the interfering terminals are randomly distributed according to a specific probability density function (pdf) of their positions. The GBSMs are applicable to a wide variety of wireless network environments without and with interferer clustering. The proposed methodology would provide useful insight on the design and performance assessment of future networks, featured by opportunistic, randomized, and dense placement of nodes.

Microwave power transfer (MPT) delivers energy wirelessly from stations called power beacons (PBs) to mobile devices by microwave radiation. This provides mobiles practically infinite battery lives and eliminates the need of power cords and chargers. To enable MPT for mobile recharging, this paper proposes a new network architecture that overlays an uplink cellular network with randomly deployed PBs for powering mobiles, called a hybrid network. The deployment of the hybrid network under an outage constraint on data links is investigated based on a stochastic-geometry model where single-antenna base stations (BSs) and PBs form independent homogeneous Poisson point processes (PPPs) with densities λband λp, respectively, and single-antenna mobiles are uniformly distributed in Voronoi cells generated by BSs. In this model, mobiles and PBs fix their transmission power at p and q, respectively; a PB either radiates isotropically, called isotropic MPT, or directs energy towards target mobiles by beamforming, called directed MPT. The model is used to derive the tradeoffs between the network parameters (p, λb, q, λp) under the outage constraint. First, consider the deployment of the cellular network. It is proved that the outage constraint is satisfied so long as the product pλbα/2is above a given threshold where α is the path-loss exponent. Next, consider the deployment of the hybrid network assuming infinite energy storage at mobiles. It is shown that for isotropic MPT, the product qλpλbα/2has to be above a given threshold so that PBs are sufficiently dense; for directed MPT, zmqλpλbα/2with zmdenoting the array gain should exceed a different threshold to ensure short distances between PBs and their target mobiles. Furthermore, similar results are derived for the case of mobiles having small energy storage.

Far-field wireless recharging based on microwave power transfer (MPT) will free mobile devices from interruption due to finite battery lives. Integrating MPT with wireless communications to support simultaneous information-and-power transfer (SIPT) allows the same spectrum to be used for dual purposes without compromising the quality of service. In this paper, we propose the novel approach of realizing SIPT in a broadband downlink system where users are assigned orthogonal frequency sub-channels and a base station transfers information and energy to users over spatially separated channels called the data and MPT channels. Optimizing the power control for such a system results in a new class of multiuser power-control problems featuring the circuit-power constraints, namely that the wirelessly transferred power must be sufficiently large for operating receiver circuits. Solving these problems gives a set of power-control algorithms that exploit channel diversity in frequency for simultaneously enhancing the throughput and MPT efficiency. For the single-user SIPT system, the optimal power allocation is shown to perform water filling in frequency with water levels for different users depending on the corresponding MPT sub-channel gains. Next, an efficient power-control algorithm is proposed for the multiuser SIPT system based on sequential scheduling of mobiles by comparing their data rates and circuit-power constraints. This algorithm is proved to be optimal for the practical scenario of highly correlated data and MPT channels.

Wireless networks can be self sustaining by harvesting energy from ambient sources such as kinetic activities or electromagnetic radiation. In this paper, the spatial throughput of a mobile ad hoc network powered by energy harvesting is analyzed using a stochastic-geometry model where transmitters are Poisson distributed and powered by randomly arriving energy and each transmitter transmits with fixed power to an intended receiver under an outage constraint. We assume that harvested energy is stored in batteries with large capacity. The probability that a transmitter transmits, called transmission probability, is proved using the random-walk theory to be equal to one if the energy-arrival rate is larger than transmission power or otherwise equal to their ratio. This result and the stochastic-geometry theory are applied to maximize the network throughput by optimizing transmission power for a given energy-arrival rate. The maximum network throughput is shown to be proportional to the optimal transmission probability that is equal to one if the transmitter density is below a given function of the energy-arrival rate; otherwise the probability is smaller than one as derived. Moreover, the maximum network throughput is also obtained for the extreme cases of high energy-arrival rates or sparse/dense transmitters.

Designing mobiles to harvest ambient energy such as kinetic activities or electromagnetic radiation will enable wireless networks to be self-sustaining. In this paper, the spatial throughput of a mobile ad hoc network powered by energy harvesting is analyzed using a stochastic-geometry model. In this model, transmitters are distributed as a Poisson point process and energy arrives at each transmitter randomly with a uniform average rate called the energy arrival rate. Upon harvesting sufficient energy, each transmitter transmits with fixed power to an intended receiver under an outage-probability constraint for a target signal-to-interference-and-noise ratio. It is assumed that transmitters store energy in batteries with infinite capacity. By applying the random-walk theory, the probability that a transmitter transmits, called the transmission probability, is proved to be equal to the smaller of one and the ratio between the energy-arrival rate and transmission power. This result and tools from stochastic geometry are applied to maximize the network throughput for a given energy-arrival rate by optimizing transmission power. The maximum network throughput is shown to be proportional to the optimal transmission probability, which is equal to one if the transmitter density is below a derived function of the energy-arrival rate or otherwise is smaller than one and solves a given polynomial equation. Last, the limits of the maximum network throughput are obtained for the extreme cases of high energy-arrival rates and sparse/dense networks.

Multicell cooperation (MCC) is an approach for mitigating intercell interference in dense cellular networks. Existing studies on MCC performance typically rely on either oversimplified Wyner-type models or complex system-level simulations. The promising theoretical results (typically using Wyner models) seem to materialize neither in complex simulations nor in practice. To more accurately investigate the theoretical performance of MCC, this paper models an entire plane of interfering cells as a Poisson random tessellation. The base stations (BSs) are then clustered using a regular lattice, whereby BSs in the same cluster mitigate mutual interference by beamforming with perfect channel state information. Techniques from stochastic geometry and large-deviation theory are applied to analyze the outage probability as a function of the mobile locations, scattering environment, and the average number of cooperating BSs per clusterl. For mobiles near the centers of BS clusters, it is shown that outage probability diminishes asO(e-lν1) with 0 ≤ ν1≤ 1 if scattering is sparse, and asO(l-ν2) with ν2proportional to the signal diversity order if scattering is rich. For randomly located mobiles, regardless of scattering, outage probability is shown to scale asO(l-ν3) with 0 ≤ ν3≤ 0.5. These results confirm analytically that cluster-edge mobiles are the bottleneck for network coverage and provide a plausible analytic framework for more realistic analysis of other multicell techniques.

Wireless networks can be self-sustaining by harvesting energy from ambient radio-frequency (RF) signals. Recently, researchers have made progress on designing efficient circuits and devices for RF energy harvesting suitable for low-power wireless applications. Motivated by this and building upon the classic cognitive radio (CR) network model, this paper proposes a novel method for wireless networks coexisting where low-power mobiles in a secondary network, called secondary transmitters (STs), harvest ambient RF energy from transmissions by nearby active transmitters in a primary network, called primary transmitters (PTs), while opportunistically accessing the spectrum licensed to the primary network. We consider a stochastic-geometry model in which PTs and STs are distributed as independent homogeneous Poisson point processes (HPPPs) and communicate with their intended receivers at fixed distances. Each PT is associated with a guard zone to protect its intended receiver from ST's interference, and at the same time delivers RF energy to STs located in its harvesting zone. Based on the proposed model, we analyze the transmission probability of STs and the resulting spatial throughput of the secondary network. The optimal transmission power and density of STs are derived for maximizing the secondary network throughput under the given outage-probability constraints in the two coexisting networks, which reveal key insights to the optimal network design. Finally, we show that our analytical result can be generally applied to a non-CR setup, where distributed wireless power chargers are deployed to power coexisting wireless transmitters in a sensor network.

Multi-cell cooperation (MCC) is a promising approach for mitigating inter-cell interference in dense cellular networks. To study the MCC performance, existing work typically relies on the over-simplified Wyner-type models that fail to account for mobile spatial statistics, irregular locations of base stations (BSs) and the resultant highly variable path-loss. Unsurprisingly, real-world systems show gains far below those predicted using such idealized models. This paper adopts a stochastic-geometry model for a cellular downlink network with MCC where cells are modeled as a Poisson random tessellation generated by Poisson distributed BSs, these BSs are then clustered using a hexagonal lattice, and BSs in the same cluster mitigate mutual interference by spatial interference avoidance. We analyze the effects of scattering on network coverage as the average number of cooperative BSs, K, increases. For mobiles near the centers of cooperative BS clusters, we show that the outage probability diminishes with increasing K at least sub-exponentially for sparse scattering and following a power law for rich scattering where the exponent is proportional to the signal diversity order. For randomly located mobiles, the outage probability is shown to decrease with increasing K following a power law independent with scattering.

Interference alignment (IA) is a joint-transmission technique that achieves the capacity of the interference channel for high signal-to-noise ratios (SNRs). However, most prior works on IA are based on the impractical assumption that perfect and global channel-state information(CSI) is available at all transmitters, resulting in overwhelming feedback overhead. To substantially suppress the feedback overhead, this paper proposes an efficient design of the feedback framework for IA in the K-user multiple-input multiple-output (MIMO) interference channel. The proposed feedback topology supports sequential CSI exchange (feedback and feedforward) between transmitters and receivers and reduces the feedback overhead from a cubic function of K to a linear one, compared to conventional feedback approaches. Given the proposed feedback topology, we consider the limited feedback channel from the receivers to corresponding interferers and analyze the effect of quantization error which generates the residual interference. Also, an efficient feedback-bit allocation algorithm that minimizes the upper-bound of sum residual interference is proposed.

Interference between nodes is a critical impairment in mobile ad hoc networks. This paper studies the role of multiple antennas in mitigating such interference. Specifically, a network is studied in which receivers apply zero-forcing beamforming to cancel the strongest interferers. Assuming a network with Poisson-distributed transmitters and independent Rayleigh fading channels, the transmission capacity is derived, which gives the maximum number of successful transmissions per unit area. Mathematical tools from stochastic geometry are applied to obtain the asymptotic transmission capacity scaling and characterize the impact of inaccurate channel state information (CSI). It is shown that, if each node cancels interferers, the transmission capacity decreases as as the outage probability vanishes. For fixed , as grows, the transmission capacity increases as where is the path-loss exponent. Moreover, CSI inaccuracy is shown to have no effect on the transmission capacity scaling as vanishes, provided that the CSI training sequence has an appropriate length, which we derive. Numerical results suggest that canceling merely one interferer by each node may increase the transmission capacity by an order of magnitude or more, even when the CSI is imperfect.

This paper addresses the stability and queueing delay of space-division multiple access (SDMA) systems with bursty traffic, where zero-forcing beamforming enables simultaneous transmissions to multiple mobiles. Computing beamforming vectors relies on quantized channel state information (CSI) feedback (limited feedback) from mobiles. Define the stability region for SDMA as the set of multiuser packet-arrival rates for which the steady-state queue lengths are finite. Given perfect feedback of channel-direction information (CDI) and equal power allocation over scheduled queues, the stability region is proved to be a convex polytope having the derived vertices. A similar result is obtained for the case with perfect feedback of CDI and channel-quality information (CQI), where CQI allows scheduling and power control for enlarging the stability region. For any set of arrival rates in the stability region, multiuser queues are shown to be stabilized by the joint queue-and-beamforming control policy that maximizes the departure-rate-weighted sum of queue lengths. The stability region for limited feedback is found to be the perfect-CSI region multiplied by one minus a small factor. The required number of feedback bits per mobile is proved to scale logarithmically with the inverse of the above factor as well as linearly with the number of transmit antennas minus one. The effect of limited feedback on queueing delay is also quantified. CDI quantization errors are shown to multiply average queueing delay by a factorM>; 1. For givenM→ 1, the number of feedback bits per mobile is proved to beO(-log2(1-1/M)) .

Multi-antenna precoding effectively mitigates the interference in wireless networks. However, the resultant performance gains can be significantly compromised in practice if the precoder design fails to account for the inaccuracy in the channel state information (CSI) feedback. This paper addresses this issue by considering finite-rate CSI feedback from receivers to their interfering transmitters in the two-user multiple-input-multiple-output (MIMO) interference channel, called cooperative feedback, and proposing a systematic method for designing transceivers comprising linear precoders and equalizers. Specifically, each precoder/equalizer is decomposed into inner and outer components for nulling the cross-link interference and achieving array gain, respectively. The inner precoders/equalizers are further optimized to suppress the residual interference resulting from finite-rate cooperative feedback. Furthermore, the residual interference is regulated by additional scalar cooperative feedback signals that are designed to control transmission power using different criteria including fixed interference margin and maximum sum throughput. Finally, the required number of cooperative precoder feedback bits is derived for limiting the throughput loss due to precoder quantization.

Multi-cell cooperation is a promising approach for mitigating inter-cell interference in dense cellular networks. Quantifying the performance of multi-cell cooperation is challenging as it integrates physical-layer techniques and network topologies. For tractability, existing work typically relies on the over-simplified Wyner-type models. In this paper, we propose a new stochastic- geometry model for a cellular network with multi-cell cooperation, which accounts for practical factors including the irregular locations of base stations (BSs) and the resultant path-losses. In particular, the proposed network-topology model has three key features: i) the cells are modeled using a Poisson random tessellation generated by Poisson distributed BSs, ii) multi-antenna BSs are clustered using a hexagonal lattice and BSs in the same cluster mitigate mutual interference by spatial interference avoidance, iii) BSs near cluster edges access a different sub- channel from that by other BSs, shielding cluster-edge mobiles from strong interference. Using this model and assuming sparse scattering, we analyze the shapes of the outage probabilities of mobiles served by cluster-interior BSs as the average number K of BSs per cluster increases. The outage probability of a mobile near a cluster center is shown to be proportional to e(-c(2- √v)2) K where v is the fraction of BSs lying in the interior of clusters and c is a constant. Moreover, the outage probability of a typical mobile is proved to scale proportionally with e(-c(2- √v)2) K where c' is a constant.

Transmit beamforming increases throughput and transmission range of a wireless communication system. However, the required feedback of channel state information (CSI) consumes radio resources that otherwise can be used for data transmission. This makes "Feedback or no feedback?" a relevant question to ask. This paper answers this question by proposing intelligent feedback control using a Markov decision process. The feedback controller turns feedback on/off according to the channel state and the criterion of maximum net throughput, namely throughput minus average feedback cost. Assuming channel isotropicity and Markovity, the state of the feedback controller reduces to two channel parameters. This allows the optimal control policy to be efficiently computed using dynamic programming. The optimal control policy is proved to be of the threshold type. Under this policy, feedback is performed whenever a channel parameter indicating the accuracy of transmit CSI is below a threshold, which varies with channel power. The above result holds regardless of whether the controller's state space is discretized or continuous. Simulation shows that feedback control increases net throughput by up to 0.5 bit/s/Hz without requiring additional bandwidth or antennas.

Cognitive beamforming (CB) is a promising technique for efficient spectrum sharing between primary users (PUs) and secondary users (SUs) in a cognitive radio network. With CB, the multi-antenna SU transmitter is able to suppress the interference to the PU receiver and maximize the SU link throughput. Existing designs on CB assume that the SU transmitter either has prior knowledge of the interference channel to the PU receiver or can acquire this knowledge by observing the PU transmission. Both assumptions may be impractical. In this paper, we propose a new and practical design paradigm for CB based on finite-rate cooperative feedback from the PU receiver to the SU transmitter. Specifically, for the case of multiple-input single-output (MISO) SU channel and single- input single-output (SISO) PU channel, the PU receiver collaboratively communicates to the SU transmitter the channel direction information (CDI), namely the quantized shape of the SU- to-PU MISO channel, and the interference power control (IPC) signal, which specifies the maximum transmit power of the SU given the interference margin at the PU receiver. We present a CB algorithm for the SU transmitter based on the finite-rate CDI and IPC feedback. The resulting outage probability of the SU MISO channel is derived and shown to be lower-bounded by a function of the number of feedback bits, which is independent of the signal- to-noise ratio. Moreover, the optimal tradeoff between CDI and IPC feedback is analyzed.

In this paper, we consider a relay-assisted cognitive cellular downlink system dynamically accessing a spectrum licensed to a primary network, thereby improving the efficiency of spectrum usage. A cluster-based relay-assisted architecture is proposed, where relay stations are employed for minimizing the interference to users in the primary network and achieving fairness for cell-edge users. Based on this architecture, an optimal solution is derived for jointly controlling data rate, transmission power, and subband allocation to optimize the weighted sum goodput where proportional fair scheduling (PFS) is included as a special case. As shown by simulations, the proposed solution achieves significant throughput gains and better user fairness compared with existing designs.

In this paper, utilizing the relay buffers, we propose an opportunistic decode-wait-and-forward relay scheme for a point-to-point communication system with a half-duplexing mobile relay network. The proposed scheme achieves the maximum throughput of Θ(log K) at a cost of O(1) total transmission power and O(K=q) average end-to-end packet delay, where 0 ≪ q ≤ ½ measures the speed of relays' mobility. It can be proved that this system throughput is unattainable for the existing designs with low relay mobility. Therefore, the proposed relay scheme can exploit the time diversity and relays' mobility more efficiently.

In a frequency-division-duplex (FDD) system, uplink and downlink are allocated equal bandwidths. Due to the relative sparseness of uplink Internet traffic, the uplink spectrum is usually under-utilized. In this paper, we propose dynamic spectrum access algorithms allowing relay-assisted downlink to opportunistically transmit over the uplink spectrum, thereby maximizing its usage efficiency. Specifically, uplink sub-carrier allocation and scheduling based on spectrum sensing are jointly designed for minimizing received interference and thereby enhancing throughput under the constraint of interference. The proposed algorithms support both centralized and distributed implementation. Simulation results demonstrate that the proposed cognitive algorithms contribute significant capacity gains compared with existing designs.

Spectrum sharing between wireless networks improves the usage efficiency of radio spectrums. This paper addresses spectrum sharing between a cellular uplink and a mobile ad hoc networks. These networks use either all uplink frequency subchannels or their disjoint subsets, called spectrum underlay and spectrum overlay, respectively. Given these methods, the capacity tradeoff between the coexisting networks is analyzed in terms of transmission capacity. For a network with Poisson distributed transmitters, this metric is defined as the maximum density of transmitters subject to an outage constraint for a given signal-to-interference ratio (SIR). Using stochastic geometry, the transmission-capacity tradeoff between the coexisting networks is derived, where both spectrum overlay and underlay as well as successive interference cancellation (SIC) are considered. In particular, for small target outage probability, the transmission capacities of the coexisting networks are proved to satisfy a linear equation. Its coefficients depend on the spectrum sharing method and whether SIC is applied. This linear equation shows that spectrum overlay is more efficient than spectrum underlay.

Spectrum sharing between wireless networks improves the efficiency of spectrum usage, and thereby alleviates spectrum scarcity due to growing demands for wireless broadband access. To improve the usual underutilization of the cellular uplink spectrum, this paper addresses spectrum sharing between a cellular uplink and a mobile ad hoc networks. These networks access either all frequency subchannels or their disjoint subsets, called spectrum underlay and spectrum overlay, respectively. Given these spectrum sharing methods, the capacity trade-off between the coexisting networks is analyzed based on the transmission capacity of a network with Poisson distributed transmitters. This metric is defined as the maximum density of transmitters subject to an outage constraint for a given signal-to-interference ratio (SIR). Using tools from stochastic geometry, the transmissioncapacity trade-off between the coexisting networks is analyzed, where both spectrum overlay and underlay as well as successive interference cancelation (SIC) are considered. In particular, for small target outage probability, the transmission capacities of the coexisting networks are proved to satisfy a linear equation, whose coefficients depend on the spectrum sharing method and whether SIC is applied. This linear equation shows that spectrum overlay is more efficient than spectrum underlay. Furthermore, this result also provides insight into the effects of network parameters on transmission capacities, including link diversity gains, transmission distances, and the base station density. In particular, SIC is shown to increase the transmission capacities of both coexisting networks by a linear factor, which depends on the interference-power threshold for qualifying canceled interferers.

Interference between nodes directly limits the capacity of mobile ad hoc networks. This paper focuses on spatial interference cancellation with perfect channel state information (CSI), and analyzes the corresponding network capacity. Specifically, by using multiple antennas, zero-forcing beamforming is applied at each receiver for canceling the strongest interferers. Given spatial interference cancellation, the network transmission capacity is analyzed in this paper, which is defined as the maximum transmitting node density under constraints on outage and the signal-to-interference-plus-noise ratio. Assuming that the locations of network nodes are Poisson distributed and spatially i.i.d. Rayleigh fading channels, mathematical tools from stochastic geometry are applied for deriving scaling laws for transmission capacity. Specifically, for a large number of antennas per node, the transmission capacity scales with the number of antennas raised to a fractional power, which depends only on the path-loss exponent. Moreover, for small target outage probability, transmission capacity is proved to increase following a power law, where the exponent is the inverse of the size of antenna array or larger depending on the pass-loss exponent. As shown by simulations, spatial interference cancellation increases transmission capacity by an order of magnitude or more even if only one extra antenna is added to each node.

On a multi-antenna downlink channel, separation of multiple users by transmit beamforming enables simultaneous transmission from the base station to the users, resulting in high sum throughput. This paper proposes and analyzes a practical algorithm for joint scheduling and orthogonal beamforming, which is enabled by feedback of quantized channel state information (CSI). In this approach, each user quantizes CSI using a codebook comprised of multiple orthonormal vector sets and sends back quantized CSI. Using feedback CSI, the base station jointly selects a set of orthogonal beamforming vectors and schedules a subset of feedback users for downlink transmission such that the throughput is maximized. For moderate to large numbers of users, the proposed algorithm achieves higher sum capacities than the conventional ones.

Space division multiple access (SDMA) is capable of achieving sum capacity that grows double logarithmically with the number of users. The sum rate for channel state information (CSI) feedback, however, increases linearly with the number of users, reducing the effective uplink capacity. To address this problem, a novel SDMA design is proposed, where the sum feedback rate is upper-bounded by a constant. This design consists of algorithms for CSI quantization, threshold based CSI feedback, and joint beamforming and scheduling. The key feature of the proposed approach is the use of feedback thresholds to select feedback users with large channel gains and small CSI quantization errors such that the sum feedback rate constraint is satisfied. Despite this constraint, the proposed SDMA design is shown to achieve a sum capacity growth rate close to the optimal one. Numerical results show that the proposed SDMA design is capable of attaining higher sum capacities than existing ones, even though the sum feedback rate is bounded.

For a wireless multi-antenna network with a large number of users, the sum capacity scales at most linearly with the number of antennas and double logarithmically the number of users. Achieving this optimal capacity scaling potentially requires feedback of channel state information (CSI) from all users, leading to overflow of the feedback channel. This paper proposes a limited feedback strategy that provides feedback control such that a sum CSI feedback rate constraint is satisfied. It is proved that a wireless multi-antenna network using the proposed limited feedback strategy achieves the optimal capacity scaling for the broadcast channel.

A novel method based on Markov chain theory is proposed for analyzing the effect of feedback delay on a transmit beamforming system with limited feedback. Using this method, the ergodic capacity with delayed feedback of channel state information is derived. The capacity gain with respect to the case of no feedback is shown to decrease at least exponentially with the feedback delay. From these results, useful design guidelines can be derived for choosing system parameters including the vehicular speed and the tolerable feedback delay.

A novel method is proposed for reducing the feedback rate of a transmit beamforming system with feedback of quantized channel state information. Specifically, the channel is modeled as a Markov chain and the feedback bits are compressed by truncating the Markov chain transition probabilities. Using the proposed method, the feedback rate can be compressed by more than 100% without degrading the system performance.

Multiple antenna wireless systems with feedback of quantized channel information, called "limited feedback” systems, are attractive choices for improving the quality of downlink (DL) transmission. Most work in this area use the block-fading channel model where the DL channel is assumed constant in each block and different blocks uncorrelated. In this paper, we consider limited feedback for a temporally correlated DL channel. Markov models are introduced for characterizing the temporal correlation and probability distribution of the DL channel. Using the Markov models, average feedback rates are derived. Numerical results show that the feedback rates are proportional to the Doppler frequency.

New unified linear preceding and decoding techniques are presented in this paper, which are suitable for block transmission systems such as orthogonal frequency division multiplexing (OFDM) systems, synchronous code division multiple access (CDMA) systems, single carrier systems and multiple-input-multiple-output (MIMO) systems. First, a linear precoder that achieves minimum symbol-error-rate (MSER) is proposed and analyzed. Motivated by the fact that the conventional and the MSER precoders achieve a diversity order of one, a new method of applying linear preceding over subchannels, named multichannel precoding (MP), is developed to exploit the diversity gain. It is shown that even if a suboptimal linear decoder is used, MP improves the SER performance significantly. Given the block and linear nature of the linearly preceded system, lattice decoding is a suitable and efficient method for optimally detecting data symbols. Some analytical results are presented for the linearly preceded system with lattice decoding. The SER performance of different linearly preceded systems are also compared numerically.

In this letter, we supplement the derivation in Laurent's paper by mathematically proving that combining binary continuous phase modulation (CPM) signals in different bit durations results in the decomposed CPM expression consisting of a set of continuous amplitude-modulated pulse functions.

A DS-CDMA RAKE receiver architecture is proposed. Unlike the conventional receiver, the proposed /spl Sigma//spl Delta/-CDMA receiver does not require a /spl Sigma//spl Delta/ demodulator and a root-raised-cosine (RRC) filter, and hence its despreader can be implemented mainly using simple XNOR gates and 2-bit adders. The conditional BER for DS-CDMA forward link is derived and the averaged BER is obtained by Monte Carlo simulation. It is shown that the BER performance of the proposed receiver can match that of the conventional DS-CDMA receiver, which is implemented using a /spl Sigma//spl Delta/ analog-to-digital converter (ADC), with the proper choice of over-sampling ratio. And the proposed receiver achieves a much smaller gate-count than the conventional receiver when implemented on FPGA.