This work examines the path planning problem for data collection by an unmanned aerial vehicle (UAV) over clusters of smart poles (or streetlights). The smart poles within each cluster are assumed to be interconnected via wired or wireless links and, thus, the UAV needs to collect data from only one pole in each cluster. A small subset of smart poles is equipped with charging facilities to replenish the UAV’s battery when visited. To minimize the UAV’s total flight distance in a data collection cycle, we first propose the energy-aware minimum distance (EA-MinDist) path planning algorithm based on the dynamic programming principle. The algorithm takes into consideration the UAV’s need to visit charging poles along the path to sustain its travel over these clusters and can be extended to accommodate an infinite number of data collection cycles while maintaining a constant memory requirement. The sequence of visited smart poles converges to a periodic cycle. Alternatively, to reduce the cost of charging, we also propose the energy-aware minimum charging (EA-MinCharge) algorithm, which aims to minimize the total number of charges needed to traverse these clusters. Simulation results demonstrate the efficacy of the proposed algorithms in comparison to several baseline algorithms.

The rise of wireless technologies has made the Internet of Things (IoT) ubiquitous, but the broadcast nature of wireless communications exposes IoT to authentication risks. Physical layer authentication (PLA) offers a promising solution by leveraging unique characteristics of wireless channels. As a common approach in PLA, hypothesis testing yields a theoretically optimal Neyman-Pearson (NP) detector, but its reliance on channel statistics limits its practicality in real-world scenarios. In contrast, deep learning-based PLA approaches are practical but tend to be not optimal. To address these challenges, we proposed a learning-based PLA scheme driven by hypothesis testing and conducted extensive simulations and experimental evaluations using Wi-Fi. Specifically, we incorporated conditional statistical models into the hypothesis testing framework to derive a theoretically optimal NP detector. Building on this, we developed LiteNP-Net, a lightweight neural network driven by the NP detector. Simulation results demonstrated that LiteNP-Net could approach the performance of the NP detector even without prior knowledge of the channel statistics. To further assess its effectiveness in practical environments, we deployed an experimental testbed using Wi-Fi IoT development kits in various real-world scenarios. Experimental results demonstrated that the LiteNP-Net outperformed the conventional correlation-based method as well as state-of-the-art Siamese-based methods.

In Contention Resolution Diversity Slotted ALOHA (CRDSA) and Irregular Repetition Slotted ALOHA (IRSA), iterative decoding is typically assumed to be instantaneous, overlooking the decoding latency encountered in practical systems. This paper proposes a Partially Parallel Decoding ($\textsf {PPD}$) framework that offers a tunable trade-off between latency, complexity, and throughput. The proposed framework supports both CRDSA and IRSA with AWGN and Rayleigh fading by incorporating, in each iteration of the decoding process, a scheduling algorithm that selects target packets based on the number of available decoders, along with a slot selection algorithm that identifies a subset of time slots for the equalization step to manage complexity. Simulation results show that in both AWGN and fading environments, the PPD framework achieves performance close to that of sequential decoding while significantly reducing latency—by up to$64\times $for CRDSA and$16\times $for IRSA—with a practical number of decoders.

This work examines the use of unmanned aerial vehicles (UAVs) as mobile anchors to determine the locations of ground users based on the received signal strength (RSS). This is motivated by the significance of user location information in search and rescue operations, post-disaster recovery, and wireless communication systems. By utilizing the Cramér-Rao lower bound (CRLB) as a measure of localization accuracy, we jointly optimize the UAV’s flight trajectory and the users’ transmission scheduling for localization. We propose an iterative solution in which the transmission scheduling and trajectory design subproblems are solved in turn until convergence. We utilize a successive convex approximation (SCA) approach to address the non-convexity of the transmission scheduling subproblem and adopt a gradient descent method to solve the UAV trajectory optimization subproblem. Extensive simulation results verify that our proposed solution outperforms various baselines.

This work examines robust beamforming for integrated sensing and communication (ISAC) systems. We consider a vehicle-to-infrastructure (V2I) downlink system with a dual-functional radar-communication (DFRC) setup, where the communication signal sent by the roadside unit to the target vehicle is also used for sensing of the vehicle's angular position. The angle information is then utilized to further improve the communication in the next time slot. We aim to design transmit signals that can simultaneously achieve communication rate and sensing requirements under angle uncertainty. The transmit precoder for each time slot is optimized using angle estimates from the previous time slot, incorporating prediction errors into the performance metrics. The proposed robust beamforming design minimizes power consumption while meeting constraints on the achievable rate and the Bayesian Cramér-Rao bound (BCRB) of the angle estimate, for all potential angular positions. We employ the region of interest (ROI) approach to incorporate angle uncertainty and adopt semidefinite relaxation (SDR) techniques to efficiently solve the optimization problem. Simulation results show the effectiveness and robustness of the proposed algorithm in the presence of prediction errors.

Recent advances in Large Language Models (LLMs) have demonstrated the emergence of capabilities (learned skills) when the number of system parameters and the size of training data surpass certain thresholds. The exact mechanisms behind such phenomena are not fully understood and remain a topic of active research. Inspired by the skill-text bipartite graph model proposed by Arora and Goyal for modeling semantic languages, we develop a mathematical theory to explain the emergence of learned skills, taking the learning (or training) process into account. Our approach models the learning process for skills in the skill-text bipartite graph as an iterative decoding process in Low-Density Parity Check (LDPC) codes and Irregular Repetition Slotted ALOHA (IRSA). Using density evolution analysis, we demonstrate the emergence of learned skills when the ratio of the number of training texts to the number of skills exceeds a certain threshold. Our analysis also yields a scaling law for testing errors relative to this ratio. Upon completion of the training, the association of learned skills can also be acquired to form a skill association graph. We use site percolation analysis to derive the conditions for the existence of a giant component in the skill association graph. Our analysis can also be extended to the setting with a hierarchy of skills, where a fine-tuned model is built upon a foundation model. It is also applicable to the setting with multiple classes of skills and texts. As an important application, we propose a method for semantic compression and discuss its connections to semantic communication.

This work examines the impact of recommendation on both the shaping of user preference and the caching decisions at wireless edge devices. While most studies focus on the optimization from a network perspective, we investigate the joint caching and recommendation optimization for wireless device-to-device (D2D) networks from both network and user perspectives and identify their key differences. To achieve this goal, optimization problems for network offloading and user offloading probabilities as well as their tradeoff are first formulated. Two types of preference-shaping models are considered. The first type assumes that the user preference can be arbitrarily shaped whereas the second type only selectively enhances the users’ original preference. The proposed optimization problems are solved using alternating optimization where the users’ caching variables and the recommendation variables are optimized in turn until convergence. The corresponding convergence and complexity analyses are also provided. Extensive simulations are conducted to validate the efficacy of the solution approaches. Results show that designs obtained from different perspectives can lead to very different performance behaviors, and the difference is especially large when there is little similarity among users’ preferences.

The Internet of Things (IoT) is ubiquitous thanks to the rapid development of wireless technologies. However, the broadcast nature of wireless transmissions results in great vulnerability to device authentication. Physical layer authentication emerges as a promising approach by exploiting the unique channel characteristics. However, a practical scheme applicable to dynamic channel variations is still missing. In this paper, we proposed a deep learning-based physical layer channel state information (CSI) authentication for mobile scenarios and carried out comprehensive simulation and experimental evaluation using IEEE 802.11n. Specifically, a synthetic training dataset was generated based on the WLAN TGn channel model and the autocorrelation and the distance correlation of the channel, which can significantly reduce the overhead of manually collecting experimental datasets. A convolutional neural network (CNN)-based Siamese network was exploited to learn the temporal and spatial correlation between the CSI pair and output a score to measure their similarity. We adopted a synergistic methodology involving both simulation and experimental evaluation. The experimental testbed consisted of WiFi IoT development kits and a few typical scenarios were specifically considered. Both simulation and experimental evaluation demonstrated excellent generalization performance of our proposed deep learning-based approach and excellent authentication performance. Demonstrated by our practical measurement results, our proposed scheme improved the area under the curve (AUC) by 0.03 compared to the fully connected network-based (FCN-based) Siamese model and by 0.06 compared to the correlation-based benchmark algorithm.

This work examines the trajectory design and energy charging strategy of a data-gathering unmanned aerial vehicle (UAV). The UAV utilizes laser charging from high-altitude platforms (HAPs) to replenish its battery, enabling sustained travel across multiple data-gathering points. The trajectory is determined by a sequence of hovering positions at which the UAV stays to perform both data collection and energy charging. The UAV's hovering positions affect both the sensors’ transmission rates and the laser-charging efficiency. To minimize the total task completion time, it is necessary to choose hovering positions that consider both data upload and energy charging times. In this work, we first propose the Minimum Completion Time Trajectory and Charging Optimization (MinTime-TCO) algorithm, where the hovering positions and charging energies are optimized in turn using a block coordinate descent approach. Given the UAV's hovering positions, we propose the Minimum Charge Rate Search (MCRS) algorithm to optimize the charging energies at these positions. We show that MCRS is optimal in terms of minimizing the total task completion time. Then, given the charging energies, we propose the Hovering Position Optimization (HPO) algorithm, employing successive convex approximation to address the non-convexity of the optimization problem. We also propose a low-complexity alternative based on dynamic programming to further reduce computational complexity. Simulation results demonstrate the effectiveness of the proposed algorithms against several baseline strategies.

Due to its simplicity and scalability, the Irregular Repetition Slotted ALOHA (IRSA) system that uses the successive interference cancellation (SIC) technique is a promising solution for uncoordinated multiple access of a massive number of Internet-of-Things (IoT) devices. In this paper, we propose two parallel decoding algorithms for IRSA in an additive white Gaussian noise channel. Our first algorithm is limited to SIC-decoupling matrices that correspond to the SIC decoding process in IRSA. For this, we propose a message-passing algorithm to find the optimal SIC-decoupling matrix that can minimize the accumulated noise power when the induced user-slot bipartite graph of an IRSA system is acyclic. This includes the Contention Resolution Diversity Slotted ALOHA (CRDSA) system that sends exactly two copies for each packet as a special case. Our second algorithm extends the first one by finding the optimal decoupling matrix for CRDSA through an optimal combination of two SIC-decoupling matrices. Using a random graph analysis, we derive the throughput for the two parallel decoding algorithms of CRDSA in a threshold-based decoding model. We then conduct various numerical experiments to illustrate the tradeoffs between sequential decoding with a limited number of iterations and parallel decoding with a predefined signal-to-noise ratio (SNR) threshold. Finally, we demonstrate how to extend our parallel decoding scheme to bipartite graphs with cycles.

This work examines the task assignment, transmission scheduling, and trajectory design of image-surveillance UAVs dispatched to serve on-demand image capture and delivery services to ground users, e.g., from drivers seeking images of traffic jams or security units requesting images of private homes. In each task, the UAVs are required to capture the image of a specified surveillance region and deliver the image to the requesting user before the deadline. The task assignment, transmission scheduling, and trajectory design are jointly determined to maximize the total surveillance area of the completed tasks. We first examine the single-UAV problem and propose an alternating optimization approach that adopts the exact penalty method to promote near-binary solutions and employ successive convex approximation to deal with the nonconvex trajectory optimization. Then, we extend to the multiple-UAV scenario where cross-UAV tasks may require images to be captured and delivered by different UAVs. To enable distributed implementation, we introduce auxiliary deadlines to limit the time available for local tasks and, thus, decouple the joint optimization problem into multiple single-UAV problems that can be solved in parallel following the procedure derived in the previous case. Numerical simulations are provided to demonstrate the effectiveness of the proposed solutions.

This work examines a novel wireless knowledge caching framework where machine learning models (i.e., knowledge) are cached at local small cell base-stations (SBSs) to facilitate both federated training and access of the models by users. We first consider a single-SBS scenario, where the caching decision, user selection, and wireless resource allocation are jointly determined by minimizing a training error bound subject to constraints on the cache capacity, the communication and computation latency, and the energy consumption. The solution is obtained by first computing the minimum achievable training loss for each model, followed by the optimization of the binary caching variables, which reduces to a 0-1 knapsack problem. The proposed framework is then extended to the multiple-SBS scenario where the user association among SBSs is further examined. We adopt a dual-ascent method where Lagrange multipliers are introduced and updated in each iteration to regularize the dependence among user selection and association. Given the Lagrange multipliers, the caching decision, user selection, resource allocation and user association variables are optimized in turn using a block coordinate descent algorithm. Simulation results show that the proposed scheme can achieve a training error bound that is lower than preference-only and random caching policies in both scenarios.

The Internet of Things (IoT) is ubiquitous thanks to the rapid development of wireless technology. However, the broadcast nature of wireless transmission results in great challenges to the security authentication for large-scale IoT. In this paper, we propose a novel physical layer authentication approach for mobile scenarios employing deep learning and channel state information (CSI). Specifically, the convolution neural network (CNN) is designed to learn the temporal and spatial similarity between CSIs and output a score to measure the difference between the input CSIs. Device authentication is achieved by comparing the score to an empirically obtained threshold. We build a WiFi-based testbed and carry out a comprehensive experimental evaluation. The performance of using the CSI magnitude and real & imaginary parts is compared. The effect of the distance between legitimate and rogue devices on authentication performance is studied. The generalization performance of the CNN model in different test scenarios is also evaluated. Experiment results demonstrate the effectiveness of the proposed CNN-based authentication over conventional correlation-based authentication schemes.

This work examines the downlink channel assignment for integrated access and backhaul (IAB) networks employing multihop and multi-connectivity operations. Multi-connectivity allows user equipments (UEs) and base stations (BSs) to receive information from multiple upstream BSs and, thus, increases the flexibility of spectrum utilization. We formulate the channel assignment problem as a hierarchical multiple knapsack with discrete fractional assignments problem that aims to maximize the total accommodated data-rate demands of the UEs. We propose a multi-connectivity-aware hierarchical resource allocation (MuCH-RA) algorithm that consists of two stages: a sequential multiple knapsack assignment (SMKA) stage and a UE deselection (UED) stage. The SMKA stage assigns channels to UEs and BSs by solving a sequence of single knapsack problems at the BSs in a bottom-up tier-by-tier fashion, followed by the efficient removal of redundant assignments. The UED stage removes the UEs that could not be fully served and releases their channels for possible reassignment in the next iteration. The proposed MuCH-RA algorithm jointly considers the load and the channel quality of BSs and UEs and, thus, is able to serve larger overall data-rate demands than pure load-based and channel-based greedy algorithms. Numerical simulations are provided to demonstrate the effectiveness of the proposed algorithm.

This work examines a UAV-enabled surveillance mission over multiple restricted regions and aims to determine the optimal UAV trajectory that minimizes the mission completion time. The UAV is prohibited from entering the restricted regions due to government regulations or adversarial concerns. However, during the surveillance of a region, the UAV can move along the region's boundary to reduce its distance to the next region once the local task is completed. To exploit this advantage, we propose a minimum completion time (MinTime) algorithm that first determines the visiting order of the regions by employing an approximate solution of the traveling salesman problem (TSP) and then optimizes the UAV trajectory over the sequence of restricted regions using dynamic programming. In the presence of obstacles, we further propose an obstacle-aware MinTime (OA-MinTime) algorithm that treats each obstacle as an additional restricted region with zero surveillance duration, allowing the UAV to avoid the obstacles in a more efficient manner. A modified TSP solution is also proposed by taking into consideration the additional distance required to circumvent the obstacles on each inter-POI path. Simulation results show that the proposed MinTime and OA-MinTime algorithms can significantly reduce the total completion time compared to conventional minimum-distance approaches.

Due to its simplicity and scalability, the Irregular Repetition Slotted ALOHA (IRSA) system that uses the successive interference cancellation (SIC) technique is a promising solution for uncoordinated multiple access of a massive number of Internet-of-Things (IoT) devices. However, the peeling (iterative) decoder for IRSA is sequential in nature, and it might lead to cascading errors due to imperfect SIC. In this paper, we propose a parallel decoding algorithm for IRSA in an Additive White Gaussian Noise (AWGN) channel. Inspired by a recent advance in collision resolution for random access, our approach is to find a SIC-decoupling matrix so that the receiver can perform interference cancellation based on the received signals only. We propose a message-passing algorithm to find the optimal SIC-decoupling matrix when the induced user-slot bipartite graph of an IRSA system is acyclic. This includes the Contention Resolution Diversity Slotted ALOHA (CRDSA) system that sends exactly two copies for each packet. Using a random graph analysis, we derive the throughput for parallel decoding of CRDSA in a threshold-based decoding model. We also conduct various numerical experiments to illustrate the tradeoffs between sequential decoding with a limited number of iterations and parallel decoding with a predefined signal-to-noise ratio (SNR) threshold. Our numerical results show that one can significantly reduce the decoding time and achieve comparable throughput by parallel decoding when the SNR is substantially larger than the decoding threshold.

Conventional sparse phase retrieval schemes can recover sparse signals from the magnitude of linear measurements only up to a global phase ambiguity. This work proposes a novel approach that instead utilizes the magnitude of affine measurements to achieve ambiguity-free signal reconstruction. The proposed method relies on two-stage approach that consists of support identification followed by the exact recovery of nonzero signal entries. In the noise-free case, perfect support identification using a simple counting rule is guaranteed subject to a mild condition on the signal sparsity, and subsequent exact recovery of the nonzero signal entries can be obtained in closed-form. The proposed approach is then extended to two noisy scenarios, namely, sparse noise (or outliers) and non-sparse bounded noise. For both cases, perfect support identification is still ensured under mild conditions on the noise model, namely, the support size for sparse outliers and the power of the bounded noise. Under perfect support identification, exact signal recovery can be achieved using a simple majority rule for the sparse noise scenario, and reconstruction up to a bounded error can be achieved using linear least-squares (LS) estimation for the non-sparse bounded noise scenario. The obtained analytic performance guarantee for the latter case also sheds light on the construction of the sensing matrix and bias vector. In fact, we show that a near optimal performance can be achieved with high probability by the random generation of the nonzero entries of the sparse sensing matrix and bias vector according to the uniform distribution over a circle. Computer simulations using both synthetic and real-world data sets are provided to demonstrate the effectiveness of the proposed scheme.

This paper addresses the coexistence problem among multiple wireless body area networks (WBANs), where co-channel interference may occur among different WBANs if the channels are not allocated properly, leading to performance degradation in both energy efficiency and packet transmission reliability. We formulate the channel allocation problem as a graph coloring problem, and develop a solution to increase the co-channel reuse and the number of WBANs with assigned channels. We propose a distributed two-hop incomplete coloring (DTIC) algorithm that adopts a game-theoretic approach to solve the graph coloring problem. The DTIC algorithm exploits two-hop information to enable high channel reuse among two-hop neighbors and allows for incomplete coloring when the number of colors (or channels) is insufficient to color all vertices without conflict. A distributed message-passing protocol is also proposed to achieve collision-free message exchange, and to ensure that consistent coloring information is shared among WBANs. Simulation results show that our proposed algorithm achieves better co-channel reuse and higher throughput than existing methods.

This work examines the use of image surveillance UAVs for relay communication between ground users and a remote base station (BS). UAVs take aerial images of the surveillance region and forward them to the BS while serving the uplink transmission demands of ground users. We first consider the single-UAV scenario and jointly determine the UAV’s trajectory, task assignment, user association, and rate allocation by maximizing the sum-log-throughput of the users subject to constraints on the surveillance coverage, image transmission requirements, and relay capacity. The resulting mixed-integer nonlinear programming problem is solved by an inexact block coordinate descent (BCD) algorithm where we inherit ideas from the exact penalty method for mathematical programming with equilibrium constraints to relax the integer constraints and the successive convex approximation approach to address the non-convexity of the trajectory optimization problem. Then, we extend the proposed framework to the case with multiple UAVs that are dispatched to cover a wide surveillance region. The UAVs may complete both relay and surveillance tasks more efficiently through cooperation and proper task allocation for UAVs. A similar BCD algorithm is adopted to solve the problem. Numerical simulations are provided to demonstrate the effectiveness of the proposed scheme over several baseline methods.

This work examines a novel wireless content distribution problem where machine learning models (e.g., deep neural networks) are cached at local small cell base-stations to facilitate access by users within their coverage. The models are trained by federated learning procedures which allow local users to collaboratively train the models using their locally stored data. Upon the completion of training, the model can also be accessed by all other users depending on their application demand. Different from conventional wireless caching problems, the placement of machine learning models should depend not only on the users' preferences but also on the data available at the users and their channel conditions. In this work, we propose to jointly optimize the caching decision, user selection, and wireless resource allocation, including transmit powers and bandwidth of the selected users, to minimize a training error bound. The problem is reduced to minimizing a weighted sum of local dataset sizes subject to constraints on the cache storage capacity, the communication and computation latency, and the total energy consumption. We first derive the minimum loss achievable for each cached model, and, then, determine the optimal models to cache by solving an equivalent 0–1 Knapsack problem that minimizes the total average loss. Simulations show that the proposed scheme can achieve lower extremity error bounds compared to preference-only and random caching policies.

As user preferences can be influenced by the recommendation system, it has been shown that the joint recommendation and caching policy design can significantly improve the caching networks where caching is at the BSs. However, whether and how the joint recommendation and caching policy design can provide benefits to cache-aided device-to-device (D2D) networks have not been well-understood. This paper thus contributes in this direction by modeling the offloading probability of the cache-aided D2D network and proposing a social-aware joint recommendation caching policy design. Specifically, considering the preferences and social relationship of users as well as the caching and recommendation policies of the network, we formulate an offloading probability optimization problem which is non-convex. Then, an iterative algorithm with monotonicity and convergence property is proposed to solve the problem. By simulations, we show that the proposed joint recommendation and caching policy design can significantly outperform designs that only optimize the caching policy and other reference designs.

Advancement in speech technology has brought convenience to our life. However, the concern is on the rise as speech signal contains multiple personal attributes, which would lead to either sensitive information leakage or bias toward decision. In this work, we propose an attribute-aligned learning strategy to derive speech representation that can flexibly address these issues by attribute-selection mechanism. Specifically, we propose a layered-representation variational autoencoder (LR-VAE), which factorizes speech representation into attribute-sensitive nodes, to derive an identity-free representation for speech emotion recognition (SER), and an emotionless representation for speaker verification (SV). Our proposed method achieves competitive performances on identity-free SER and a better performance on emotionless SV, comparing to the current state-of-the-art method of using adversarial learning applied on a large emotion corpora, the MSP-Podcast. Also, our proposed learning strategy reduces the model and training process needed to achieve multiple privacy-preserving tasks.

This work examines the deployment of multiple fixed-wing unmanned aerial vehicles (UAVs) for data-gathering from ground IoT devices, and the corresponding device association policy. Each UAV is assumed to hover above its associated devices following a circular trajectory. The device association and the UAVs' trajectory centers and radii are jointly optimized to maximize the energy-savings relative to a constant transmission power scheme. Given the trajectory centers and radii, the device association problem is modeled as a multiple 0-1 knapsack problem, taking into consideration the load demands of different devices as well as UAVs' service capacities. A two-stage maximum energy-saving device association policy is proposed, where each UAV first solves a single knapsack problem based on all connectable devices, and then resolves conflict with others by a maximum profit assignment. Moreover, given the device association, the UAVs' trajectory centers and radii are optimized by an iterative load-balancing algorithm, where the trajectory centers are chosen as a load-dependent weighted sum of the associated devices' locations. The device association and the UAV deployment are optimized in turn until convergence. Simulation results show that our proposed schemes outperform candidate algorithms in terms of the total energy-savings of IoT devices.

This work proposes a deep-learning (DL) based coordinated precoder design for multicell downlink systems with rate-limited exchange of channel state information (CSI) among base-stations (BSs). Two CSI compression techniques are proposed, one based on a binarized convolutional neural network (CNN) and one based on a learned vector-quantization (VQ) codebook. The former utilizes a CNN-based CSI feature extractor to directly compute the binary feature vector that is to be exchanged with other BSs. The latter utilizes a DL-based VQ codebook to encode the CSI feature vector that is obtained at the output of the feature extractor. In both cases, each BS takes the rate-limited CSI received from other BSs as input to a precoder network that produces the normalized precoding vectors and the transmit powers using a multitask learning architecture. By using solutions of the weighted minimum mean square error (WMMSE) algorithm as the output labels, end-to-end training of both the CSI compression and transmit precoder networks is performed jointly at all BSs. By doing so, the CSI compression networks will be able to extract the CSI features that are most effective for precoder computation at the BSs. Our simulation results show that the proposed schemes can achieve weighted sum rates close to that in the full CSI scenario, even when the number of exchanged bits is small, and outperform existing random VQ methods.

Gait phase segmentation is the process of identifying the start and end of different phases within a gait cycle. It is essential to many medical applications, such as disease diagnosis or rehabilitation. This work utilizes inertial measurement units (IMUs) mounted on the individual's foot to gather gait information and develops a gait phase segmentation method based on the collected signals. The proposed method utilizes a weighted dynamic time warping (DTW) algorithm to measure the distance between two different gait signals, and a k-nearest neighbors (kNN) algorithm to obtain the gait phase estimates. To reduce the complexity of the DTW-based kNN search, we propose a neural network-based graph embedding scheme that is able to map the IMU signals associated with each gait cycle into a distance-preserving low-dimensional representation while also producing a prediction on the k nearest neighbors of the test signal. Experiments are conducted on self-collected IMU gait signals to demonstrate the effectiveness of the proposed scheme.

This work examines the trajectory optimization of an unmanned aerial vehicle (UAV) for the purpose of data-gathering from a backscattering wireless sensor network. The sensors are assumed to be remotely powered by distributed power-beacons using wireless power transfer (WPT) technology. The energy signals are backscattered towards the UAV, carrying information about the sensors' observations. Under a strict deadline constraint, the number of time slots that can be used to gather data is limited and, thus, the sensors must carefully determine their activation time slots according to the UAV's position at given times. The UAV trajectory and sensor activation decisions are coupled and, thus, jointly determined by minimizing the mean-squared error of the reconstructed sensor observations at the UAV. The trajectory is constrained by the UAV's maximum flight speed and minimum altitude, and the sensors' transmissions suffer from altitude-dependent path loss. An iterative procedure is proposed where the UAV trajectory, elevation angle and sensor activation are updated in turn until convergence. Performance comparison is provided through numerical simulations.

This work proposes a collaborative sensor caching and data reconstruction method based on the sequential compressed sensing framework. Here, multiple caches are assumed to exist in the wireless sensor network to store the most recent data gathered from sensors within their respective coverage areas. To reduce the cache size and the data-acquisition overhead, each cache accesses measurements only from a small subset of sensors. This work proposes a collaborative sparse-signal reconstruction method that exploits the presence of sensors simultaneously accessible by multiple caches as anchor nodes to introduce dependency in the reconstruction. The reconstruction is based on the use of the alternating direction method of multipliers (ADMM), which enables distributed implementation of the algorithm. Simulations are provided to demonstrate the effectiveness of the proposed scheme.

This work proposes power-efficient trajectory adjustment and temporal routing algorithms for a network of unmanned aerial vehicles (UAV) that are deployed to monitor or gather data from underlying sensors in the field. Here, we consider fixed-wing UAVs that are assumed to follow circular trajectories whose radius can be adjusted to reduce power consumption while maintaining coverage over its responsible service area. Given the multihop transmission paths from the UAVs to the data-gathering node, power-efficient flight-radius adjustment strategies are proposed based on the total power minimization and lifetime maximization criteria while maintaining the existence of the paths. Then, by establishing the relationship between routing in UAV networks and that in general temporal graphs, we propose a power-efficient (PE) temporal path algorithm based on the minimization of the accumulated square of the minimum achievable powers of all UAVs on the path. Computer simulations are provided to demonstrate the effectiveness of the radius adjustment strategies in terms of both total power minimization and lifetime maximization, and the power-savings provided by the PE temporal path algorithm.

This work proposes efficient methods for the detection of malicious crowdsourcing workers using only privacy-aware group queries. In the proposed system, the crowdsourcing platform first issues a series of standard tasks to the workers, and allows users (i.e., data owners) to access aggregate responses from the workers through group queries that can be described by sparse encoding vectors. The identities of workers associated with individual responses are not explicitly revealed. By exploiting the sparse nature of the encoding vectors, we first propose an approximate maximum a posteriori probability (Approx. MAP) detector to perform the detection. Then, to further reduce computational complexity, we devise a generalized likelihood ratio test (GLRT) where probable malicious workers are first identified before a simple hypothesis test is performed. The identification of malicious workers is performed by a low-complexity probability-based rule that exploits a certain sparse structure inherent in the crowd data as well as the associated statistical assumptions. Computer simulations show that the proposed methods outperform the conventional energy detector.

This paper proposes a dynamic precoding and power allocation policy for mutually cooperative device-to-device (D2D) transmitter-receiver pairs that underlay a cellular system in the uplink. The cooperative transmission consists of two phases: a data-sharing phase (i.e., phase 1) and a joint transmission phase (i.e., phase 2). Multicast precoders are used in phase 1 and coordinated block-diagonalization precoders are considered in phase 2. The precoders are jointly designed to maximize the long-term utility of the D2D users subject to long-term individual power and rate-gain constraints and an instantaneous interference constraint at the base-station. The long-term objective and constraints allow cooperating users to adapt their resources more flexibly over time, but increase the complexity of the design. By adopting the Lyapunov optimization framework and by constructing virtual queues to record the temporal evolution of the system states, the long-term utility maximization problem can be decoupled into a series of short-term weighted-rate-minus-energy-penalty (WRMEP) optimization problems that can be solved efficiently. A low-complexity algorithm is further proposed for solving the WRMEP problem when multicasting in the data-sharing phase is performed by a spatially white input. Theoretical performance guarantees and a bound on the virtual queue backlogs are also derived.

This work examines joint beamforming and power allocation schemes as well as trajectory learning and clustering mechanisms for dynamically connectable unmanned aerial vehicle (UAV) base- stations (BSs). Here, UAV-BSs are allowed to join together dynamically and form physically-connected collocated antenna arrays that enable joint transmission and cooperative energy sharing among the connected UAVs. A joint design of the transmit powers and beamformers in each time slot is first proposed based on the maximization of the sum signal-to-leakage-plus-noise-ratio (SLNR) of all users. The solution is obtained via alternating optimization where the transmit powers and beamformers are optimized in turn until convergence. Then, based on the per-time-slot design, a dynamic UAV moving and clustering policy is proposed where the expected sum rate of the system is maximized while adapting to changes in the users' locations and environment. Here, UAV locations are adjusted gradually in each time slot according to the stochastic gradient of the expected system sum rate. The UAV clusters are updated every T time slots by combining the two clusters that yield the maximum increase in the expected system sum rate. Simulations are provided to demonstrate the effectiveness of the proposed schemes.

This work proposes a multiuser hybrid beamforming scheme for non-orthogonal multiple access (NOMA) systems using the minimum mean square error (MMSE) approach to weighted sum rate maximization. While NOMA may be effective in terms of enhancing user fairness, hybrid beamforming is necessary to reduce the transceiver cost as the system moves towards higher frequency. The design is divided into two stages. In the first stage, a fully digital multiuser beamformer is derived by maximizing the weighted sum rate of all users under no constraint on the number of RF chains. This problem is then transformed into a weighted sum MSE minimization problem, which facilitates the use of alternating optimization to obtain an efficient local solution. In the second stage, the previously obtained multiuser beamformer is then split into RF and baseband beamformers by using orthogonal matching pursuit (OMP). A user role selection algorithm is then proposed to determine the role of strong and weak users. Simulation results are provided to demonstrate the effectiveness of the proposed schemes.

This work examines a cross-layered caching problem for distributed estimation in wireless sensor networks (WSNs). In WSNs, large amounts of data are produced continuously over time, and storing all the data collected from the sensors can be costly. In distributed estimation applications, sensors first gather information about a common phenomenon, and then forward the information to a fusion center where the final estimate is computed. By assuming that the parameters are correlated over time, the estimation quality at the fusion center can be improved by combining both present and past information, where the latter can be obtained from cached data. Different from conventional caching problems, where the goal is to reconstruct the sensors' observations, our caching strategy is designed to minimize the long term average mean-square error (MSE) of the final estimate. This problem can be modelled as a Markov decision process but, due to the curse of dimensionality, is solved here using a greedy one-step-ahead caching strategy, which only minimizes the expected MSE in the next time slot. This results in a nonlinear fractional programming problem that is solved approximately using semi-definite relaxation and a modified Dinkelbach's algorithm. The effectiveness of the proposed scheme is demonstrated through numerical simulations.

This paper proposes a vector-quantization-based secret key generation (SKG) procedure to efficiently extract shared secret keys from correlated channel observations at two communicating terminals, Alice and Bob. Most existing SKG schemes utilize scalar quantization to extract secret key bits separately from each individual channel observation. This approach is simple to implement but yields higher key disagreement probability (or lower key entropy) compared with vector-quantization-based approaches. However, regardless of the quantizer design, quantization for SKG often suffers from the so-called cell-boundary problem, which occurs when the channel observations at Alice and Bob lie close to the quantization cell boundaries, resulting in high probability of key disagreement. In this paper, a general SKG procedure that utilizes sample and quantizer selection techniques to avoid this problem is first proposed. The vector quantizer adopted in the above procedure is designed by minimizing the quadratic distortion between the true channel vector and the noisy observation at Alice (or Bob). Then, by considering the case where the eavesdropper (Eve) may observe a channel vector that is correlated with that observed by Alice and Bob, a clustered key mapping scheme that assigns each secret key to multiple quantization cells in different clusters is also proposed to induce additional randomness at Eve and, thus, maintain high conditional key entropy. The effectiveness of the proposed schemes is demonstrated through computer simulations.

This paper considers the joint design of training and data transmission in physical-layer secret communications, and examines the role of artificial noise (AN) in both of these phases. In particular, AN in the training phase is used to prevent the eavesdropper from obtaining accurate channel state information (CSI), whereas AN in the data transmission phase can be used to mask the transmission of confidential messages. By considering AN-assisted training and secrecy beamforming, we first derive bounds on the achievable secrecy rate and utilize them to obtain approximate secrecy rate expressions that are asymptotically tight at high SNR. By maximizing these expressions, power allocation policies between signal and AN in both training and data transmission phases are then proposed for conventional and AN-assisted training-based schemes, respectively. We show that the optimal AN power at high SNR should be non-vanishing with respect to the total power, and that AN usage can be more effective in the training phase than in the data transmission phase when the coherence time is large. However, at low SNR, we show that AN cannot be effectively utilized due to the lack of accurate CSI, and thus, one can often do better without. Numerical results are presented to verify our theoretical claims.

This paper provides new insights on the convergence of a locally connected network of pulse coupled oscillator (PCOs) (i.e., a bioinspired model for communication networks) to synchronous and desynchronous states, and their implication in terms of the decentralized synchronization and scheduling in communication networks. Bioinspired techniques have been advocated by many as fault-tolerant and scalable alternatives to produce self-organization in communication networks. The PCO dynamics, in particular, have been the source of inspiration for many network synchronization and scheduling protocols. However, their convergence properties, especially in locally connected networks, have not been fully understood, prohibiting the migration into mainstream standards. This paper provides further results on the convergence of PCOs in locally connected networks and the achievable convergence accuracy under propagation delays. For synchronization, almost sure convergence is proved for three nodes and accuracy results are obtained for general locally connected networks, whereas for scheduling (or desynchronization), results are derived for locally connected networks with mild conditions on the overlapping set of maximal cliques. These issues have not been fully addressed before in the literature.

The feasibility of practical in-band full-duplex radios has recently been demonstrated experimentally. One way to leverage full-duplex in a network setting is to enable three-node full-duplex, where a full-duplex access point (AP) transmits data to one node yet simultaneously receives data from another node. Such three-node full-duplex communication, however, introduces inter-client interference, directly impacting the full-duplex gain. It hence may not always be beneficial to enable three-node full-duplex transmissions. In this paper, we present a distributed full-duplex medium access control (MAC) protocol that allows an AP to adaptively switch between full-duplex and half-duplex modes. We formulate a model that determines the probabilities of full-duplex and half-duplex access so as to maximize the expected network throughput. A MAC protocol is further proposed to enable the AP and clients to contend for either full-duplex or half-duplex transmissions based on their assigned probabilities in a distributed way. Our evaluation shows that, by combining the advantages of centralized probabilistic scheduling and distributed random access, our design improves the overall throughput by 1.53 times, on average, as compared with the greedy downlink-uplink client pairing.

This work examines the user-pair selection problem for distributed-input distributed-output (DIDO) wireless systems. A DIDO system refers to a network of densely deployed transmitter and receiver pairs, where the transmitters are connected to and coordinated by a DIDO server. The system sum rate is known to grow without bound as the number of transmitter-receiver pairs increases. However, when zero-forcing (ZF) beamforming is adopted across the transmitters (as assumed in most existing works on DIDO), the effect of power amplification due to ill-conditioned channel matrices may significantly reduce the system sum rate. In this work, a decremental user-pair selection (DUPS) algorithm is proposed to determine the set of transmitter-receiver pairs that should be simultaneously active in order to reduce the impact of power amplification and increase the system sum rate. A low-complexity variant of DUPS is also proposed and an asymptotic lower bound of its sum rate is derived using extreme value theory. Moreover, inspired by the semi-orthogonal user selection (SUS) algorithm, often adopted in the conventional multiple-input multiple-output (MIMO) literature, a semi-orthogonal DUPS algorithm is also proposed by taking into consideration the orthogonality of the users' channel vectors in the selection process. Simulations are provided to demonstrate the effectiveness of the proposed schemes.

This work proposes a jamming-resistant frequency hopping (FH) system that utilizes local channel observations for secret key generation (SKG). FH is a spread spectrum technique used in both military and consumer wireless applications to avoid jamming attacks, but requires pre-shared secret keys among communicating terminals, say Alice and Bob, to ensure that the same FH sequence is used at both sides. In our scheme, Alice and Bob utilize local observations of the channel between them as the source of common randomness to generate the shared secret key. By gathering multiple time slots into a frame, the sequence of channels observed in each frame can be used to determine the FH sequence in the next frame. In this case, the key generation rate must be high enough to identify the FH sequence in the next frame and, thus, to sustain the operation over time. However, by further considering the data transmission, an interesting tradeoff exists between the power allocated for SKG and channel estimation in the training phase and that for communication in the data transmission phase. Given the number of FH channels and the number of channels that the adversary can jam at once, we derive the minimum pilot signal power required for sustainability and also determine the optimal power allocation between pilot and data signals that maximizes the ergodic rate between the two users. Simulations are provided to demonstrate the effectiveness of the proposed scheme.

The pulse-coupled synchronization and scheduling (PulseSS) protocol is proposed in this paper for simultaneous synchronization and scheduling of communication activities in clustered wireless sensor networks (WSNs), by emulating the emergent behavior of pulse-coupled oscillator (PCO) networks in mathematical biology. Different from existing works that address synchronization and scheduling (i.e., desynchronization) separately, PulseSS provides a coordination signaling mechanism that achieves decentralized network synchronization and time division multiple access scheduling simultaneously at different time scales for clustered WSNs. Here, we assume that the nodes are connected only locally via their respective cluster heads. Moreover, PulseSS addresses the issue of propagation delays, that may plague the accuracy of PCO synchronization in practice, by providing ways to estimate and precompensate for these values locally at the sensors (i.e., PCOs). At the same time the protocol retains the adaptivity and light-weight nature of PCO protocols both in terms of signaling and computations. Simulations of both physical and medium access control layers show a synchronization accuracy of factions of microseconds above 15 dB of signal to interference and noise ratio for a five cluster network. A hardware implementation of PulseSS using TinyOS is also provided to corroborate the real world applicability of our protocol.

This paper presents a systematic approach for solving wireless max-min utility fairness optimization problems in multiuser wireless networks with general monotonic constraints. These problems are often challenging to solve due to their nonconvexity. By establishing a connection between this class of optimization problems and the class of conditional eigenvalue problems that can be addressed by a generalized nonlinear Perron-Frobenius theory, we show how these problems can be solved optimally using an iterative algorithm that converges geometrically fast. The mathematical development in this paper unifies previous work and allows us to handle a broader class of competitive utility functions with general nonlinear monotonic constraints. Several representative applications illustrate the effectiveness of the proposed framework, including the max-min quality-of-service subject to robust interference temperature constraints in cognitive radio networks, the min-max weighted mean-square error subject to signal-to-interference-and-noise ratio constraints in multiuser downlink systems, the max-min throughput subject to nonlinear power constraints in energyefficient wireless networks, the max-min sigmoid utility in multimedia wireless networks, and the min-max outage probability subject to outage constraints in heterogeneous wireless networks. Numerical results are presented to demonstrate the fast-convergence behavior of the algorithms to the optimal fixed-point solution characterized by our generalized nonlinear Perron-Frobenius theoretic framework.

This paper proposes secrecy-enhancing signaling schemes to exploit the advantages of having only channel state information (CSI) at the transmitter, but no (or limited) CSI at the receiver and the eavesdropper in fast-fading wiretap channels and also in wiretap channels with Gaussian interference. With only CSI at the transmitter, the transmit signaling can be designed to precompensate for the distortion or the interference on the main channel to facilitate decoding at the receiver while leaving the eavesdropper confused by the uncertainties of its own channel. In fading wiretap channels, truncated channel-inversion and phase-compensation schemes are proposed to achieve this task. Here, truncation is used so that transmission occurs only when the main channel is sufficiently reliable. In wiretap channels with Gaussian interference, a truncated interference-nulling scheme is proposed to enable interference precancellation at the transmitter. The proposed signaling schemes exploit the advantages of having only CSI at the transmitter without employing more complex encoding schemes. The achievable secrecy rates are derived using Gaussian input, and approximate expressions are proposed for the optimization of system parameters. The effectiveness of the proposed transmission schemes and the advantages of having only CSI at the transmitter are demonstrated through numerical simulations.

Power and on-off control problems are examined for renewable energy enabled base-stations (BSs) and relay nodes (RNs) in cooperative multicast networks. Renewable energy is utilized at BSs and RNs to reduce the overall grid energy cost. By considering a practical energy consumption model and the statistics of the renewable energy arrival, the optimal transmit powers are first determined by minimizing the expected grid energy consumption subject to an average outage probability constraint at MUs. The optimal solution is found via line search in the general case and is obtained in closed-form at high SNR. In addition, an on-off control policy is also proposed to further reduce the basic operational energy costs. The joint on-off and power control problems are solved approximately using two sequential deflation techniques, namely, the subset-search and the convex-relaxation-based approaches. The power control problem is also extended to the multicarrier scenario with unequal transmit powers and is solved using successive convex approximation. Simulations using the photovoltaic energy arrival model are provided to demonstrate the effectiveness of the proposed schemes. The results show that expending more power at RNs allows for more efficient use of renewable energy and, thus, increases energy-savings.

This work develops a tractable model to analyze the performance of downlink heterogeneous cellular networks (HCNs) with both power-grid-connected base stations (PG-BSs) and energy harvesting small cell access points (EH-SAPs). Each EH-SAP forms a personal cell that is active (and is available to serve others) only when its own priority user requests service and its battery contains sufficient energy to transmit. By modeling the battery dynamics of an EH-SAP as a discrete-time Markov chain and by considering a practical power consumption model, the rate coverage, network throughput, and energy efficiency are derived as functions of the PG-BS and EH-SAP densities, transmission powers, cell association biases, and energy harvesting capabilities. Cell association biases control the traffic load among different tiers and transmission powers affect EH-SAPs' probability of being active. Exact expressions of the performance metrics are first derived for the general multitier scenario and are then used to obtain approximate closed-form expressions for two-tier networks using the mean load approximation. The analytic results of the two-tier network provide valuable insights on the achievable performance and the choice of system parameters, and are further validated with numerical results.

The feasibility of practical in-band full-duplex radios has recently been demonstrated experimentally. One way to leverage full-duplex in a network setting is to enable three-node full-duplex, where a full-duplex access point (AP) transmits data to one node yet simultaneously receives data from another node. Such three-node full-duplex communication however introduces inter-client interference, directly impacting the full-duplex gain. It hence may not always be beneficial to enable three-node full-duplex transmissions. In this paper, we present a distributed full-duplex medium access control (MAC) protocol that allows an AP to adaptively switch between full-duplex and half-duplex modes. We formulate a model that determines the probabilities of full-duplex and half-duplex access so as to maximize the expected network throughput. A MAC protocol is further proposed to enable the AP and clients to contend for either full-duplex or half-duplex transmissions based on their assigned probabilities in a distributed way. Our evaluation shows that, by combining the advantages of centralized probabilistic scheduling and distributed random access, our design improves the overall throughput by 3.16× and 1.44×, on average, as compared to half-duplex 802.11 and greedy downlink-uplink client pairing.

In this paper, we develop an analytical framework to evaluate the network throughput and traffic offloading efficiency in heterogeneous cellular networks (HCNs) with multiple energy-harvesting personal cells. The network consists of a tier of power-grid connected base-stations (BSs) and multiple tiers of energy-harvesting (personal) small cell access points (EH-SAPs). Each EH-SAP is personally owned by a priority user and becomes active only when its priority user requests communication and when its residual battery energy is sufficient to support the transmission. The energy arrival at each EH-SAP is modeled as a Bernoulli random process and the battery energy dynamics are modeled as a discrete time Markov chain. The network throughput is derived and its dependence on the energy arrival probability, cell association bias, and transmission power are examined based on the analysis. We show that, when the transmission power of EH-SAPs is sufficiently small, network throughput can be increased by increasing the density of EH-SAPs and by offloading more traffic to them. Our results offer insights on the design of spectral-efficient HCNs with EH-SAPs.

We consider the block Rayleigh fading multiple-input multiple-output (MIMO) wiretap channel with no prior channel state information (CSI) available at any of the terminals. The channel gains remain constant within a coherence interval of T symbols, and then change to another independent realization in the next coherence interval. The transmitter, the legitimate receiver, and the eavesdropper have nt, nr, and ne antennas, respectively. We determine the exact secure degrees of freedom (s.d.o.f.) of this system when T ≥ 2min(nt,nr). We show that, in this case, the s.d.o.f. is exactly equal to (min(nt,nr)-ne)+(T -min(nt,nr))/T. The first term in this expression can be interpreted as the eavesdropper with ne antennas taking away ne antennas from both the transmitter and the legitimate receiver. The second term can be interpreted as a fraction of the s.d.o.f. being lost due to the lack of CSI at the legitimate receiver. In particular, the fraction loss, min(nt,nr)/T, can be interpreted as the fraction of channel uses dedicated to training the legitimate receiver for it to learn its own CSI. We prove that this s.d.o.f. can be achieved by employing a constant norm channel input, which can be viewed as a generalization of discrete signalling to multiple dimensions.

Discriminatory channel estimation (DCE) is a secrecy-enhancing training and channel estimation technique previously proposed in the literature to enhance the effective channel quality difference between the main and the eavesdropper channels (i.e., the channels experienced by the legitimate receiver and the eavesdropper, respectively) in the channel estimation phase. In the past, this was achieved by developing techniques to first provide the transmitter with preliminary estimates of the main channel and by then emitting training signals that embed AN in the null space of the estimated main channel to disrupt the channel estimation at the eavesdropper. Extending upon previous works on DCE, this work proposes a general AN design that does not rely on the availability of the null space of the estimated main channel and, thus, does not require the transmitter to have more antennas than the receiver. In particular, the AN covariance matrix, the pilot signal power, and the linear estimator are jointly determined to minimize the channel estimation error at the receiver subject to a constraint below on the channel estimation error at the eavesdropper. The design is obtained by adopting an alternating optimization approach where the AN and pilot signals at the transmitter and the estimator at the receiver are optimized in turn until no further decrease in channel estimation error is observed. The effectiveness of the proposed scheme is demonstrated through computer simulations.

The effectiveness of epidemic routing for information dissemination in energy harvesting wireless sensor networks is examined in this work. Here, information is to be disseminated from a few nodes to a considerable fraction of all other nodes in the network. The use of epidemic routing is motivated by the fact that, when sensors are supported solely by harvested energy, the sensors' availability and the network topology may change dynamically due to the uncertainty of the energy arrival at the sensors. With epidemic routing, a node will receive and forward a packet to all other nodes in its neighborhood whenever it is able to do so. Each node will keep the packet only for a certain time duration depending on its recovery rate. By utilizing only energy harvested from the environment, the transmission radius of each sensor (and, thus, the network connectivity) is affected by its local energy arrival and the time in between transmissions. The less frequently it transmits, the further the distance it is able to reach. Two cases are examined in this work: 1) the case with identical transmission radius and 2) the case with identical inter-transmission time. The first case occurs when sensors transmit with fixed power and the second case occurs when the sensors operate under a fixed sleep-wake cycle or TDMA scheduling. The transmission radius and intertransmission time required to guarantee that a considerable fraction of nodes receive the information is derived for a given sensor density and recovery rate. Computer simulations are provided to validate our theoretical claims.

We consider the block Rayleigh fading multiple-input multiple-output (MIMO) wiretap channel with no prior channel state information (CSI) available at any of the terminals. The channel gains remain constant in a coherence time of T symbols, and then change to another independent realization. The transmitter, the legitimate receiver and the eavesdropper have nt, nrand neantennas, respectively. We determine the exact secure degrees of freedom (s.d.o.f.) of this system when T ≥ 2 min(nt, nr). We show that, in this case, the s.d.o.f. is exactly (min(nt, nr) − ne)+(T − min(nt, nr))/T. The first term can be interpreted as the eavesdropper with neantennas taking away neantennas from both the transmitter and the legitimate receiver. The second term can be interpreted as a fraction of s.d.o.f. being lost due to the lack of CSI at the legitimate receiver. In particular, the fraction loss, min(nt, nr)/T, can be interpreted as the fraction of channel uses dedicated to training the legitimate receiver for it to learn its own CSI. We prove that this s.d.o.f. can be achieved by employing a constant norm channel input, which can be viewed as a generalization of discrete signalling to multiple dimensions.

This paper presents a unifying and systematic framework to solve wireless max-min utility fairness optimization problems in multiuser wireless networks with generalized monotonic constraints. These problems are often challenging to solve due to their nonlinearity and non-convexity. Our framework leverages a general result in nonlinear Perron-Frobenius theory to characterize the global optimal solution of these problems analytically, and to design scalable and fast-convergent algorithms for the computation of the optimal solution. This work advances the state-of-the-art in handling wireless utility optimization problems with nonlinear monotonic constraints, which existing methodologies cannot handle, and also unifies previous works in this area. Several representative applications are considered to illustrate the effectiveness of the proposed framework, including max-min quality of service subject to robust interference temperature constraints in cognitive radio networks, min-max outage subject to outage constraints in heterogeneous networks, and min-max weighted MSE subject to SINR constraints in multiuser downlink system.

This work examines the large-scale deployment of energy-harvesting sensors for the purpose of sensing and reconstruction of a spatially correlated Gaussian random field. The sensors are assumed to be deployed randomly according to a spatially non-homogeneous Poisson point process and our goal is to determine the optimal spatially-dependent sensor density to minimize the field reconstruction error. During each observation period, each sensor takes a local sample of the random field and transmits a scaled version of its observation to the sink node. The sink node then performs reconstruction of the random field based on the received information. The transmit power of each sensor is converted from ambient energy and thus, depends highly on the energy arrival at each location. For the purpose of field reconstruction, the sensors should, on the one hand, be deployed uniformly in space to gather more informative samples, but should, on the other hand, be placed at locations with large energy arrival or large channel gain to the sink node to increase the reliability of data transmission. The optimal sensor intensity and the optimal energy-aware transmission policy, which maximizes the effectiveness of energy usage at the sensors, are determined by minimizing an upper bound of the average mean-square reconstruction error. The efficacy of the proposed scheme is demonstrated through numerical simulations.

This work examines offline and online power control policies for efficient usage of renewable energy in the downlink of a multi-antenna wireless system. Two multiuser beamforming schemes are considered, namely, channel inversion (CI) and maximal ratio transmit (MRT) beamforming schemes. Power control policies are derived for both schemes, respectively, with the goal of maximizing the sum throughput by a deadline subject to energy causality and battery storage constraints. With CI beamforming, the power control problem can be formulated as a convex optimization problem, whose solution can be obtained using the directional water-filling algorithm. With MRT beam-forming, the power control problem becomes non-convex, due to interference between the signals intended for different users, and, thus, is difficult to solve exactly. However, an efficient solution can be obtained by performing successive approximation into a sequence of geometric programming problems, i.e., by employing the condensation method. Offline power control policies are first derived assuming non-causal knowledge of the energy arrival and channel coefficients over time. Online power control policies are then proposed based on observations gained from the offline policy. The performance of the proposed schemes are demonstrated through numerical simulations.

Multirelay networks exploit spatial diversity by transmitting user's messages through multiple relay paths. Most works in the literature on cooperative or relay networks assume that all terminals are fully cooperative and neglect the effect of possibly existing malicious relay behaviors. In this work, we consider a multirelay network that consists of both cooperative and malicious relays, and aims to obtain an improved understanding on the optimal behaviors of these two groups of relays via information-theoretic mutual information games. By modeling the set of cooperative relays and the set of malicious relays as two players in a zero-sum game with the maximum achievable rate as the utility, the optimal transmission strategies of both types of relays are derived by identifying the Nash equilibrium of the proposed game. Our main contributions are twofold. First, a generalization to previous works is obtained by allowing malicious relays to either listen or attack in Phase 1 (source-relay transmission phase). This is in contrast to previous works that only allow the malicious relays to listen in Phase 1 and to attack in Phase 2 (relay-destination transmission phase). The latter is shown to be suboptimal in our problem. Second, the impact of CSI knowledge at the destination on the optimal attack strategy that can be adopted by the malicious relays is identified. In particular, for the more practical scenario where the interrelay CSI is unknown at the destination, the constant attack is shown to be optimal as opposed to the commonly considered Gaussian attack.

A channel-and-sensing-aware channel access (CSCA) policy is proposed for multi-channel interweave cognitive radio systems, where multiple secondary users (SUs) are competing for transmission to a common access point. The proposed CSCA policy consists of two key elements: (i) a decentralized channel selection policy that allows each SU to utilize knowledge of both the spectrum occupancy information and its local channel state information to make local channel access decisions and (ii) a channel reservation policy that allows each SU to compete for use of its selected channel by emitting short reservation packets at the beginning of each frame. In the reservation period, a channel-aware splitting algorithm is utilized to resolve collision among SUs that are competing for the same channel. The splitting procedure is optimized with respect to SUs' channel selection policy and ensures that the SU with the best channel quality prevails when collision is resolved. The CSCA policy is derived with the goal of maximizing the SU's throughput subject to a constraint on the probability of collision with primary users (PUs). To satisfy the collision probability constraint, a minimum channel gain threshold is set on each channel to limit the probability that the channel is accessed by SUs. An iterative algorithm is proposed to optimize the parameters in the channel selection and reservation policies, and a low-complexity policy is devised for use in systems with large numbers of channels. The proposed CSCA policies allow SUs to exploit optimally the tradeoff between spectrum availability and channel quality.

We propose a reservation-based channel access policy for multi-channel cognitive radio networks. To enhance the throughput of secondary users (SUs), SUs are allowed to select channels opportunistically according to both the local channel state information (CSI) and the spectrum sensing outcomes. SUs will then compete for the right of transmission on the chosen channel by emitting reservation packets to the access point sequentially according to their local CSI. We further devise a proper threshold on channel gains such that only the SUs whose channel gains are sufficiently high can reserve channels and the interference from SUs to the licensed network can be limited. A channel aware splitting algorithm is adopted to schedule the SU with the highest channel gain to transmit at each time instant. From simulations, the proposed channel access policy outperforms the policies that take into consideration only CSI or sensing outcomes.

In this paper, we consider the joint design of training and data transmission signals for wiretap channels where the transmitter is to send a secrect massage to the receiver without being intercepted by the eavesdropper. The celebrated secrecy beamforming scheme, which may or may not be assisted by artificial-noise (AN), is adopted in the data transmission phase to achieve this task. The achievable secrecy rate for practical systems with channel estimation error is first derived. Based on the achievable secrecy rate, we find the optimal tradeoff between the energy used for training and data signals. The optimal solutions in the low and high energy regimes are characterized analytically. We show that AN does not provide any advantages in the low energy regime, while it may have significant impact in the high energy regime. Numerical results are presented to verify our theoretical claims.

In this work, we consider an interference-mitigation based cognitive radio system where a secondary transmitter is to communicate with its corresponding receiver without affecting the communication between a primary transmitter-receiver pair. In this case, the secondary transmitter must satisfy a coexistence constraint which requires that no rate degradation occurs at the primary user (PU), even when the latter utilizes only a single-user decoder. To achieve a non-zero rate of the secondary user (SU) under this constraint, Jovicic and Viswanath previously proposed a scheme (referred to as the JV scheme) that utilizes relaying by the secondary transmitter to overcome the interference caused by the simultaneous transmission of the SU's message. In this case, the interference caused by the signals corresponding to PU's message at the secondary receiver (from both the direct and the relay links) is mitigated by employing dirty paper coding (DPC) at the secondary transmitter. However, the interference caused by the SU's message at the primary receiver is not eliminated in this case and, thus, will limit the power (and, hence, the rate) that can be used by the secondary transmitter to transmit its own message. In our work, the use of clean relaying by the secondary transmitter and/or receiver (i.e., relaying without simultaneous transmission of SU's own message) is proposed to improve the quality of the relayed signal and, thereby, increases the rate achievable by the SU. Two improved transmission schemes are proposed: (i) clean relaying by secondary transmitter (CT) and (ii) clean relaying by secondary transmitter and receiver (CTR). The CT scheme utilizes DPC to mitigate interference at the secondary receiver whereas the CTR scheme utilizes coding for multiple access channels with common messages to enable decoding of both PU's and SU's messages at the secondary receiver. The CT scheme can be viewed as a generalization of the JV scheme and, therefore, performs at least as well as the latter. The CTR scheme, on the other hand, is shown to outperform the CT scheme in terms of the multiplexing gain achievable under full channel state information at the transmitter (CSIT) and in terms of the rate achievable with statistical CSIT. Numerical simulations are provided to illustrate these advantages.

Cooperative relaying refers to a technique that allows the source to transmit its messages to the destination via the relaying of multiple cooperative partners and exploits the spatial diversity gains inherent in multiuser wireless systems. In this work, we examine cooperative networks with cooperative and malicious relays and determine the optimal behavior for both kinds of relays using a game-theoretic approach. We formulate the problem as a zero-sum game, determining the optimal relay strategies by identifying the Nash equilibrium of the proposed game under individual power constraints. We prove, with Rayleigh fading, the optimal strategy for malicious relays is to transmit independent Gaussian noise using full power at each relay and for cooperative relays is to independently re-encode the source's message into Gaussian signals and forward them to the destination. Inter-cooperation among relays is unnecessary. The results are verified through numerical simulations.

The use of opportunistic multicast scheduling (OMS) for systems with multiple multicast groups is examined in this work. Here, a general downlink scenario where a single basestation is to transmit independent data streams to multiple groups of users is considered. Within each group, common information is transmitted, but between groups, the source information is independent. In the literature, the OMS scheme has been proposed for the case where all users belong to a single multicast group. This scheme allows the base-station to transmit at a higher rate in each time slot by scheduling the transmission to only a subset of users in the group. By encoding the data stream using fountain codes, each user will be able to decode the message whenever a sufficient number of data bits are received, regardless of which specific time slots the user was able to receive in. In this work, the use of OMS is extended to systems with multiple multicast groups and the so-called multicast throughput region is defined to characterize the performance of the multigroup OMS scheme. The analytical results based on extreme value theory are utilized to accurately predict the optimal multicast group-sizes and the optimal power allocation policy when maximizing the weighted sum throughput. By choosing the weights appropriately, it is shown that the method can be further utilized to ensure proportional fairness among the multiple multicast groups. The efficacy of the proposed OMS schemes is shown through numerical simulations.

Information theoretic security, also called secrecy analysis, provides theoretical limit for secret data transmission even in wireless networking environment, and it is more focused in these days because of increasing number of private information transmission through wireless channel and its inherent broadcasting characteristic. However, many studies in secrecy analysis assume that there exists only one eavesdropper or there is no cooperation among multiple eavesdroppers, which makes research results difficult to be applied in real situations. In this paper, we conduct a game theoretic analysis of eavesdropper cooperation in MISO wireless communication system where a secret information sender, Alice, has a counter-strategy against eavesdropping. We assume that Alice has a method for preventing eavesdroppers from overhearing. It is triggered by eavesdropper detection and degrades eavesdropping channel by forwarding artificial noise. For this reason, eavesdroppers' cooperation can pose a high risk to them and we utilize game theory to investigate eavesdropper behavior based on mutual information I (X; Yeve) between the input X from Alice and the output Yeveto an eavesdropper. We derive that non-cooperation is Nash Equilibrium in one shot eavesdropper cooperation game and show necessary conditions for eavesdropper cooperation in infinitely repeated game.

Recently, in multi-antenna wireless systems, the use of artificial noise (AN) in training and data transmission phases has been respectively proposed to achieve performance discrimination between a legitimate receiver (LR) and an unauthorized receiver (UR). For data transmission, an AN-aided beamforming (ANBF) scheme has been proposed where the message is sent towards LR using beamforming while AN is imposed in the null space of LR's channel to disrupt UR's reception. For channel estimation, the so-called discriminatory channel estimation (DCE) scheme has been proposed where a multi-stage training scheme is employed and AN is imposed in the null space of the estimated LR's channel obtained in previous stages to degrade the channel estimation performance of UR. In this work, the optimal power allocation between DCE and ANBF (as well as AN in both phases) is derived with the goal of maximizing the receive signal-to-interference-plus-noise ratio (SINR) of LR subject to a constraint on the maximum achievable SINR of UR. The simulation results show that, with the joint power allocation of DCE and ANBF, the SINR at LR and UR can be effectively discriminated even when UR is equipped with more antennas than the transmitter. Moreover, it is observed that the proposed joint DCE and ANBF scheme would allocate more power to the channel estimation phase compared with that using conventional channel estimation (without considering URs) in the training phase.

This paper examines the use of two-way training in multiple-input multiple-output (MIMO) wireless systems to discriminate the channel estimation (and, thus, data detection) performance between two receivers, namely, a legitimate receiver (LR) and an unauthorized receiver (UR). This work extends upon the discriminatory channel estimation (DCE) proposed in our prior work, where it was previously assumed that training signals can only be sent by the transmitter. The DCE design criterion is to minimize the channel estimation error at the LR while confining the channel estimation error at the UR above a minimum level. In the case of two-way training, training signals can first be transmitted on the reverse link to enable channel estimation at the transmitter and allow the transmitter to insert artificial noise (AN) along with the training signal in the forward link to disrupt the training at the UR, while minimizing the interference on the LR. The optimal power allocation between training and AN signals is devised for systems that are subject to both average and peak power constraints. Numerical results demonstrate the efficacy of the proposed two-way training scheme when used in discriminating the performances between LR and UR.

The advantage of using channel feedback information to determine the optimal pilot placement is examined for amplify-and-forward OFDM relay links. This work extends upon the previously proposed feedback-aided pilot placement scheme to systems that employ subcarrier pairing at the relay. With channel feedback, the pilot subcarriers can be properly chosen to reduce the channel estimation error and increase the effective signal-to-noise ratio (SNR) of data subcarriers. When subcarrier pairing is employed, the relay reselects the subcarriers used to forward the source's data based on the effective SNR of each subcarrier, which in order is affected by the pilot placement policy. Here, the minimum effective SNR observed by all subcarriers' data is utilized as the performance measure since it is what dominates the symbol-error-rate (SER) at high SNR. For a given pilot placement, the best subcarrier pairing scheme is found to be the inverse mapping of the source-to-relay (SR) and relay-to-destination (RD) channels based on the quality of their effective SNR. A lower bound is derived for the gain achievable with any feedback-aided pilot placement scheme. Due to the complexity of finding the optimal pilot placement, a more efficient iterative pilot relocation (IPR) scheme is then proposed. In this scheme, the subcarrier with the worst equivalent channel is used to replace one of the originally selected pilot locations in each iteration. The efficacy of the proposed policy is demonstrated through numerical simulations.

The impact of quantized channel direction information (CDI) on the achievable secrecy rate is studied for multiple antenna wiretap channels. By assuming that the eavesdropper's channel is unknown at the transmitter, we adopt the transmission scheme where artificial noise (AN) is imposed in the null space of the legitimate receiver's channel to disrupt the eavesdropper's reception. It has been shown that, in the ideal case where perfect CDI is available at the transmitter, the achievable secrecy rate can be made arbitrarily large by increasing the transmission power. However, when only quantized CDI is available, the AN that was originally intended to jam the eavesdropper may now leak into the legitimate receiver's channel, causing significant secrecy rate loss. For a given number of feedback bits B and transmission power P, we derive the optimal power allocation among the message-bearing signal and the AN to maximize the secrecy rate under AN leakage. We show that, when B is sufficiently large, one should allocate power evenly among the message-bearing signal and the AN; whereas when B is small, one should be more conservative in allocating power to the AN. Moreover, by showing that the achievable secrecy rate under quantized CDI is bounded by a constant, we derive a scaling law between B and P that is necessary to maintain a constant secrecy rate loss compared to the perfect CDI case. The scaling of B is shown to be logarithmic of P. These results are first derived for the multiple-input single-output single-antenna-eavesdropper scenario and are later extended to the multiple-input multiple-output multiple-antenna-eavesdropper case. Numerical simulations are provided to verify our theoretical claims.

Physical layer multicasting with opportunistic user selection and optimized space-time transmission is examined in this work. We consider a multi-antenna downlink scenario where a multi-antenna base-station transmits common information to a given set of users, each with a single receive antenna. In the past, most existing multi-antenna multicasting solutions have been restricted to spatial multiplexing and transmit beamforming. Here, we adopt the opportunistic multicast scheduling (OMS) scheme, where messages are encoded using fountain codes and an optimal subset of users is targeted for reception in each time slot. Capitalizing on extreme value theory, we derive analytical expressions for the average system throughput and utilize this result to obtain the optimal user selection ratio for both spatial multiplexing and transmit beamforming scenarios. To better utilize the channel state information available at the transmitter, we further consider an optimized space-time transmission (OST) scheme where the statistics of the space-time codeword is chosen to maximize the rate of the worst selected user. For reduced complexity, we utilize a semi-orthogonal user selection algorithm to determine the spatial dimensions of the signal and derive optimal power allocation across these dimensions. The proposed OST is a generalization of transmit beamforming and spatial multiplexing and, thus, outperforms both conventional schemes.

In this work, we examine the impact of quantized channel direction feedback on the achievable secrecy rate of multiple-antenna wiretap channels. To guarantee secrecy without knowledge of the eavesdropper's channel, we consider the transmission scheme proposed by Goel and Negi where artificial noise (AN) is imposed in the null space of the legitimate receiver's channel to disrupt the eavesdropper's reception. When perfect knowledge of the legitimate receiver's channel direction information (CDI) is available at the transmitter, the secrecy rate can be made arbitrarily large by increasing the transmission power. However, perfect CDI is difficult to achieve in practice due to rate-limitations on the feedback channel. When only quantized CDI is available at the transmitter, the AN that is only intended to disrupt the eavesdropper's reception may leak into the legitimate receiver's channel, causing significant loss in secrecy rate. In fact, we show that the achievable secrecy rate under quantized CDI is bounded by a constant even as the transmission power increases. To guarantee a constant rate loss compared to the perfect CDI case, we show that the number of feedback bits must scale at least logarithmically with the transmission power. These theoretical claims are verified by computer simulations.

Subcarrier allocation, power allocation, and partner selection algorithms are examined for amplify-and-forward cooperative multicarrier systems. Consider a network that consists of multiple cooperative pairs employing a two-phase cooperation scheme where the users first transmit their own messages on each of their subcarriers in Phase I and either retransmit their own messages or relay the messages of their partners in Phase II, i.e., the cooperative phase. In general, subcarrier allocation and partner selection problems are known to be intractable for systems with large number of users and subcarriers. In this work, we propose a mathematically tractable approach to address these issues and show its effectiveness compared to existing algorithms. Specifically, we consider a relaxed problem formulation, where each subcarrier is able to retransmit or relay for all other subcarriers in Phase II over orthogonal channels, and derive the optimal power allocation for this scenario. Efficient subcarrier allocation policies can then be derived based on the power allocation result and a sufficient condition is obtained to determine when cooperation is helpful among two users. A partner selection algorithm is then devised based on the sufficient condition. Given the subcarrier allocation scheme, the optimal power allocation among subcarriers in the cooperative phase can be shown to be a convex optimization problem and, thus, can be solved efficiently using standard convex optimization tools. The efficacy of the proposed algorithms is demonstrated through numerical simulations.

This paper proposes a collaborative change detection scheme for cognitive radio networks, where secondary users can collaboratively and efficiently detect the departure and arrival of the primary user's signal. Specifically, we consider a system where each of the secondary users in the network can make a local detection in each time frame and forward its decision to the fusion center where a CUSUM-based change detector is used to efficiently and reliably detect the presence of the primary user. The change detector is developed to maximize the throughput of the secondary network subject to constraints on the interference imposed on the primary user. In accordance with the considered system model and the adopted detection scheme, this paper also analyzes the average throughput of the secondary network and the average interference time to primary users. The simulation results show that the proposed spectrum sensing scheme outperforms the fixed-sample-size detector in terms of the average throughput of the secondary network.

We examine a cooperative uplink CDMA network where multiple sources simultaneously access the cooperative channel using different spreading codes and compete for the resources at the relays. To efficiently utilize the limited energy and bandwidth resources at the relays, we propose in this work a decentralized reduced-rank multiuser relaying (RR-MUR) where the data received at each relay is compressed into a limited number of dimensions and forwarded with optimal power allocation among the different dimensions. The proposed scheme follows upon our previous work where a centralized strategy has been proposed. Specifically, we propose the minimum mean square error principle component analysis (MMSE-PCA) approach that can be used to design the relay precoders in a decentralized manner. We show that the MMSE-PCA scheme is the optimal relay precoder design when only one relay exists in the network. Through numerical simulations, we show that the proposed scheme outperforms the Q-selection scheme, where only a selected group of sources are served by each relay.

Multiuser and cooperative diversity gains have been studied extensively in recent years to improve communication reliability and throughput of wireless networks. Multiuser diversity gains have been exploited by scheduling users with the best channel to transmit in each time slot while cooperative diversity gains have been exploited by having users relay each other's messages to the destination. Most works in the literature consider these advantages separately and in systems that rely on a central scheduler. In this work, we show that the throughput of distributed random access systems can be improved by exploiting both cooperative and multiuser diversity where the transmission probabilities of users are adjusted according to their local channel state information. The fully-loaded stability region is derived as the performance measure of our proposed transmission strategy. The optimal transmission control that maximizes the fully-loaded region is shown to be a threshold-like function. That is, each user shall transmit only when its effective channel is sufficiently reliable. The effective channel in this case is defined as each user's contribution to the weighted sum throughput of the network.

In recent years, several models introduced in mathematical biology and natural science have been used as the foundation of networking algorithms. These bio-inspired algorithms often solve complex problems by means of simple and local interactions of individuals. In this work, we consider the development of decentralized scheduling in a small network of self-organizing devices that are modeled as pulse-coupled oscillators (PCOs). By appropriately designing the dynamics of the PCO, the network of devices can converge to a desynchronous state where the nodes naturally separate their transmissions in time. Specifically, by following Peskin's PCO model with inhibitory coupling, we first show that round-robin scheduling can be achieved with weak convergence, where the nodes' transmissions are separated by a constant duration, but the differences of their local clocks continue to shift over time. Then, by having each node accept coupling only from the pulses emitted by a subset of neighboring nodes, we show that it is possible to achieve strict desynchronization, where the difference between local clocks remain fixed over time. More interestingly, by having each node maintain two local clocks, we show that it is possible to further achieve proportional fair scheduling, where the time alloted to each node is proportional to their demands. The convergence of these algorithms is studied both analytically and numerically.

In cooperative systems, users achieve spatial diversity and multihop gains by transmitting packets over multiple independent fading paths provided by their partners. Most previous works on cooperative communications focus on the physical layer aspects such as coding, modulation, and transceiver signal processing techniques. In this work, we study the advantages of user cooperation from a MAC layer perspective and devise queueing strategies to exploit cooperative gains in random access networks. Based on the conventional slotted ALOHA protocol, we propose a simple cooperative transmission mechanism for a two-user cooperative pair. We derive the two-user stability region of the proposed system and show the improvements compared to noncooperative systems. The benefits can be attributed to both physical layer cooperation, where users with good channels may relay for those with bad channels, and MAC layer cooperation, where system parameters can be chosen to enhance cooperation and reduce competition. Then, we extend the proposed strategy to a finite-user system that consists of multiple cooperative pairs. By treating each pair as a single transmission entity, we derive inner bounds for the finite-user stability region and propose a ranking system to characterize the transmission entities' relative tendency of being stable (or unstable).

In this work, we analyze the throughput, delay, and energy efficiency of random access sensor networks that employ Slepian-Wolf distributed source coding (DSC) and study the impact of MAC protocol design on these performances. Suppose that N sensors observe correlated information from the environment and that their local data are sent to a sink node through direct transmission links. To eliminate data redundancy, we allow sensors to encode their local messages using the Slepian-Wolf DSC method. We assume that sensors are ordered sequentially and that each sensor's message is compressed by exploiting the joint data statistics between itself and the sensors earlier in the sequence. Due to properties of DSC, a message can be decoded only if all messages transmitted by sensors earlier in the sequence are successfully decoded. The loss of one message may cause failure in decoding many other messages. Hence, the sensors' messages are not of equal importance and should be given different transmission priorities by the MAC. Based on the properties of DSC, we provide analytical tools to study the throughput, delay, and energy efficiency of slotted ALOHA random access protocols. Utilizing these tools, we compare between the performance of different transmission probability assignments and study the impact of MAC protocol design on the performance of these systems. Furthermore, an adaptive MAC protocol is also proposed to improve upon the throughput and delay of the original system.

Optimized opportunistic multicast scheduling (OMS) is studied for cellular networks, where the problem of efficiently transmitting a common set of fountain-encoded data from a single base station to multiple users over quasi-static fading channels is examined. The proposed OMS scheme better balances the tradeoff between multiuser diversity and multicast gain by transmitting to a subset of users in each time slot using the maximal data rate that ensures successful decoding by these users. We first analyze the system delay in homogeneous networks by capitalizing on extreme value theory and derive the optimal selection ratio (i.e., the portion of users that are selected in each time slot) that minimizes the delay. Then, we extend results to heterogeneous networks where users are subject to different channel statistics. By partitioning users into multiple approximately homogeneous rings, we turn a heterogeneous network into a composite of smaller homogeneous networks and derive the optimal selection ratio for the heterogeneous network. Computer simulations confirm theoretical results and illustrate that the proposed OMS can achieve significant performance gains in both homogeneous and heterogeneous networks as compared with the conventional unicast and broadcast scheduling.

Pulse coupled oscillators (PCOs) are pulsing devices that pulse individually in a periodic manner but alter their pulsing patterns in response to the pulsing of other nodes. A network of PCOs can produce a number of different dynamics from their pulsing activities, among which the synchrony of pulsing is perhaps the most well known. In this paper, we study the primitive that falls into the class of ldquodesynchronizationrdquo. Specifically, we propose a simple pulse-coupling mechanism that allows each node in the network to converge to a desynchronized state where the nodes will pulse periodically with a constant spacing among each others firing times. We discuss the convergence of the PCO mechanism and propose to apply this primitive to resolve contention in the reservation phase of a reservation-based MAC protocol.

Optimized opportunistic multicast scheduling (OMS) has been studied previously by the authors for homogeneous cellular networks, where the problem of efficiently transmitting a common set of data from a single base station to multiple users that have identical channel statistics was examined. It has been demonstrated that OMS can achieve significant performance improvement by exploiting the optimal tradeoff between multiuser diversity and multicast gain. In this work, we extend our studies to heterogeneous networks with users subject to different channel statistics. Specifically, we consider a single cell wireless network with users uniformly distributed in a circular region around the base station. Since users with low SNR are the ones that hinder system throughput, we argue that system performance may be predicted by the behavior of users in the outmost ring of the cell, which are approximately homogeneous. Using extreme value theory and results obtained from the homogeneous case, we determine the optimal user selection ratio for a homogeneous ring of users near the edge of the cell and then use it to derive the optimal selection ratio over the entire heterogeneous network. Simulations confirm theoretical results and illustrate the effectiveness of the proposed scheme.

Cooperative relaying has been studied extensively in the literature to exploit spatial diversity gains by having each source transmit its messages through multiple independently fading relay paths. In multiuser systems where multiple sources may access the same set of relays simultaneously, CDMA spreading techniques along with multiuser detection schemes have been proposed in the literature to eliminate multiple access interference (MAI). In order for each relay to forward messages from all sources, a tremendous increase in dimensions (or spreading gain) is used to accommodate the relay transmissions. To reduce the required bandwidth or dimensions, we propose a reduced-rank multiuser relaying (RR-MUR) scheme where the data received from multiple users are first compressed into lower dimensions before being retransmitted. More specifically, linear compression precoders at the relays and decoder at the destination are found by imposing a recursive joint optimization procedure with the objective of minimizing the mean square error (MMSE) of the estimate at the destination. We show through numerical simulations that the RR-MUR scheme outperforms the often adopted Q-selection scheme in terms of increased spectral efficiency.

The impact of imperfect channel state information (CSI) on the transmission control of channel-aware slotted ALOHA networks is studied in this work. By taking into consideration the statistics of the channel estimation error to maximize the achievable stable throughput, we obtain the optimal transmission control policy that determines the transmission probability, rate, and power that should be adopted under each channel state. Specifically, with imperfect CSI, users allocate transmission probabilities to channel states that allow for high probability of successful transmission, which is similar to the case with perfect CSI, but the rate allocation must be performed more conservatively in order to avoid transmission error due to imprecise channel estimates. The policies are first developed for single carrier systems but later extended to multi-carrier systems with maximum per-user power constraints. The asymptotic stable throughput is analyzed for both the single and multiple carrier systems as the number of users goes to infinity. We observe that, with channel estimation error, the proposed transmission control policy that takes into consideration the error statistics may significant increase the throughput compared to strategies that are derived under the perfect CSI assumption.

Physical-layer secrecy in wireless fading channels has been studied extensively in recent years to ensure reliable communication between the transmitter and the receiver subject to constraints on the information attainable by the eavesdropper. With multiple antennas at the transmitter, Goel and Negi proposed the use of artificial noise (AN) in the null space of the receiver's channel to corrupt the eavesdropper's reception, which helps guarantee secrecy without knowledge of the eavesdropper's channel. It has been shown that the secrecy capacity can be made arbitrarily large by increasing the transmission power, when perfect knowledge of the receiver's channel direction information (CDI) is available. However, in practice, this is not possible due to rate-limitations on the feedback channel. This paper studies the impact of quantized channel feedback on the secrecy capacity achievable with artificial noise.We show that, with imperfect CDI at the transmitter, the AN that was originally intended only for the eavesdropper may leak into the receiver's channel and limit the achievable secrecy rate. To maintain a constant performance degradation, the number of feedback bits must increase at least logarithmically with the transmission power. Moreover, we observe that the portion of power allocated to the transmission of AN should decrease as the number of quantization bits decreases to alleviate the degradation due to noise leakage.

The impact of imperfect channel state information (CSI) on the transmission control of channel-aware slotted ALOHA networks is studied in this work. By taking into consideration the statistics of the channel estimation error to maximize the achievable stable throughput, we obtain the optimal transmission control policy that determines the transmission probability, rate, and power that should be adopted under different channel states. Specifically, with imperfect CSI, we find that high transmission probabilities should be assigned to channel states that allow for high probability of successful transmission (which may be affected by the estimation errors), but rate allocation must be performed more conservatively (compared to the case with perfect CSI) in order to avoid transmission errors due to imprecise channel estimates. The policies are first developed for single carrier systems and then extended to multicarrier systems with maximum per-user power constraints. The asymptotic maximal stable throughput is analyzed for both the single and multiple carrier systems as the number of users goes to infinity. We observe that, with error in the channel estimate, the proposed transmission control policy that takes into consideration the error statistics may significantly increase the throughput compared to strategies that are derived under the perfect CSI assumption.

In this work, we study the finite-user stability region of a slotted ALOHA random access network with cooperative users. The network consists of multiple cooperating pairs where the users in each pair is allowed to cooperate by relaying each others' messages to the access point (AP). Spatial diversity gains are achieved since the packet of a cooperative user can be transmitted through independent fading channels, eg. the direct transmission path and the cooperative relaying path. Most works in the literature on cooperative communications focus on the physical layer aspects such as coding, modulation, transceiver signal processing etc. In this paper, we study the advantages of user cooperation from a MAC layer perspective and devise queueing strategies to exploit the cooperative diversity gains in a random access network. Extending upon our previous results for the two-user case, we study the stability of the finite-user cooperative system that consists of multiple cooperating pairs. By treating each cooperative pair as a transmission entity, we derive inner bounds for the finite-user stability region and propose a ranking system to characterize the transmission entities' relative tendency of being stable (or unstable).

In this work, we study the effect of imperfect channel-state information (CSI) on the stability and delay of a two-user channel-aware slotted ALOHA system. We assume that the channel is quantized into two states and channel estimation error may occur between channel states. By taking into account the estimation error, we derive the optimal transmission control function that adjusts the users' transmission probabilities based on the estimated channel state. To evaluate the performance, we derive the stability region and average delay of the system and show the loss in performance due to channel estimation errors.

In this paper, we examine the uplink of a cooperative CDMA network, where users cooperate by relaying each other's messages to the base station. When spreading waveforms are not orthogonal, multiple access interference (MAI) exists at the relays and the destination, causing cooperative diversity gains to diminish. To address this issue, we adopt the multiuser detection (MUD) technique to mitigate MAI in achieving the full advantages of cooperation. Specifically, the relay-assisted decorrelating multiuser detector (RAD-MUD) is proposed to separate interfering signals at the destination with the help of preceding at the relays along with pre-whitening at the destination. Unlike the conventional zero-forcing (ZF) precoder or the decorrelating MUD, the proposed RAD-MUD experiences neither power expansion at the relays nor noise amplification at the destination. Three cooperative transmission strategies are considered on top of RAD-MUD; namely, transmit beamforming, selective relaying and distributed space-time coding. Since the reliability of each source-relay and/or relay-destination links are different, relay transmissions are weighted accordingly in our schemes to further combat MAI. The advantages of RAD-MUD over ZF precoding and other existing cooperative MUD schemes are shown through computer simulations.

Group testing has been used in many applications to efficiently identify rare events in a large population. In this paper, the concept of group testing is generalized to applications with correlated source models to derive scheduling policies for sensors' adopting cooperative transmissions. The tenet of our work is that in a wireless sensor network it is advantageous to allocate the same channel dimensions to all sensor sources that have the same response to a sequence of queries or tests. That is, nodes that have the same data attributes should transmit as a cooperative super-source. Specifically, we consider the case where sensors' data are modeled spatially as a one-dimensional Markov chain. Two strategies are considered: the recursive algorithm and the tree-based algorithm. The recursive scheme allows us to illustrate the performance of group testing for finite populations while the tree-based algorithm is used to derive the achievable scaling performances of the class of group testing strategies as the number of sensors increases. We show that the total number of queries required to gather all sensors' data scales in the order of the joint entropy. A further generalization of this concept provides the basis of deriving efficient data-gathering algorithms for correlated sources.

The use of multi-user detection (MUD) in a cooperative CDMA network is investigated for the uplink in synchronous CDMA systems. Suppose that, at any instant in time, part of the users serve as sources while the others serve as relays. The proposed MUD scheme decorrelates the sources' messages at the destination with the help of precoding at the relays. Three cooperation methods are considered: (1) transmit beamforming, (2) selective relaying and (3) distributed space- time coding. The optimal weighting factors of each method are determined by taking the quality of the source-to-relay and/or the relay-to-destination links into account. We show that significant improvements in terms of the spatial diversity and multiple-access interference (MAI) mitigation can be attained when precoding is employed at the relays to aid the decorrelation at the destination. The advantages are even more pronounced when selective relaying is combined with the other two schemes.

Three power control schemes for the space-time coded amplify-and-forward (AF) relaying scheme targeting at wireless sensor network applications are examined and compared. The opportunistic scheme performs the best by considering the signal-to-noise-ratio (SNR) of the received signal. However, if the power for the relay is limited, the performance of the opportunistic scheme degrades due to the loss of active relay nodes that have better channel conditions. Since the battery lifetime of nodes for wireless sensor networks is limited and the loss of relay nodes is critical to system performance, we propose an SNR-constrained power reduction scheme to prolong the relay lifetime for the opportunistic scheme. It is demonstrated by computer simulation that the opportunistic scheme with SNR-constrained power reduction is power efficient and the relay lifetime of dense relay networks can be significantly prolonged.

A decentralized sensor positioning algorithm is proposed using an adaptive weighted-interpolation method. The proposed method utilizes in-network processing among sensors to compute the location of the target, which is in contrast to most existing algorithms that rely on the joint processing of raw measurements from all sensors at a central server. Specifically, the target location is computed by taking the weighted average of the local estimates based on the sensors' reliability. The average is attained iteratively with each iteration being performed by a different sensor in the network. During each iteration, a sensor computes a new estimate of the target's location based on its own observation and the most recent update passed over by the sensor responsible for the previous iteration. The newest location estimate and the update process is circulated among the sensors in the close-vicinity of the target, similar to that of a token- ring topology. A message-passing protocol is proposed for the inter-sensor communication and is used to adaptively select the participating sensors as the target moves around the area. Energy and bandwidth efficiency is achieved since the system need not expend large amounts of resources in transmitting the raw data to the central server. Simulation results demonstrate the fast convergence of the iterative method and the effectiveness of the proposed positioning scheme compared to other methods.

In this work, we analyze the stability region of the slotted ALOHA random access network with cooperative users. With cooperation, the packets corresponding to each user can be transmitted to the destination through the multiple relaying paths formed by the other users. The diversity introduced by the multi-path relaying reduces significantly the probability of outage. We propose two queuing strategies for the cooperative system. In the first strategy, we assume that each user is equipped with two buffers: a source buffer, to record its own packets, and the relay buffer, to store the packets received from its partner. When both buffers are non-empty, the user chooses randomly among the two buffers based on a predetermined probability. An inner bound of the stability region is derived and is shown to outperform the non-cooperative system. In the second case, we assume that each user utilizes only one buffer to store all packets. The relaying packets are stored at the front of the queue at the cooperative user upon arrival. The second scheme outperforms the non-cooperative case only for certain channel conditions and arrival rates. However, the scheme has low complexity and can be implemented with little changes to the original system.

Discrete power allocation strategies for amplify- and-forward cooperative networks are proposed based on selective relaying methods. The goal of power allocation is to maximize the network lifetime, which is defined as the duration of time for which the outage probability at the destination can be maintained above a certain level. The discrete power levels enable a low cost implementation and a close integration with high speed digital circuits. We propose three power allocation strategies that take into consideration both the channel state information (CSI) and the residual energy information (REI) at each node. By modeling the residual energy of each node as the states of a Markov Chain, we are able to derive the network lifetime analytically by computing the expected number of transitions to the absorbing states, i.e., the energy states for which the outage probability is no longer achievable. The performance of the three strategies are compared through numerical simulations and a significant improvement in network lifetime is shown, when compared with the case considering only the local CSI.

The throughput performance of adaptive multi-input multi-output orthogonal frequency division multiplexing (MIMO-OFDM) system without channel station information (CSI) in priori is investigated in this research. First, throughput scaling factor (pre-log factor) of an nttransmit antennas, nrreceive antennas MIMO-OFDM with total N subcarriers is asymptotically equal to n* (1- n* L/(MN)), where n* = min(nt, nr) and M is the number of OFDM blocks experienced a constant channel. Second, feedback directed adaptive schemes can achieve the pre-log factor of the lower bound. Third, diversity-multiplexing gain tradeoff of variable-rate systems has been shown to have the same behavior as MIMO systems employed with random Gaussian codewords. Finally, it is shown that throughput of OFDMA with opportunistic scheduling performs better than traditional OFDM-TDMA scheduling with constant enhancement independent of SNR in multiuser downlink environment.

Power allocation strategies are devised to maximize the network lifetime of amplify-and-forward (AF) cooperative networks. The paper considers the scenario where one source and multiple partners cooperate to transmit messages to the destination. The powers emitted by the users are subject to the SNR requirement at the destination. First, the power allocation strategy that demands the minimum instantaneous aggregate transmit power of all cooperating partners is described and analyzed. The optimal solution results in a form of selective relaying; namely, the user with the best channel condition is selected to help in relaying the message. However, this instantaneous power minimization strategy does not necessarily maximize the lifetime of battery-limited systems. Then, three AF cooperative schemes were proposed to exploit the channel state information (CSI), the residual battery energy and the QoS requirement. It is shown that the network lifetime can be extended considerably by taking all these three factors into account.

The reliability and delay of Slepian-Wolf distributed source coding (DSC) in sensor networks is analyzed under the random access setting. Consider a network of N sensors that observes correlated information from the environment and sends the local data to a central processor through direct transmission links. Due to the low message rate in sensor networks, we adopt the slotted ALOHA random access protocol where the time is divided into synchronized time slots and each sensor is allowed to access the time slots with independent probabilities. To eliminate the redundancy in the transmitted data, the sensors encode the local messages based on the Slepian-Wolf DSC method. Specifically, we assume that the sensors' message are encoded with a sequential dependency among each other and, thus, must be decoded one after the other such that the decoding of a particular message is reliant on the successful decoding of all the messages encoded earlier in the sequence. In this case, the loss of one message may result in the failure of other messages and the delay in the successful decoding of a particular message also varies from sensor to sensor. In this work, we analyze the performance of Slepian-Wolf DSC in random access networks in terms of the rate of successful decoding and the average delay of each message. Specifically, we propose and compare different transmission probability assignments for DSC in the ALOHA network and emphasize the importance of the MAC design.

Broadcasting is a method that allows the distributed nodes in a wireless sensor network to share its data efficiently among each other. Due to the limited energy supplies of a sensor node, energy efficiency has become a crucial issue in the design of broadcasting protocols. In this paper, we analyze the energy savings provided by a cooperative form of broadcast, called the opportunistic large arrays (OLA), and compare it to the performance of conventional multi-hop networks where no cooperation is utilized for transmission. The cooperation in OLA allows the receivers to utilize for detection the accumulation of signal energy provided by the transmitters that are relaying the same symbol. In this work, we derive the optimal energy allocation policy that minimizes the total energy cost of the OLA network subject to the SNR (or BER) requirements at all receivers. Even though the cooperative broadcast protocol provides significant energy savings, we prove that the optimum energy assignment for cooperative networks is an NP-complete problem and, thus, requires high computational complexity in general. We then introduce several suboptimal yet scalable solutions and show the significant energy-savings that one can obtain even with the approximate solutions

The goal of group testing is to efficiently classify the state of a set of distributed agents through a sequence of tests by imposing each test simultaneously upon groups of agents. In this work, we describe the concept of group testing in a generalized framework and propose to apply this concept to solve the scheduling and multiple access problem in a large scale wireless sensor network. Since the standard approach is to dedicate a single channel to each sensor, we discuss the efficiency of group testing by comparing it to the case where each sensor is tested individually. Through the sequence of tests, the group testing strategy successively refines the observation space of the set of sensors and eventually identifies the status of each sensor when the space is refined to only one element. We show that the successive refinement property of group testing (similar to that of arithmetic coding) plays an important role in its performance. Based on this concept, we provide insight into choosing optimal group testing strategies for general applications.

Synchronization is considered a particularly difficult task in wireless sensor networks due to its decentralized structure. Interestingly, synchrony has often been observed in networks of biological agents (e.g., synchronously flashing fireflies, or spiking of neurons). In this paper, we propose a bio-inspired network synchronization protocol for large scale sensor networks that emulates the simple strategies adopted by the biological agents. The strategy synchronizes pulsing devices that are led to emit their pulses periodically and simultaneously. The convergence to synchrony of our strategy follows from the theory of Mirollo and Strogatz, 1990, while the scalability is evident from the many examples existing in the natural world. When the nodes are within a single broadcast range, our key observation is that the dependence of the synchronization time on the number of nodes N is subject to a phase transition: for values of N beyond a specific threshold, the synchronization is nearly immediate; while for smaller N, the synchronization time decreases smoothly with respect to N. Interestingly, a tradeoff is observed between the total energy consumption and the time necessary to reach synchrony. We obtain an optimum operating point at the local minimum of the energy consumption curve that is associated to the phase transition phenomenon mentioned before. The proposed synchronization protocol is directly applied to the cooperative reach-back communications problem. The main advantages of the proposed method are its scalability and low complexity.

This paper proposes the use of a distributed synchronization mechanism, which locks in phase the pulse-coupled oscillators, to rapidly alert the nodes in a sensor network of a change detected by a group of the sensors. By encoding into an abrupt variation of the phase their positive detection of a change, the nodes force all other nodes to reach a new synchronization equilibrium. Therefore, the information about the change is implicitly encoded in the phase transitions. While the local detection problem at each sensor can be addressed using the standard change detection algorithms, the interesting aspect of this work is the unconventional way through which the nodes broadcast their information to each other and fuse their decisions. The main advantages of the proposed method is the scalability and low complexity of the fusion algorithm.

In this paper we consider the multiple access problem with distributed dependent sources. We derive the optimal designs for the case of N correlated binary sources whose data is modelled as a two-state Markov chain. The solution can be classified as a group testing technique where data values at the sensors are determined through the successive refinements of the tests over smaller groups. The tests form, progressively, an accurate map of the sensor data at the central receiver. We derive the conditions on the parameters of the data model for which the group testing approach is superior to time sharing. In contrast to standard multiple access techniques, this is the first method proposed for data retrieval from distributed dependent sources which is content-based rather than user-based.

In this work, we explore the concept of group testing to efficiently acquire data from a distributed sensor field and to reconstruct the sensor field at a central station. We show that group testing techniques are not only an efficient tool to schedule multiple access transmissions, they are also transmission techniques that allow the central node to rapidly discriminate the information from the sensor field when a large number of sources generates data with low aggregate entropy. Our method enables the sensors to reconstruct a map of the entire sensor field with bandwidth requirements that depend on the precision of the reconstructed field and, thus, do not grow linearly with the increased number of nodes when the network density increases.