Large Language Model (LLM) training is increasingly concentrated in homogeneous datacenters, while private data and underutilized GPUs across universities, laboratories, and edge sites remain difficult to use. This extended abstract presents preliminary results from a geo-distributed LLM training prototype that treats networking constraints as first-order design concerns. The prototype connects three heterogeneous GPU sites via cloud-hosted parameter servers, outbound-only gRPC streams, two-stage delta compression (INT8 quantization + Huffman coding, achieving up to 4× payload reduction), and fault-tolerant rejoin. In real deployments, GPT-2 Medium pretraining achieves stable loss reduction and reaches the target loss 15.2% faster in wall-clock time than the best tested baseline; Llama3-1B pretraining remains stable under larger communication pressure; and cross-site latency traces reveal site-dependent WAN spikes of up to 200s. These results motivate adaptive networking support for synchronization, compression, placement, telemetry, and recovery in geo-distributed LLM training.

The problem of bistatic integrated sensing and communications over memoryless relay channels is considered, where destination concurrently decodes the message sent by the source and estimates unknown parameters from received signals with the help of a relay. A state-dependent discrete memoryless relay channel is considered to model this setup, and the fundamental limits of the communication-sensing performance tradeoff are characterized by the capacity-distortion function. An upper bound on the capacity-distortion function is derived, extending the cut-set bound results to address the sensing operation at the destination. A hybrid-partial-decode-and-compress-forward coding scheme is also proposed to facilitate source-relay cooperation for both message transmission and sensing, establishing a lower bound on the capacity-distortion function. It is found that the hybrid-partial-decode-and-compress-forward scheme achieves optimal sensing performance when the communication task is ignored. Furthermore, the upper and lower bounds are shown to coincide for three specific classes of relay channels. Numerical examples are provided to illustrate the communication-sensing tradeoff and demonstrate the benefits of integrated design.

This paper studies multimodal semantic communication through a unified contextual embedding space, where meaning is the core unit across modalities. We introduce encoding and decoding functions that map between this shared space and modality-specific expressions. This framework connects to information-theoretic limits and improves resource efficiency. As a case study, we consider a broadcast setting in which a multimodal source of text and images is transmitted to two receivers. Regardless of the input modality, the first receiver generates a text while the second generates an image, making the task coherent cross-modal generation. Simulations over AWGN and Rayleigh fading channels demonstrate that the proposed model achieves higher fidelity scores than baselines under resource constraints. This suggests the feasibility of building semantic communication systems on a universal meaning space and supports unified designs across data types and communication settings.

Federated learning (FL) has emerged as a promising distributed learning framework that preserves data privacy on edge devices. Over-the-air federated learning (OTA-FL) considers the shared multiple access medium and leverages the superposition property of wireless channels to aggregate local updates, making it particularly well-suited for communication-efficient FL. In this paper, we consider energy harvesting edge devices as clients and develop participation (client-selection) mechanisms for energy efficient OTA-FL in a distributed manner, i.e., each edge device decides whether to participate in the global learning iteration based on its local knowledge. Specifically, each edge device independently decides on participation in a learning round under imperfect CSI, non-i.i.d. data, and fading channels. By defining a device importance metric that captures the contribution of each device to global loss and gradient divergence, we formulate a Lyapunov online optimization framework, allowing each device to balance local computation steps with harvested energy constraints. We provide the convergence analysis for the proposed algorithm. Numerical results demonstrate that our solution significantly outperforms baselines, highlighting its effectiveness for large-scale, heterogeneous, and energy-constrained FL deployments.

We consider the Age of Information (AoI) in a network, where each user is both a source and a monitor, in the presence of transmission delays. Update beacons are transmitted over a shared channel that is accessed according to a Carrier Sense Multiple Access (CSMA) scheme where, due to an adversarial attack, a portion of the beacon transmissions take longer than expected. The users collectively have the ability to end a transmission that is taking too long. The users select the rate at which transmissions are reset and the individual rates at which each user begins a transmission from when the channel becomes idle with the goal of minimizing the AoI. The adversary selects the average increase in transmission duration with the goal of maximizing the AoI minus a cost. We use a Stackelberg game formulation with the legitimate users as the leader and the adversary as the follower. We provide limiting analysis in the system parameters and numerical results.

Semantic communications in a relay network is considered. In particular, we focus on efficient semantic text communication. We propose semantic sentence forwarding (SSF), which utilizes the attention mechanism to transmit a single embedding capturing the entire message semantics. At the relay, SSF decodes the source signal autoregressively, one token at a time, incorporating previously generated tokens and the context embedding from the source node. In addition to traditional BLEU and SBERT-based semantic similarity metrics, we introduce a GPT-enabled semantic similarity score for semantic evaluation of the reconstructed messages. Extensive experiments demonstrate that SSF outperforms both semantic-agnostic baselines, including classical transmission and autoencoder-based designs. SSF also outperforms existing token-level semantic methods SLF and SPF, demonstrating the effectiveness of sentence-level semantic text communication.

This paper introduces an information-theoretic framework for text summarization. We define the summarizer rate-distortion function and show that it provides a fundamental lower bound on summarizer performance. We describe an iterative procedure, similar to Blahut-Arimoto algorithm, for computing this function. To handle real-world text datasets, we also propose a practical method that can calculate the summarizer rate-distortion function with limited data. Finally, we empirically confirm our theoretical results by comparing the summarizer rate-distortion function with the performances of different summarizers used in practice.

Authorship identification is the problem of estimating the author of a written text from a set of possible authors using clues from the written text itself. While this problem has been around for over 130 years, there yet remain several important questions regarding the fundamental limits of existing techniques. This paper is the first of our knowledge to build an information-theoretic framework around the problem of authorship identification. We present a model by which the limits of the problem may be studied as well as key definitions and metrics rooted in information theory. We also provide a test case from the historical archives of the authorship identification research, namely the Federalist Papers problem, and apply our methods to this well-studied set of texts. Given the adhoc nature of much of the research surrounding authorship identification (and related) problems, and the wealth of open fundamental questions regarding even modern methods based in machine learning and large language models, we anticipate that this work will lead to a number of future contributions in both authorship attribution and basic machine learning.

This paper develops a clustering algorithm using the rate-distortion-perception (RDP) framework. First, we address the problem of clustering$n$points into$k$clusters using a distortion metric in the rate-distortion (RD) context. Then, we extend the scope to the RDP framework by introducing a perception constraint to the clustering distortion objective to better preserve the source distribution. We propose an algorithm for the extended objective and prove its convergence. Numerical experiments with samples from probability distributions demonstrate that the proposed algorithm aligns with the RDP tradeoff characteristics. We also demonstrate through experiments on real-world datasets that the proposed algorithm provides better semantic clustering by improving the classification accuracy in next-generation communication networks.

This work considers a secret sharing problem where the dealer uses a classical-quantum channel to divide and distribute shares to two users by encoding the secret so that the channel observation of each user is their share. The channel observations of a single user contains very little information about the secret, but the two users together can recover the secret. We derive a one-shot and an asymptotic achievability result for secret sharing over a quantum broadcast channel. Additionally, our achievability results coincide with the secret sharing capacity for the classical broadcast channel.

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International audience

To accommodate new applications such as extended reality, fully autonomous vehicular networks and the metaverse, next generation wireless networks are going to be subject to much more stringent performance requirements than the fifth-generation (5G) in terms of data rates, reliability, latency, and connectivity. It is thus necessary to develop next generation advanced transceiver (NGAT) technologies for efficient signal transmission and reception. In this tutorial, we explore the evolution of NGAT from three different perspectives. Specifically, we first provide an overview of new-field NGAT technology, which shifts from conventional far-field channel models to new near-field channel models. Then, three new-form NGAT technologies and their design challenges are presented, including reconfigurable intelligent surfaces, flexible antennas, and holographic multi-input multi-output (MIMO) systems. Subsequently, we discuss recent advances in semantic-aware NGAT technologies, which can utilize new metrics for advanced transceiver designs. Finally, we point out other promising transceiver technologies for future research.

A state-dependent discrete memoryless multiple access channel is considered to model an integrated sensing and communication system, where two transmitters wish to convey messages to a receiver while simultaneously estimating the state parameter sequences through echo signals. In particular, the sensing state parameters are assumed to be correlated with the channel state. In this setup, improved inner and outer bounds for capacity-distortion region are derived. The inner bound is based on an achievable scheme that combines message cooperation and joint compression of past transmitted codewords and echo signals at each transmitter, resulting in unified cooperative communication and sensing. The outer bound is based on the ideas of dependence balance for communication rate, genie-aided state estimator and rate-limited constraints on sensing distortion. The proposed inner and outer bounds are proved to improve the state-of-the-art bounds. Finally, numerical examples are provided to demonstrate that our new inner and outer bounds strictly improve the existing results.

Task-oriented semantic communication enhances transmission efficiency by conveying semantic information rather than exact messages. Deep learning (DL)-based semantic communication can effectively cultivate the essential semantic knowledge for semantic extraction, transmission, and interpretation by leveraging massive labeled samples for downstream task training. In this paper, we propose a self-supervised learning-based semantic communication framework (SLSCom) to enhance task inference performance, particularly in scenarios with limited access to labeled samples. Specifically, we develop a task-relevant semantic encoder using unlabeled samples, which can be collected by devices in real-world edge networks. To facilitate task-relevant semantic extraction, we introduce self-supervision for learning contrastive features and formulate the information bottleneck (IB) problem to balance the tradeoff between the informativeness of the extracted features and task inference performance. Given the computational challenges of the IB problem, we devise a practical and effective solution by employing self-supervised classification and reconstruction pretext tasks. We further propose efficient joint training methods to enhance end-to-end inference accuracy over wireless channels, even with few labeled samples. We evaluate the proposed framework on image classification tasks over multipath wireless channels. Extensive simulation results demonstrate that SLSCom significantly outperforms conventional digital coding methods and existing DL-based approaches across varying labeled data set sizes and SNR conditions, even when the unlabeled samples are irrelevant to the downstream tasks.

This paper considers an information theoretic model for secure integrated sensing and communication (ISAC) with the goal of establishing fundamental limits in low-latency scenarios. In this secure ISAC model, a message is transmitted through a state-dependent wiretap channel with decoder-side state availability. The model is studied under a strong secrecy constraint when only a part of the transmitted message should be kept secret. First, the secrecy-distortion rate region is established for a degraded channel by treating the model as a special case of a feed-backed secure ISAC model. Finite-length inner bounds are then proved by applying nonasymptotic random binning techniques. Bounds on the rates have a similar form to common finite-length bounds, and the distortion bound follows from a bound for letter-typical sequences.

We propose semantic communication over wireless channels for various modalities, e.g., text and images, in a task-oriented communications setup where the task is classification. We present two approaches based on memory and learning. Both approaches rely on a pre-trained neural network to extract semantic information but differ in codebook construction. In the memory-based approach, we use semantic quantization and compression models, leveraging past source realizations as a codebook to eliminate the need for further training. In the learning-based approach, we use a semantic vector quantized autoencoder model that learns a codebook from scratch. Both are followed by a channel coder in order to reliably convey semantic information to the receiver (classifier) through the wireless medium. In addition to classification accuracy, we define system time efficiency as a new performance metric. Our results demonstrate that the proposed memory-based approach outperforms its learning-based counterpart with respect to system time efficiency while offering comparable accuracy to semantic agnostic conventional baselines.

Over-the-air federated learning (OTA-FL) provides bandwidth-efficient learning by leveraging the inherent superposition property of wireless channels. Personalized federated learning balances performance for users with diverse datasets, addressing real-life data heterogeneity. We propose the first personalized OTA-FL scheme through multi-task learning, assisted by personal reconfigurable intelligent surfaces (RIS) for each user. We take a cross-layer approach that optimizes communication and computation resources for global and personalized tasks in time-varying channels with imperfect channel state information, using multi-task learning for non-i.i.d data. Our PROAR-PFed algorithm adaptively designs power, local iterations, and RIS configurations. We present convergence analysis for non-convex objectives and demonstrate that PROAR-PFed outperforms state-of-the-art on the Fashion-MNIST dataset.

Federated learning (FL) is a distributed learning framework that by design allows local edge devices to keep their training data. However, privacy leakage occurs through model updates and is a privacy protection concern that needs to be addressed. Over-the-air FL (OTA-FL) is a variant of FL designed for wireless edge networks by utilizing the inherent superposition property of the wireless medium. The wireless physical layer (PHY), in addition to providing resource and communication-efficient collaborative training via OTA-FL, can also be leveraged to enhance privacy for FL. This paper presents the PHY design to ensure differentially private (DP) OTA-FL. Specifically, by leveraging the Gaussian noise naturally present in the wireless channel, and deploying a dedicated artificial noise generator (cooperative jammer) when needed, a fully decentralized, dynamic power control strategy is proposed. This design relies on a resource-efficient FL framework with first-order approximation applied at every even iteration, thereby reducing the amount of information needed from clients. This approach can eliminate the need for artificial noise injection at the client side, typically required to achieve DP; the cooperative jammer is used for higher privacy requirement without transmission efficiency loss. The privacy analysis is provided via the Moments Accountant method, providing a tight privacy assessment. The convergence analysis is provided for non-convex learning objectives. Experiments conducted on real-world non-i.i.d. data demonstrate that our scheme outperforms the state-of-the-art method under the same DP requirement and illustrate the effectiveness of cooperative jammer in the case of stringent privacy requirements.

A relay-aided bistatic integrated sensing and communication (ISAC) system is considered, where the destination concurrently decodes a message and estimates unknown state parameters from its received signals. This system is modeled by a generalized state-dependent relay channel. Its fundamental limits of the communication-sensing performance tradeoff, characterized by the capacity-distortion function, are established. Specifically, an upper bound on the capacity-distortion function extending the cutset bound for relay channels to address the state estimation at the destination is developed. Additionally, a hybrid partial-decode-and-compress-forward coding scheme is proposed to facilitate source-relay cooperation for both message transmission and state estimation, establishing a lower bound on the capacity-distortion function. It is found that partial-decode-and-compress-forward scheme achieves optimal sensing performance when the communication task is ignored. Furthermore, the upper and lower bounds are shown to coincide for some special classes of channels. Two numerical examples are provided to illustrate the communication-sensing tradeoff in the considered ISAC system.

Consider a scenario involving multiple users and a fusion center. Each user possesses a sequence of bits and can communicate with the fusion center through a one-way public channel. The fusion center's task is to compute the sum of all the sequences under the privacy requirement that a set of colluding users, along with the fusion center, cannot gain more than a predetermined amount$\delta$of information, measured through mutual information, about the sequences of other users. Our first contribution is to characterize the minimum amount of necessary communication between the users and the fusion center, as well as the minimum amount of necessary shared randomness at the users. Our second contribution is to establish a connection between secure summation and secret sharing by showing that secret sharing is necessary to generate the local randomness needed for private summation, and prove that it holds true for any$\delta\geqslant 0$.

This work models a secure integrated sensing and communication (ISAC) system as a wiretap channel with action-dependent channel states and channel output feedback, e.g., obtained through reflections. The transmitted message is split into a common and a secure message, both of which must be reliably recovered at the legitimate receiver, while the secure message needs to be kept secret from the eavesdropper. The transmitter actions, such as beamforming vector design, affect the corresponding state at each channel use. The action sequence is modeled to depend on both the transmitted message and channel output feedback. For perfect channel output feedback, the secrecy-distortion regions are provided for physically-degraded and reversely-physically-degraded secure ISAC channels with transmitter actions. The corresponding rate regions when the entire message should be kept secret are also provided. The results are illustrated through characterizing the secrecy-distortion region of a binary example.

In this paper, we address task-oriented (or goal-oriented) communications where an encoder at the transmitter learns compressed latent representations of data, which are then transmitted over a wireless channel. At the receiver, a decoder performs a machine learning task, specifically for classifying the received signals. The deep neural networks corresponding to the encoder-decoder pair are jointly trained, taking both channel and data characteristics into account. Our objective is to achieve high accuracy in completing the underlying task while minimizing the number of channel uses determined by the encoder's output size. To this end, we propose a multi-round, multi-task learning (MRMTL) approach for the dynamic update of channel uses in multi-round transmissions. The transmitter incrementally sends an increasing number of encoded samples over the channel based on the feedback from the receiver, and the receiver utilizes the signals from a previous round to enhance the task performance, rather than only considering the latest transmission. This approach employs multi-task learning to jointly optimize accuracy across varying number of channel uses, treating each configuration as a distinct task. By evaluating the confidence of the receiver in task decisions, MRMTL decides on whether to allocate additional channel uses in multiple rounds. We characterize both the accuracy and the delay (total number of channel uses) of MRMTL, demonstrating that it achieves the accuracy close to that of conventional methods requiring large numbers of channel uses, but with reduced delay by incorporating signals from a prior round. We consider the CIFAR-10 dataset, convolutional neural network architectures, and AWGN and Rayleigh channel models for performance evaluation. Our results show that MRMTL significantly improves the efficiency of task-oriented communications, balancing accuracy and latency effectively.

This paper considers the Gaussian multiple access wiretap channel (GMAC-WT) with selfish transmitters, i.e., who are each solely interested in maximizing their individual secrecy rate. The question then arises as to whether selfish transmitters can increase their individual secrecy rate by participating in a collective, i.e, multiple access, protocol instead of operating on their own. If yes, the question arises whether there is a protocol that satisfies all the participating transmitters simultaneously, in the sense that no transmitter has an incentive to deviate from the protocol. Utilizing coalitional game theory, these questions are addressed for the degraded GMAC-WT with an arbitrary number of transmitters and for the non-degraded GMAC-WT with two transmitters. In particular, for the degraded GMAC-WT, cooperation is shown to be in the best interest of all transmitters, and the existence of protocols that incentivize all transmitters to participate is established. Furthermore, a unique, fair, stable, and achievable secrecy rate allocation is determined. For the non-degraded GMAC-WT, depending on the channel parameters, there are cases where cooperation is not in the best interest of all transmitters, and cases where it is. In the latter cases, a unique, fair, stable, and achievable secrecy rate allocation is determined.

Over-the-air federated learning (OTA-FL) integrates communication and model aggregation by exploiting the innate superposition property of wireless channels. The approach renders bandwidth efficient learning, but requires care in handling the wireless physical layer impairments. In this paper, federated edge learning is considered for a network that is heterogeneous with respect to client (edge node) data set distributions and individual client resources, under a general non-convex learning objective. We augment the wireless OTA-FL system with a Reconfigurable Intelligent Surface (RIS) to enable a propagation environment with improved learning performance in a realistic time varying physical layer. Our approach is a cross-layer perspective that jointly optimizes communication, computation and learning resources, in this general heterogeneous setting. We adapt the local computation steps and transmission power of the clients in conjunction with the RIS phase shifts. The resulting joint communication and learning algorithm, RIS-assisted Over-the-air Adaptive Resource Allocation for Federated learning (ROAR-Fed) is shown to be convergent in this general setting. Numerical results demonstrate the effectiveness of ROAR-Fed under heterogeneous (non i.i.d.) data and imperfect CSI, indicating the advantage of RIS assisted learning in this general set up.

It is our pleasure to share with you this Special Issue, which brings together a diverse set of articles dealing with various aspects of semantic and goal-oriented communications, providing a snapshot of research activities in this highly active research area. Wireless communications and networking research has traditionally focused on improving the capacity and throughput of the underlying wireless network. However, recent explosion in data-driven machine learning applications and their reliance on huge datasets collected by edge devices have raised legitimate concerns that the increasing data traffic might soon overwhelm the capacity of current networks despite ongoing efforts to increase their capacity and efficiency. Also, most of the edge intelligence applications impose stringent delay constraints, which cannot be met by naive forwarding of data samples for processing at the receiver end. This made it obvious to researchers in both academia and industry that it is essential to analyze the “value” or “relevance” of collected data, and filter and prioritize the delivery of data based on its value/relevance as well as the wireless channel and network conditions. In this context, data value will be closely connected to the underlying signals and processes that generate the data, e.g., text, image, video, or sensor data, and what the receiver intends to do with the received data. This subjectivity of data value makes semantic and goal-oriented communication a rather elusive research topic, which has led to both an increasingly rich and active area of investigation, but also a controversial one, mainly due to the lack of clear and widely agreed-upon definitions of some of the core concepts and formulations. Despite these disagreements, there is almost unanimous consensus on the importance and potential impact of this line of investigation for the design of future communication systems and networks.

Communication systems to date primarily aim at reliably communicating bit sequences. Such an approach provides efficient engineering designs that are agnostic to the meanings of the messages or to the goal that the message exchange aims to achieve. Next generation systems, however, can be potentially enriched by folding message semantics and goals of communication into their design. Further, these systems can be made cognizant of the context in which communication exchange takes place, thereby providing avenues for novel design insights. This tutorial summarizes the efforts to date, starting from its early adaptations, semantic-aware and task-oriented communications, covering the foundations, algorithms and potential implementations. The focus is on approaches that utilize information theory to provide the foundations, as well as the significant role of learning in semantics and task-aware communications.

Reconfigurable Intelligent Surfaces (RIS) are a new paradigm which, with judicious deployment and alignment, can enable more favorable propagation environments and better wireless network design. As such, they can offer a number of potential benefits for next generation wireless systems including improved coverage, better interference management and even security. In this paper, we consider an uplink next generation wireless system where each user is assisted with an RIS. We study the uplink power control problem in this distributed RIS-assisted wireless network. Specifically, we aim to minimize total uplink transmit power of all the users subject to each user's reliable communication requirements at the base station by a joint design of power, receiver filter and RIS phase matrices. We propose an iterative power control algorithm, combined with a successive convex approximation technique to solve the problem with non-convex phase constraints. Numerical results illustrate that distributed RIS assistance leads to uplink power savings when direct links are weak.

This work considers mitigation of information leakage between communication and sensing operations in joint communication and sensing systems. Specifically, a discrete memoryless state-dependent broadcast channel model is studied in which (i) the presence of feedback enables a transmitter to simultaneously achieve reliable communication and channel state estimation; (ii) one of the receivers is treated as an eavesdropper whose state should be estimated but which should remain oblivious to a part of the transmitted information. The model abstracts the challenges behind security for joint communication and sensing if one views the channel state as a characteristic of the receiver, e.g., its location. For independent and identically distributed (i.i.d.) states, perfect output feedback, and when part of the transmitted message should be kept secret, a partial characterization of the secrecy-distortion region is developed. The characterization is exact when the broadcast channel is either physically-degraded or reversely-physically-degraded. The characterization is also extended to the situation in which the entire transmitted message should be kept secret. The benefits of a joint approach compared to separation-based secure communication and state-sensing methods are illustrated with a binary joint communication and sensing model.

This paper studies federated learning (FL) with non-identical data generating distributions and under the notion of proportional fairness. Users are partitioned into disjoint clusters based on their data distributions, and a distinct model is learnt for each cluster. Different than previous work that considered clustering to optimize personalized learning performance, here clustering is inspected under the disparate impact doctrine which requires that protected classes (e.g., those of certain race or gender) have representations in every cluster that are approximately equal to their representation in the overall set of users. A family of iterative algorithms that balance the learning performance and proportional fairness through cluster assignments as randomized functions of the learning losses is proposed. The trade-off induced by our algorithms between accuracy of cluster estimation and the introduced randomization level is characterized. The proposed algorithm is examined on a real dataset to evaluate its performance.

In status update systems, multiple features carried by the status updating process require pursuit of objectives beyond timeliness measured by the age of information of updates. We consider such a problem where the transmitter sends status update messages through a noiseless binary energy harvesting channel that is equivalent to a timing channel. The transmitter aims to amplify or mask the energy state information that is carried in the updating process. The receiver extracts encoded information, infers the energy state sequence while maintaining timeliness of status updates. Consequently, the timings of the updates must be designed to control the message rate, the energy state uncertainty, and the age of information. We investigate this three-way trade-off between the achievable rate, the reduction in energy arrival state uncertainty, and the age of information, for zero and infinite battery cases.

This paper considers over-the-air federated learning (OTA-FL). OTA-FL exploits the superposition property of the wireless medium, and performs model aggregation over the air for free. Thus, it can greatly reduce the communication cost incurred in communicating model updates from the edge devices. In order to fully utilize this advantage while providing comparable learning performance to conventional federated learning that presumes model aggregation via noiseless channels, we consider the joint design of transmission scaling and the number of local iterations at each round, given the power constraint at each edge device. We first characterize the training error due to such channel noise in OTA-FL by establishing a fundamental lower bound for general functions with Lipschitz-continuous gradients. Then, by introducing an adaptive transceiver power scaling scheme, we propose an over-the-air federated learning algorithm with joint adaptive computation and power adjustment (ACPA-OTA-FL). We provide the convergence analysis for ACPA-OTA-FL in training with non-convex objective functions and heterogeneous data. We show that the convergence rate of ACPA-OTA-FL matches that of FL with noise-free communications.

We study fully decentralized learning in which agents learn collaborative, yet personalized prediction models. Specifically, when learners’ local datasets are non-IID, a collaboratively trained global model (such as those learned through most federated learning algorithms to minimize the sum of losses across all agents) may sacrifice the local performance on agents’ private datasets. To address this issue and enable personalized learning, we propose DiPLe : an algorithm for Directed Personalized Learning. Through our algorithm, each agent identifies "relevant" agents with whom to exchange model information. This leads to a weighted and directed collaboration graph. Agents repeatedly update this graph, and then exchange information with neighboring agents on this learned graph, to collaboratively train their personalized models. We provide analytical results on the generalization error bounds and convergence of our proposed learning method. We verify the performance of DiPLe through numerical experiments, and show its advantages in terms of personalization compared to a number of existing federated learning and personalized learning algorithms.

Abstract Modern automobiles are equipped with connectivity features to enhance the user’s comfort. Bluetooth is one such communication technology that is used to pair a personal device with an automotive infotainment unit. Upon pairing, the user could access the personal information on the phone through the automotive head unit with minimum distraction while driving. However, such connectivity introduces a possibility for privacy attacks. Hence, performing an in-depth analysis of the system with privacy constraints is extremely important to prevent unauthorized access to personal information. In this work, we perform a systematic analysis of the Bluetooth network of an automotive infotainment unit to exploit security and privacy-related vulnerabilities. We model the identified threat with respect to privacy constraints of the system, emphasize the severity of attacks through a standardized rating metric and then provide potential countermeasures to prevent the attack. We perform System Theoretic Process Analysis for Privacy as a part of the systematic analysis and use the Common Vulnerability Scoring System to derive attack severity. The identified vulnerabilities are due to design flaws and assumptions on Bluetooth protocol implementation on automotive infotainment systems. We then elicit the vulnerability by performing a privacy attack on the Automotive system in an actual vehicle. We use Android Open-Source Project to report our findings and propose defense strategies.

We study age of information (AoI) minimization in a network consisting of energy harvesting transmitters that are scheduled to send status updates to their intended receivers. We consider the user scheduling problem over a communication session. To solve online user scheduling with causal knowledge of the system state, we formulate an infinite-state Markov decision problem and adopt model-free on-policy deep reinforcement learning (DRL), where the actor-critic algorithm with deep neural network function approximation is implemented. Comparable AoI to the offline optimal is demonstrated, verifying the efficacy of learning for AoI-focused scheduling and resource allocation problems in wireless networks.

Timeliness of information transfer is critical in real-time applications. Prioritizing timeliness, however, often comes at the cost of rate inefficiency, especially in block coding. In this work, motivated by the sequential nature of encoding and decoding in arithmetic source coding, the timeliness of arithmetic coding is investigated. For a generate-at-will source model, an upper bound is provided on the average peak age of information (PAoI). This upper bound builds on the arithmetic coding scheme of Shayevitz et al. (2007) which has a finite look-ahead parameter$d$. It captures interesting trade-offs between PAoI, compression rate, and the look-ahead parameter$d$. For periodic sources, rate efficiency is argued to be less critical than the look-ahead parameter$d$in minimizing the peak age, especially when the traffic load is moderate and small. Through simulations, two observations are made: (i) the optimal look-ahead parameter$d$is an increasing function of the traffic load, and (ii) asymptotically, as the traffic load gets close to its limit 1, the classical arithmetic coding (without a finite bound on$d$) performs better than the state-of-the-art age-optimal block codes.

This paper provides a first study of utilizing energy harvesting for sustainable machine learning in distributed networks. We consider a distributed learning setup in which a machine learning model is trained over a large number of devices that can harvest energy from the ambient environment, and develop a practical learning framework with theoretical convergence guarantees. We demonstrate through numerical experiments that the proposed framework can significantly out- perform energy-agnostic benchmarks. Our framework is scalable, requires only local estimation of the energy statistics, and can be applied to a wide range of distributed training settings, including machine learning in wireless networks, edge computing, and mobile internet of things.

Potential environmental impact of machine learning in large-scale wireless networks is a major challenge for the sustainability of next-generation intelligent systems. Federated learning is a recent framework for communication-efficient training of machine learning models over the data collected, stored, and processed by millions of wireless devices. In this paper, we introduce a sustainable machine learning framework for federated learning, using rechargeable devices that can collect energy from the ambient environment. In particular, we propose a practical federated learning framework that utilizes intermittent energy arrivals for training, with provable convergence guarantees. Our framework can be applied to both cross-device and cross-silo federated learning settings, including federated learning in wireless edge networks and the Internet-of-Things. Our experiments demonstrate that the proposed framework can provide significant performance improvement over the benchmark energy-agnostic federated learning settings.

This paper considers a cache-aided device-to-device (D2D) system where the users are equipped with cache memories of different size. During low traffic hours, a server places content in the users' cache memories, knowing that the files requested by the users during peak traffic hours will have to be delivered by D2D transmissions only. The worst-case D2D delivery load is minimized by jointly designing the uncoded cache placement and linear coded D2D delivery. Next, a novel lower bound on the D2D delivery load with uncoded placement is proposed and used in explicitly characterizing the minimum D2D delivery load (MD2DDL) with uncoded placement for several cases of interest. In particular, having characterized the MD2DDL for equal cache sizes, it is shown that the same delivery load can be achieved in the network with users of unequal cache sizes, provided that the smallest cache size is greater than a certain threshold. The MD2DDL is also characterized in the small cache size regime, the large cache size regime, and the three-user case. Comparisons of the server-based delivery load with the D2D delivery load are provided. Finally, connections and mathematical parallels between cache-aided D2D systems and coded distributed computing (CDC) systems are discussed.

This paper studies device to device (D2D) coded-caching with information theoretic security guarantees. A broadcast network consisting of a server, which has a library of files, and end users equipped with cache memories, is considered. Information theoretic security guarantees for confidentiality are imposed upon the files. The server populates the end user caches, after which D2D communications enable the delivery of the requested files. Accordingly, we require that a user must not have access to files it did not request, i.e., secure caching. First, a centralized coded caching scheme is provided by jointly optimizing the cache placement and delivery policies. Next, a decentralized coded caching scheme is developed that does not require the knowledge of the number of active users during the caching phase. Both schemes utilize non-perfect secret sharing and one-time pad keying, to guarantee secure caching. Furthermore, the proposed schemes provide secure delivery as a side benefit, i.e., any external entity which overhears the transmitted signals during the delivery phase cannot obtain any information about the database files. The proposed schemes provide the achievable upper bound on the minimum delivery sum rate. Lower bounds on the required transmission sum rate are also derived using cut-set arguments indicating the multiplicative gap between the lower and upper bounds. Numerical results indicate that the gap vanishes with increasing memory size. Overall, the work demonstrates the effectiveness of D2D communications in cache-aided systems even when confidentiality constraints are imposed at the participating nodes and against external eavesdroppers.

We consider authentication of messages sent from transmitters to a receiver over a multiple access channel, where each transmitter shares a secret key with the legitimate receiver. Additionally, there exists a computationally unbounded opponent who has access to noisy observations of the messages transmitted and can initiate impersonation or substitution attacks. We require that the legitimate receiver must be able to authenticate the messages he receives with respect to predetermined groups of transmitters, but at the same time must be kept ignorant of the transmitter's identity of a given message in a given group. We propose an information-theoretic formulation of these anonymity constraints as well as an authentication coding scheme for which the asymptotic probability of successful attack is shown to optimally scale with the length of the secret keys shared between each transmitter and the legitimate receiver. Our results quantify the positive impact of the multiple access setting compared to the single-user setting on the probability of successful attack.

Secure communication over a wiretap channel is investigated, in which an active adversary modifies the state of the channel and the legitimate transmitter has the opportunity to sense and learn the adversary's actions. The adversary has the ability to switch the channel state and observe the corresponding output at every channel use while the encoder has causal access to observations that depend on the adversary's actions. A joint learning/transmission scheme is developed in which the legitimate users learn and adapt to the adversary's actions. For some channel models, it is shown that the achievable rates, defined precisely for the problem, are arbitrarily close to those obtained with hindsight, had the transmitter known the actions ahead of time. This initial study suggests that there is much to exploit and gain in physical-layer security by learning the adversary, e.g., monitoring the environment.

Age of information (AoI) has been recently considered as a performance metric to measure the freshness of information for time-critical wireless communications application. In this paper, we consider AoI minimization in a wireless ad hoc network, where nodes exchange status updates with one another over shared spectrum. The network needs to be formed in a dynamic fashion in the sense that each node either broadcasts or receives updates in a slot and attempts to keep the updates in both directions fresh. We aim to minimize the average AoI of each node by a joint broadcast scheduling and power control policy. Each node decides its transmitting/receiving mode and the transmission power based on its local observation of the system state. We formulate a Markov game and develop a multi-agent deep reinforcement learning algorithm based on deep recurrent Q-network. The simulation results show that the proposed approach outperforms the baselines significantly.

In this work, we consider a cache-aided network where the users share the end-caches. In particular, a user has access to only one of the caches and the number of caches is less than the number of users. We propose a coded placement scheme that exploits the asymmetry in the number of users associated with each cache. Some of the signals sent to the overloaded caches facilitate the decoding of the coded subfiles stored at the underloaded caches. We present an explicit caching scheme and fully characterize the coded placement gain for two-cache systems. Then, we generalize our scheme to larger networks, where the optimal parameters are characterized by solving a linear program. We observe that, with the proposed scheme, as the asymmetry in the users' connectivity increases, the gain from coded placement is more evident.

The metric age of information (AoI) has recently been widely employed to quantify the freshness of the information delivered to the destination. This paper investigates long-term average AoI minimization of an energy harvesting secondary user (SU) in a cognitive radio network setting. Specifically, the paper focuses on the impact of imperfect spectrum sensing on AoI minimization in this setting. The SU makes a decision whether to sense the presence of a primary user, and if it determines the spectrum to be unoccupied, may send out a status update. Sensing and updating both cost energy and the sensing decision may be incorrect due to imperfect spectrum sensing. This setting is formulated as an infinite horizon partially observable Markov decision process (POMDP) to derive the optimal policy that minimizes the long-term average AoI of the SU. The existence of the optimal stationary sensing and update policy is proved and the threshold structure of the policy is shown. Numerical results are presented to demonstrate the SU's AoI performance.

The Gaussian multiple-access wiretap channel when the number of transmitters grows unbounded and at most linearly with the blocklength is studied. Its capacity region is characterized when the eavesdropper channel is degraded and when the transmitters' activities are random. Unlike the conventional Gaussian multiple-access wiretap channel, the capacity region is independent of the power of the transmitters and depends only on the sum of the message lengths of the transmitters.

We propose a model of secure communication over wireless channels in which the legitimate parties leverage Radio Tomographic Imaging (RTI) to learn the adversary. Specifically, we model the results of RTI as an "in band" sensing channel that provides causal information about the eavesdropper's path-loss to the transmitter. This ability to learn the path-loss is exploited to achieve secrecy, even in presence of an eavesdropper that moves to optimize its path-loss and improves its eavesdropping. We show that the secrecy rates achieved are the same as those that would have been obtained with hindsight, had the transmitter known the average path-loss ahead of time.

This work considers a network consisting of a server and a layer of relay nodes equipped with cache memories which aim to deliver content to end nodes that also have cache memories. The server and the end nodes consider the intermediate relay caches to be untrusted with the content. As a result, the server must design strategies to place content in relay caches not only to serve end users, but also to ensure that any a subset of them, even when colluding, cannot gain any information about the contents of the server database. The end users randomly connect to a subset of these untrusted caches at the beginning of the delivery phase via multicast links. For this network model, a coded caching scheme is developed by jointly optimizing the cache placement and delivery phases using secure regenerating codes. In addition, the scheme is extended to the setup of combination networks with untrusted relays, where the untrusted relays are connected to the end users via unicast links. The study highlights the benefits of cooperating with untrusted caches by designing the end users' caches to provide multicast opportunities in order to minimize the delivery load.

We consider information theoretic security in a two-hop combination network where there are groups of end users with distinct degrees of connectivity served by a layer of relays. The model represents a network set up with users having access to asymmetric resources, here the number of relays that they are connected to, yet demand security guarantees uniformly. We study two security constraints separately and simultaneously: secure delivery where the information must be kept confidential from an external entity that wiretaps the delivery phase; and secure caching where each cache-aided end-user can retrieve the file it requests and cannot obtain any information on files it does not. The achievable schemes we construct are multi-stage where each stage completes requests by a class of users.

Age of information (AoI) is recently introduced to serve as a performance metric that quantifies the freshness of information for wireless applications with time-sensitive data. This paper studies AoI of wireless energy harvesting nodes in a multiuser cognitive radio network. In the envisioned network, primary users (PUs) and secondary users (SUs) coexist in such way that SUs harvest wireless energy from active PUs and access the primary spectrum opportunistically to transmit status updates. We model the distributions of PUs and SUs by a homogeneous Poisson point process and a binomial point process, respectively. We consider a greedy policy where each SU transmits an update whenever it harvests sufficient energy. A nonlinear energy harvesting model is used for SUs. Under the greedy policy, the average AoI of the secondary network is analyzed. An upper bound and a lower bound of the average AoI are derived. Simulation results demonstrate that the bounds closely approximate the average AoI.

This paper studies average age of information (AoI) minimization in cognitive radio energy harvesting communications. The secondary user is an energy harvesting sensor that harvests ambient energy with which it performs spectrum sensing and status updates. Status-update data is sent by opportunistically accessing the primary spectrum. Specifically, the secondary user aims to minimize the average AoI by adaptively making sensing and update decisions based on its energy availability and the availability of the primary spectrum. The sequential decision problem is formulated as a partially observable Markov decision process and solved by dynamic programming. The properties of the optimal sensing and updating policies are investigated and shown to have threshold structure. Numerical results confirm the analytical findings.

In cache-aided networks, the server populates the cache memories at the users during low-traffic periods in order to reduce the delivery load during peak-traffic hours. In turn, there exists a fundamental tradeoff between the delivery load on the server and the cache sizes at the users. In this paper, we study this tradeoff in a multicast network, where the server is connected to users with unequal cache sizes and the number of users is less than or equal to the number of library files. We propose centralized uncoded placement and linear delivery schemes which are optimized by solving a linear program. Additionally, we derive a lower bound on the delivery memory tradeoff with uncoded placement that accounts for the heterogeneity in cache sizes. We explicitly characterize this tradeoff for the case of three end-users, as well as an arbitrary number of end-users when the total memory size at the users is small, and when it is large. Next, we consider a system where the server is connected to the users via rate-limited links of different capacities and the server assigns the users' cache sizes subject to a total cache budget. We characterize the optimal cache sizes that minimize the delivery completion time with uncoded placement and linear delivery. In particular, the optimal memory allocation balances between assigning larger cache sizes to users with low capacity links and uniform memory allocation.

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This paper considers secret-key generation between several agents and a base station that observe independent and identically distributed realizations of correlated random variables. Each agent wishes to generate the longest possible individual key with the base station by means of public communication. All keys must be jointly kept secret from all external entities. In this many-to-one secret-key generation setting, it can be shown that the agents can take advantage of a collective protocol to increase the sum rate of their generated keys. However, when each agent is only interested in maximizing its own secret-key rate, agents may be unwilling to participate in a collective protocol. Furthermore, when such a collective protocol is employed, how to fairly allocate individual key rates arises as a valid issue. This paper studies the tension between cooperation and self-interest with a game-theoretic treatment. This paper establishes that cooperation is in the best interest of all individualistic agents and that there exist individual secret-key rate allocations that incentivize the agents to follow the protocol. In addition, an explicit coding scheme that achieves such allocations is proposed.

An additive white Gaussian noise energy-harvesting channel with an infinite-sized battery is considered. The energy arrival process is modeled as a sequence of independent and identically distributed random variables. The channel capacity 1/2 log(1 + P) is achievable by the so-called best-effort and save-and-transmit schemes where P denotes the battery recharge rate. This paper analyzes the save-and-transmit scheme whose transmit power is strictly less than P and the best-effort scheme as a special case of save-and-transmit without a saving phase. In the finite blocklength regime, we obtain new nonasymptotic achievable rates for these schemes that approach the capacity with gaps vanishing at rates proportional to 1/√n and ((log n)/n)1/2respectively where n denotes the blocklength. The proof technique involves analyzing the escape probability of a Markov process. When P is sufficiently large, we show that allowing the transmit power to back off from P can improve the performance for save-and-transmit. The results are extended to a block energy arrival model where the length of each energy block L grows sublinearly in n. We show that the save-and-transmit and best-effort schemes achieve coding rates that approach the capacity with gaps vanishing at rates proportional to √(L/n) and (max{log n, L}/n)1/2, respectively.

In this paper, new two-user multiple access wiretap channel models are studied. First, the multiple access wiretap channel II with a discrete memoryless main channel under different wiretapping scenarios is introduced. The wiretapper, as in the classical wiretap channel II model, chooses a fixed-size subset of the channel uses, in which it obtains noise-free observations of one of the codewords: a deterministic function, e.g., superposition, of the two codewords or each of the two codewords. A fourth wiretapping scenario is considered, in which the wiretapper, in each position it chooses, decides to observe either one of the codewords or both codewords, with an overall budget on the number of its noiselessly observed symbols. These, thus, extend the recently examined wiretap channel II with a noisy main channel to a multiple access setting with a variety of attack models for the wiretapper. Next, the proposed multiple access wiretap channel II models are further generalized to the case when the wiretapper observes the outputs of a discrete memoryless channel, instead of erasures, outside the subset of noiseless observations. Achievable strong secrecy rate regions for all the proposed models are derived. Achievability is established by solving dual multi-terminal secret key agreement problems in the source model and converting the solution to the original channel models using probability distribution approximation arguments. The derived achievable rate regions quantify the secrecy cost due to the additional capabilities of the wiretapper with respect to the previous multiple access wiretap models.

This paper considers a device-to-device (D2D) coded caching system where the users have differing cache sizes. During low traffic hours, the server places subsets of the files at the users' cache memories, in a manner that enables serving the users' requests via D2D transmissions during peak traffic hours. The objective is to jointly design the users' cache contents and the D2D transmissions in order to minimize the D2D delivery load. In particular, we seek to identify the optimal uncoded placement and linear delivery schemes. We propose a novel lower bound on the D2D delivery load under uncoded placement, which enables us to explicitly characterize the minimum D2D delivery load under uncoded placement for several cases of interest.

This paper studies a two-hop network with wireless energy transfer consisting of one source, multiple relays, and multiple destinations. The relays' main objective is to communicate their own messages to their own destinations. The message of each relay is transmitted to its associated destination along with the source's information that is intended for the same destination. As an incentive for relaying, the source offers wireless energy transfer to the relays via radio frequency signals. The relays harvest energy and receive information one by one. The relays that are further down in the order in which they are powered incur delay, but are able to harvest from previous time slots and thus are able to accumulate more energy until it is their turn to transmit, thus establishing an energy-delay trade-off. We formulate a multi-leader-follower Stackelberg game to capture the self-interest and hierarchically competing nature of the nodes. The relay-destination pairs play as leaders and the source-destination pairs as followers. We incorporate data rate, energy cost and delay in the utility functions. The existence and the uniqueness of the Stackelberg equilibrium (SE) are proved, and two algorithms that achieve SE in centralized and distributed fashion are provided. Numerical results verify analytical findings.

We consider an energy harvesting transmitter sending status updates regarding a physical phenomenon it observes to a receiver. Different from the existing literature, we consider a scenario where the status updates carry information about an independent message. The transmitter encodes this message into the timings of the status updates. The receiver needs to extract this encoded information, as well as update the status of the observed phenomenon. The timings of the status updates, therefore, determine both the age of information (AoI) and the message rate (rate). We study the tradeoff between the achievable message rate and the achievable average AoI. We propose several achievable schemes and compare their rate-AoI performances.

An additive white Gaussian noise (AWGN) energy-harvesting (EH) channel is considered where the transmitter is equipped with an infinite-sized battery which stores energy harvested from the environment. The energy arrival process is modeled as a sequence of independent and identically distributed (i.i.d.) random variables. The capacity of this channel is known and is achievable by the so-called best-effort and save-and-transmit schemes. This paper investigates the best-effort scheme in the finite blocklength regime and establishes the first nonasymptotic achievable rate for it. The first-order term of the nonasymptotic achievable rate equals the capacity, and the second-order term is proportional to -√{logn/n}-where n denotes the blocklength. The proof technique involves analyzing the escape probability of a Markov process. In addition, we use this new proof technique to analyze the save-and-transmit and obtain a new non-asymptotic achievable rate for it, whose first-order and second-order terms achieve the capacity and the scaling -1/√n respectively. For all sufficiently large signal-to-noise ratios (SNRs), our new achievable rate outperforms the existing ones.

We consider the wiretap channel when a (secure) cache memory is added to the legitimate receiver. With the goal of utilizing coded caching for improving secrecy, during the cache placement phase, the receiver caches a function of the files, and the secret key shared with the transmitter subject to the memory constraint. The signals transmitted to serve the receiver's request during the delivery phase are observed by an eavesdropper over a wiretap channel. We characterize the secrecy capacity of the wiretap channel with a cache, i.e., the maximum achievable file size while keeping the overall database secure, for both the discrete memoryless and the Gaussian channels. The optimal caching scheme maximizes the utilization from the transmission over the delivery phase by sharing sufficient amount of keys between the legitimate communication nodes during the placement phase. Interestingly, we demonstrate that the existence of cache memory is an enabler of secure communication, i.e., the secrecy capacity remains positive, even when the main channel is degraded with respect to the eavesdropper channel.

This paper introduces the notion of cache-tapping into information theoretic models of coded caching. In particular, the wiretap II model with two receivers equipped with fixed-size cache memories is considered. The adversary chooses a set of symbols from cache placement, delivery, or both to tap into. The legitimate parties know neither whether cache placement, delivery, or both transmissions are tapped, nor the positions of tapped symbols. Only the size of overall tapped set is known. The strong secrecy capacity, i.e., the maximum achievable file rate while keeping the overall library strongly secure, is identified for the instance of two library files. Achievability is established using a code design which combines wiretap coding, security embedding codes, one-time pad keys, and coded caching. The study overall demonstrates that information theoretic security guarantees are possible against a powerful adversary which optimizes its attack over both phases of a cache-aided communication system.

We study a two-hop network with wireless energy transfer (WET) from the source to multiple energy harvesting relays. Both the source and relays intend to transmit dedicated information to the destination. The source, without direct reliable channels to the destination, needs the relays to forward signals, while the relays are short of energy and have to harvest energy from the source to transmit their own data and relaying the source's data. Relays use time division to harvest then transmit. For the multiple access channel (MAC) from the relays to the destination, we consider both time division multiple access (TDMA) between the relays and simultaneous transmission (ST) by all relays. The source and the relays are all selfish and aim to maximize their own utility. We take a game theoretic viewpoint to model the hierarchical competition between the source and the relays. In particular, multi-leader Stackelberg games are formulated where the relays play as the leaders and the source plays as the follower. The existence and the uniqueness of Stackelberg equilibrium (SE) are analyzed, based on which algorithms are proposed to achieve the SE. The numerical results verify that the proposed algorithms improve the system performance comparing to the baseline scheme.

This paper considers a two-hop network architecture known as a combination network, where a layer of relay nodes connects a server to a set of end users. In particular, a new model is investigated, where the intermediate relays employ caches in addition to the end users. First, a new centralized coded caching scheme is developed that utilizes maximum distance separable coding, jointly optimizes cache placement and delivery phase, and enables decomposing the combination network into a set virtual multicast sub-network. It is shown that if the sum of the memory of an end user and its connected relay nodes is sufficient to store the database, then the server can disengage in the delivery phase and all the end users' requests can be satisfied by the caches in the network. Lower bounds on the normalized delivery load using genie-aided cut-set arguments are presented along with second hop optimality. Next, recognizing the information security concerns of coded caching, this new model is studied under three different secrecy settings: 1) secure delivery where we require an external entity that must not gain any information about the database files by observing the transmitted signals over the network links; 2) secure caching, where we impose the constraint that end users must not be able to obtain any information about files that they did not request; and 3) both secure delivery and secure caching, simultaneously. We demonstrate how network topology affects the system performance under these secrecy requirements. Finally, we provide numerical results demonstrating the system performance in each of the settings considered.

We consider strongly secure communication over a discrete memoryless multiple access wiretap channel with two transmitters. No degradation or symmetry assumptions are made on the channel. Our main result is that any rate pair known to be achievable with a random coding like proof, is also achievable with an explicit and low-complexity polar coding scheme. Moreover, if the rate pair is known to be achievable without time-sharing, then time-sharing is not needed in our polar coding scheme as well. Our proof technique relies on rate-splitting, which introduces two virtual transmitters, and cooperative jamming strategies implemented by these virtual transmitters. Specifically, our coding scheme combines point-to-point codes that either aim at secretly conveying a message to the legitimate receiver or at performing cooperative jamming. Each point-to-point code relies on block Markov encoding to be able to deal with an arbitrary channel and strong secrecy. Consequently, our coding scheme is the combination of inter-dependent block Markov constructions. We assess reliability and strong secrecy through a detailed analysis of the dependencies between the random variables involved in the scheme.

Lossy coding of correlated sources over a multiple access channel (MAC) is studied. First, a joint source-channel coding scheme is presented when the decoder has correlated side information. Next, the optimality of separate source and channel coding that emerges from the availability of a common observation at the encoders or side information at the encoders and the decoder is investigated. It is shown that separation is optimal when the encoders have access to a common observation whose lossless recovery is required at the decoder, and the two sources are independent conditioned on this common observation. Optimality of separation is also proved when the encoder and the decoder have access to shared side information conditioned on which the two sources are independent. These separation results obtained in the presence of side information are then utilized to provide a set of necessary conditions for the transmission of correlated sources over a MAC without side information. Finally, by specializing the obtained necessary conditions to the transmission of binary and Gaussian sources over a MAC, it is shown that they can potentially be tighter than the existing results in the literature, providing a novel converse for this fundamental problem.

This paper studies the fundamental limits of storage for structured data, where statistics and structure are both critical to the application. Accordingly, a framework is proposed for optimal lossless and lossy compression of subsets of the possible realizations of a discrete memoryless source (DMS). For the lossless subset-compression problem, it turns out that the optimal source code may not index the conventional source-typical sequences, but rather index certain subset-typical sequences consistent with the subset of interest. Building upon an achievability and a strong converse, an analytic expression is given, based on the Shannon entropy, relative entropy, and subset entropy, which identifies such subset-typical sequences for a broad class of subsets of a DMS. Intuitively, subset-typical sequences belong to those typical sets which highly intersect the subset of interest but are still closest to the source distribution in the sense of relative entropy. For the lossy subset-compression problem, an upper bound is derived on the subset rate-distortion function in terms of the subset mutual information optimized over the set of conditional distributions that satisfy the expected distortion constraint with respect to the subset-typical distribution and over a set of certain auxiliary subsets. By proving a strong converse result, this upper bound is shown to be tight for a class of symmetric subsets. As shown in our numerical examples, more often than not, one achieves a gain in the fundamental limits, in that the optimal compression rate for the subset in both the lossless and lossy settings can be strictly smaller than the source entropy and the source rate-distortion function, respectively, although exceptions are also possible.

In this paper, a new wiretap channel model is proposed, where the legitimate transmitter and receiver communicate over a discrete memoryless channel. The wiretapper has perfect access to a fixed-length subset of the transmitted code word symbols of her choosing. Additionally, she observes the remainder of the transmitted symbols through a discrete memoryless channel. This new model subsumes the classical wiretap channel and wiretap channel II with noisy main channel as its special cases, and is termed as the generalized wiretap channel for that reason. The strong secrecy capacity of the proposed channel model is identified. Achievability is established by solving a dual secret key agreement problem in the source model, and converting the solution to the original channel model using probability distribution approximation arguments. In the dual problem, a source encoder and decoder, who observe random sequences independent and identically distributed according to the input and output distributions of the legitimate channel in the original problem, communicate a confidential key over a public error-free channel using a single forward transmission, in the presence of a compound wiretapping source who has perfect access to the public discussion. The security of the key is guaranteed for the exponentially many possibilities of the subset chosen at the wiretapper by deriving a lemma which provides a doubly-exponential convergence rate for the probability that, for a fixed choice of the subset, the key is uniform and independent from the public discussion and the wiretapping source's observation. The converse is derived by using Sanov's theorem to upper bound the secrecy capacity of the generalized wiretap channel by the secrecy capacity when the tapped subset is randomly chosen by nature.

This paper considers a K-user Gaussian broadcast channel (BC) where receivers are equipped with cache memories. Lower and upper bounds are established on the capacity-memory tradeoff, i.e., the largest rate achievable for given cache-memories. The lower bound is based on a joint cache-channel coding scheme which generalizes the recently proposed piggyback coding to Gaussian BCs with unequal cache sizes. This paper also establishes lower and upper bounds on the global capacity-memory tradeoff, i.e., the maximum capacity-memory tradeoff over all possible cache assignments subject to a total cache memory constraint. The bounds match when the total cache memory is sufficiently large. It is shown that significantly larger rates can be achieved by carefully assigning larger cache memories to weaker receivers. In particular, cache allocation allows communication at rates that are (fundamentally) impossible to achieve with equal cache assignment. This shows the merit in carefully designing the cache size allocation in conjunction with channel qualities.

This paper considers centralized coded caching, where the server not only designs the users' cache contents, but also assigns their cache sizes under a total cache memory budget. The server is connected to each user via a link of given finite capacity. For given link capacities and total memory budget, we minimize the worst-case delivery completion time by jointly optimizing the cache sizes, the cache placement and delivery schemes. The optimal memory allocation and caching scheme are characterized explicitly for the case where the total memory budget is smaller than that of the server library. Numerical results confirm the savings in delivery time obtained by optimizing the memory allocation.

The degraded K-receiver broadcast channel (BC) is studied when receivers are aided with cache memories. Lower and upper bounds are derived on the capacity-memory tradeoff, i.e., on the largest rate that can be achieved as a function of the receivers' cache sizes. The lower bounds are achieved by two new coding schemes that benefit from non-uniform cache assignment. The paper also provides lower and upper bounds on the global capacity-memory tradeoff of degraded BCs, i.e., on the largest capacity-memory tradeoff that can be attained by optimizing the receivers cache-assignment subject to a total cache memory budget. The bounds coincide when the total cache memory budget is sufficiently small or sufficiently large, with the thresholds depending on the BC statistics. For a small total cache budget M, it is optimal to assign all the cache memory to the weakest receiver. In this regime, the global capacity-memory tradeoff grows as M/D, where D denotes the total number of files in the system. For a large total cache budget, it is optimal to assign a positive cache memory to every receiver, where weaker receivers are assigned larger cache memories than stronger receivers. When the total cache budget M exceeds a threshold, then the global capacity-memory tradeoff grows as 1/K.M/D.A uniform cache-assignment policy is suboptimal.

We consider secret-key generation between several agents and a base station that observe independent and identically distributed (i.i.d.) realizations of correlated random variables. Each agent wishes to generate the longest possible individual key with the base station by means of public communication. All keys must be jointly kept secret from all external entities. We do not require them to be kept secret among the agents. In this many-to-one secret-key generation setting, it can be shown that the agents can take advantage of a collective protocol to increase the sum-rate of all the generated keys. However, when each agent is only interested in maximizing its own secret-key rate, agents may be unwilling to participate in a collective protocol. Furthermore, when such a collective protocol is employed, how to fairly allocate individual key rates arises as a valid issue. We study this tension between cooperation and self-interest with a game-theoretic treatment. We establish that cooperation is in the best interest of all agents and that there exists individual secret-key rate allocations that incentivize the agents to follow the protocol. Additionally, we propose an explicit and low-complexity coding scheme based on polar codes and hash functions that achieves such allocations.

We study the degraded Gaussian multiple access wiretap channel with selfish transmitters, i.e., they are each solely interested in maximizing their individual secrecy rate. The question then arises as to whether selfish transmitters can increase their individual secrecy rate by participating in a collective, i.e, multiple access, protocol instead of operating on their own. If yes, the question arises if there is a protocol that satisfies all the participating transmitters, in the sense that no transmitter has an incentive to deviate from the protocol. We answer these questions in the positive utilizing coalitional game theory. In particular, we show that cooperation is in the best interest of all transmitters and that there exist protocols that incentivize all transmitters to participate. Furthermore, we determine a unique, fair, and stable achievable secrecy rate allocation.

We study the Gaussian multiple access channel in presence of an adversary, who is simultaneously able to eavesdrop and jam, i.e., an active wiretapper. We assume that the adversary has a power constraint, which she can utilize to have any arbitrary jamming strategy. The multiple access channel between the legitimate transmitters and the receiver thus becomes arbitrarily varying. We derive inner and outer bounds on the secrecy rate region of our model. In the case of a degraded channel, we characterize the optimal secrecy sum-rate, and within 0.5 bits per channel use the optimal individual rate constraints. As a special case, we obtain the secrecy capacity of the point-to-point Gaussian wiretap channel when the eavesdropper is able to arbitrarily jam.

We study the lossy communication of correlated sources over a multiple access channel (MAC). In particular, we provide a new set of necessary conditions for the achievability of a distortion pair over a given channel. The necessary conditions are then specialized to the case of bivariate Gaussian sources and doubly symmetric binary sources over a Gaussian multiple access channel. Our results indicate that the new necessary conditions provide the tightest conditions to date in certain cases.

A new model for the interference channel with confidential messages (IC-CM) is introduced, where each receiver, besides his noisy observations, is provided with a fixed-length subset, of his choosing, of noiseless observations for the transmitted codewords of both users, making confidential communication more challenging than the previous such model. In addition, in the same spirit, a broadcast channel with confidential messages (BC-CM), where the receivers noiselessly tap into subsets of their choice of the transmitted codeword, is considered. Achievable strong secrecy rate regions for both models are derived. In both models, the size of the subset quantifies a secure rate trade-off between the two receivers. The case of the new BC-CM model with one receiver's noisy observations are degraded with respect to the other receiver, and only the degraded receiver is provided with the subset of noiseless observations, is highlighted. In this case, the receiver with the degraded noisy observations has a positive rate after a certain threshold of his noiseless observations, i.e., with the aid of these symbols.

A new broadcast wiretap channel (B-WTC) with a wiretapper who noiselessly taps into a fixed-length subset of the transmitted symbols of her choice, and observes the remainder through a noisy channel, is studied. An achievable strong secrecy rate region which extends Marton's inner bound to the proposed setting, is derived. Strong secrecy capacity regions for two classes of the new B-WTC, namely the new B-WTC with deterministic receivers, and the new B-WTC with degraded receivers and more noisy wiretapper, are identified. These results extend the recently proposed new wiretap channel model to the broadcast setting.

We analyze the problem of secure communication over a wiretap channel with an active adversary, in which the legitimate transmitter has the opportunity to sense and learn the adversary's actions. Specifically, the adversary has the ability to switch between two channels and to observe the corresponding output at every channel use; the encoder, however, has causal access to observations impacted by adversary's actions. We develop a joint learning/transmission scheme in which the legitimate users learn and adapt to the adversary's actions. For some channel models, we show that the achievable rates, which we define precisely, are arbitrarily close to those obtained with hindsight, had the transmitter known the actions ahead of time. This suggests that there is much to exploit and gain in physical-layer security by monitoring the environment.

We consider a system where a local cache maintains a collection of N dynamic content items that are randomly requested by local users. A capacity-constrained link to a remote network server limits the ability of the cache to hold the latest version of each item at all times, making it necessary to design an update policy. Using an age of information metric, we show under a relaxed problem formulation that an asymptotically optimal policy updates a cached item in proportion to the square root of the item's popularity. We then show experimentally that a physically realizable policy closely approximates the asymptotic optimal policy.

We study a two-hop cache-aided network, where a layer of relay nodes connects a server and a set of end users, i.e., a combination network. We consider the case where both the relay nodes and the end users have caching capabilities. We provide upper and lower bounds which are applicable to any combination network, noting that previous work had focused on models where the relays do not have caches as well as schemes that were suitable for a special class of combination networks. Utilizing maximum distance separable (MDS) codes, we jointly optimize the placement and the delivery phases, demonstrating the impact of cache memories in alleviating the delivery load over the two hop communications. Moreover, we show how cooperation between the relay nodes and the end users can effectively replace the server during the delivery phase whenever the total memory at each end user and its connected relay nodes is sufficient to store the database.

Coded caching can improve fundamental limits of communication, utilizing storage memory at individual users. This paper considers a centralized coded caching system, introducing heterogeneous cache sizes at the users, i.e., the users' cache memories are of different size. The goal is to design cache placement and delivery policies that minimize the worst-case delivery load on the server. To that end, the paper proposes an optimization framework for cache placement and delivery schemes which explicitly accounts for the heterogeneity of the cache sizes. We also characterize explicitly the optimal caching scheme, for the case where the sum of the users' cache sizes is smaller than or equal to the library size.

We consider a two-source two-destination two-hop relay network, where all data communication must be kept secret from the relay node. The model considered is the simplest primitive that embodies a multi-transmitter multi-receiver network that needs to communicate sharing an untrusted relay node. We focus on two scenarios. In the first scenario, each source aims to send two messages to be kept secret from the relay: a common message that should be decoded by both destinations, and a private message that should be decoded by the first destination while kept secret from the second one. We define an achievable rate region by utilizing stochastic encoding at the sources, the Gaussian noise cooperative jamming from the destinations, and compress-and-forward at the relay. In the second scenario, each source aims to send a confidential message to its intended destination, which should be kept secret from the other one as well as the relay. We define an achievable rate region using a combination of nested lattice codes and random binning at the sources, structured cooperative jamming from destinations, and scaled compute-and-forward at the relay. We also derive genie-aided outer bounds on the secrecy rate regions. We present numerical results that demonstrate the performance of the proposed achievable schemes. Overall, this paper provides insights into how to utilize an untrusted relay to communicate to destinations with different levels of security clearance, and how intentional interference is an enabler of communication.

We consider a binary energy harvesting communication channel with a finite-sized battery at the transmitter. In this model, the channel input is constrained by the available energy at each channel use, which is driven by an external energy harvesting process, the size of the battery, and the previous channel inputs. We consider an abstraction where energy is harvested in binary units and stored in a battery with the capacity of a single unit, and the channel inputs are binary. Viewing the available energy in the battery as a state, this is a state-dependent channel with input-dependent states, memory in the states, and causal state information available at the transmitter only. We find an equivalent representation for this channel based on the timings of the symbols, and determine the capacity of the resulting equivalent timing channel via an auxiliary random variable. We present achievable rates based on certain selections of this auxiliary random variable, which resemble lattice coding for the timing channel. We develop upper bounds for the capacity by using a genie-aided method, and also by quantifying the leakage of the state information to the receiver. We show that the proposed achievable rates are asymptotically capacity achieving for small energy harvesting rates. We extend the results to the case of ternary channel inputs. We numerically observe that our achievable rates are notably close to the upper bounds, and outperform basic Shannon strategies that only consider instantaneous battery states, for all parameter values.

We study the interactive compression of an arbitrary function of two discrete sources with zero-error. The information on the joint distribution of the sources available at the two sides is asymmetric, in that one user knows the true distribution, whereas the other user observes a different distribution. This paper considers the minimum worst-case zero-error codeword length under such asymmetric prior distributions. We investigate the cases for which reconciling the information mismatch is better or worse than not reconciling it, but instead using an encoding scheme that ensures zero-error with possibly increased communication rate. Our results indicate a reconciliation-communication tradeoff and that there exist cases for which partially reconciling the mismatched information is better than both perfect reconciliation and no reconciliation.

We consider a Gaussian network consisting of a source that aims to communicate to its legitimate destination via an untrusted relay node in the presence of an external eavesdropper. The source wishes to send two independent messages to the destination: one message must be kept secret from the external eavesdropper only, while the other message must be kept secret from the external eavesdropper and the untrusted relay both. We identify achievable secure rates under these layered secrecy constraints. Considering a two-hop half-duplex setup, we employ the destination as a cooperative jammer in the first phase in order to help provide secrecy from the relay and the external eavesdropper, and the source as a cooperative jammer in the second phase in order to detriment the external eavesdropper. The source encodes its messages using stochastic encoding and security embedding coding. We provide the secrecy analysis and present numerical results to demonstrate the performance of the proposed achievability technique. Our study points to the value of the source serving as a cooperative jammer as well as the need for power control policies at the legitimate nodes in order to ensure secrecy in this system.

We study how to communicate semantic information in the presence of an agent that can influence the decoder by providing side information. The agent's true intentions, which may be adversarial or helpful, is unknown to the communicating parties. Actions taken by the agent are governed by its intentions, and they may improve or deteriorate the communication performance. We characterize the optimal transmission policies to minimize the end-to-end average semantic error, i.e., difference between the meanings of intended and recovered messages, under the uncertainty in the agent's true intentions. We formulate the semantic communication problem as a Bayesian game, and investigate the conditions under which a pure strategy Bayesian Nash equilibrium exists. We then explore the structure of the encoding and decoding functions under the mixed strategy Bayesian Nash equilibrium, which for the semantic communication problem at hand always exists. Our results show that the optimal policies are strongly influenced by the belief the parties hold about the agent's true intention.

This paper develops a simplified dependency model for sources on social networks that is shown to improve the quality of fact-finding -- assessing veracity of observations shared on social media. Recent literature developed a mathematical approach for exploiting social networks, such as Twitter, as noisy sensor networks that report observations on the state of the physical world. It was shown that the quality of state estimation from such noisy data, known as fact-finding, was a function of assumptions made regarding the independence of sources or lack thereof. When sources propagate information they hear from others (without verification), correlated errors may arise that degrade fact-finding performance. This work advances the state of the art by developing a simplified model of dependencies between sources and designing an improved dependency-aware estimator to assess veracity of observations, taking into account the observed dependency structure. A fundamental error bound is derived for this estimator to understand the gap in its performance from optimal. It is shown that the new estimator outperforms state of the art fact-finders and, in some cases, yields an accuracy close to the fundamental error bound.

The paper develops a recursive state estimator for social network data streams that allows exploitation of social networks, such as Twitter, as sensor networks to reliably observe physical events. Recent literature suggested using social networks as sensor networks leveraging the fact that much of the information upload on the former constitutes acts of sensing. A significant challenge identified in that context was that source reliability is often unknown, leading to uncertainty regarding the veracity of reported observations. Multiple truth finding systems were developed to solve this problem, generally geared towards batch analysis of offline datasets. This work complements the present batch approaches by developing an online recursive state estimator that recovers ground truth from streaming data. In this paper, we model physical world state by a set of binary signals (propositions, called assertions, about world state) and the social network as a noisy medium, where distortion, fabrication, omissions, and duplication are introduced. Our recursive state estimator is designed to recover the original binary signal (the true propositions) from the received noisy signal, essentially decoding the unreliable social network output to obtain the best estimate of ground truth in the physical world. Results show that the estimator is both effective and efficient at recovering the original signal with a high degree of accuracy. The estimator gives rise to a novel situation awareness tool that can be used for reliably following unfolding events in real time, using dynamically arriving social network data.

We consider strongly secure communication over a discrete memoryless multiple access wiretap channel with two transmitters - no degradation or symmetry assumptions are made on the channel. Our main result is that any rate pair known to be achievable with a random coding like proof, is also achievable with a low-complexity polar coding scheme. Moreover, if the rate pair is known to be achievable without time-sharing, then time-sharing is not needed in our polar coding scheme as well. Our proof technique relies on rate-splitting and different cooperative jamming strategies. Specifically, our coding scheme combines several point-to-point codes that either aim at secretly conveying a message to the legitimate receiver or at performing cooperative jamming. Each point-to-point code relies on a chaining construction to be able to deal with an arbitrary channel and strong secrecy. We assess reliability and strong secrecy through a detailed analysis of the dependencies between the random variables involved in the scheme.

We consider authentication of messages sent by L legitimate transmitters to a legitimate receiver over a noisy multiple access channel. We assume the presence of a computationally unbounded opponent who has access to noisy observations of the messages transmitted, and can perform impersonation or substitution attacks. In addition, we consider anonymity constraints where the legitimate receiver must be able to authenticate the messages he receives with respect to predetermined groups of transmitters, but must be kept ignorant of the transmitter's identity of a given message in a given group. Our main result is an authentication coding scheme for which asymptotically matching upper and lower bounds on the probability of successful attack are derived. Our result analytically quantifies the impact of a multiuser setting compared to a single-user setting, as well as the negative impact of anonymity constraints on the probability of successful attack.

We study lossy communication of correlated sources over a multiple access channel. In particular, we provide a joint source-channel coding scheme for transmitting correlated sources with decoder side information, and study the conditions under which separate source and channel coding is optimal. For the latter, the encoders and/or the decoder have access to a common observation conditioned on which the two sources are independent. By establishing necessary and sufficient conditions, we show the optimality of separation when the encoders and the decoder both have access to the common observation. We also demonstrate that separation is optimal when only the encoders have access to the common observation whose lossless recovery is required at the decoder. As a special case, we study separation for sources with a common part. Our results indicate that side information can have significant impact on the optimality of source-channel separation in lossy transmission.

In this paper, a new wiretap channel (WTC) model with a discrete memoryless (DM) main channel and a wiretapper who noiselessly observes a fixed portion, of her choice, of the transmitted symbols, while observing the remaining transmitted symbols through another DM channel (DMC), is considered. The strong secrecy capacity of the model is identified. The achievability is established using the output statistics of random binning framework which exploits the duality between source and channel coding problems. The converse is derived by upper bounding the secrecy capacity of an equivalent model with the secrecy capacity of a DM-WTC. This result generalizes both the classical DM-WTC and the WTC-II with a DM main channel.

A two transmitter multiple access wiretap channel II (MAC-WT-II) with a discrete memoryless (DM) main channel is investigated. Two models for the wiretapper, who chooses a fixed-length subset of the channel uses and observes erasures outside this subset, are proposed. In the first model, in each position of the subset, the wiretapper noiselessly observes either the first or the second user's symbol, while in the second model, the wiretapper observes a noiseless superposition of the two symbols. Achievable strong secrecy rate regions for the two models are derived. The achievability is established by solving a dual secret key agreement problem in the source model. The secrecy of the keys in the dual source model is established by deriving a lemma which provides a doubly exponential convergence rate for the probability of the keys being uniform and independent from the wiretapper's observation. The results extend the recently examined WTC-II with a DM main channel to a multiple access setting.

A new model for the two-user multiple-access wiretap channel is considered. In this model, the legitimate (main) channel is a discrete memoryless channel (DMC), and the wiretapper chooses a fixed-length subset of the channel uses where she has perfect access to the transmitted symbols of the both users, while observing the remainder of the transmitted codewords through a second DMC. As such, it generalizes the existing multiple-access wiretap channel models and extends the recently proposed new wiretap channel model to a multiple access setting. An achievable strong secrecy rate region for the model is derived. The achievability is established by solving a dual multi-terminal secret key agreement problem in the source model, where two independent sources are communicating confidential keys to a common decoder over a public channel in the presence of a compound wiretapping source. The secrecy of the two keys in the dual source model is established by deriving a lemma which provides a doubly exponential convergence rate for the probability of the keys being uniform and independent from the public discussion and the wiretapping source observation.

We consider a wireless ad hoc network composed of N transmitters and M receivers which are all selfish in the sense that they wish to optimize their individual utilities rather than a network wide utility. Each node can acquire energy from a supplier at a price to power the transmission or reception of data. For such a network, we consider a matching game played between the transmitters and the receivers. The transmitters compute the optimal rate for them and propose this to a receiver. The receivers determine the best proposal they have received to maximize their utilities. We identify the optimal decisions for all nodes and the resulting utilities. We next consider a Vickrey auction between transmitters which have proposed to the same receiver. We show that the transmitters can compete with each other by offering energy transfer to the receiver. The energy transfer reduces the processing costs of the receiver and influences its decision, thereby pointing to the merit of energy cooperation. We observe that populating the network with additional nodes generally results in more options for all nodes to choose from, and larger rates for the entire network, which are improved even further by energy cooperation.

We consider a distributed storage system where data storage nodes are equipped with energy harvesting transmitters. In particular, F files are stored over n storage nodes using regenerating codes. The main operations of the distributed storage system are serving the file requests of data collectors and repairing the content of storage nodes that fail or leave the system. Each operation has an associated energy expenditure. Under the intermittent energy arrival profile, we study the problem of maximizing the number of retrieved files given a deadline. Additionally, we consider the problem of minimizing the repair time of a failed node. Both optimization problems turn out to be equivalent to binary programs, for which we provide a tractable solution in two steps. First, we determine necessary and sufficient conditions on the harvested energy that ascertain the feasibility of retrieving (repairing) M files in T time slots. Using these conditions, we develop two algorithms that reduce the formulated optimization problems to a single feasibility problem. Then, we solve the feasibility problem using forward and backward algorithms. Additionally, we study the online setup where only causal knowledge of energy arrivals is available at the network nodes. We present numerical results on the short and long term performance of the system operations under the proposed algorithms.

This paper studies energy harvesting transmitters in the single user channel, the two-way channel, and the two-way relay channel with block fading. Each transmitter is equipped with a finite battery to store the harvested energy, and a finite buffer to store the data that arrive during the communication session. We consider delay sensitive applications and maximize throughput while enabling timely delivery of data with delay constraints. We show that the resulting delay limited throughput maximization problem can be solved using alternating maximization of two decoupled problems termed the energy scheduling problem and the data scheduling problem. We solve the energy scheduling problem using a modified directional waterfilling algorithm with right permeable taps, water pumps, and overflow bins and the data scheduling problem with forward induction. Additionally, we identify the online optimum policy for throughput maximization. We provide numerical results to verify our analytical findings and to demonstrate the impact of the finite data buffer capacity and the delay requirements on the throughput. We observe that larger buffer sizes become useful for more lenient delay requirements, and a data buffer size that is comparable to the throughput within one time slot accounts for the majority of the increase in throughput.

We study the fundamental performance limits of energy harvesting channels with short-length channel codes that expend less processing energy and facilitate low-latency communications. In particular, we examine the zero-battery case, i.e, energy harvesting transmitters with no energy storage such as passive RFID tags, in which energy must be spent as it arrives or is lost. To analyze practical finite- length channel codes, we develop a second-order approximation to the communication rate for such channels with energy information causally known at the transmitter. We present two binary examples for which we explicitly calculate the channel capacity and channel dispersion and interestingly observe that a slight increase in the energy arrival probability can significantly boost the achievable rate, and further that the rate loss due to energy intermittency is more pronounced for channels with lower noise levels.

In this paper, we study a cooperative two-hop network with multiple sources and multiple relays where the energy required for the relays is transferred by the sources. In return, the relays transmit the sources' data, along with their own data, to the destination. We consider the setup where each node's objective is to maximize the amount of its own data delivered to the destination. We take a game theoretic approach and first model the selfish cooperation scenario with one source and one relay as a Stackelberg game where (i) the relay or (ii) the source is the leader. We demonstrate how the leader of the game takes advantage of its ability to compute the follower's optimal strategy to influence the follower and improve its own utility. In both cases, we also consider the case with multiple followers. We employ Vickrey auctions to model the inter-follower competition. We identify the winner of the auction in both cases and observe that the followers must compromise their individual utilities to win the auction. Consequently, the leader's utility turns out to be nondecreasing in the number of competing followers.

The secure degrees of freedom (s.d.o.f.) of a multiantenna Gaussian wiretap channel with N antennas at the transmitter and receiver and an arbitrary number of antennas, M, at the wiretapper is characterized when a multiantenna cooperative jammer (CJ) is available as a helper. This generalizes our previous result that assumed the same number of antennas at the eavesdropper as the legitimate parties. In particular, for arbitrary values of N andM, the s.d.o.f. is derived for all possible values of the number of antenna at the CJ, K. The achievability is based on a variety of signalling, beamforming, and alignment techniques which vary according to the value of K, whether M is larger than, smaller than, or equal to N, and whether the s.d.o.f. is integer valued or not an integer. The converse is based on combining an upper bound for the s.d.o.f. which allows for cooperation between the transmitter and CJ and holds for some values of K, with another upper bound which exploits the secrecy and reliability constraints and holds for other values of K.

This paper studies the impact of side information on the lossy compression of a remote source, one which is indirectly accessed by the encoder. In particular, we identify the conditions under which sharing side information between the encoder and the decoder may be superior or inferior to having two-sided, i.e., correlated but not identical, side information. As a special case, we characterize the optimal rate-distortion function for a direct binary source with two-sided information by proposing an achievable scheme and proving a converse. This example suggests a hierarchy on the impact of side information, in that the performance is mainly determined by how well the decoder learns about the source and then by how well the encoder learns about the decoder's observation.

In this paper, a wiretap channel where the transmitter and receiver communicate through a discrete memoryless channel, and the eavesdropper (Eve) has perfect access to a fixed fraction of transmitted symbols (of its choosing) is considered. An outer bound for the rate-equivocation region of the channel, for all such fractions, is derived. An achievable scheme, which provides an inner bound for the rate-equivocation region, is proposed. The achievability is established by defining a class of good codebooks for which there exists a good partition that achieves the required level of equivocation no matter what subset of symbols Eve chooses. It is shown that, for a uniform input distribution, the probability of this class of good codes approaches 1 as the block length increases. This generalizes the wiretap II model to one with a noisy main channel.

A noiseless binary energy harvesting channel with on-off fading is considered. When causal fading state information is available at the transmitter only, an equivalent timing channel with additive geometric noise and noise information known at the transmitter is obtained. In this channel, the transmitter's strategy is a stopping rule with respect to the channel fade levels given the message and the additive noise. Next, capacity when energy arrival information is available at the receiver and capacity when both energy arrival and fading information are available at the receiver are obtained. Additionally, several achievable schemes are proposed and evaluated.

The past decade has witnessed significant effort towards establishing reliable and information theoretically secure rates in communication networks, taking advantage of the properties of the communication medium. Such efforts include those in the wireless medium where simultaneous transmissions and the ensuing interference can prove advantageous from an information theoretic secrecy point of view. With the goal of obtaining a secrecy rate that scales with transmit power, structured signaling with simultaneous favorable signal alignment at the legitimate receiver(s) and unfavorable signal alignment at the eavesdropper(s) has proven particularly useful in multi-terminal Gaussian channels. Many challenges remain however in realizing the vision of absolute security provided by the wireless physical layer including handling more realistic models. In this paper, we provide a brief overview of the state of the art, the forward look and argue for an additional asset that could be utilized for secrecy, i.e., bidirectional signaling. Taking the bidirectional wiretap channel as an example, Gaussian signaling is demonstrated to be as good as structured signaling from the degrees of freedom point of view, while observed to be performing better with finite transmit power. Moreover, taking bidirectional signals explicitly into account for encoding performs even better and provides a way forward to synergistically combine physical layer based secrecy and encryption.

We consider an energy harvesting network where the transmitter harvests energy from nature, and the harvested energy can be saved in an imperfect battery which suffers from charging/ discharging inefficiency. In particular, when E units of energy is to be stored in the battery, only ηE units is saved and (1 - η)E is lost due to charging/discharging inefficiency, where 0 ≤ η ≤ 1 represents the storing efficiency. We determine the optimum offline transmit power schedule for such a system for single-user and broadcast channel models, for static and fading channels, with and without a finite battery size. We show that the optimum policy is a double-threshold policy: specifically, we store energy in the battery only when the harvested energy is above an upper threshold, and retrieve energy from the battery only when the harvested energy is below a lower threshold; when the harvested energy is in between these two thresholds, we use it in its entirety in the current slot. We show that the two thresholds remain constant unless the battery is depleted or full. We provide an algorithm to determine the sequence of optimum thresholds. For the case with fading, we develop a directional water-filling algorithm which has a double-threshold structure. Finally, we formulate the online problem using dynamic programming, and numerically observe that the online policy exhibits a double-threshold structure as well.

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.

We consider a two-hop wireless network where the source(s) in the network have the ability to wirelessly power the relay(s) who also have their own data to send to the destination. Considering the fact that each node in the network aims to maximize its own metric, we adopt a game theoretic approach that foresees offering relaying of the sources' data in exchange for energy provided to the relays, and simultaneously offering energy to the relays in exchange for their relaying services. We first study a Stackelberg competition with the single relay node as the leader, and investigate the impact of having multiple source nodes in the system. We next study the reciprocal Stackelberg game with the single source as the leader, and investigate the inter-relay competition with multiple relays. We find that in the Stackelberg games, the leader can improve its individual utility by influencing the follower's decision accordingly, even more so when there are multiple followers. We next formulate a noncooperative game between the source and the relay and show the existence of a unique Nash equilibrium by an appropriate pricing mechanism. The equilibrium maximizes the total utility of the network and allows the destination to choose how much data to receive from each node.

Physical-layer security utilizes resources of the transmission medium to guarantee secure communication against an adversary with unlimited computational power. Rooted in information theory, physical-layer security advocates for a foundational approach by requiring security of communicated information as well as its reliability at the outset. The past decade has seen an unprecedented effort in physical-layer security research resulting in promising new design insights. The majority of these advances has been in wireless communications security, well-motivated by the fact that most data at large, including those of sensitive nature, flow over wireless links that are more vulnerable to security breaches, e.g., eavesdropping. At the same time, the open broadcast nature of wireless brings possibilities of cooperation by the network entities for improving security, e.g., resistance to eavesdropping. This article aims to provide an overview of research results in information-theoretic security with multiple wireless transmitters, and focuses on distilling insights for designing wireless systems with confidentiality guarantees.

In this paper, we study the two-way relay channel with energy harvesting nodes. In particular, we find transmission policies that maximize the sum-throughput for two-way relay channels when the relay does not employ a data buffer. The relay can perform decode-and-forward, compress-and-forward, compute-and-forward, or amplify-and-forward relaying. Furthermore, we consider throughput improvement by dynamically choosing relaying strategies, resulting in hybrid relaying strategies. We show that an iterative generalized directional water-filling algorithm solves the offline throughput maximization problem, with the achievable sum-rate from an individual or hybrid relaying scheme. In addition to the optimum offline policy, we obtain the optimum online policy via dynamic programming. We provide numerical results for each relaying scheme to support the analytic findings, pointing out to the advantage of adapting the instantaneous relaying strategy to the available harvested energy.

This paper considers multiterminal networks with energy harvesting transmitter nodes that are also capable of wirelessly transferring energy to or receiving energy from other nodes in the network. In particular, the jointly optimal transmit power and energy transfer policies that maximize sum-throughput for the two-way, two-hop, and multiple access channels are identified. It is shown for nodes with infinite-sized batteries that delaying energy transfers until energy is needed immediately at the receiving node is sum-throughput optimal. Focusing on such procrastinating policies without loss of optimality, the stated joint optimization problem can be decomposed into energy transfer and consumed energy allocation problems which are solved in tandem. This decomposition is shown to hold for the finite-sized battery case as well, using partially procrastinating policies that avoid battery overflows. It is observed that for the two-hop channel, the proposed algorithm has a two fluid water-filling interpretation, and for the multiple access channel, it reduces to a single transmitter problem with aggregate energy arrivals. Numerical results demonstrate the throughput improvement with bi-directional energy cooperation over no cooperation and uni-directional cooperation.

This paper considers relay assisted transmission for multiuser networks when the relay has no access to the codebooks used by the transmitters. The relay is called oblivious for this reason. Of particular interest is the generalized compress-and-forward (GCF) strategy, where the destinations jointly decode the compression indices and the transmitted messages, and their optimality in this setting. The relay-to-destination links are assumed to be out-of-band with finite capacity. Two models are investigated: 1) the multiple access relay channel (MARC) and 2) the interference relay channel (IFRC). For the MARC with an oblivious relay, a new outerbound is derived and it is shown to be tight by means of achievability of the capacity region using GCF scheme. For the IFRC with an oblivious relay, a new strong interference condition is established, under which the capacity region is found by deriving a new outerbound and showing that it is achievable using GCF scheme. The result is further extended to establish the capacity region of M-user MARC with an oblivious relay, and multicast networks containing M sources and K destinations with an oblivious relay.

Online social communities often exhibit complex relationship structures, ranging from close friends to political rivals. As a result, persons are influenced by their friends and foes differently. Network applications can benefit from accompanying these structural differences in propagation schemes. In this paper, we study the optimal influence propagation policies for networks with positive and negative relationship types. We tackle the problem of minimizing the end-to-end propagation cost of influencing a target person in favor of an idea by utilizing the relationship types in the underlying social graph. The propagation cost is incurred by social and physical network dynamics such as frequency of interaction, the strength of friendship and foe ties, propagation delay or the impact factor of the propagating idea. We extend this problem by incorporating the impact of message deterioration and ignorance. We demonstrate our results in both a controlled environment and the Epinions dataset. Our results show that judicious propagation schemes lead to a significant reduction in the average cost and complexity of influence propagation compared to naïve myopic algorithms.

Semantics of communicated data can lead to conclusions with varying degrees of priorities. Depending on the interests of the communicating parties, some facts lead to conclusions that carry a high risk when ignored, and others may not be worth the resources to share the facts leading to those uninteresting conclusions. This paper studies the worst-case semantic data compression problem for sharing facts that lead to conclusions with such varying priorities. We establish the performance bounds by utilizing the partial dependencies between the ideas and the priority distributions on the conclusions. We show that multiple term descriptions of the facts and conclusions improve the compression performance when combined with judicious partitioning of the fact space.

In this paper, we consider the energy allocation problem for energy harvesting and energy cooperating nodes with finite-sized batteries. In particular, we solve the sum-throughput maximization problem in a two-way channel with energy harvesting nodes that can also transfer energy to one another. To do so, we non-trivially extend a class of policies which originally rely on an infinite-sized battery to be optimal, to the finite battery case. We observe that when we partition transferred energy into immediately used and stored components, an optimal policy has a non-zero stored component only when the battery of the transferring user is full. This enables the decomposition of the sum-throughput maximization problem into separate energy transfer and power allocation problems. Utilizing properties of this optimal class of policies, we solve the power allocation problem using a two dimensional directional water-filling algorithm with restricted transfers, where energy transfers only take place at full battery instances. Numerical results demonstrate that energy cooperation notably improves sum-throughput as one node gets energy deprived.

In this paper, a single user channel is considered with an energy harvesting transmitter that receives its energy and data intermittently. The transmitter is equipped with a finite battery as well as a finite data buffer. The throughput maximization problem with a deadline is solved and the optimal transmission policy is obtained. The optimization problem is shown to yield a directional waterfilling solution with energy pumps. An alternative algorithmic solution is also presented that utilizes the recursive shortest path solution that was shown to be optimal for infinite data buffers in earlier work. Numerical results are provided to demonstrate the throughput performance of optimal policies as well as to assess the impact of the finite buffer on the throughput.

We determine the capacity of a discrete memoryless communication channel with an energy harvesting transmitter and its battery state information available at the transmitter and the receiver. This capacity is an upper bound for the problem where side information is available only at the transmitter. Since channel output feedback does not increase the capacity in this case, we equivalently study the resulting finite-state Markov channel with feedback. We express the capacity in terms of directed information. Additionally, we provide sufficient conditions under which the capacity expression is further simplified to include the stationary distribution of the battery state. We also obtain a single-letter expression for the capacity with battery state information at both sides and an infinite-sized battery. Lastly, we consider achievable schemes when side information is available only at the transmitter for the case of an arbitrary finite-sized battery. We numerically evaluate the capacity and achievable rates with and without receiver side information.

We consider a binary energy harvesting channel (BEHC) where the encoder has unit energy storage capacity. We first show that an encoding scheme based on block indexing is asymptotically optimal for small energy harvesting rates. We then present a novel upper bounding technique, which upper bounds the rate by lower-bounding the rate of information leakage to the receiver regarding the energy harvesting process. Finally, we propose a timing based hybrid encoding scheme that achieves rates within 0.03 bits/channel use of the upper bound; hence determining the capacity to within 0.03 bits/channel use.

A multiple antenna Gaussian wiretap channel with a multiantenna cooperative jammer (CJ) is considered and the secure degrees of freedom (s.d.o.f.), with N antennas at the sender, receiver, and eavesdropper, is derived for all possible values of the number of antennas at the cooperative jammer, K. In particular, the upper and lower bounds for the s.d.o.f. are provided for different ranges of K and shown to coincide. Gaussian signaling both for transmission and jamming is shown to be sufficient to achieve the s.d.o.f. of the channel, when the s.d.o.f. is integer-valued. By contrast, when the channel has a non-integer s.d.o.f., structured signaling and joint signal space and signal scale alignment are employed to achieve the s.d.o.f.

We determine the capacity of a discrete memoryless communication channel with an energy harvesting transmitter and the energy arrival information available at the receiver as well as the transmitter. We obtain an n-letter capacity expression and prove that the capacity is achieved by an encoding scheme that depends only on the current battery state. Moreover, the capacity is invariant to the non-causal knowledge of energy arrivals. Finally, we show that the capacity expression is equivalently the maximum directed mutual information and that the channel output feedback does not increase the capacity in this case. We obtain upper and lower bounds on the capacity and numerically evaluate them for comparison.

In this paper, we consider a binary energy harvesting transmitter that wishes to control the amount of side information the receiver can obtain about its energy harvests. Specifically, we study state amplification and state masking, which define the maximum and minimum amount of state information conveyed to the receiver for a given message rate, respectively. For an independent and identically distributed energy harvesting process, we first find the amplification and masking regions for a transmitter without a battery and a transmitter with an infinite battery. Next, we find inner bounds for these regions for a unit-sized battery at the transmitter using two different encoding schemes, using instantaneous Shannon strategies and using a scheme based on the equivalent timing channel introduced in our previous work. We observe that the former provides better state amplification, while the latter provides better state masking.

This paper considers a Gaussian multiple access channel aided by a relay. Specifically, the relay facilitates communication between multiple sources and a destination to which the sources have no direct link. In this set up, the relay node is considered to be untrusted, i.e., honest but curious, from whom the source messages need to be kept secret. We identify an achievable secrecy rate region utilizing cooperative jamming from the destination, and using compress-and-forward at the relay. Additionally, an outer bound on the secrecy rate region is derived. Numerical results indicate that the outer bound is tight in some cases of interest.

In this paper, we consider Quality-of-Information (QoI) aware transmission policies for a dynamic environment. In particular, we focus on the time-varying nature of the observation quality of the environment in practical networks which leads to uncertainty in satisfying QoI requirements specified by end users. The goal of this paper is to meet QoI requests from end users with minimum resources. Specifically, power is allocated dynamically depending on observation accuracies and QoI requirements. We formulate a dynamic scheme for scheduling with the objective of minimizing the energy consumption at the network while satisfying constraints on outage probability for QoI. Lyapunov stability arguments are used to define a policy based on the instantaneous observation qualities and QoI requirement satisfaction levels. Numerical results demonstrate that significant improvements in delivered QoI are realized with identical power expenditure using our QoI-aware resource allocation algorithm compared with traditional maximum-rate schedulers.

This paper presents a novel cross-tier interference management solution for coexisting two-tier networks by exploiting cognition and coordination between tiers via the use of agile radios. The cognitive users sense their environment to determine the receivers they are interfering with, and adapt to it by designing their precoders using interference alignment (IA) in order to avoid causing performance degradation to nearby receivers. The proposed approach judiciously chooses the set of users to be aligned at each receiver as a subset of the cross-tier interferers, hence is termed selective IA. The proposed solution includes identification of the subspace in which cross-tier interference signals would be aligned followed by a distributed algorithm to identify the precoders needed at the selected interferers. The intra-tier interference is then dealt with using minimum mean squared error (MMSE) interference suppression. Numerical results demonstrate the effectiveness of selective IA for both uplink and downlink interference management.

We study a multi-antenna broadcast channel with two legitimate receivers and an external eavesdropper. We assume that the channel matrix of the eavesdropper is unknown to the legitimate terminals but satisfies a maximum rank constraint. As our main result we characterize the associated secrecy degrees of freedom for the broadcast channel with common and private messages. We show that a direct extension of the single-user wiretap codebook does not achieve the secrecy degrees of freedom. Our proposed optimal scheme involves decomposing the signal space into a common subspace, which can be observed by both receivers, and private subspaces which can be observed by only one of the receivers, and carefully transmitting a subset of messages in each subspace. We also consider the case when each user's private message must additionally remain confidential from the other legitimate receiver and characterize the s.d.o.f. region in this case.

Recent results have shown that structured codes can be used to construct good channel codes, source codes, and physical layer network codes for Gaussian channels. For Gaussian channels with secrecy constraints, however, efforts to date rely on Gaussian random codes. In this paper, we advocate that structure in random code generation is useful for providing secrecy as well. In particular, a Gaussian wiretap channel in the presence of a cooperative jammer is studied. Previously, the achievable secrecy rate for this channel was derived using Gaussian signaling, which saturated at high signal-to-noise ratio (SNR), owing to the fact that the cooperative jammer simultaneously helps by interfering with the eavesdropper, and hurts by interfering with the intended receiver. In this paper, a new achievable rate is derived through imposing a lattice structure on the signals transmitted by both the source and the cooperative jammer, which are aligned at the eavesdropper but remain separable at the intended receiver. We prove that the achieved secrecy rate does not saturate at high SNR for all values of channel gains except when the channel is degraded.

In this paper, a class of information theoretic secrecy problems is addressed where the eavesdropper channel state is completely unknown to the legitimate parties. In particular, a Gaussian MIMO wiretap channel is considered, where the eavesdropper channel state can vary from one channel use to the next, and the overall channel state sequence is known only to the eavesdropper. When the eavesdropper has fewer antennas than the transmitter and its intended receiver, a positive secrecy rate in the sense of strong secrecy is proved to be achievable and shown to match with the converse in secure degrees of freedom. This yields the conclusion that secure communication is possible regardless of the location or channel states of the eavesdropper. Additionally, it is observed that, the present setting renders the secrecy capacity problems for some multiterminal wiretap-type channels more tractable as compared to the case with full or partial knowledge of eavesdropper channel states. To demonstrate this observation, secure degrees of freedom regions are derived for the Gaussian MIMO multiple access (MAC) wiretap channel and the two-user Gaussian MIMO broadcast (BC) wiretap channel, where the transmitter(s) and intended receiver(s) have the same number of antennas.

This paper investigates the degrees of freedom (DoF) of the L-cluster, K-user MIMO multiway relay channel, where users in each cluster wish to exchange messages within the cluster, and they can only communicate through the relay. A novel DoF upper bound is derived by providing users with carefully designed genie information. Achievable DoF is identified using signal space alignment and multiple-access transmission. For the two-cluster MIMO multiway relay channel with two users in each cluster, the DoF is established for the general case when users and the relay have arbitrary number of antennas, and it is shown that the DoF upper bound can be achieved using signal space alignment or multiple-access transmission, or a combination of both. The result is then generalized to the three user case. For the L-cluster K-user MIMO multiway relay channel in the symmetric setting, conditions under which the DoF upper bound can be achieved are established. In addition to being shown to be tight in a variety of scenarios of interests of the multiway relay channel, the newly derived upperbound also establishes the optimality of several previously established achievable DoF results for multiuser relay channels that are special cases of the multiway relay channel.

This paper considers uplink interference management for two-tier cellular systems by way of Interference Alignment (IA). In order to manage the uplink interference caused by macrocell users at the femtocell base stations (FBS), cooperation between macrocell users with the closest femtocell base stations is proposed with the goal of aligning the received signals of macrocell users in the same subspace at multiple FBSs. The precoder design for macrocell users is accomplished using successive semidefinite programming relaxations. The proposed solution aims to minimize the cross-tier interference leaked to the femtocells while providing the macrocell users with a minimum received signal to interference plus noise ratio (SINR) at the macrocell base station (MBS). Intra-tier femtocell interference is dealt with minimum mean squared error (MMSE) interference suppression. Numerical results demonstrate that the proposed two-tier interference management approach improves the performance of femtocell users, while maintaining the desired quality of the communication channel of macrocell users.

An interference limited multitier multiuser MIMO cellular uplink is considered. Specifically, an interference management scheme is proposed where interference from subsets of macrocell users is aligned at the femtocell base stations in order to ensure acceptable service for the femtocell users. The scheme employs interference alignment (IA) at each femtocell base station (FBS), to the set of macrocell users (MU) that are causing the high interference specifically at that FBS, and hence is termed selective IA. The proposed IA algorithm determines the interference subspaces at each FBS and precoders for each MU in a distributed fashion. Numerical results demonstrate the performance advantage of selective IA.

Communication gains from relaying can be acquired even in the absence of altruistic or dedicated relays. To accommodate this, spectrum leasing for cooperation paradigm prescribes that the potential relaying node is rewarded for its relaying role with a fraction of source's bandwidth. Recently, a two-hop spectrum leasing scheme was proposed that enables the source to employ the relay only to an extent it finds beneficial, preserving the remaining bandwidth for its direct transmission, if necessary. Such a solution adds flexibility that can be particularly useful for implementation in networks involving more than two hops. In this paper, we extend this setup to include the relay selection problem in a two-hop network scenario with multiple potential relays. Since the potential relays are selfish nodes, their competitive attitude for accessing the spectrum needs to be taken into account. In addition to Stackelberg game framework used in the original setting with one relay, auction theory is applied to model the relay selection process. Analysis and numerical results are provided, demonstrating that the source-destination pair can achieve significant benefits emanating from the competitive nature of the relays.

This paper investigates the degrees of freedom (DoF) of the two-cluster multi-way relay channel. Two cases are investigated: the case when there are 2 users in each cluster with arbitrary number of antennas and the case when there are 3 users in each cluster in the symmetric setting, i.e., all the users have the same number of antennas. For the 2-user case, a DoF upper bound is derived based on cut set bound by allowing user cooperation between clusters. Conditions when the DoF upper bound can be achieved using signal space alignment are established based on the relative number of antennas between the users and the relay. For the 3-user case, a new DoF upper bound is derived using genie-aided approach and channel enhancement. The DoF upper bound can be achieved using signal space alignment for several scenarios of interests. The results point out the insight that increasing the number of users in each cluster cannot provide a further DoF gain compared to the 2-user case, when the relay has limited number of antennas.

This paper investigates the degrees of freedom (DoF) of the L-cluster LT-user MIMO multi-way relay channel. A DoF upperbound is derived by providing users with carefully designed genie information, and properly enhancing the received signal of one of the users. For the L-cluster LT-user MIMO multi-way relay channel in the symmetric setting, conditions under which the DoF upperbound can be achieved using either multiple access transmission or signal space alignment are established, demonstrating that the newly derived upperbound is the first tight DoF upperbound for the general MIMO multi-way relay channel. Additionally, this new upperbound proves the optimality of the achievable DoF for several special cases of the MIMO multi-way relay channel obtained in previous works. The results provide the insight that, with fixed spatial dimension at the relay, increasing the number of users and clusters cannot provide any DoF gain. In addition, it is observed that allowing three or more users to share the spatial dimension of the relay cannot provide any DoF gain.

We consider the capacity of an energy harvesting communication channel with a finite-sized battery. As an abstraction of this problem, we consider a system where energy arrives at the encoder in multiples of a fixed quantity, and the physical layer is modeled accordingly as a finite discrete alphabet channel based on this fixed quantity. Further, for tractability, we consider the case of binary energy arrivals into a unit-capacity battery over a noiseless binary channel. Viewing the available energy as state, this is a state-dependent channel with causal state information available only at the transmitter. Further, the state is correlated over time and the channel inputs modify the future states. We show that this channel is equivalent to an additive geometric-noise timing channel with causal information of the noise available at the transmitter. We provide a single-letter capacity expression involving an auxiliary random variable, and evaluate this expression with certain auxiliary random variable selection, which resembles noise concentration and lattice-type coding in the timing channel. We evaluate the achievable rates by the proposed auxiliary selection and extend our results to noiseless ternary channels.

This paper considers two-hop communication networks where the transmitters harvest their energy in an intermittent fashion. In this network, communication is carried out by signal cooperation, i.e., relaying. Additionally, the transmitters have the option of transferring energy to one another, i.e., energy cooperation. Energy is partially lost during transfer, exposing a trade-off between energy cooperation and use of harvested energy for transmission. A multi-access relay model is considered and transmit power allocation and energy transfer policies that jointly maximize the sum-rate are found. It is shown that a class of power policies achieves the optimal sum-rate, allowing a separation of optimal energy transfer and optimal power allocation problems. The optimal energy transfer policy is shown to be an ordered node selection, where nodes with better energy transfer efficiency and worse channels transfer all their energy to the relay or other source nodes via the relay. For the special case of single source, the optimal policy requires the direction of energy transfer to remain unchanged unless either node depletes all of its energy. Overall, the findings provide the insight that cooperation of the source nodes by sharing energy with the relay node leads to them indirectly cooperating with each other, and that such cooperation can be carried out in a last-minute fashion.

The multiuser communication channel, in which multiple users exchange information with the help of a relay terminal, termed the multiway relay channel (mRC), is introduced. In this model, multiple interfering clusters of users communicate simultaneously, such that the users within the same cluster wish to exchange messages among themselves, i.e., each user multicasts its message to all the other users in its own cluster. It is assumed that the users cannot receive each other's signals directly. Hence, the relay terminal in this model is the enabler of communication. In particular, restricted encoders are considered, such that the encoding function of each user depends only on its own message and the received signal is used only for decoding the messages of the other users in the cluster. Achievable rate regions and an outer bound are characterized for the Gaussian mRC, and their comparison is presented in terms of the exchange rate, the symmetric rate point in the capacity region in a symmetric Gaussian mRC scenario. It is shown that the compress-and-forward (CF) protocol achieves exchange rates within a constant bit offset of the optimal exchange rate, independent of the power constraints of the terminals in the network. A finite bit gap between the exchange rates achieved by the CF and the amplify-and-forward protocols is also shown. The two special cases of the mRC, the full data exchange model, in which every user wants to receive messages of all other users, and the pairwise data exchange model which consists of multiple two-way relay channels, are investigated in detail. In particular for the pairwise data exchange model, in addition to the proposed random coding-based achievable schemes, a nested lattice coding-based scheme is also presented and is shown to achieve exchange rates within a constant bit gap of the exchange capacity.

A two-transmitter Gaussian multiple access wiretap channel with multiple antennas at each of the nodes is investigated. The channel matrices of the legitimate users are fixed and revealed to all the terminals, whereas the channel matrices of the eavesdropper are arbitrarily varying and only known to the eavesdropper. The secrecy degrees of freedom (s.d.o.f.) region under a strong secrecy constraint is characterized. A transmission scheme that orthogonalizes the transmit signals of the two users at the intended receiver, and uses a single-user wiretap code for each user, is shown to achieve the s.d.o.f. region. The converse involves establishing an upper bound on a weighted-sum-rate expression. This is accomplished by using induction, where at each step one combines the secrecy and multiple-access constraints associated with an adversary eavesdropping a carefully selected group of sub-channels.

We consider a Gaussian two-hop network where the source and the destination can communicate only via a relay node who is both an eavesdropper and a Byzantine adversary. Both the source and the destination nodes are allowed to transmit, and the relay receives a superposition of their transmitted signals. We propose a new coding scheme that satisfies two requirements simultaneously: the transmitted message must be kept secret from the relay node, and the destination must be able to detect any Byzantine attack that the relay node might launch reliably and fast. The three main components of the proposed scheme are the nested lattice code, the privacy amplification scheme, and the algebraic manipulation detection (AMD) code. Specifically, for the Gaussian two-hop network, we show that lattice coding can successfully pair with AMD codes enabling its first application to a noisy channel model. We prove, using this new coding scheme, that the probability that the Byzantine attack goes undetected decreases exponentially fast with respect to the number of channel uses, while the loss in the secrecy rate, compared to the rate achievable when the relay is honest, can be made arbitrarily small. In addition, in contrast with prior work in Gaussian channels, the notion of secrecy provided here is strong secrecy.

Most practical communication links are bidirectional. In these models, since the source node also receives signals, its encoder has the option of computing its output based on the signals it received in the past. On the other hand, from a practical point of view, it would also be desirable to identify the cases where such an encoder design may not improve communication rates. This question is particularly interesting for the case where the transmitted messages and the feedback signals are subject to eavesdropping. In this paper, we investigate the question of how much impact the feedback has on the secrecy capacity by studying two fundamental models. First, we consider the Gaussian two-way wiretap channel and derive an outer bound for its secrecy capacity region. We show that the secrecy rate loss can be unbounded when feedback signals are not utilized except for a special case we identify, and thus conclude that utilizing feedback can be highly beneficial in general. Second, we consider a half-duplex Gaussian two-way relay channel where the relay node is also an eavesdropper, and find that the impact of feedback is less pronounced compared to the previous scenario. Specifically, the loss in secrecy rate, when ignoring the feedback, is quantified to be less than 0.5 bit per channel use when the relay power goes to infinity. This achievable rate region is obtained with simple time sharing along with cooperative jamming, which, with its simplicity and near optimum performance, is a viable alternative to an encoder that utilizes feedback signals.

It has been recently shown by Lapidoth and Steinberg that strictly causal state information can be beneficial in multiple access channels (MACs). Specifically, it was proved that the capacity region of a two-user MAC with independent states, each known strictly causally to one encoder, can be enlarged by letting the encoders send compressed past state information to the decoder. In this study, a generalization of the said strategy is proposed whereby the encoders compress also the past transmitted codewords along with the past state sequences. The proposed scheme uses a combination of long-message encoding, compression of the past state sequences and codewords without binning, and joint decoding over all transmission blocks. The proposed strategy has been recently shown by Lapidoth and Steinberg to strictly improve upon the original one. Capacity results are then derived for a class of channels that include two-user modulo-additive state-dependent MACs. Moreover, the proposed scheme is extended to state-dependent MACs with an arbitrary number of users. Finally, output feedback is introduced and an example is provided to illustrate the interplay between feedback and availability of strictly causal state information in enlarging the capacity region.

A state-dependent degraded broadcast diamond channel is studied where the source-to-relays cut is modeled with two noiseless, finite-capacity digital links with a degraded broadcasting structure, while the relays-to-destination cut is a general multiple access channel controlled by a random state. It is assumed that the source has noncausal channel state information and the relays have no state information. Under this model, first, the capacity is characterized for the case where the destination has state information, i.e., has access to the state sequence. It is demonstrated that in this case, a joint message and state transmission scheme via binning is optimal. Next, the case where the destination does not have state information, i.e., the case with state information at the source only, is considered. For this scenario, lower and upper bounds on the capacity are derived for the general discrete memoryless model. Achievable rates are then computed for the case in which the relays-to-destination cut is affected by an additive Gaussian state. Numerical results are provided that illuminate the performance advantages that can be accrued by leveraging noncausal state information at the source.

Channel state information (CSI) at the transmitters (CSIT) is of importance for interference alignment schemes to achieve the optimal degrees of freedom (DoF) for wireless networks. This paper investigates the impact of half-duplex relays on the DoF of the X channel and the interference channel when the transmitters are blind in the sense that no CSIT is available. In particular, it is shown that adding relay nodes with global CSI to the communication model is sufficient to recover the DoF that is the optimal for these models with global CSI at the transmitters. The relay nodes in essence help steer the directions of the transmitted signals to facilitate interference alignment to achieve the optimal DoF with CSIT. The general M × N X channel with relays and the -user interference channel are both investigated, and sufficient conditions on the number of antennas at the relays and the number of relays needed to achieve the optimal DoF with CSIT are established. Using relays, the optimal DoF can be achieved in finite channel uses. The DoF for the case when relays only have delayed CSI is also investigated, and it is shown that with delayed CSI at the relay the optimal DoF with full CSIT cannot be achieved. Special cases of the X channel and interference channel are investigated to obtain further design insights.

A multi-hop line network is considered, where each node can receive signals transmitted by its two neighbors. As such, the model embodies both the interference and broadcast aspects of wireless networks. The leftmost node wishes to send messages to the rightmost node, while keeping these messages confidential from all the intermediate relay nodes. In this setting where any or all of the relay nodes can be eavesdroppers, it is shown that end-to-end secure and reliable communication is possible. Notably, it is shown that an end-to-end secrecy rate that is independent of the number of hops, i.e., intermediate eavesdroppers, is achievable by means of a carefully designed transmission schedule, compute-and-forward relaying and coding strategy utilizing nested lattice codes. The achievable rate obtained indicates that imposing secrecy constraints penalizes the capacity by at most 1 bit per channel use. Therefore, it is concluded that information theoretic secrecy can be guaranteed for this model irrespective of eavesdropping relays and a fixed modest cost for the end-to-end rate.

Cooperative jamming paradigm in secure communications enlists network nodes to transmit noise or structured codewords, in order to impair the eavesdropper's ability to decode messages to be kept confidential from it. Such an approach can significantly help in facilitating secure communication between legitimate parties but, by definition, assumes dedicated and/or altruistic nodes willing to act as cooperative jammers. In this paper, it is demonstrated that cooperative jamming leads to meaningful secrecy rate improvements even when this assumption is removed. A distributed mechanism is developed that motivates jamming participation of otherwise non-cooperative terminals, by compensating them with an opportunity to use the fraction of legitimate parties' spectrum for their own data traffic. With the goal of maximizing their data transmission rate priced by the invested power, cooperative jammers provide the jamming/transmitting power that is generally proportional to the amount of leased bandwidth. The fully decentralized framework is facilitated through a game-theoretic model, with the legitimate parties as the spectrum owners acting as the game leader, and the set of assisting jammers constituting the follower. To facilitate the behavior of non-cooperative and competitive multiple jammers, auctioning and power control mechanisms are applied for a follower sub-game in a two-layer leader-follower game framework.

In this work we considered the Gaussian two-user interference channel where the eavesdropper channel is arbitrarily varying, all channel matrices have rank less than or equal to 2, and the eavesdropper has 1 antenna. We identify a class of these channel models for which the secrecy degrees of freedom (s.d.o.f.) region is achieved by letting only one user transmit a time. We also provided a non-trivial example for which such a transmission strategy is sub-optimal in terms of s.d.o.f. region. The achievable scheme for this example introduces a new technique to achieve secrecy for MIMO wiretap channels, in which the transmitter-receiver pair uses linear precoding so that effectively the signals are beam-formed toward a direction that can not be attained by the eavesdropper and is at the same time orthogonal to interference from the other user.

This paper investigates the impact of half-duplex relays on the degrees of freedom (DoF) of the K-user X channel when the transmitters are blind in the sense that no channel state information (CSI) at the transmitter (CSIT) is available. It is shown that adding relay nodes with CSI to the communication model is sufficient to recover the optimal DoF with CSIT. In particular, the relay nodes help steer the directions of the transmitted signals to facilitate interference alignment to achieve optimal DoF. It is shown that one relay node with K-1 antennas is sufficient to achieve the optimal DoF. When relays do not have multiple antennas, it is sufficient to have (K - 1)2relays to achieve the optimal DoF. The achievability is established using partial interference alignment and joint beamforming. DoF for the case when relays only have delayed CSI is also investigated.

This work considers a Gaussian multiple access channel with two energy harvesting transmitters with lossy energy storage. The power allocation policy maximizing the average weighted sum rate given the energy harvesting profiles is found. In particular, it is shown that the optimal policy has a double-threshold structure on each of the transmit powers, while the two transmit powers interact through the multivariate achievable rate function which determines the thresholds. For the special case of sum rate maximization in a Gaussian MAC channel, it is shown that the thresholds apply to the sum power, and the optimal policy consists of three thresholds, rather than four, which enables the user with a more efficient battery to be given priority in energy storage.

Spectrum leasing for cooperation is a promising paradigm that motivates relaying participation of non-altruistic nodes. It prescribes the trade where the node is rewarded for its relaying role with a fraction of source's bandwidth. Proposed spectrum leasing schemes typically involve hard decision at the source, whether to completely rely on the relay or preserve the whole bandwidth for itself. This approach can pose limitations in multihop networks, with the source refusing to share its bandwidth with multiple nodes on its route, and instead choosing to transmit directly to its destination. The benefits for both the source and potential relays would thus be lost. In this paper, we propose a two-hop spectrum leasing scheme that enables the source to employ the relay only to an extent it finds beneficial, preserving the remaining bandwidth for its direct transmission, if necessary. Unlike the previous mechanisms, the proposed scheme is based on explicit interaction and negotiation about the leased bandwidth. Stackelberg game is used to model this interaction. Comprehensive analysis for the proposed scheme and numerical results demonstrate significant benefits for all the participating nodes.

In this work, we investigate the impact of placing a half-duplex relay in the X channel in the presence of limited channel state information (CSI). Specifically, we consider the scenarios when the sources have no CSI, while the relay either has perfect CSI or delayed CSI. We develop transmission strategies for each scenario to facilitate interference alignment with the help of the relay. We show that for both scenarios, with single antenna at each source and destination, degree of freedom 4/3 can be achieved. This matches the degree of freedom established in previous work when perfect CSI is available at all nodes. For the case with perfect CSI at the relay, relay only needs to have 1 antenna, and can perform beamforming with the sources to facilitate interference alignment using the proposed scheme. For the case with delayed CSI at the relay, the relay can compensate the staleness of the CSI by adding an antenna. An important observation from this work is that relay can increase the degrees of freedom for the X channel when the sources have no CSI, in contrast with the case when perfect CSI is available at all nodes, where a relay cannot improve the degrees of freedom. The results are also extended to multi-antenna settings.

This paper studies the Gaussian interference channel (IC) with a relay, which transmits and receives in a band that is orthogonal to the IC. The channel associated with the relay is thus an out-of-band relay channel (OBRC). The focus is on a symmetric channel model, in order to assess the fundamental impact of the OBRC on the signal interaction of the IC, in the simplest possible setting. First, the linear deterministic model is investigated and the sum capacity of this channel is established for all possible channel parameters. In particular, it is observed that the impact of OBRC, as its links get stronger, is similar to that of output feedback for the IC. The insights obtained from the deterministic model are then used to design achievable schemes for the Gaussian model. The interference links are classified as extremely strong, very strong, strong, moderate, weak, and very weak. For strong and moderate interference, separate encoding is near optimal. For very strong and extremely strong interference, the interference links provide side information to the destinations, which can help the transmission through the OBRC. For weak or very weak interference, an extension of the Han-Kobayashi scheme for the IC is utilized, where the messages are split into common and private. To achieve higher rates, it is beneficial to further split the common message into two parts, and the OBRC plays an important role in decoding the common message. It is shown that our strategy achieves the symmetric capacity to within 1.14625 bits per channel use with duplexing factor 0.5, and 1.27125 bits per channel use for arbitrary duplexing factors, for all channel parameters. An important observation from the constant gap result is that strong interference can be beneficial with the presence of an OBR.

Wireless networks with energy harvesting battery equipped nodes are quickly emerging as a viable option for future wireless networks with extended lifetime. Equally important to their counterpart in the design of energy harvesting radios are the design principles that this new networking paradigm calls for. In particular, unlike wireless networks considered to date, the energy replenishment process and the storage constraints of the rechargeable batteries need to be taken into account in designing efficient transmission strategies. In this work, such transmission policies for rechargeable nodes are considered, and optimum solutions for two related problems are identified. Specifically, the transmission policy that maximizes the short term throughput, i.e., the amount of data transmitted in a finite time horizon is found. In addition, the relation of this optimization problem to another, namely, the minimization of the transmission completion time for a given amount of data is demonstrated, which leads to the solution of the latter as well. The optimum transmission policies are identified under the constraints on energy causality, i.e., energy replenishment process, as well as the energy storage, i.e., battery capacity. For battery replenishment, a model with discrete packets of energy arrivals is considered. The necessary conditions that the throughput-optimal allocation satisfies are derived, and then the algorithm that finds the optimal transmission policy with respect to the short-term throughput and the minimum transmission completion time is given. Numerical results are presented to confirm the analytical findings.

This paper proposes a method for applying the idea of Interference Alignment (IA) in femtocell networks. In order to manage the uplink interference caused by macrocell users at the femtocell base stations (FBS), cooperation between macrocell users with the closest femtocell base stations could be used to align the received signals of macrocell users in the same subspace at multiple FBS simultaneously. We develop a method to apply IA while providing the QoS requirements of macrocell users, in terms of minimum received SINR at the macrocell base station (MBS). With this approach, the BER performance of femtocell users is shown to improve, while maintaining the quality of the communication channel of macrocell users.

A two-receiver MIMO broadcast-wiretap channel is considered where the channel state of the eavesdropper is arbitrarily varying. It is assumed that the eavesdropper knows this channel state perfectly whereas the legitimate nodes have no knowledge of it. It is further assumed that the eavesdropper experiences no additive noise. The channel between the transmitter and the two legitimate receivers is a constant MIMO Gaussian broadcast channel. This paper establishes the secrecy degrees of freedom region for transmitting a common-confidential message as well as a private- confidential message to each receiver. It is observed that a straightforward extension of single user random binning does not achieve the optimal secrecy degrees of freedom (s.d.o.f.) region. The proposed coding scheme that achieves the s.d.o.f. region involves simultaneous diagonalization of the channel matrices of the two legitimate receivers using the generalized singular value decomposition (GSVD) as well as a particular \emph{structured binning} across codebooks that minimizes the rate of the fictitious message. While the focus is on achieving weak secrecy for ease of exposition, an outline is provided on how the results can be extended for achieving strong secrecy.

This work studies the Gaussian interference channel (IC) with an out-of-band relay (OBR) from an information-theoretical perspective. The relay works in bands orthogonal to the IC. The focus is on a symmetric channel model, in order to understand the fundamental impact of the OBR on the signal interaction of the IC, in the simplest possible setting. We classify the interference links as extremely strong, very strong, strong, moderate, weak, and very weak. For strong and moderate interference, separate encoding is close to optimal. For very strong and extremely strong interference, the interference links provide side information to the destinations, which can help the transmission through the OBRC. In particular, when interference is extremely strong, the channel acts as if there are two disjoint OBRC helping each source-destination pair. For moderate or weak interference, we use the Han-Kobayashi scheme for the IC, where we split the messages into common and private messages. We find that it is beneficial to further split the common message into two parts, and the OBRC plays an important role in decoding the common messages. It is shown that our strategy achieves the symmetric capacity to within 1.14625 bits per channel use for all channel parameters.

Deploying energy harvesting nodes can significantly extend the lifetime of a battery powered wireless network. The nature of these systems calls for specific design principles to efficiently utilize the dynamic energy resources. In contrast to the conventional wireless networks, transmission policies for these systems should take into account the recharge process of the node as well as its battery capacity. In this work, we focus on the problem of finding the optimal transmission policy to maximize the short-term throughput of an energy harvesting transmitter node with power control and a limited energy storage capacity. The transmission policy in this problem is strictly constrained with both the energy arrival process and the energy storage capacity of the node. The analysis of the problem is founded on the increasing concave nature of the power-rate relationship information theoretic findings suggest. A discrete model with packets of energy arrivals is considered for energy replenishment, and an algorithm to yield the short-term throughout maximizing policy is sought. The necessary conditions such a policy should satisfy are identified and an algorithm to yield the unique policy that satisfies these conditions is presented. The performance of this algorithm is then observed through numerical results.

Wireless systems comprised of rechargeable nodes have a significantly prolonged lifetime and are sustainable. A distinct characteristic of these systems is the fact that the nodes can harvest energy throughout the duration in which communication takes place. As such, transmission policies of the nodes need to adapt to these harvested energy arrivals. In this paper, we consider optimization of the transmission policy of an energy harvesting transmitter which has a limited battery capacity, communicating in a wireless fading channel. In particular, we identify the optimal offline transmission policies that maximize the number of bits delivered by a deadline, and minimize the transmission completion time of the communication session. We introduce a directional water-filling algorithm which provides a simple and concise interpretation of the necessary optimality conditions as well as energy storage capacity and causality. We solve the throughput maximization problem for the fading channel using the directional water-filling algorithm, which simultaneously adapts to the energy harvested as well as the channel variations in time. We then solve the transmission completion time minimization problem by utilizing its equivalence to its throughput maximization counterpart.

This work investigates providing information theoretically secure communication in a scenario where the eavesdropper is more powerful as compared to models considered to date. Specifically, we consider the setting where the eavesdropper, based on signals it received in the past, modifies its channel state in order to benefit its reception of the legitimate parties' messages. Natural to this setting is that the legitimate parties do not have any knowledge of the eavesdropper's channel state. In this setting, we study the Gaussian two-way wiretap channel, namely two legitimate nodes connected by a bi-directional link in the presence of an eavesdropper that receives the superposition of signals from both nodes. We show that a positive secrecy rate in the sense of strong secrecy is achievable even under these assumptions. The secrecy rate obtained scales with transmit power. The achievable strategy involves cooperative jamming pointing out to its robustness to the adaptive nature of the eavesdropper channel.

The state-dependent multiple access channel (MAC) is considered where the state sequences are known strictly causally to the encoders. First, a two-user MAC with two independent states each known strictly causally to one encoder is revisited, and a new achievable scheme inspired by the recently proposed noisy network coding is presented. This scheme is shown to achieve a rate region that is potentially larger than that provided by recent work for the same model. Next, capacity results are presented for a class of channels that include modulo-additive state-dependent MACs. It is shown that the proposed scheme can be easily extended to an arbitrary number of users. Finally, a similar scheme is proposed for a MAC with common state known strictly causally to all encoders. The corresponding achievable rate region is shown to reduce to the one given in the previous work as a special case for two users.

Secure communication rates can be facilitated or enhanced via deployment of cooperative jammers in a multi-terminal environment. Such an approach typically assumes dedicated and/or altruistic jamming nodes, investing their resources for the good of the whole system. In this paper, we demonstrate that jammers can be recruited to provide significant improvements of secrecy rates even when this assumption is alleviated. A game-theoretic framework is proposed where a source node, towards the maximization of its secrecy rate, utilizes the jamming services from a set of non-altruistic nodes, compensating them with a fraction of its bandwidth for transmission of their user data. With the goal of maximizing their user-data transmission rate priced by the invested power, potential cooperative jammers will provide the jamming/transmitting power that is generally proportional to the amount of leased bandwidth. Elaborating initially on a single-jammer scenario, interaction between the source and a cooperative jammer is modeled as the Stackelberg leader-follower game. The scheme is further extended to involve multiple potential jammers, applying competition mechanisms such as the auctioning and power control game, while maintaining the Stackelberg framework.

We investigate Quality of Information (QoI) aware scheduling in task processing networks. Specifically, we consider the scenario where a network sequentially receives tasks from an end user, utilizes its resources to process them, and sends back its response. The utility derived by the end user from this response depends on both the accuracy and the freshness of the information. There is often a trade-off between these two attributes and we present a model that quantifies this dependence. Using dynamic programming and optimal stopping theory, we characterize the optimal scheduling policy that maximizes the time average utility delivered by the network. We show that for many scenarios of practical interest, the optimal policy has a simple threshold structure. We also propose a method to approximately compute the threshold in closed-form. This work takes a step towards incorporating application aware objectives in making optimal scheduling decisions.

Wireless systems comprised of rechargeable nodes have a significantly prolonged lifetime and are sustainable. A distinct characteristic of these systems is the fact that the nodes can harvest energy throughout the duration in which communication takes place. As such, transmission policies of the nodes need to adapt to these harvested energy arrivals. In this paper, we consider optimization of point-to-point data transmission with an energy harvesting transmitter which has a limited battery capacity, communicating in a wireless fading channel. We consider two objectives: maximizing the throughput by a deadline, and minimizing the transmission completion time of the communication session. We optimize these objectives by controlling the time sequence of transmit powers subject to energy storage capacity and causality constraints. We, first, study optimal offline policies. We introduce a directional water-filling algorithm which provides a simple and concise interpretation of the necessary optimality conditions. We show the optimality of an adaptive directional water-filling algorithm for the throughput maximization problem. We solve the transmission completion time minimization problem by utilizing its equivalence to its throughput maximization counterpart. Next, we consider online policies. We use stochastic dynamic programming to solve for the optimal online policy that maximizes the average number of bits delivered by a deadline under stochastic fading and energy arrival processes with causal channel state feedback. We also propose near-optimal policies with reduced complexity, and numerically study their performances along with the performances of the offline and online optimal policies under various different configurations.

This paper investigates the effect of eavesdroppers on network connectivity, using a wiretap model and percolation theory. The wiretap model captures the effect of eavesdroppers on link security. A link exists between two nodes only if the secrecy capacity of that link is positive. Network connectivity is defined in a percolation sense, i.e., connectivity exists if an infinite connected component exists in the corresponding secrecy graph. We consider uncertainty in location of eavesdroppers, which is modeled directly at the network level as correlated failures in the secrecy graph. Our approach attempts to bridge the gap between physical layer security under uncertain channel state information and network level connectivity under secrecy constraints. For square and triangular lattice secrecy graphs, we obtain bounds on the percolation threshold, which is the critical value of the probability of occurrence of an eavesdropper, above which network connectivity does not exist. For Poisson secrecy graphs, degree distribution and mean value of upper and lower bounds on node degree are obtained. Further, inner and outer bounds on the achievable region for network connectivity are obtained. Both analytic and simulation results show that uncertainty in location of eavesdroppers has a dramatic effect on network connectivity in a secrecy graph.

The 31 papers in this special issue focus on using the physical layer for securing the next generation of communication systems.

The many-to-one interference channel has received interest by virtue of embodying the essence of an interference network while being more tractable than the generalK-user interference channel. In this paper, we introduce information theoretic secrecy to this model and consider the many-to-one interference channel with confidential messages, in which each receiver, in particular, the one subject to interference, is also one from which the interfering users' messages need to be kept secret from. We derive the achievable secrecy sum rate for this channel using nested lattice codes, as well as an upper bound on the secrecy sum rate for all possible channel gain configurations. We identify several nontrivial cases where the gap between the upper bound and the achieved secrecy sum rate is only a function of the number of the usersK, and is uniform over all possible channel gain configurations in each case. In addition, we identify the secure degrees of freedom for this channel and show it to be equivalent to its degrees of freedom, i.e., the secrecy in high SNR comes for free.

We consider the Gaussian interference channel with an intermediate relay as a main building block for cooperative interference networks. On the achievability side, we consider compress-and-forward based strategies. Specifically, a generalized compress-and-forward strategy, where the destinations jointly decode the compression indices and the source messages, is shown to improve upon the compress-and-forward strategy which sequentially decodes the compression indices and source messages, and the recently proposed generalized hash-and-forward strategy. We also construct a nested lattice code based compute-and-forward relaying scheme, which outperforms other relaying schemes when the direct link is weak. In this case, it is shown that, with a relay, the interference link can be useful for decoding the source messages. Noting the need for upperbounding the capacity for this channel, we propose a new technique with which the sum rate can be bounded. In particular, the sum capacity is upperbounded by considering the channel when the relay node has abundant power and is named potent for that reason. For the Gaussian interference relay channel with potent relay, we study the strong and the weak interference regimes and establish the sum capacity, which, in turn, serve as upperbounds for the sum capacity of the GIFRC with finite relay power. Numerical results demonstrate that upperbounds are tighter than the cut-set bound, and coincide with known achievable sum rates for many scenarios of interest. Additionally, the degrees of freedom of the GIFRC are shown to be 2 when the relay has large power, achievable using compress-and-forward.

We consider two sources in a wireless network exchanging stochastically varying traffic using an intermediate relay. Each relay use incurs some cost, which, for example, could be transmission energy. This cost is shared between the sources when packets from both are transmitted simultaneously by the relay using network coding. If the relay transmits a packet originating from one source only, the cost is incurred by that source only. In this setting, we study transmission policies that tradeoff the average cost with the average packet delay. We first present the cost-delay tradeoff for a centralized scheme using Lyapunov stability arguments. Next, we consider a distributed policy, where each source aims to optimize its own cost-delay tradeoff. We determine the Nash equilibrium of the resulting non-cooperative game and show that it performs worse than the centralized algorithm. To overcome this limitation, we introduce a pricing mechanism at the relay, which is shown to achieve the centralized performance. These algorithms, though oblivious to the arrival statistics, do require global knowledge of queue backlogs. Lastly, we consider distributed algorithms that overcome this requirement. Among those, we observe that simple queue-length threshold algorithms perform remarkably well.

A usual concern against physical layer security is that the legitimate parties would need to have (partial) channel state information (CSI) of the eavesdropper in order to design transmission schemes that provide secrecy. In this work, to overcome this concern, we consider the model where the eavesdropper's CSI is completely unknown at the legitimate transmitter(s) and the receiver. A static channel setting, and multiple antennas are considered for all parties, and it is assumed that the eavesdropper has perfect self-CSI. In this setting, assuming that the legitimate parties can employ a larger number of antennas than the eavesdropper, we provide a positive secure communication rate in the sense of strong secrecy. The achievable (guaranteed) secrecy rate we derive for the MIMO wiretap channel matches its converse in terms of secure degrees of freedom. As a side result of our approach, we also derive the secure degrees of freedom region for the MIMO MAC-wiretap channel where the transmitters and the intended receiver have the same number of antennas.

We investigate the fundamental communication limits when messages are sent via a Gaussian two-way channel, which must at the same time be kept secret from an external eavesdropper. In this two-way wiretap channel that models two legitimate transceivers and an eavesdropping receiver, there are two techniques to provide confidentiality for the messages: one entails the legitimate nodes to jam the eavesdropper, i.e., cooperative jamming, while the other entails generating keys from the feedback signals received by the two legitimate nodes and using them to encrypt the messages. Previous work has shown that both methods can be used concurrently to improve the secrecy rates of a channel with a degradedness condition. In this work, we consider the general case, and derive a new outer bound for the secrecy capacity region of this channel. A case is identified where the loss in secrecy rate, due to ignoring the backward (feedback) link at each legitimate transmitter from the other, is bounded by a constant which only depends on the channel gains. This is the case when the power of the two legitimate nodes increases proportionally. In all other cases, we show that ignoring feedback signals causes unbounded loss in the secrecy rate. The loss is measured as the gap between the achievable rate when the feedback signals are taken into account, and the upper bound when the feedback is not used, and hence is not affected by the choice of the achievable scheme. This result therefore establishes that, for the Gaussian two-way channel with an external eavesdropper, the encoders need to be designed with memory. This is in contrast to the result for this channel in the absence of an eavesdropper.

We consider the Gaussian interference channel with an intermediate relay. The known achievable schemes for this channel are based on decode-and-forward (DF) relaying, whose performance depends on the signal-to-noise (SNR) ratio of the received signal at the relay. Consequently, when the source-to-relay (S-R) links are weak, the resulting achievable rates have room for improvement. In this work, we design achievable schemes that provide this improvement. First, we consider compress-and-forward (CF) relaying to overcome the weakness of the DF schemes. Second, we consider employing structured codes and observe that when the signal strength from the direct link is subject to severe attenuation, using nested lattice codes yields higher rates than both the DF and CF schemes. Numerical results are presented to demonstrate the performance of different achievable schemes with the Potent Relay Outerbound derived in our previous work.

In this paper, we consider end-to-end secure communication in a large wireless network, where the locations of eavesdroppers are uncertain. Our framework attempts to bridge the gap between physical layer security under uncertain channel state information of the eavesdropper and network level connectivity under security constraints, by modeling location uncertainty directly at the network level as correlated node and link failures in a secrecy graph. Bounds on the percolation threshold are obtained for square and triangular lattices, and bounds on mean degree are obtained for Poisson secrecy graphs. Both analytic and simulation results show the dramatic effect of uncertainty in location of eavesdroppers on connectivity in a secrecy graph.

We consider the ergodic fading Gaussian interference relay channel (EF-GIFRC) with individual power constraints at the nodes. Aiming at design insights to emerge from the optimal power allocation, we focus on scenarios similar to that of the degraded/reversely degraded relay channels. In particular, we focus on models where the source-to-relay (S - R) links are either stronger than direct links, or completely blocked, i.e., with an “on-and-off” relay. To characterize the capacity of EF-GIFRC with an on-and-off relay, we first investigate the parallel interference relay channel. We propose an achievable scheme based on partial decode-and-forward (DF) strategy and show that the capacity for the parallel IFRC can be achieved under strong interference and degradedness conditions. Based on the achievable rate region for parallel IFRC, we propose an achievable rate region for EF-GIFRC, and present the properties of optimal power allocation. We also present a sum capacity result when the EF-GIFRC satisfies certain channel conditions.

This paper considers the problem of key management in wireless networks. In particular, we investigate the effect of dynamic key compromise and recovery on connectivity in large networks. A queuing model with a finite buffer is used to model the dynamics of key compromise. The exact distribution of the fraction of keys compromised is obtained. The result of the queuing analysis is used to determine the probability of outage, where an outage occurs whenever instantaneous end-to-end connectivity, in percolation sense, is not present. Numerical results show that in order to obtain a low outage probability, it is critical that key compromises are detected accurately, and that the average key recovery rate has a weak influence on the outage probability. Thus, for the same average key recovery rate the system must be designed to have a high key recovery probability rather than a large number of key recoveries per unit time with a low key recovery probability.

In this work, we investigate secret key generation from channel states. We point out, by means of a packet-delay-based attack, that observing its own channel states is not the only way an adversary can learn about the channel states of the legitimate communicating parties. The attack suggests that it is not secure to transmit data via the channel whose states generate secret keys. However, not using the channel at all would result in a waste of bandwidth. Hence, we propose using this channel to transmit the bits needed to reconcile the channel state estimates at the transmitter and the receiver. This is a necessary step in secret key generation that required a separate channel in previous work. Although the scheme proposed here in effect prohibits the use of an adaptive transmitter, we show, for the Rayleigh fading channel, that a decent key rate that outperforms existing schemes is obtained. This is due to the fact that collection of the channel state information and transmission of the reconciliation bits are performed concurrently rather than via time sharing.

We consider the communication scenario where a source-destination pair wishes to keep the information secret from a relay node despite wanting to enlist its help. For this scenario, an interesting question is whether the relay node should be deployed at all. That is, whether cooperation with an untrusted relay node can ever be beneficial. We first provide an achievable secrecy rate for the general untrusted relay channel, and proceed to investigate this question for two types of relay networks with orthogonal components. For the first model, there is an orthogonal link from the source to the relay. For the second model, there is an orthogonal link from the relay to the destination. For the first model, we find the equivocation capacity region and show that answer is negative. In contrast, for the second model, we find that the answer is positive. Specifically, we show, by means of the achievable secrecy rate based on compress-and-forward, that by asking the untrusted relay node to relay information, we can achieve a higher secrecy rate than just treating the relay as an eavesdropper. For a special class of the second model, where the relay is not interfering itself, we derive an upper bound for the secrecy rate using an argument whose net effect is to separate the eavesdropper from the relay. The merit of the new upper bound is demonstrated on two channels that belong to this special class. The Gaussian case of the second model mentioned above benefits from this approach in that the new upper bound improves the previously known bounds. For the Cover-Kim deterministic relay channel, the new upper bound finds the secrecy capacity when the source-destination link is not worse than the source-relay link, by matching with achievable rate we present.

The authors used superposition coding as opposed to the more standard random binning approach to wire-tap channels.

We consider a multiuser two-way relay network where multiple pairs of users exchange information with the assistance of a relay node, using orthogonal channels per pair. For a variety of two-way relaying mechanisms, such as decode- and- forward (DF), amplify-and-forward (AF) and compress-and-forward (CF), we investigate the problem of optimally allocating relay's power among the user pairs it assists such that an arbitrary weighted sum rate of all users is maximized, and solve the problem as one or a set of convex problems for each relaying scheme. Numerical results are presented to demonstrate the performance of the optimum relay power allocation as well as the comparison among different two-way relaying schemes.

In this work, we prove that a positive secure degree of freedom is achievable for a large class of real Gaussian channels as long as the channel is not degraded and the channel is fully connected. This class includes the MAC wiretap channel, the 2-user interference channel with confidential messages, the 2-user interference channel with an external eavesdropper. Best known achievable schemes to date for these channels use Gaussian signaling. In this work, we show that structured codes outperform Gaussian random codes at high SNR when channel gains are real numbers.

We consider the Gaussian interference channel with an intermediate relay. The relay is assumed to have abundant power and is named potent for that reason. A main reason to consider this model is to find good outerbounds for the Gaussian interference relay channel (GIFRC) with finite relay power. By setting the power of the relay constraint to infinity, we show that the capacity region is asymptotically equivalent to the case when the relay-destination links are noiseless and orthogonal to other links. The capacity region of the latter provides an outerbound for the GIFRC with finite relay power. We then show the capacity region of the former can be upper bounded by a single-input-multiple-output interference channel with an antenna common to both receivers. To establish the sum capacity of this channel, we study the strong and the weak interference regimes. For both regimes, we show that the upperbounds we find are achievable, thus establishing the sum capacity of GIFRC with the potent relay. Both results, in turn, serve as upperbounds for the sum capacity of the GIFRC with finite relay power. Numerical results show that the upperbounds are close to the known achievable rates for many scenarios of interest.

We consider a multi-access two-way relay network where multiple pairs of users exchange information with their pre-assigned partners with the assistance of an intermediate relay node. Each pair is assumed to have a shared channel which is orthogonal to the channels used by the remaining pairs. We investigate the relay power allocation problem for two-way relaying protocols that allow a variety of forwarding mechanisms, such as decode-and-superposition-forward (DSF), decode-and-XOR-forward (DXF), amplify-and-forward (AF) and compress-and-forward (CF). Different from one-way communications, in two-way relaying, the rates of the two communication directions between a pair of partners constrain each other, and the relay power allocated to one user pair simultaneously affects the rates of both directions. For each relaying scheme, we solve the problem of optimally allocating relay's power among the user pairs such that an arbitrary weighted sum rate of all users is maximized. Simulation results are presented to demonstrate performance of optimum relay power allocation, as well as the comparison among different two-way relaying schemes.

The multi-user communication channel, in which multiple users exchange information with the help of a single relay terminal, called the multi-way relay channel, is considered. In this model, multiple interfering clusters of users communicate simultaneously, where the users within the same cluster wish to exchange messages among themselves. It is assumed that the users cannot receive each other's signals directly, and hence the relay terminal is the enabler of communication. A relevant metric to study in this scenario is the symmetric rate achievable by all users, which we identify for amplify-and-forward (AF), decode-and-forward (DF) and compress-and-forward (CF) protocols. We also present an upper bound for comparison. The two extreme cases, namely full data exchange, in which every user wants to receive messages of all other users, and pairwise data exchange, consisting of multiple two-way relay channels, are investigated and presented in detail.

We investigate the K-user many-to-one interference channel with confidential messages in which the Kth user experiences interference from all other K - 1 users, and is at the same time treated as an eavesdropper to all the messages of these users. We derive achievable rates and an upper bound on the sum rate for this channel and show that the gap between the achievable sum rate and its upper bound is log2(K - 1) bits per channel use under very strong interference, when the interfering users have equal power constraints and interfering link channel gains. The main contributions of this work are: (i) nested lattice codes are shown to provide secrecy when interference is present, (ii) a secrecy sum rate upper bound is found for strong interference regime and (iii) it is proved that under very strong interference and a symmetric setting, the gap between the achievable sum rate and the upper bound is constant with respect to transmission powers.

We consider a communication scenario where the source and the destination can communicate only via a relay node who is both an eavesdropper and a Byzantine attacker. Hence for secure communication, two requirements must be met simultaneously: the transmitted message must be kept secret, and a Byzantine attack must be detected reliably. Both a discrete noiseless adder model with the relay receiving the real sum of two signals and a Gaussian model are considered. In both models, the loss in rate due to Byzantine detection can be made arbitrarily small. For the discrete adder model, we show that the probability that the adversary wins decreases exponentially with the number of channel uses. For the Gaussian model, we show that this probability decreases exponentially with the square root of the number of channel uses. The rate derived in this paper is the strong secrecy rate, and the rate loss incurred due to the untrusted and Byzantine relay is measured with respect to the achievable secrecy rate when the relay is untrusted but honest. The result is obtained via a careful combination of the algebraic manipulation detection (AMD) code, the linear wire-tap code constructed from low density parity check (LDPC) code, randomly generated wire-tap code and for the Gaussian model the lattice code.

In this work, we consider achievable secrecy rates for symmetric K-user (K ges 3) interference channels with confidential messages. We find that nested lattice codes and layered coding are useful in providing secrecy for these channels. Achievable secrecy rates are derived for very strong interference. In addition, we derive the secure degrees of freedom for a range of channel parameters. As a by-product of our approach, we also demonstrate that nested lattice codes are useful for K-user symmetric interference channels without secrecy constraints in that they yield higher degrees of freedom than previous results.

We consider a multiuser two-way relay network where multiple pairs of users communicate with their preassigned partners, using a common intermediate relay node, in a two-phase communication scenario employing code division multiple access (CDMA). By taking advantage of the bidirectional communication structure, we first propose that each pair of partners share a common spreading signature and design a jointly demodulate-and-XOR forward (JD-XOR-F) relaying scheme, where all users transmit to the relay simultaneously followed by the relay broadcasting an estimate of the XORed symbol for each user pair. We derive the decision rules and the corresponding bit error rates (BERs) at the relay and at the users' receivers. We then investigate the joint power control and receiver optimization problem for each phase for this multiuser two-way relay network with JD-XOR-F relaying. We solve each optimization problem by constructing the iterative power control and receiver updates that converge to the corresponding unique optimum. Simulation results are presented to demonstrate the performance of the proposed multiuser two-way JD-XOR-F relaying scheme in conjunction with the joint power control and receiver optimization algorithms. Specifically, we observe significant power savings and user capacity improvement with the proposed communication scheme as compared to the designs with a "one-way" communication perspective.

We consider a source-destination pair that can communicate only through an unauthenticated intermediate relay node. In this two-hop communication scenario, where the cooperation from the relay node is essential, we investigate whether achieving non-zero secrecy rate is possible. Specifically, we treat the relay node as an eavesdropper from whom the source information needs to be kept secret, despite the fact that its cooperation in relaying this information is needed. We find that a positive secrecy rate is indeed achievable, with the aid of the destination node or an external node that jams the relay, i.e., by cooperative jamming. We derive an upper bound on the secrecy rate by means of an eavesdropper-relay separation argument. We remark that this upper bound is the first of its kind in Gaussian channels with cooperative jamming. The upper bound is strictly smaller than the channel capacity without secrecy constraints. The achievable secrecy rates are found using stochastic encoding and compress-and-forward at the relay. Numerical results show that the gap between the bound and the achievable rate is small when the relay's power is larger than the power of the jammer and the source. In essence, this paper shows that a cooperative jammer enables secure communication to take place using an untrusted relay which would be otherwise impossible.

We investigate multiuser two-way relaying strategies for interference limited systems where multiple pairs of users exchange information with their partners via an intermediate relay node in a two-phase communication scenario. To take advantage of the bidirectional communication structure, we propose that each pair of users share a common spreading signature instead of using distinct signatures as in a traditional CDMA setting. We design the jointly demodulate-and-XOR forward (JD-XOR- F) relaying scheme and derive the decision rule which enables the relay to generate the XORed symbol upon reception of the superposition of user symbols. When the relay has limited computational capability, amplify-and-forward relaying can be applied instead. We evaluate the BER performance for the proposed relaying schemes and show that they significantly outperform the traditional "one-way" CDMA systems.

This paper addresses the joint estimator and power optimization problem for a sensor network whose mission is to estimate an unknown parameter. We assume a two-hop network where each sensor collects observations from the source that transmits the quantity to be estimated, then amplifies and forwards its observations to a fusion center. The fusion center combines the observations using a Linear Minimum Mean Squared Error (LMMSE) estimator. We study the scenario where multiple parallel channels are available between the source and each sensor as well as between the sensors and the fusion center. We find the global optimal power allocation and estimator design for this network model. We present two practical scenarios of interest that utilize spatial and temporal diversity for which this solution applies, namely, a clustered network model and a single cluster model with an ergodic fading channel.

We consider the communication scenario where a source-destination pair wishes to keep the information secret from a relay node despite wanting to enlist its help. A class of relay channels with orthogonal components is considered and an upper bound on the secrecy rate is derived. For the class of relay channels in consideration, we prove the relay and the eavesdropper can be separated to obtain an ‘enhanced’ channel in terms of secrecy capacity. We then consider two special cases of this channel model: (i) the Gaussian orthogonal relay channel, and (ii) the Gaussian Cover-Kim deterministic relay channel. For the former, the upper bound found is tighter than the previously known bound. For the latter, we show that the bound yields the secrecy capacity when the source-destination link is not worse than the source-relay link. This second case also provides the first example where secrecy capacity is achieved with compress and-forward at the relay.

We consider a pair of nodes who wish to communicate with each other via intermediate relays. In this bi-directional network with stochastic flows, we develop the throughput optimal control policy, i.e., a policy that stabilizes the network whenever the arrival rates are within the region established. We investigate the effect of implementing different practical transmission protocols and network coding. The network control policies we present offer diverse possibilities in relaying and cooperation structure depending on the channel and the queue states.

This paper considers the design of relay assisted F/TDMA ad hoc networks with multiple relay nodes each of which assists the transmission of a predefined subset of source nodes to their respective destinations. Considering the sum capacity as the performance metric, we solve the problem of optimally allocating the total power of each relay node between the transmissions it is assisting. We consider four different relay transmission strategies, namely regenerative decode-and-forward (RDF), nonregenerative decode-and-forward (NDF), amplify- and-forward (AF) and compress-and-forward (CF). We first obtain the optimum power allocation policies for the relay nodes that employ a uniform relaying strategy for all nodes. We show that the optimum power allocation for the RDF and NDF cases are modified water-filling solutions. Weobserve that for a given relay transmit power, NDF always outperforms RDF whereas CF always provides higher sum capacity than AF. When CF and NDF are compared, it is observed that either of CF or NDF may outperform the other in different scenarios. This observation suggests that the sum capacity can be further improved by having each relay adopt its relaying strategy in helping different source nodes. We investigate this problem next and determine the optimum power allocation and relaying strategy for each source node that relay nodes assist. We observe that optimum power allocation for relay nodes with hybrid relaying strategies provides higher sum capacity than pure RDF, NDF, AF or CF relaying strategies.

The general Gaussian multiple-access wiretap channel (GGMAC-WT) and the Gaussian two-way wiretap channel (GTW-WT) are considered. In the GGMAC-WT, multiple users communicate with an intended receiver in the presence of an eavesdropper who receives their signals through another GMAC. In the GTW-WT, two users communicate with each other over a common Gaussian channel, with an eavesdropper listening through a GMAC. A secrecy measure that is suitable for this multiterminal environment is defined, and achievable secrecy rate regions are found for both channels. For both cases, the power allocations maximizing the achievable secrecy sum rate are determined. It is seen that the optimum policy may prevent some terminals from transmission in order to preserve the secrecy of the system. Inspired by this construct, a new scheme cooperative jamming is proposed, where users who are prevented from transmitting according to the secrecy sum rate maximizing power allocation policy ldquojamrdquo the eavesdropper, thereby helping the remaining users. This scheme is shown to increase the achievable secrecy sum rate. Overall, our results show that in multiple-access scenarios, users can help each other to collectively achieve positive secrecy rates. In other words, cooperation among users can be invaluable for achieving secrecy for the system.

We consider the Gaussian multiple access wire-tap channel (GMAC-WT). In this scenario, multiple users communicate with an intended receiver in the presence of an intelligent and informed wire-tapper who receives a degraded version of the signal at the receiver. We define suitable security measures for this multiaccess environment. Using codebooks generated randomly according to a Gaussian distribution, achievable secrecy rate regions are identified using superposition coding and time-division multiple access (TDMA) coding schemes. An upper bound for the secrecy sum-rate is derived, and our coding schemes are shown to achieve the sum capacity. Numerical results are presented showing the new rate region and comparing it with the capacity region of the Gaussian multiple-access channel (GMAC) with no secrecy constraints, which quantifies the price paid for secrecy.

We consider a source-destination pair assisted by parallel regenerative decode-and-forward relays operating in orthogonal channels. We investigate distributed power allocation strategies for this system with limited channel state information at the source and the relay nodes. We first propose a distributed decision mechanism for each relay to individually make its decision on whether to forward the source data. The decision mechanism calls for each relay that is able to decode the information from the source to compare its relay-to-destination channel gain with a given threshold. We identify the optimum distributed power allocation strategy that minimizes the total transmit power while providing a target signal-to-noise ratio at the destination with a target outage probability. The strategy dictates the optimum choices for the source power as well as the threshold value at the relays. Next, we consider two simpler distributed power allocation strategies, namely the passive source model where the source power and the relay threshold are fixed, and the single relay model where only one relay is allowed to forward the source data. These models are motivated by limitations on the available channel state information as well as ease of implementation as compared to the optimum distributed strategy. Simulation results are presented to demonstrate the performance of the proposed distributed power allocation schemes. Specifically, we observe significant power savings with proposed methods as compared to random relay selection.

We consider two-way wire-tap channels, where two users are communicating with each other in the presence of an eavesdropper, who has access to the communications through a multiple-access channel. We find achievable rates for two different scenarios, the Gaussian two-way wire-tap channel, (GTW-WT), and the binary additive two-way wire-tap channel, (BATW- WT). It is shown that the two-way channels inherently provide a unique advantage for wire-tapped scenarios, as the users know their own transmitted signals and in effect help encrypt the other user's messages, similar to a one-time pad. We compare the achievable rates to that of the Gaussian multiple-access wiretap channel (GMAC-WT) to illustrate this advantage.

We consider the downlink throughput maximization problem for interference limited multiuser systems. Our goal is to characterize the optimum base station transmission strategy, i.e., whether the base station transmits to one-user (TDMA) or multiple users (CDMA). Specifically, we aim at determining the optimum number of users to be scheduled and finding the corresponding power allocation. We model the interference by the aid of the orthogonality factor, and determine the throughput maximizing transmission strategy for a range of the values of the orthogonality factor, and the channel gains, subject to a total power constraint. Although the resulting optimization problem may turn out to be non-convex, we show that valuable observations regarding the structure of the optimum solution can be obtained by examining the performance metric from an individual user's point of view. We propose an exact and a near-exact algorithm to determine whether one-user-transmission is the optimum strategy, or more than one user should be transmitted to. Numerical results to support our analysis, as well as the modifications to the proposed algorithms in the presence of individual power constraints are presented.

In this paper, we investigate the outage performance of cognitive wireless relay networks where source nodes communicate to their destinations via multiple hops facilitated by intermediate cognitive nodes able to acquire spectrum holes. Specifically, we consider a model that consists of a source node, a destination node, and a group of network clusters each consisting of a number of cognitive (unlicensed) relay nodes and a primary (licensed) node. Cognitive nodes relay information from the source depending on their geographical proximity and their ability to acquire the spectrum hole successfully. We investigate the high SNR approximation of the outage probability of the resulting two-hop system to obtain the diversity order. We show that full diversity is achieved only if each relay node successfully identifies the spectrum hole unoccupied by the corresponding primary node in the cluster, and that the diversity order can be significantly less for imperfect spectrum acquisition. Thus, we set out to improve the outage performance by incorporating a specific intracluster cooperation scheme where neighboring cognitive relay nodes in a cluster collaborate with a desired cognitive relay node. We show that the combination of this intra-cluster cooperation along with the system level cooperation via relaying through cognitive nodes improves the outage performance significantly, and the full diversity can be achieved if the proper number of neighboring relay nodes participate in the intra-cluster cooperation.

We propose a novel collision resolution scheme termed the Optimal Protocol Tree (OPT), which is based on the tree search algorithm for RFID systems. The basic principle of OPT relies on taking advantage of the similarities in the identification strings of different tags, having the reader prompt the tags to send only the mutually exclusive sub-portion of their identification strings. The aim of OPT is to significantly reduce the total identification time, in order to render the deployment of dense RFID systems feasible. Simulation results are presented to demonstrate the performance of OPT, and the considerable improvement it provides with respect to the existing tree search protocols.

In this paper, we introduce a novel medium access control (MAC) protocol for Radio Frequency Identification (RFID) systems which exploits the statistical information collected at the reader. The protocol, termed Adaptive Slotted ALOHA Protocol (ASAP), is motivated by the need to significantly improve the total read time performance of the currently suggested MAC protocols for RFID systems. In order to accomplish this task, ASAP estimates the dynamic tag population and adapts the frame size in the subsequent round. We demonstrate that ASAP provides significant improvement in total read time performance over the current RFID MAC protocols. We extend the design to mobile RFID systems where tags move at constant velocity in the reader's field, and show that ASAP performs well in mobile scenarios as well.

In this paper, we study the power allocation problem at the relay nodes for two-hop F/TDMA networks with multiple sources and destinations. Each relay node is assumed to be capable of assisting multiple source nodes and of selecting one of regenerative decode-and-forward (RDF), nonregenerative decode-and-forward (NDF), amplify-and-forward (AF) and compress-and-forward (CF) relaying strategies to assist each source node. Considering the sum capacity as the performance metric, we solve the problem of optimally allocating the total power of each relay node between the transmissions it is assisting. We first obtain the optimum power allocation policies for the relay nodes when the relaying strategies of all source nodes are given. Next, to obtain higher sum capacities, we investigate the optimum power allocation problem jointly with relaying strategy selection. We observe that optimum power allocation with the appropriate hybrid relaying strategies provides higher sum capacity than pure RDF, NDF, AF or CF relaying strategies.

We consider the Gaussian multiple access wire-tap channel (GMAC-WT). In this scenario, multiple users communicate with an intended receiver in the presence of an intelligent and informed wire-tapper who receives a degraded version of the signal at the receiver. We define a suitable security measure for this multi-access environment. We derive an outer bound for the rate region such that secrecy to some pre-determined degree can be maintained. We also find, using Gaussian codebooks, an achievable such secrecy region. Gaussian codewords are shown to achieve the sum capacity outer bound, and the achievable region coincides with the outer bound for Gaussian codewords, giving the capacity region when inputs are constrained to be Gaussian. We present numerical results showing the new rate region and compare it with that of the Gaussian multiple-access channel (GMAC) with no secrecy constraints

We consider efficient scheduling for a delay constrained CDMA Wireless Sensor Network (WSN). Given a two- tiered WSN model, we first find the optimum schedule for the intra-cluster communications, that minimizes the total transmit power of the sensor nodes, while maintaining the short term average throughput at each sensor. We show that the specifics of the scheduling problem enables it polynomially solvable. Next, We consider the inter-cluster communications where cluster heads are capable of employing two antennas and use Alamouti scheme to achieve the transmit diversity (TD). We observe that our proposed scheduling protocol applied to the inter- cluster communications provides a near-optimum solution, with a modest sacrifice in performance and significant savings in computational complexity as compared to the optimum scheduler. Simulation results are presented to demonstrate the performance of the proposed scheduling protocols, and the considerable power savings they provide with respect to the TDMA-type scheduling.

We investigate the transmit precoder design problem for a multiple input multiple output (MIMO) link with correlated receive antennas, considering the effect of channel estimation. We work with the total mean squared error (MSE) as the performance measure, and develop transceiver structures considering the effect of channel estimation and the correlation of the MIMO link. The proposed transceiver structures are optimum in the sense of minimizing the total MSE and distributing the total MSE equally among the parallel data streams. Motivated by the substantial effect the channel estimation process can have on the system performance, we next investigate the problem of how the correlated MIMO link should distribute its total available power between power expended for channel estimation versus data transmission. The optimum power allocation problem between the training sequences for channel estimation and data transmission for the correlated MIMO link is shown to have a unique solution, that is different than the uncorrelated case. It is observed that the proposed transceiver achieves near-minimum MSE values via a relatively wide range of power allocation parameters. This is in contrast to the transceiver that is oblivious to the estimation errors when a more precise power allocation strategy is needed to achieve the best performance. Our results demonstrate that the correlation structure of the MEMO link has a profound effect on the performance, and that the transceiver optimization should be done by taking both the correlation and the channel estimation process into account

We investigate power allocation strategies for distributed decode-and-forward (DF) parallel relay networks. We first propose a distributed decision mechanism for the relay nodes to make decisions on whether to forward the source data. Specifically, we identify the optimum distributed power allocation strategy that minimizes the total transmit power while providing a target signal-to-noise ratio (SNR) at the destination with a target outage probability. We also consider two simple distributed power allocation models, where the source does not contribute to the relay selection in the first model, and single relay is employed in the second model. Simulation results are presented to demonstrate the performance of the proposed distributed power allocation schemes, and the considerable power savings they provide with respect to random relay selection.

We investigate the transmit precoder design problem for a multiple input multiple output (MIMO) link with correlated receive antennas, considering the effect of channel estimation. We propose transceiver structures that are optimum in the sense of minimizing the total MSE and distributing the total MSE equally among the parallel data streams. We also investigate the problem of how the correlated MIMO link should distribute its total available power between power expended for channel estimation versus data transmission. The optimum power allocation problem between the training sequences for channel estimation and data transmission for the correlated MIMO link is shown to have a unique solution, that is different than the uncorrelated case. The performance of the system is observed to be more sensitive to transceiver design rather than the power allocation. The results demonstrate that the correlation structure of the MIMO link has a profound effect on the performance, and that the transceiver optimization should be done by taking both the correlation and the channel estimation process into account.

We consider the problem of signature sequence selection for the uplink of CDMA systems with multiple receiver antennas. We consider the sum capacity, the systemwide MSE, and the effective total squared correlation as performance metrics and formulate iterative algorithms that improve these metrics. The convergence analysis of the algorithms is given and the numerical evidence that supports the analysis is presented along with our observations related to the resulting optimum signature sets.

A set of enhancements to the IEEE 802.11 standard, viz. the IEEE 802.11e have been proposed to meet the increasing demand for quality of service. The standard provides a means for service differentiation by using multiple traffic categories at each node, where each traffic category has its own individual parameters such as priority, inter-frame space and contention window size. After each successful transmission, the contention window size is decreased based on a static equation, which may result in poor channel utilization and a decrease on the system throughput. In this paper, we propose a new protocol, called enhanced distributed coordination function with dual-measurement (EDCF-DM), to address this issue. EDCF-DM is based on the idea of reducing the number of idle slots by dynamically varying the contention window size according to the current traffic state of the traffic categories at each node. Meanwhile, it carefully adapts the contention window size based on the network condition of the system to avoid incurring extra collisions. Extensive simulations are performed to evaluate the proposed protocol. Simulation results demonstrate that EDCF-DM provides a good service differentiation and outperforms the standard 802.11e in terms of channel utilization, throughput and packet delay.

Efficient interference suppression techniques are needed to maximally utilize the potential gains of code-division multiple-access systems. In this letter, a receiver structure which combines multiuser detection (temporal filtering) and receiver beamforming (spatial filtering) in a multipath environment is considered. Following previous work, we model the receiver as a linear matrix filter and use the minimum mean-squared error (MMSE) as the performance criterion. Motivated by the high complexity of the optimum receiver, we propose rank constrained temporal-spatial filters which are simpler and near-optimum. The MSE is minimized subject to a structural constraint, using an iterative algorithm based on alternating minimization. The constraint on the receiver matrix filter narrows down the solution space, which helps to solve the optimization problem more efficiently. The constraint can be set appropriately by the system designer to achieve the desired tradeoff between performance and complexity. Numerical results indicate that a performance close to that of the optimum filter can be achieved with a simple iterative structure, even in highly loaded systems. Adaptive implementation of the rank constrained filters is derived. A new adaptive scheme is proposed which is a combination of the alternating minimization and the least mean squares methods. The convergence properties are investigated along with the effect of the number paths.

Optimization of the capacity of a single-cell code-division multiple-access (CDMA) system, both from the perspective of the maximum number of users that can be served at a required quality of service level and from the information theoretic perspective, has been recently shown to be achieved by the same joint transmit and receive strategies. We propose an alternating minimization based iterative algorithm that updates the transmitters and the corresponding receivers of the users. The algorithm is suitable for online implementation, and the objective function is suitable for extension to multicell networks, both of which are in contrast with the previously proposed algorithms. We show that the algorithm is provably convergent to the optimum signature sequences and the corresponding receivers.

In this paper, a receiver structure which combines multiuser detection (temporal filtering) and receiver beamforming (spatial filtering) in a multipath environment is considered. Following Yener et al. [2001] and Miller and Schwartz [1995], we model the receiver as a linear matrix filter and use the minimum mean-squared error (MMSE) as the performance criterion. Motivated by the complexity of the optimum receiver, we propose rank constrained temporal-spatial filters which are simpler and near optimum. The MSE is minimized subject to a structural constraint, using an iterative alternating minimization algorithm. Numerical results indicate that a performance close to that of the optimum filter can be achieved with a simple iterative structure, even in highly loaded systems. Least mean squares (LMS) is used to formulate the adaptive implementations and the convergence properties are investigated along with the effect of the number of multipath components.

The optimum receiver to detect the bits of multiple code-division multiple access (CDMA) users has an exponential complexity in the number of active users in the system. Consequently, many suboptimum receivers have been developed to achieve good performance with less complexity. We take the approach of approximating the solution of the optimum multiuser detection problem (OMUD) using nonlinear programming relaxations. First, we observe that some popular suboptimum receivers indeed correspond to relaxations of the optimal detection problem. In particular, one proposed approximation method yields to iterative solutions which correspond to previously proposed heuristic nonlinear detectors. Using a nonlinear programming approach, we identify the convergence properties of these iterative detectors. Secondly, we propose a relaxation that yields a receiver which we call the generalized minimum mean squared error detector. We give a simple iterative implementation of the detector. Its performance is evaluated and comparisons to other suboptimum detection schemes are given.

The optimum receiver to detect the bits of multiple CDMA users has exponential complexity in the number of active users in the system. Previous work showed that the successive and parallel soft interference cancellers correspond to nonlinear programming relaxations of the optimum multiuser detection problem. We use this approximation method combined with the slowest descent approach to improve the performance of soft interference cancellers. The aim is to achieve a performance closer to the performance of the optimum receiver without significantly compromising the low complexity of the resulting receiver. We derive the resulting detectors and evaluate their performance. Results show that they can achieve near-optimum performance and outperform several previously proposed multiuser detectors.

Given the user distribution in a cell, we investigate the two problems of how to appropriately sectorize the cell such that we minimize the total received power and the total transmit power of all the users, while giving each user acceptable quality of service in both cases. For the received power optimization problem, we show that the optimum arrangement equalizes the number of users in each sector. The transmit power optimization is formulated as a graph partitioning problem that is polynomially solvable. We provide an algorithm that finds the best sectorization assignment as well as the optimal transmit powers for all the users. The computational complexity of the algorithm is polynomial in the number of users and sectors. For both the received power optimization and the transmit power optimization, under nonuniform traffic conditions, we show that the optimum arrangement can be quite different from uniform cell sectorization (equal width sectors). We also formulate and solve the transmit power optimization and cell sectorization problem in a multicell scenario that would improve the capacity of a hot spot in the network. We observe that, with adaptive sectorization, where the sector boundaries are determined in response to users' locations, received and transmit power savings are achieved, and the number of users served by the system (system capacity) is increased compared to uniform sectorization of the cell.

Among the ambitious challenges to be met by the third-generation systems is to provide high-capacity flexible services. Code-division multiple access (CDMA) emerges as a promising candidate to meet these challenges. It is well known that CDMA systems are interference-limited, and interference management is needed to maximally utilize the potential gains of this access scheme. Several methods of controlling and/or suppressing the interference through power control, multiuser detection (temporal filtering), and receiver beamforming (spatial filtering) have been proposed to increase the capacity of CDMA systems up to date. We investigate the capacity increase that is possible by combining power control with intelligent temporal and spatial receiver filter design. The signal-to-interference ratio maximizing joint temporal-spatial receiver filters in unconstrained and constrained filter spaces are derived. Two-step iterative power control algorithms that converge to the optimum powers and the joint temporal and spatial receiver filters in the corresponding filter domains are given. A power control algorithm with a less complex filter update procedure is also given. We observe that significant savings in total transmit power are possible if filtering in both domains is utilized compared with conventional power control and joint optimal power control and filtering in only one domain.

We consider connectionless packet switched CDMA systems where all users have to share a common bandwidth and signature sequence on a contention basis. Multiple access is achieved by the simultaneous acquisition of different users' spread spectrum transmissions. The throughput of a pure random access system of this kind suffers from acquisition errors and timing mismatches of the active users as well as instability issues. We consider schemes that use information available at the physical layer, i.e., at the output of the multiuser access detector (MUAD) designed for this random access system, to improve and stabilize the throughput. We consider timing randomization, pseudo-Bayesian stabilization and collision resolution algorithms and report the corresponding numerical results.

We consider the problem of adaptive cell sectorization in a CDMA system. Specifically, given the user distribution in a cell, we investigate the two problems of how to appropriately sectorize the cell such that we minimize the total received power and the total transmit power spent by all the users, in both cases, while giving each user acceptable quality of service. The optimum arrangement equalizes the number of users in each sector for the received power optimization problem. The transmit power optimization can be formulated as a graph partitioning problem which can be solved in polynomial time. In both cases, the optimum arrangement can be quite different from uniform cell sectorization (equal partitioning) under non-uniform traffic conditions. We observe that, with adaptive sectorization, received and transmit power savings are achieved and the number of users served by the system (system capacity) is increased compared to uniform sectorization of the cell.