The time allocation problem in multi-function cognitive radar systems focuses on the trade-off between scanning for newly emerging targets and tracking the previously detected targets. We formulate this as a multi-objective optimization problem and employ deep reinforcement learning to find Pareto-optimal solutions and compare deep deterministic policy gradient (DDPG) and soft actor-critic (SAC) algorithms. Our results demonstrate the effectiveness of both algorithms in adapting to various scenarios, with SAC showing improved stability and sample efficiency compared to DDPG. We further employ the NSGA-II algorithm to estimate an upper bound on the Pareto front of the considered problem. This work contributes to the development of more efficient and adaptive cognitive radar systems capable of balancing multiple competing objectives in dynamic environments.

In this paper, we develop a learning-based framework for an unmanned aerial vehicle (UAV) to sense, track and pursue mobile users as well as communicate with them. UAV is assumed to be equipped with a radar unit, and radar measurements are incorporated into the extended Kalman filter (EKF) algorithm to track the position of a mobile user. An integrated sensing and communication (ISAC) approach is considered, and resource management for the allocation of radar dwell time for target tracking and the communication time for data transfer is addressed. Based on the estimated location, the UAV's task is to select actions to approach and pursue the target users and also to perform time management between radar sensing and data communication. A bootstrapped deep Q-network (DQN) based reinforcement learning model is utilized to generate a set of actions for directing UAV movement and allocating time within the ISAC system. The eventual goal for the UAV is to optimize the data communication by tracking and pursuing mobile users. The numerical results demonstrate that with the designed approach, UAV is able to approach the target user and communicate efficiently. When applied to a multi-target scenario, the proposed approach enables the UAV to pursue all targets and perform data communication effectively.

In this paper, we analyze collaborative inference in a mobile edge computing (MEC) network aided by a reconfigurable intelligent surface (RIS). In particular, we consider multiple user equipments (UEs) with collaborative inference tasks that require the execution of deep neural networks. The goal is to minimize the long-term average energy consumption subject to a long-term average computing queue backlog constraint. We first transform the considered problem into a Lyapunov optimization problem and then propose a deep reinforcement learning (DRL)-based algorithm to solve it. An optimization subroutine is embedded in the proposed algorithm to directly obtain the optimal RIS coefficients, while the UEs’ deep neural network (DNN) partition decisions and computational resource allocations at the MEC server are obtained from the DRL-based algorithm. Via numerical results, it is shown that the proposed algorithm solves the problem efficiently, and the introduced RIS improves the long-term average energy consumption significantly. Furthermore, it is demonstrated that system parameters (such as communication bandwidth and the maximum CPU frequency at the MEC server) can have significant impact on the energy consumption and computing backlog levels.

In this paper, we consider a multi-access mobile edge computing (MEC) network with multiple sensors and one MEC server in industrial Internet of Things networks, where the MEC server provides a joint computation service (in the computation phase) for a set of sub-tasks offloaded by different sensors (in the communication phase). Due to the requirements of low latency and ultra reliability, we utilize finite blocklength information theory to characterize the reliability of the communication phase and exploit extreme value theory to investigate the delay violation probability in the computation phase. Following these characterizations, we derive the average end-to-end error probability of the entire service and provide two average end-to-end reliability-optimal design frameworks considering fixed frames structure and dynamic frames structure, in both of which the goal is to minimize the average end-to-end error probability by optimally allocating the total time length to each frame, as well as allocating each frame length to the communication phase and the computation phase. For the fixed frames structure, the original problem is decomposed, and the joint convexity of the decomposed sub-problems is rigorously proved, and the optimal solutions are obtained by the proposed optimal time allocation algorithm. Moreover, for the dynamic frames structure, we reformulate the optimization problem by introducing an average time constraint. By exploiting Lagrange multipliers, we transform the reformulated optimization problem into a dual problem with strong duality, the solutions of which can be obtained by the proposed time allocation algorithm. Via simulations, we validate the proven convexity and the approximation in our analytical model and evaluate the performance for both fixed frames length structure and dynamic frames length structure.

In this paper, we analyze collaborative inference in a mobile edge computing (MEC) network aided by a reconfigurable intelligent surface (RIS). In particular, we consider multiple user equipments (UEs) with collaborative inference tasks. Our objective is to minimize the long-term average energy consumption subject to a long-term average computing queue backlog constraint. We first transform the considered problem into a Lyapunov optimization problem and then propose a deep reinforcement learning (DRL)-based algorithm to solve it. An optimization subroutine is embedded in the proposed algorithm to directly obtain the optimal RIS coefficients, while the UEs’ deep neural network (DNN) partition decisions and computational resource allocations at the MEC server are obtained from the DRL-based algorithm. Numerical results show that the proposed algorithm solves the problem efficiently, and the introduced RIS improves the long-term average energy consumption significantly. Furthermore, the impact of bandwidth is analyzed and the effectiveness of our proposed algorithm is validated.

In this paper, scanning and multi-target tracking in a radar system are considered, and adaptive radar resource management is analyzed. In particular, time management in radar scanning and tracking of multiple maneuvering targets subject to budget constraints is studied with the goal to jointly maximize the tracking and scanning performances of a cognitive radar. The constrained optimization of the dwell time allocation to each target is addressed via deep Q-network (DQN) based reinforcement learning. In the proposed constrained deep reinforcement learning (CDRL) algorithm, both the parameters of the DQN and the dual variable are learned simultaneously. Numerical results show that radar can autonomously allocate more time to the tracking task that requires greater attention while providing time for scanning and also constraining the total time budget below the predefined threshold.

Federated learning (FL) is a framework which allows multiple users to jointly train a global machine learning (ML) model by transmitting only model updates under the coordination of a parameter server, while being able to keep their datasets local. One key motivation of such distributed frameworks is to provide privacy guarantees to the users. However, preserving the users' datasets locally is shown to be not sufficient for privacy. Several differential privacy (DP) mechanisms have been proposed to provide provable privacy guarantees by introducing randomness into the framework, and majority of these mechanisms rely on injecting additive noise. FL frameworks also face the challenge of communication efficiency, especially as machine learning models grow in complexity and size. Quantization is a commonly utilized method, reducing the communication cost by transmitting compressed representation of the underlying information. Although there have been several studies on DP and quantization in FL, the potential contribution of the quantization method alone in providing privacy guarantees has not been extensively analyzed yet. We in this paper present a novel stochastic quantization method, utilizing a mixed geometric distribution to introduce the randomness needed to provide DP, without any additive noise. We provide convergence analysis for our framework and empirically study its performance.

With the advancement of Mobile Edge Computing (MEC), effective solutions for communication scenarios, including the industrial Internet of Things (IoT) and the Internet of Vehicles (IoV), are becoming increasingly feasible. Unmanned aerial vehicles (UAVs) can further enhance flexibility in delivering computational services within MEC contexts. Addressing the urgent need for information freshness, we propose a three-tier IoV-MEC system, supported by multiple UAVs and a cloud center, aiming to minimize the system's average age of information (AoI). We propose a heterogeneous multi-agent reinforcement learning algorithm based on the actor-critic framework, where vehicles act as data sources, UAVs serve as edge devices, and the cloud acts as a control center. All three tiers learn interaction strategies cooperatively based on the observations. To further enhance system performance, we implement an efficient federated learning method, allowing same-tier agents to share learning parameters, thus improving system performance and convergence speed. Extensive simulation results demonstrate that the proposed algorithm outperforms baseline algorithms in terms of average AoI and convergence speed.

Federated learning (FL) aims at keeping client data local to preserve privacy. Instead of gathering the data itself, the server only collects aggregated gradient updates from clients. Following the popularity of FL, there has been considerable amount of work revealing the vulnerability of FL approaches by reconstructing the input data from gradient updates. Yet, most existing works assume an FL setting with unrealistically small batch size, and have poor image quality when the batch size is large. Other works modify the neural network architectures or parameters to the point of being suspicious, and thus, can be detected by clients. Moreover, most of them can only reconstruct one sample input from a large batch. To address these limitations, we propose a novel and analytical approach, referred to as the maximum knowledge orthogonality reconstruction (MKOR), to reconstruct clients' data. Our proposed method reconstructs a mathematically proven high-quality image from large batches. MKOR only requires the server to send secretly modified parameters to clients and can efficiently and inconspicuously reconstruct images from clients' gradient updates. We evaluate MKOR’s performance on MNIST, CIFAR-100, and ImageNet datasets and compare it with the state-of-the-art baselines. The results show that MKOR outperforms the existing approaches, and draw attention to a pressing need for further research on the privacy protection of FL so that comprehensive defense approaches can be developed. The code is available at: https://github.com/wfwf10/MKOR.

In this paper, we investigate a reconfigurable intelligent surface (RIS)-assisted mobile edge computing (MEC) network aiming to maximize the energy efficiency in the finite blocklength (FBL) regime under both coding length and maximum decoding error rate constraints. We first analyze the single user equipment (UE) case and propose a three-step alternating optimization algorithm to solve the problem. Extending the system model, we subsequently investigate a network with multiple UEs, in which non-orthogonal multiple access (NOMA) transmission is adopted. In this more general setting, we also conduct a convergence analysis. Furthermore, we introduce a UE-grouping scheme for hybrid NOMA-TDMA transmission and develop a dynamic CPU frequency allocation algorithm at the mobile edge computing (MEC) server. Numerical results show that the proposed algorithms solve the problem efficiently. Via numerical results, we also identify the impact of various parameters (e.g., coding blocklength, the number of RIS elements, computational resources, number of UEs) on the energy efficiency. Furthermore, with the numerical results, we verify the validity of UE grouping method and demonstrate that the proposed dynamic CPU frequency allocation can enhance the performance substantially.

Unmanned aerial vehicles (UAVs) have various applications in different settings, including for example, surveillance, packet delivery, emergency response, data collection in the Internet of Things (IoT), and connectivity in cellular networks. However, this technology comes with many risks and challenges such as vulnerabilities to malicious cyber-physical attacks. This article studies the problem of path planning for UAVs under GPS sensor permanent faults in a cyber-physical system (CPS) perspective. Based on studying and analyzing the CPS architecture of the UAV, the cyber “attacks and threats” are differentiated from attacks on sensors and communication components. An efficient way to address this problem is to introduce a novel approach for UAV’s path planning resilience to cyber-attack artificial potential field (RCA-APF) algorithm. The proposed algorithm completes the three stages in a coordinated manner. In the first stage, the permanent faults on the GPS sensor of the UAV are detected, and the UAV starts to divert from its initial path planning. In the second stage, we estimated the location of the UAV under GPS permanent fault using received signal strength (RSS) trilateration localization approach. In the final stage of the algorithm, we implemented the path planning of the UAV using an open source UAV simulator. Experimental and simulation results demonstrate the performance of the algorithm and its effectiveness, resulting in efficient path planning for the UAV.

In this paper, we consider a reconfigurable intelligent surface (RIS) aided mobile edge computing (MEC) network and investigate the minimization of the energy consumption for collaborative inference. Within the collaborative inference framework, the user equipments (UEs) are allowed to offload parts of computation-intensive perception services to the MEC server and thereby reduce the energy consumption subject to their latency constraints. In this setting, considering the collaborative inference task and the transmission model, we aim to minimize the global energy consumption under the UEs' latency constraints. A three-step optimization algorithm is devised to address the considered energy minimization problem in which the RIS phase shift matrix, inference partition decisions and CPU frequency allocations at the MEC server are optimally determined. Simulation results on the collaborative inference task demonstrate the effectiveness of our proposed approach in reducing the energy consumption levels while satisfying the UEs' latency constraints.

In this paper, we investigate the reliability in an unmanned aerial vehicle (UAV) assisted caching-based downlink network where non-orthogonal multiple access (NOMA) transmission and finite blocklength (FBL) codes are adopted. In this network, the ground user equipments (GUEs) request contents from a distant base station (BS) but there are no direct links from the BS to the GUEs. A UAV with limited cache size is employed to assist the BS to complete the communication by either first requesting the uncached contents from the BS and then serving the GUEs or directly sending the cached contents to the GUEs. In this setting, we first introduce the decoding error rate in the FBL regime as well as the caching policy at the UAV, and subsequently we construct an optimization problem aiming to minimize the maximum end-to-end decoding error rate among all GUEs under both coding length and maximum UAV transmission power constraints. A two-step alternating algorithm is proposed to solve the problem and numerical results demonstrate that our algorithm can solve the optimization problem efficiently. More specifically, loosening the FBL constraint, enlarging the cache size and having a higher transmission power budget at the UAV lead to an improved performance.

In this work, we propose a constrained deep reinforcement learning (CDRL) based approach to address resource allocation for multi-target tracking in a radar system. In the proposed CDRL algorithm, both the parameters of the deep Q-network (DQN) and the dual variable are learned simultaneously. The proposed CDRL framework consists of two components, namely online CDRL and offline CDRL. Training a DQN in the deep reinforcement learning algorithm usually requires a large amount of data, which may not be available in a target tracking task due to the scarcity of measurements. We address this challenge by proposing an offline CDRL framework, in which the algorithm evolves in a virtual environment generated based on the current observations and prior knowledge of the environment. Simulation results show that both offline CDRL and online CDRL are critical. Offline CDRL provides more training data to stabilize the learning process and the online component can sense the change in the environment and make the corresponding adaptation.

In this paper, we consider multi-agent deep reinforcement learning (deep RL) based network slicing agents in a dynamic environment with multiple base stations and multiple users. We develop a deep RL based jammer with limited prior information and limited power budget. The goal of the jammer is to minimize the transmission rates achieved with network slicing and thus degrade the network slicing agents’ performance. We design a jammer with both listening and jamming phases and address jamming location optimization as well as jamming channel optimization via deep RL. We evaluate the jammer at the optimized location, generating interference attacks in the optimized set of channels by switching between the jamming phase and listening phase. We show that the proposed jammer can significantly reduce the victims’ performance without direct feedback or prior knowledge on the network slicing policies.

Intelligent reflecting surfaces (IRSs) provide the ability to tune the wireless environments by introducing passive scattering elements between the base station (BS) or access points (APs) and users, and it is a promising technology for next-generation wireless networks. In this article, we consider joint activity detection and channel estimation in both centralized and distributed IRS-assisted Internet of Things networks. We determine the explicit distribution for the equivalent channel coefficients from the BS or APs to the users. It is shown that this distribution can be equivalently represented with a Gaussian scale mixture (GSM) model. With the generalized approximate message passing (GAMP) algorithm, the received pilot signals at the BS or APs are decoupled into scalar Gaussian noise corrupted versions of the effective channel coefficients. Subsequently, a minimum mean square error (MMSE) estimate of the effective channel coefficients and threshold detection rules is acquired. Finally, the optimal fusion rule is used to obtain the activity detection results of each user. As an additional low-complexity approach, we approximate the equivalent channel coefficients with a Gaussian approximation (GA) model. We derive the theoretical mean square error for the MMSE estimate of the effective channel coefficients using the GA model and the state evolution equations of the GAMP algorithm, under the assumption that the direct links from the BS to the users can be neglected due to unfavorable propagation conditions. Numerical results show that the GA for the distribution of the equivalent channel coefficients is accurate even for the moderate number of elements at the IRS. And the theoretical mean square error for the MMSE estimate of the effective channel coefficients under the assumption that direct link is negligible is shown to overlap with the corresponding numerical simulations.

In this paper, we investigate jamming-resilient UAV path planning strategies for data collection in Internet of Things (IoT) networks, in which the typical UAV can learn the optimal trajectory to elude such jamming attacks. Specifically, the typical UAV is required to collect data from multiple distributed IoT nodes under collision avoidance, mission completion deadline, and kinematic constraints in the presence of jamming attacks. We first design a fixed ground jammer with continuous jamming attack and periodical jamming attack strategies to jam the link between the typical UAV and IoT nodes. Defensive strategies involving a reinforcement learning (RL) based virtual jammer and the adoption of higher SINR thresholds are proposed to counteract against such attacks. Secondly, we design an intelligent UAV jammer, which utilizes the RL algorithm to choose actions based on its observation. Then, an intelligent UAV anti-jamming strategy is constructed to deal with such attacks, and the optimal trajectory of the typical UAV is obtained via dueling double deep Q-network (D3QN). Simulation results show that both non-intelligent and intelligent jamming attacks have significant influence on the UAV’s performance, and the proposed defense strategies can recover the performance close to that in no-jammer scenarios.

To support ultra-reliable and low-latency services for mission-critical applications, transmissions are usually carried via short blocklength codes, i.e., in the so-called finite blocklength (FBL) regime. Different from the infinite blocklength regime where transmissions are assumed to be arbitrarily reliable at the Shannon’s capacity, the reliability and capacity performances of an FBL transmission are impacted by the coding blocklength. The relationship among reliability, coding rate, blocklength and channel quality has recently been characterized in the literature, considering the FBL performance model. In this paper, we follow this model, and prove the joint convexity of the FBL error probability with respect to blocklength and transmit power within a region of interest, as a key enabler for designing systems to achieve globally optimal performance levels. Moreover, we apply the joint convexity to general use cases and efficiently solve the joint optimization problem in the setting with multiple users. We also extend the applicability of the proposed approach by proving that the joint convexity still holds in fading channels, as well as in relaying networks. Via simulations, we validate our analytical results and demonstrate the advantage of leveraging the joint convexity compared to other commonly-applied approaches.

Enabling ultra-reliable and low-latency communication services while providing massive connectivity is one of the major goals to be accomplished in future wireless communication networks. In this paper, we investigate the performance of a hybrid multi-access scheme in the finite blocklength (FBL) regime that combines the advantages of both non-orthogonal multiple access (NOMA) and time-division multiple access (TDMA) schemes. Two latency-sensitive application scenarios are studied, distinguished by whether the queuing behaviour has an influence on the transmission performance or not. In particular, for the latency-critical case with one-shot transmission, we aim at a certain physical-layer quality-of-service (QoS) performance, namely the optimization of the reliability. And for the case in which queuing behaviour plays a role, we focus on the link-layer QoS performance and provide a design that maximizes the effective capacity. For both designs, we leverage the characterizations in the FBL regime to provide the optimal framework by jointly allocating the blocklength and transmit power of each user. In particular, for the reliability-oriented design, the original problem is decomposed and the joint convexity of sub-problems is shown via a variable substitution method. For the effective-capacity-oriented design, we exploit the method of Lagrange multipliers to formulate a solvable dual problem with strong duality to the original problem. Via simulations, we validate our analytical results of convexity/concavity and show the advantage of our proposed approaches compared to other existing schemes.

Federated learning has attracted growing interest as it preserves the clients' privacy. As a variant of federated learning, federated transfer learning utilizes the knowledge from similar tasks and thus has also been intensively studied. However, due to the limited radio spectrum, the communication efficiency of federated learning via wireless links is critical since some tasks may require thousands of Terabytes of uplink payload. In order to improve the communication efficiency, we in this paper propose the feature-based federated transfer learning as an innovative approach to reduce the uplink payload by more than five orders of magnitude compared to that of existing approaches. We first introduce the system design in which the extracted features and outputs are uploaded instead of parameter updates, and then determine the required payload with this approach and provide comparisons with the existing approaches. Subsequently, we analyze the random shuffling scheme that preserves the clients' privacy. Finally, we evaluate the performance of the proposed learning scheme via experiments on an image classification task to show its effectiveness.

To address the anomaly detection problem in the presence of noisy observations and to tackle the tuning and efficient exploration challenges that arise in deep reinforcement learning algorithms, we in this paper propose a soft actor-critic deep reinforcement learning framework. To evaluate the proposed framework, we measure its performance in terms of detection accuracy, stopping time, and the total number of samples needed for detection. Via simulation results, we demonstrate the performance when soft actor-critic algorithms are employed, and identify the impact of key parameters, such as the sensing cost, on the performance. In all results, we further provide comparisons between the performances of the proposed soft actor-critic and conventional actor-critic algorithms.

In this paper, the power control problem is addressed in a wireless interference network in which there exist multiple transmitter-receiver pairs sharing the same bandwidth for information exchange. The goal is to train a common deep neural network (DNN) for power allocation at each transmitter. Recent studies in the literature have addressed this problem via deep reinforcement learning (DRL). However, training DRL algorithms can become costly in wireless networks since the DRL algorithm may converge slowly in specific problems and hence require a large amount of training data. Besides, the converged model may fail in a new environment, which is not preferable in a wireless network due to its dynamic and time-varying nature. In this work, we address these considerations by proposing a meta-DRL framework that incorporates the method of Model-Agnostic Meta-Learning (MAML). Within the proposed framework, a common initialization is trained for similar power control tasks. From the initialization, we show that only a few gradient descent steps are required for adapting to an unseen task. Simulation results demonstrate that the proposed framework can outperform conventional DRL and joint-learning (which trains a global model for similar tasks) for power control in wireless interference networks.

In this paper, we investigate jamming-resilient unmanned aerial vehicle (UAV) path planning strategies for data collection in Internet of Things (IoT) networks, in which the typical UAV can learn the optimal trajectory to elude such jamming attacks. Specifically, the typical UAV is required to collect data from multiple distributed IoT nodes under collision avoidance, mission completion deadline, and kinematic constraints in the presence of jamming attacks. We first design an intelligent UAV jammer, which utilizes reinforcement learning to choose actions based on its observation. Then, an intelligent UAV anti-jamming strategy is constructed to deal with such attacks, and the optimal trajectory of the typical UAV is obtained via dueling double deep Q-network (D3QN). Simulation results show that the intelligent jamming attack has great influence on the UAV's performance, and the proposed defense strategy can recover the performance close to that in no-jammer scenarios.

In this paper, we present a multi-agent deep reinforcement learning (deep RL) framework for network slicing in a dynamic environment with multiple base stations. We first introduce the wireless network virtualization (WNV) and the interference channel model. Then, we formulate the network slicing problem in the dynamic environment in which fading varies, users have mobility, and requests are randomly generated over time. Subsequently, we propose a deep RL framework with multiple actors and centralized critic (MACC) to maximize the reward over all base stations instead of pursuing local optimization. The actors are implemented as pointer networks to fit the varying dimension of input. Finally, we evaluate the performance of the proposed deep RL algorithm via simulations to demonstrate its effectiveness.

We consider a set of data samples such that a fraction of the samples are arbitrary outliers, and the rest are the output samples of a single-layer neural network with rectified linear unit (ReLU) activation. Our goal is to estimate the parameters (weight matrix and bias vector) of the neural network, assuming the bias vector to be non-negative. We estimate the network parameters using the gradient descent algorithm combined with either the median- or trimmed mean-based filters to mitigate the effect of the arbitrary outliers. We then prove that $\tilde{O}\left( \frac{1}{p^2}+\frac{1}{\epsilon^2p}\right)$ samples and $\tilde{O}\left( \frac{d^2}{p^2}+ \frac{d^2}{\epsilon^2p}\right)$ time are sufficient for our algorithm to estimate the neural network parameters within an error of $\epsilon$ when the outlier probability is $1-p$, where $2/3 DOI 10.52202/068431-0796

In this paper, we first present a deep reinforcement learning (deep RL) framework for network slicing in a dynamic environment. We propose three different deep RL algorithms, namely actor-critic, deep Q learning (DQN), and soft DQN, to select slices from the best recorded subset which is updated over time to adapt to the dynamic environment. We evaluate the performances of the proposed deep RL agents for network slicing and provide comparisons. Subsequently, we design intelligent jammers also as deep RL agents that significantly degrade the user's sum reward. Finally, we propose effective defensive measures to mitigate jamming attacks by determining the proper time instants to retrain the network slicing policy. Via simulations, we quantify the improvements in the performance with the defensive retraining.

Reconfigurable intelligent surfaces (RISs) are considered as an effective means to improve both the spectral efficiency and coverage in wireless systems. By properly setting the phase shift matrix, RIS can enhance the propagation environment. In this paper, we investigate an RIS aided mobile edge computing (MEC) network in the finite blocklength (FBL) regime with the goal to maximize the energy efficiency under both coding length and maximum decoding error rate constraints. We first investigate the single user equipment (UE) scenario and propose a three-step optimization algorithm. To extend the system model, we further investigate the two-UE scenario where non-orthogonal multiple access (NOMA) transmission is adopted. A revised three-step optimization algorithm is demonstrated to address the problem. Numerical results verify that the proposed three-step optimization algorithms can solve the problems in both scenarios efficiently. In particular, the numerical results show that loosening the FBL constraint can improve the performance and a larger CPU frequency at the MEC server leads to an improved energy efficiency. It is also noted that adjusting the RIS phase shift matrix can enhance the signal-to-noise ratio (SNR)/signal-to-interference-plus-noise ratio (SINR) at the BS and improve the decoding error rate under both scenarios.

To address the anomaly detection problem in the presence of noisy sensor observations and probing costs, we in this paper propose a soft actor-critic deep reinforcement learning framework. Moreover, considering adversarial jamming attacks, we design a generative adversarial network (GAN) based framework to identify the jammed sensors. To evaluate the proposed framework, we measure the performance in terms of detection accuracy, stopping time, and the total number of samples needed for detection. Via simulation results, we demonstrate the performances when soft actor-critic algorithms are sensitive to the probing cost and actively adapt to different environment settings. We analyze the impact of jamming attacks and identify the improvements achieved by GAN-based approach. We further provide comparisons between the performances of the proposed soft actor-critic and conventional actor-critic algorithms.

Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectory in multi-UAV noncooperative scenarios while collecting data from distributed Internet of Things (IoT) nodes is a challenging task. In this article, we consider a path-planning optimization problem to maximize the collected data from multiple IoT nodes under realistic constraints. The considered multi-UAV noncooperative scenarios involve a random number of other UAVs in addition to the typical UAV, and UAVs do not communicate or share information among each other. We translate the problem into a Markov decision process (MDP) with parameterized states, permissible actions, and detailed reward functions. Dueling double deep$Q$-network (D3QN) is proposed to learn the decision-making policy for the typical UAV, without any prior knowledge of the environment (e.g., channel propagation model and locations of the obstacles) and other UAVs (e.g., their missions, movements, and policies). The proposed algorithm can adapt to various missions in various scenarios, e.g., different numbers and positions of IoT nodes, different amount of data to be collected, and different numbers and positions of other UAVs. Numerical results demonstrate that real-time navigation can be efficiently performed with high success rate, high data collection rate, and low collision rate.

Cell-free massive MIMO is one of the key technologies for future wireless communications, in which users are simultaneously and jointly served by all access points (APs). In this paper, we investigate the minimum mean square error (MMSE) estimation of effective channel coefficients in cell-free massive MIMO systems with massive connectivity. To facilitate the theoretical analysis, only single measurement vector (SMV) based MMSE estimation is considered in this paper, i.e., the MMSE estimation is performed based on the received pilot signals at each AP separately. Inspired by the decoupling principle of replica symmetric postulated MMSE estimation of sparse signal vectors with independent and identically distributed (i.i.d.) non-zero components, we develop the corresponding decoupling principle for the SMV based MMSE estimation of sparse signal vectors with independent and non-identically distributed (i.n.i.d.) non-zero components, which plays a key role in the theoretical analysis of SMV based MMSE estimation of the effective channel coefficients in cell-free massive MIMO systems with massive connectivity. Subsequently, based on the obtained decoupling principle of MMSE estimation, likelihood ratio test and the optimal fusion rule, we perform user activity detection based on the received pilot signals at only one AP, or cooperation among the entire set of APs for centralized or distributed detection. Via theoretical analysis, we show that the error probabilities of both centralized and distributed detection tend to zero when the number of APs tends to infinity while the asymptotic ratio between the number of users and pilots is kept constant. We also investigate the asymptotic behavior of oracle estimation in cell-free massive MIMO systems with massive connectivity via random matrix theory. Moreover, in order to demonstrate the potential performance loss of SMV based MMSE estimation, which does not employ the correlation between the received pilot signals at different APs, the multiple measurement vector (MMV) based MMSE estimation, i.e., joint MMSE estimation with pilot signals from all APs, is analyzed via numerical results. Numerical analysis shows that the theoretical analyze with our decoupling principle for the SMV based MMSE estimation of sparse signal vectors with i.n.i.d. non-zero components matches well with the numerical results.

Cloud radio access networks (CRANs) have become a key enabling technique for the next generation wireless communications. Resource allocation in CRANs still needs to be further improved to reach the objective of minimizing power consumption and meeting demands of wireless users over a long period. Inspired by the success of Deep Reinforcement Learning (DRL) on solving complicated control problems, we present a novel framework,ReCARL, for power-efficient resource allocation in CRANs with deep reinforcement learning. Specifically, we define the state space, action space and reward function for the DRL agent, apply a deep neural network (DNN) to approximating the action-value function, and formally formulate the resource allocation problem (in each decision epoch) as a convex optimization problem. Under ReCARL, we propose two different DRL agents: one has a regular DNN structure trained with the basic deep Q-learning method (ReCARL-Basic); while the other has a context-aware DNN structure trained with a hybrid deep Q-learning method (ReCARL-Hybrid). We evaluated the performance of ReCARL along with the two DRL agents by comparing them with two widely-used baselines via extensive simulation. The simulation results show that ReCARL achieves significant power savings while meeting user demands, and it can well handle highly dynamic cases.

Power control policies that minimize the weighted combination of age of information (AoI) and total energy consumption are studied in this paper. It is assumed that the status update information is acquired at the transmitter through sensors and then sent in packets to the receiver at fixed rate over Rayleigh fading channels. Retransmission mechanism is introduced to guarantee the reliability of the received update packets, and a limit on the maximum number of transmission rounds is imposed. On the one hand, with the channel distribution information (CDI) available at the transmitter, the age-energy tradeoff is optimized by formulating the problem as a constrained Markov decision process (CMDP) which takes into account the sensing power consumption as well as the average transmission power constraint. Employing the Lagrangian relaxation, the CMDP problem is transformed into an unconstrained Markov decision process (MDP) problem. A threshold-based policy is also provided. Subsequently, a dynamic programming approach is proposed to obtain the optimal power allocation policy. On the other hand, when the environment is unknown, two probabilistic reinforcement learning algorithms are proposed to optimize the age-energy tradeoff. Through simulation results, it is shown that the age-energy tradeoffs can be improved by the proposed optimal policies compared with benchmark schemes.

Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectories for multiple UAVs while satisfying requirements of connectivity with ground base stations (GBSs) is a challenging task. In this paper, we consider non-cooperative multi-UAV scenarios, in which multiple UAVs need to fly from initial locations to destinations, while satisfying collision avoidance, wireless connectivity, and kinematic constraints. We aim to find trajectories for the UAVs with the goal to minimize their mission completion time. We first formulate the multi-UAV trajectory optimization problem as a sequential decision making problem. We, then, propose a decentralized deep reinforcement learning approach to solve the problem. More specifically, a value network is developed to obtain values given the agent’s joint state (including the agent’s information, the nearby agents’ observable information, and the locations of the nearby GBSs). A signal-to-interference-plus-noise ratio (SINR)-prediction neural network is also designed, using accumulated SINR measurements obtained when interacting with the cellular network, to map the GBSs’ locations into the SINR levels in order to predict the UAV’s SINR. Numerical results show that with the value network and SINR-prediction network, real-time navigation for multi-UAVs can be efficiently performed in various environments with high success rate.

In this paper, we investigate the performance of cell-free massive MIMO systems with massive connectivity. With the generalized approximate message passing (GAMP) algorithm, we obtain the minimum mean-squared error (MMSE) estimate of the effective channel coefficients from all users to all access points (APs) in order to perform joint user activity detection and channel estimation. Subsequently, using the decoupling properties of MMSE estimation for large linear systems and state evolution equations of the GAMP algorithm, we obtain the variances of both the estimated channel coefficients and the corresponding channel estimation error. Finally, we study the achievable uplink rates with zero-forcing (ZF) detector at the central processing unit (CPU) of the cell-free massive MIMO system. With numerical results, we analyze the impact of the number of pilots used for joint activity detection and channel estimation, the number of APs, and signal-to-noise ratio (SNR) on the achievable rates.

In this paper, a three-tier heterogeneous network (HetNet) is considered, where access points (APs), small-cell base stations (SBSs) and macrocell BSs (MBSs) transmit in terahertz (THz), millimeter wave (mmWave), microwave frequency bands, respectively. Distinguishing features of transmission in each frequency band are taken into account, including the blockage model, path loss, beamforming and small-scale fading. Path loss based association criterion is considered for user equipments (UEs). By using tools from stochastic geometry, the complementary cumulative distribution function (CCDF) of the received signal power, the Laplace transform of the aggregate interference, and the SINR coverage probability are investigated, and general expressions are obtained. Finally, numerical results show that making the THz APs more densely distributed can enhance the received signal power but decrease the SINR coverage probability.

In mobile edge computing (MEC) networks, by offloading tasks (partially or completely) to the MEC server, it becomes possible to complete computation-intensive and latency-critical applications without communicating with the cloud center, resulting in dramatic reduction both in latency and energy consumption. Performance improvements depend on the offloading decisions at the user equipments (UEs) and computational resource allocation at the MEC server. In this paper, we aim to optimize the UE offloading data ratios and MEC computational resource allocation under delay constraints with the goal to minimize the global energy consumption. Both conventional optimization method and learning-based approach are studied. Simulation results are provided to compare the performances of different schemes.

We address the problem of monitoring a set of binary stochastic processes and generating an alert when the number of anomalies among them exceeds a threshold. For this, the decision-maker selects and probes a subset of the processes to obtain noisy estimates of their states (normal or anomalous). Based on the received observations, the decision-maker first determines whether to declare that the number of anomalies has exceeded the threshold or to continue taking observations. When the decision is to continue, it then decides whether to collect observations at the next time instant or defer it to a later time. If it chooses to collect observations, it further determines the subset of processes to be probed. To devise this three-step sequential decision-making process, we use a Bayesian formulation wherein we learn the posterior probability on the states of the processes. Using the posterior probability, we construct a Markov decision process and solve it using deep actor-critic reinforcement learning. Via numerical experiments, we demonstrate the superior performance of our algorithm compared to the traditional model-based algorithms.

In this paper, we address the channel access problem in a dynamic wireless environment via meta-reinforcement learning. Spectrum is a scarce resource in wireless communications, especially with the dramatic increase in the number of devices in networks. Recently, inspired by the success of deep reinforcement learning (DRL), extensive studies have been conducted in addressing wireless resource allocation problems via DRL. However, training DRL algorithms usually requires a massive amount of data collected from the environment for each specific task and the well-trained model may fail if there is a small variation in the environment. In this work, in order to address these challenges, we propose a meta-DRL framework that incorporates the method of Model-Agnostic Meta-Learning (MAML). In the proposed framework, we train a common initialization for similar channel selection tasks. From the initialization, we show that only a few gradient descents are required for adapting to different tasks drawn from the same distribution. We demonstrate the performance improvements via simulation results.

Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectories in multi-UAV non-cooperative scenarios is a challenging task. In this paper, we consider a path planning optimization problem to maximize the collected data from multiple Internet of Things (IoT) nodes under realistic constraints. The considered multi-UAV non-cooperative scenarios involve random number of other UAVs in addition to the typical UAV, and UAVs do not communicate with each other. We translate the problem into an Markov decision process (MDP). Dueling double deep Q-network (D3QN) is proposed to learn the decision making policy for the typical UAV, without any prior knowledge of the environment (e.g., channel propagation model and locations of the obstacles) and other UAVs (e.g., their missions, movements, and policies). Numerical results demonstrate that real-time navigation can be efficiently performed with high success rate, high data collection rate, and low collision rate.

Unmanned aerial vehicles (UAVs) are expected to be an integral part of wireless networks, and determining collision-free trajectories for multiple UAVs while satisfying requirements of connectivity with ground base stations (GBSs) is a challenging task. In this paper, we first reformulate the multi-UAV trajectory optimization problem with collision avoidance and wireless connectivity constraints as a sequential decision making problem in the discrete time domain. We, then, propose a decentralized deep reinforcement learning approach to solve the problem. More specifically, a value network is developed to encode the expected time to destination given the agent’s joint state (including the agent’s information, the nearby agents’ observable information, and the locations of the nearby GBSs). An SINR-prediction network is also designed, using accumulated SINR measurements obtained when interacting with the cellular network, to map the GBSs’ locations into the SINR levels in order to predict the UAV’s SINR. Numerical results show that with the value network and SINR-prediction network, real-time navigation for multi-UAVs can be efficiently performed in various environments with high success rate.

The sparse activity detection in intelligent reflecting surface (IRS) assisted wireless networks is investigated in this paper. With generalized approximate message passing (GAMP) algorithm, we first obtain the minimum mean square error (MMSE) estimates of the equivalent effective channel coefficients from the base station (BS) to the users, and convert the received pilot signals into additive Gaussian noise corrupted versions of the equivalent effective channel coefficients. Subsequently, multiple decisions on the activity of each user are made using the likelihood ratio test based on the Gaussian noise corrupted equivalent effective channel coefficients. At last, final decisions on the activity of all users are made with the optimal fusion rule, taking into account the previous decisions on the activity of each user and the corresponding reliabilities. Numerical results show that the average error probability of the sparse activity detection method proposed in this paper diminishes as the SNR, number of pilots, number of antennas at the BS or number of elements at the IRS increases.

In this paper, the minimum mean square error (MMSE) channel estimation for intelligent reflecting surface (IRS) assisted wireless communication systems is investigated. In the considered setting, each row vector of the equivalent channel matrix from the base station (BS) to the users is shown to be Bessel K distributed, and all these row vectors are independent of each other. By introducing a Gaussian scale mixture model, we obtain a closed-form expression for the MMSE estimate of the equivalent channel, and determine analytical upper and lower bounds on the mean square error. Using the central limit theorem, we conduct an asymptotic analysis of the MMSE estimate, and show that the upper bound on the mean square error of the MMSE estimate is equal to the asymptotic mean square error of the MMSE estimation when the number of reflecting elements at the IRS tends to infinity. Numerical simulations show that the gap between the upper and lower bounds are very small, and they almost overlap with each other at medium signal-to-noise ratio (SNR) levels and moderate number of elements at the IRS.

Deep reinforcement learning (DRL) has recently been used to perform efficient resource allocation in wireless communications. In this paper, the vulnerabilities of such DRL agents to adversarial attacks is studied. In particular, we consider multiple DRL agents that perform both dynamic channel access and power control in wireless interference channels. For these victim DRL agents, we design a jammer, which is also a DRL agent. We Propose an adversarial jamming attack scheme that utilizes a listening phase and significantly degrades the users' sum rate. Subsequently, we develop an ensemble policy defense strategy against such a jamming atta.ck.er by reloa.di.ng models (saved during retraining) that have minimum transition correlation.

Drones will have extensive use cases across various commercial, government, and military sectors, ranging from delivery of consumer goods to search and rescue operations. To maintain safety and security of people and infrastructure, it becomes critically important to quickly and accurately detect non-cooperating drones. In this paper we formulate a received signal strength (RSS) based detector, leveraging the existing wireless infrastructures that might already be serving other devices. Thus the detector should be able to detect the presence of a drone signal buried in radio frequency (RF) interference and thermal noise, in a mixed line-of-sight (LOS) and non-LOS (NLOS) environment. We develop analytical expressions for the probability of false alarm and the probability of detection of a drone, which quantify the impact of aggregate interference and air-to-ground (A2G) propagation characteristics on the detection performance of individual sensors. We also provide analytical expressions for the average network probability of detection, which captures the impact of sensor density on a network's detection coverage. Finally, we find the critical sensor density that maximizes the average network probability of detection for a given requirement of probability of false alarm.

In this paper, we consider simultaneous information and energy transfer (SWIPT) in unmanned aerial vehicle (UAV)-assisted cellular Internet of Things (IoT) networks, in which the user equipment (UE) locations are modeled as Thomas cluster processes. A realistic air-to-ground communication model is incorporated into the analysis. In particular, different line of sight (LOS) and non-LOS (NLOS) path loss models are considered for the links from the UAVs to UEs and ground base stations (GBSs) to UEs. Three dimensional (3D) antenna patterns are adopted, e.g., a doughnut-shaped antenna radiation pattern is considered for UAVs and a combination of horizontal and vertical antenna pattern is utilized for GBSs. In addition, we employ the power splitting technique in the SWIPT scenario, which allows the UEs to harvest energy and decode information simultaneously using the same received signal. Association probability and energy coverage probability of the UAVs and GBSs are determined. Moreover, an analysis of the successful transmission probability which jointly addresses the energy and signal-to-interference-plus-noise ratio (SINR) coverages is provided. Finally, performance is further investigated via numerical results.

In this paper, we address the anomaly detection problem where the objective is to find the anomalous processes among a given set of processes. To this end, the decision-making agent probes a subset of processes at every time instant and obtains a potentially erroneous estimate of the binary variable which indicates whether or not the corresponding process is anomalous. The agent continues to probe the processes until it obtains a sufficient number of measurements to reliably identify the anomalous processes. In this context, we develop a sequential selection algorithm that decides which processes to be probed at every instant to detect the anomalies with an accuracy exceeding a desired value while minimizing the delay in making the decision and the total number of measurements taken. Our algorithm is based on active inference which is a general framework to make sequential decisions in order to maximize the notion of free energy. We define the free energy using the objectives of the selection policy and implement the active inference framework using a deep neural network approximation. Using numerical experiments, we compare our algorithm with the state-of-the-art method based on deep actor-critic reinforcement learning and demonstrate the superior performance of our algorithm.

Anomaly detection incurs certain sampling and sensing costs and therefore it is of great importance to strike a balance between the detection accuracy and these costs. In this work, we study anomaly detection by considering the detection of threshold crossings in a stochastic time series without the knowledge of its statistics. To reduce the sampling cost in this detection process, we propose the use of hierarchical generative adversarial networks (GANs) to perform non-uniform sampling. In order to improve the detection accuracy and reduce the delay in detection, we introduce a butter zone in the operation of the proposed GANbased detector. In the experiments, we analyze the performance of the proposed hierarchical GAN detector considering the metrics of detection delay, miss rates, average cost of error, and sampling ratio. We identify the tradeoffs in the performance as the butter zone sizes and the number of GAN levels in the hierarchy vary. We also compare the performance with that of a sampling policy that approximately minimizes the sum of average costs of sampling and error given the parameters of the stochastic process. We demonstrate that the proposed GAN-based detector can have significant performance improvements in terms of detection delay and average cost of error with a larger butter zone but at the cost of increased sampling rates.

The number of daily sUAS operations in uncontrolled low altitude airspace is expected to reach into the millions in a few years. Therefore, UAS density prediction has become an emerging and challenging problem. In this paper, a deep learning-based UAS instantaneous density prediction model is presented. The model takes two types of data as input: 1) the historical density generated from the historical data, and 2) the future sUAS mission information. The architecture of our model contains four components: Historical Density Formulation module, UAS Mission Translation module, Mission Feature Extraction module, and Density Map Projection module. The training and testing data are generated by a python based simulator which is inspired by the multi-agent air traffic resource usage simulator (MATRUS) framework. The quality of prediction is measured by the correlation score and the Area Under the Receiver Operating Characteristics (AUROC) between the predicted value and simulated value. The experimental results demonstrate outstanding performance of the deep learning-based UAS density predictor. Compared to the baseline models, for simplified traffic scenario where no-fly zones and safe distance among sUASs are not considered, our model improves the prediction accuracy by up to 15.2% and its correlation score reaches 0.947. In a more realistic scenario, where the no-fly zone avoidance and the safe distance among sUASs are maintained using A* routing algorithm, our model can still achieve 0.822 correlation score. Meanwhile, the AUROC can reach 0.951 for the hot spot prediction.

We investigate the sparse activity detection problem in cell-free massive multiple-input multiple-output (MIMO) systems in this paper. With the approximate message passing (AMP) algorithm, the received pilot signals at the access points (APs) are decomposed into independent circularly symmetric complex Gaussian noise corrupted components. By using the minimum mean-squared error (MMSE) denoiser during the AMP procedure, we obtain a threshold detection rule, and analytically describe the noise covariance matrix of the corrupted components via the state evolution equations, which is helpful for the performance analysis of the detection rule. Using the law of large numbers, it can be shown that the error probability of this threshold detection rule tends to zero when the number of APs, pilots and users tend to infinity while the ratio of the number of pilots and users is kept constant. Numerical results show that the error probability decreases while the number of APs increases, corroborating our theoretical analysis. In addition, we investigate the relationship between the error probability of the threshold detection rule and the number of symbols used for pilot transmissions during each channel coherence interval via numerical results.

Beamforming is one of the key techniques in millimeter wave (mmWave) multi-input multi-output (MIMO) communications. Designing appropriate beamforming not only improves the quality and strength of the received signal, but also can help reduce the interference, consequently enhancing the data rate. In this paper, we propose a distributed multi-agent double deep Q-learning algorithm for beamforming in mmWave MIMO networks, where multiple base stations (BSs) can automatically and dynamically adjust their beams to serve multiple highly-mobile user equipments (UEs). In the analysis, largest received power association criterion is considered for UEs, and a realistic channel model is taken into account. Simulation results demonstrate that the proposed learning-based algorithm can achieve comparable performance with respect to exhaustive search while operating at much lower complexity.

Adversarial attack strategies have been widely studied in machine learning applications, and now are increasingly attracting interest in wireless communications as the application of machine learning methods to wireless systems grows along with security concerns. In this paper, we propose two adversarial policies, one based on feed-forward neural networks (FNNs) and the other based on deep reinforcement learning (DRL) policies. Both attack strategies aim at minimizing the accuracy of a DRL-based dynamic channel access agent. We first present the two frameworks and the dynamic attack procedures of the two adversarial policies. Then we demonstrate and compare their performances. Finally, the advantages and disadvantages of the two frameworks are identified.

This paper investigates the capacity region and detailed characterizations of capacity-achieving signaling schemes of a multiple access channel (MAC) with two users communicating to a base station (BS) equipped with 1-bit quantizers. We consider Rayleigh fading channels where channel state information (CSI) is known only at BS. Towards this end, we first study the weighted sum-rate maximization problem over the set of input distributions of one user for a fixed input signal at another user. By examining a necessary and sufficient Kuhn-Tucker condition (KTC) for an input to be optimal, it is first shown that the power constraint is active, i.e., the equality in the power constraint is achieved. By further exploiting novel bounds on the output distribution functions, the optimal distribution is shown to have a bounded amplitude. In the next step, we prove that if a fixed input with bounded amplitude is used at one user, the other user also needs to use a bounded amplitude signal to maximize the weighted sum-rate. To effectively analyze the KTC, our approach is to divide the domain of fading into two disjoint regions and examine the region with non-zero measure. It is then concluded that any boundary point in the capacity region is achieved by using bounded amplitude signals, and they are π/2 circularly symmetric. Building upon these results, we turn our focus to the sum-capacity segment, and demonstrate that any π/2 circularly symmetric input distribution having a constant amplitude is sum-capacity achieving. The sum-capacity can then be established.

Anomaly detection is widely applied in a variety of domains, involving for instance, smart home systems, network traffic monitoring, IoT applications and sensor networks. In this paper, we study deep reinforcement learning based active sequential testing for anomaly detection. We assume that there is an unknown number of abnormal processes at a time and the agent can only check with one sensor in each sampling step. To maximize the confidence level of the decision and minimize the stopping time concurrently, we propose a deep actor-critic reinforcement learning framework that can dynamically select the sensor based on the posterior probabilities. We provide simulation results for both the training phase and testing phase, and compare the proposed framework with the Chernoff test in terms of claim delay and loss.

The growing demand on high-quality and low-latency multimedia services has led to much interest in edge caching techniques. Motivated by this, we in this paper consider edge caching at the base stations with unknown content popularity distributions. To solve the dynamic control problem of making caching decisions, we propose a deep actor-critic reinforcement learning based multi-agent framework with the aim to minimize the overall average transmission delay. To evaluate the proposed framework, we compare the learning-based performance with three other caching policies, namely least recently used (LRU), least frequently used (LFU), and first-in-first-out (FIFO) policies. Through simulation results, performance improvements of the proposed framework over these three caching algorithms have been identified and its superior ability to adapt to varying environments is demonstrated.

Small Unmanned Aircraft Systems (sUAS) will be an important component of the smart city and intelligent transportation environments of the near future. The demand for sUAS related applications, such as commercial delivery and land surveying, is expected to grow rapidly in next few years. In general, sUAS traffic scheduling and management functions are needed to coordinate the launching of sUAS from different launch sites and plan their trajectories to avoid conflict while considering several other constraints such as expected arrival time, minimum flight energy, and availability of communication resources. However, as the airbone sUAS density grows in a certain area, it is difficult to foresee the potential airspace and communications resource conflicts and make immediate decisions to avoid them. To address this challenge, we present a temporal and spatial routing algorithm for sUAS trajectory management in a high density urban area. It plans sUAS movements in a spatial and temporal maze with the consideration of obstacles that are either static or dynamic in time. The routing allows the sUAS to avoid static no-fly areas (i.e. static obstacles) or other in-flight sUAS and areas that have congested communication resources (i.e. dynamic obstacles). The algorithm is evaluated using an agent-based simulation platform. The simulation results show that the proposed algorithm outperforms reference route management algorithms in many areas, especially in processing speed and memory efficiency. Detailed comparisons are provided for the sUAS flight time, the overall throughput, the conflict rate and communication resource utilization. The results demonstrate that our proposed algorithm can be used as a solution to improve the efficiency of airspace and communication resource utilization for next generation smart city and smart transportation.

Efficient use of spectral and energy resources is critical in wireless networks and has been extensively studied in recent years. In particular, dynamic spectrum access and power control have been addressed primarily via optimization and game-theoretic tools. In this paper, motivated by recent advances in machine learning and, more specifically, the success of reinforcement learning for addressing dynamic control problems, we consider deep reinforcement learning to jointly perform dynamic channel access and power control in wireless interference channels. We propose a deep Q-learning model, develop an algorithm, and evaluate the performance considering different utilities and reward mechanisms. We provide comparisons with the optimal centralized strategies that require complete information and use weighted minimum mean square error (WMMSE) based power control and exhaustive search over all channel selection policies. We highlight the performance improvements with power control.

In this paper, an analytical framework is provided to analyze the energy coverage performance of unmanned aerial vehicle (UAV) energy harvesting networks with clustered user equipments (UEs). Locations of UAVs are modeled as a Poison Point Process (PPP), while locations of UEs are modeled as a Poisson Cluster Process (PCP). Two different models are considered for the line-of-sight (LOS) probability function to compare their effect on the network performance. Moreover, ultra-wideband (UWB) antennas with doughnut-shaped radiation patterns are employed in both UAVs and UEs, and the impact of practical 3D antenna radiation patterns on the network performance is also investigated. Initially, the complementary cumulative distribution function (CCDF) and probability density function (PDF) of path losses for each tier are derived. Subsequently, association probabilities with each tier are obtained. Energy coverage probability is derived for the entire network using tools from stochastic geometry. Via numerical results, we show that UAV height and antenna orientation play crucial roles on the energy coverage performance.

This paper considers simultaneous wireless information and power transfer (SWIPT) mechanisms in a relaying-assisted ultra-reliable low latency communication network operating with finite blocklength codes. The reliability of the network is maximized by the optimal selection of SWIPT parameters under both a power splitting (PS) protocol and a time switching (TS) protocol. In addition, we propose a protocol to improve the reliability performance by introducing a tradeoff between the PS and TS protocols. To further improve the reliability, a joint design is provided, which aligns the optimal selection of SWIPT parameters together with a blocklength allocation between two relaying hops. Via simulations, we validate our analytical model and show that the proposed algorithm achieves the same performance as that obtained with exhaustive search. In addition, we evaluate the considered network, and characterize the impact of blocklength, transmit power, and packet size on the reliability of the considered SWIPT-enabled relaying network. Finally, the performance advantages of the proposed protocol (in comparison with the PS and TS protocols) and the proposed joint designs are investigated.

In this paper, we jointly consider the downlink simultaneous wireless information and power transfer (SWIPT) and uplink information transmission in unmanned aerial vehicle (UAV)-assisted millimeter wave (mmWave) cellular networks, in which the user equipment (UE) locations are modeled using Poisson cluster processes (e.g., Thomas cluster processes or Matérn cluster processes). Distinguishing features of mmWave communications, such as different path loss models for line-of-sight (LOS) and non-LOS (NLOS) links and directional transmissions are taken into account. In the downlink phase, the association probability, and energy coverages of different tier UAVs and ground base stations (GBSs) are investigated. Moreover, we define a successful transmission probability to jointly present the energy and signal-to-interference-plus-noise ratio (SINR) coverages and provide general expressions. In the uplink phase, we consider the scenario that each UAV receives information from its own cluster member UEs. We determine the Laplace transform of the interference components and characterize the uplink SINR coverage. In addition, we formulate the average uplink throughput, with the goal to identify the optimal time division multiplexing between the donwlink and uplink phases. Through numerical results we investigate the impact of key system parameters on the performance. We show that the network performance is improved when the cluster size becomes smaller. In addition, we analyze the optimal height of UAVs, optimal power splitting value and optimal time division multiplexing that maximizes the network performance.

In this paper, we characterize the optimal strategies focusing on the throughput and the system energy efficiency of wireless-powered communication networks (WPCNs) in the presence of delay-limited sources. Each energy-harvesting user equipment is assumed to be subject to limitations on the buffer overflow probability, specified by the quality of service (QoS) exponent θ. Correspondingly, the time allocation strategies for downlink energy harvesting and uplink information transfer depend on these QoS constraints, potentially overriding the doubly near-far problem of WPCNs. We consider the non-orthogonal transmission and time-division multiple access protocols for the uplink information transfer of WPCN, and for both cases, we formulate energy efficiency and throughput maximizing problems to obtain the globally optimal solution that satisfy the statistical QoS constraints. Since these optimization problems fall into concave or pseudo-concave categories, Karush-Kuhn-Tucker conditions are necessary and sufficient for global optimality, using which we obtain analytical expressions for the optimal operating intervals. In addition, in several cases, due to difficulty in providing closed-form expressions, we develop algorithms to solve the problems numerically. Finally, we provide the simulation results to confirm and further analyze the theoretical characterizations. We mainly observe that QoS constraints primarily affect the optimal time allocation policies as well as achievable rate distribution among the users, enabling us to overcome the doubly near-far problem of energy-harvesting communications networks pointed out as one of the critical issues in the literature.

Energy harvesting (EH) in wireless communications has become the focus of recent transmission technology studies. Herein, energy storage modeling is one of the crucial design benchmarks that must be treated carefully. Understanding the energy storage dynamics and the throughput levels is essential especially for communication systems in which the performance depends solely on harvested energy. While energy outages should be avoided, energy overflows should also be prevented in order to utilize all harvested energy. Hence, a simple, yet comprehensive, analytical model that can represent the characteristics of a general class of EH wireless communication systems needs to be established. In this paper, invoking tools from large deviation theory along with Markov processes, a firm connection between the energy state of the battery and the data transmission process over a wireless channel is established for an EH transmitter. In particular, a simple exponential approximation for the energy overflow probability is formulated, with which the energy decay rate in the battery as a measure of energy usage is characterized. Then, projecting the energy outages and supplies on a Markov process, a discrete state model is established and an expression for the energy outage probability for given energy arrival and demand processes is provided. Finally, under energy overflow and outage constraints, theaverage data service (transmission) rateover the wireless channel is obtained and theeffective capacityof the system, which characterizes the maximum data arrival rate at the transmitter buffer under quality-of-service (QoS) constraints imposed on the data buffer overflow probability, is derived.

This paper investigates the performance of wireless systems that employ finite-blocklength channel codes for transmission and operate under queuing constraints in the form of limitations on buffer overflow or delay violation probabilities. A block fading model, in which fading stays constant in each coherence block and changes independently between blocks, is considered. It is assumed that channel coding is performed over multiple coherence blocks. A simple ARQ scheme with error-free feedback without any delay is considered. The channel coding rate with given maximal error probability is considered as the service rate and is incorporated into the effective capacity formulation, which characterizes the maximum constant arrival rate that can be supported under statistical queuing constraints. Performances of variable-rate and fixed-rate transmissions are studied. The optimum error probability for variable-rate and fixed-rate transmissions is shown to be unique. The limiting performance as the number of blocks increases is characterized. The tradeoffs and the interactions between the throughput, the number of coherence blocks over which channel coding is performed, error probabilities, channel coherence duration, and queuing constraints are identified.

In this paper, we investigate the problem of power control for streaming variable bit rate (VBR) videos over wireless links. A system model involving a transmitter (e.g., a base station) that sends VBR video data to a receiver (e.g., a mobile user) equipped with a playout buffer is adopted, as used in dynamic adaptive streaming video applications. In this setting, we analyze power control policies considering the following two objectives: 1) the minimization of the transmit power consumption and 2) the minimization of the transmission completion time of the communication session. In order to play the video without interruptions, the power control policy should also satisfy the requirement in which the VBR video data is delivered to the mobile user without causing playout buffer underflow or overflows. A directional water-filling algorithm, which provides a simple and concise interpretation of the necessary optimality conditions, is identified as the optimal offline policy. Following this, two online policies are proposed for power control based on channel side information (CSI) prediction within a short time window. Dynamic programming is employed to implement the optimal offline and the initial online power control policies that minimize the transmit power consumption in the communication session. Subsequently, reinforcement learning (RL)-based approach is employed for the second online power control policy. Through the simulation results, we show that the optimal offline power control policy that minimizes the overall power consumption leads to substantial energy savings compared with the strategy of minimizing the time duration of video streaming. We also demonstrate that the RL algorithm performs better than the dynamic programming-based online grouped water-filling (GWF) strategy unless the channel is highly correlated.

In this paper, an analytical framework is provided to analyze the uplink performance of device-to-device (D2D)-enabled millimeter-wave (mm-wave) cellular networks with clustered D2D user equipments (UEs). The locations of cellular UEs are modeled as a Poisson point process, while the locations of potential D2D UEs are modeled as a Poisson cluster process. Signal-to-interference-plus-noise ratio outage probabilities are derived for both cellular and D2D links using tools from stochastic geometry. The distinguishing features of mm-wave communications such as directional beamforming and having different path loss laws for the line-of-sight and non-line-of-sight links are incorporated into the outage analysis by employing a flexible mode selection scheme and Nakagami fading. Also, the effect of beamforming alignment errors on the outage probability is investigated to get insight into the performance in practical scenarios. Moreover, area spectral efficiency of the cellular and D2D networks is determined for both underlay and overlay types of sharing. Optimal spectrum partition factor is determined for overlay sharing by considering the optimal weighted proportional fair spectrum partition.

This paper generalizes and proves the discrete and finite nature of the optimal signaling schemes for general classes of non-Gaussian aggregate interference point-to-point and multiple access channels (MACs) under peak power constraints. Specifically, we first investigate the detailed characteristics of optimal inputs for a single-user channel that is impaired by two types of noise: a Gaussian mixture (GM) noise consisting of Gaussian elements with arbitrary means, and the interference U with an arbitrary distribution. The only very mild condition imposed on U is that its second moment is finite. To this end, we establish the Kuhn-Tucker condition (KTC) on an optimal input and prove the analyticity of the KTC using Fubini-Tonelli's and Morera's theorems. It is then shown that an optimal input is continuous if only if the KTC is zero on the entire real line. However, by examining an upper bound on the output PDF, it is demonstrated that the KTC must be bounded away from zero. As such, any optimal input must be discrete with a finite number of mass points. Finally, we exploit U having an arbitrary distribution to show that the optimal input distributions that achieve the sumcapacity of an M-user MAC under GM noise are discrete and finite. Furthermore, there exist at least two distinct points that achieve the sum capacity on the rate region.

In this work, motivated by emerging low-latency applications, we consider an amplify-and-forward relaying network operating with finite blocklength (FBL) codes subject to delay quality of service (QoS) constraints. Hence, we address both transmission delay (via FBL codes) and queueing delay (via delay QoS requirements). We first derive the QoS-constrained throughput of the network. Subsequently, we state a resource allocation problem aiming at allocating the power between the source and the relay to maximize the throughput. The convexity of the problem is proved and the optimal power allocation policy is provided. Via simulations, we confirm the accurateness of our analytical model. In addition, we provide interesting insights on the system behavior by characterizing the impact of the error probability, the QoS-exponent and coding blocklength on the throughput performance.

In this paper, an analytical framework is provided to analyze the uplink performance of device-to-device (D2D)-enabled millimeter wave (mmWave) cellular networks with clustered D2D user equipments (UEs). Locations of cellular UEs are modeled as a Poison Point Process (PPP), while locations of potential D2D UEs are modeled as a Poisson Cluster Process (PCP). Signal-to-interference-plus-noise ratio (SINR) outage probabilities are derived for both cellular and D2D links using tools from stochastic geometry. The distinguishing features of mmWave communications such as directional beamforming and having different path loss laws for line-of-sight (LOS) and non-line-of-sight (NLOS) links are incorporated into the outage analysis by employing a flexible mode selection scheme.

A K-tier heterogeneous mmWave uplink cellular network with user-centric small cell deployments is considered in this paper. In particular, the user equipments (UEs) are assumed to be clustered around small-cell base stations (BSs) according to the Thomas cluster process. Practical line-of-sight (LOS) and none-line-of-sight (NLOS) models are adopted with different parameters for different tiers. In this setting, we consider a coupled association probability model, and derive the probability that the BSs of each tier are associated with UEs from each cluster. Following the identification of the association probabilities, we characterize the Laplace transforms of the inter-cell interferences in closed-form. Using tools from stochastic geometry, we provide a general expression of the SINR coverage probability in each tier. Via numerical and simulation results, we confirm the analysis and the derived expressions, and investigate the impact of important system parameters.

In this paper, considering randomly distributed access points and energy-harvesting user equipments, we study the impact of downlink/uplink operation intervals as well as receiver power splitting factor on the performance of cellular networks. We assume that the access points transmit information and power transfer simultaneously over the downlink channels to the associated user equipments, where the link is established based on lowest path loss. Using stochastic geometry, we characterize the average harvested energy and SINR coverage probabilities as a function of system parameters. In addition, we consider outage capacity and ergodic capacity for the downlink and uplink information transfer, respectively. Furthermore, we obtain an expression for the system energy efficiency (measured by throughput per total consumed energy) in terms of uplink/downlink operating intervals and power splitting factor. Simulation results demonstrate that both energy efficiency and network throughput improve when the access points are densely populated. Furthermore, we observe that increasing the downlink transmit power level affects the optimal power splitting factor as well as downlink/uplink time allocation strategy.

In this paper, we consider a downlink multiuser network operating with finite blocklength codes under statistical quality of service (QoS) constraints. An optimal power allocation algorithm is studied to maximize the normalized sum throughput under QoS constraints. We first determine the finite blocklength (FBL) throughput formulations and subsequently state optimization problems. We show the convexity of the power allocation problem under certain conditions and propose an optimal algorithm to solve the problem. Via numerical analysis, we demonstrate the performance improvements with the optimal power allocation. In addition, we provide interesting insights on the system behavior by characterizing the impact of the error probability, the QoS-exponent and blocklength on the performance.

In this paper, energy-efficient and secure communication over wireless fading channels is investigated when data arrivals are modeled as Markovian processes (e.g., discrete Markov and Markov fluid processes) in the presence of limitations on buffer overflow probabilities. It is assumed that the transmitter knows only the distributions of the fading coefficients in both the eavesdropper's and legitimate user's channels. Consequently, data transmissions occur at fixed rates, with secrecy outage happening when the instantaneous secrecy capacity is less than the fixed rate. In this setting, by employing the effective bandwidth and effective capacity formulations, throughput expressions are derived, and energy efficiency metrics, namely minimum energy per bit and wideband slope, are determined. Overall, the impact of source and channel variations as well as the delay quality of service (QoS) requirements on the energy efficiency of secure communications is identified.

In this paper, we investigate the problem of power control for streaming variable-bit-rate (VBR) videos in a device-to-device (D2D) wireless network. A VBR video traffic model that considers video frame sizes and playout buffers at the mobile users is adopted. A setup with one pair of D2D users (DUs) and one cellular user (CU) is considered and three modes, namely cellular mode, dedicated mode and reuse mode, are employed. Mode selection for the data delivery is determined and the transmit powers of the base station (BS) and device transmitter are optimized with the goal of maximizing the overall transmission rate while VBR video data can be delivered to the CU and DU without causing playout buffer underflows or overflows. A low-complexity algorithm is proposed. Through simulations with VBR video traces over fading channels, we demonstrate that video delivery with mode selection and power control achieves a better performance than just using a single mode throughout the transmission.

In this paper, wireless video transmission over full-duplex channels under total bandwidth and minimum required quality constraints is studied. In order to provide the desired performance levels to the end-users in real-time video transmissions, quality of service requirements such as statistical delay constraints are also considered. Effective capacity is used as the throughput metric in the presence of such statistical delay constraints since deterministic delay bounds are difficult to guarantee due to the time-varying nature of wireless fading channels. A communication scenario with multiple pairs of users in which different users have different delay requirements is addressed. Following characterizations from the rate-distortion theory, a logarithmic model of the quality-rate relation is used for predicting the quality of the reconstructed video in terms of the peak signal-to-noise ratio at the receiver side. Since the optimization problem is not concave or convex, the optimal bandwidth and power allocation policies that maximize the weighted sum video quality subject to total bandwidth, maximum transmission power level and minimum required quality constraints are derived by using monotonic optimization theory.

In this paper, we consider a downlink multiuser network operating with finite blocklength (FBL) codes under statistical quality of service (QoS) constraints. Optimal power allocation algorithms are studied to maximize the normalized sum throughput under QoS constraints, while considering different types of data arrivals, namely, constant-rate, Markov, and Markov-modulated Poisson arrivals. We first determine the FBL throughput formulations and subsequently state optimization problems. We show the convexity of the power allocation problem under certain conditions and propose optimal algorithms (for scenarios with different data arrivals). In addition, the FBL performance of equal power allocation and a sub-optimal power allocation algorithm is discussed. Via numerical analysis, we demonstrate the performance improvements with the optimal power allocation. In addition, we provide interesting insights on the system behavior by characterizing the impact of the error probability, the QoS exponent, the blocklength, the number of users, and the source burstiness on the performance.

Device-to-device (D2D) communication underlaid with cellular networks is a new paradigm, proposed to enhance the performance of cellular networks. By allowing a pair of D2D users to communicate directly and share the same spectral resources with the cellular users, D2D communication can achieve higher spectral efficiency, improve the energy efficiency, and lower the traffic delay. In this paper, we propose a novel joint mode selection and channel resource allocation algorithm via the vertex coloring approach. We decompose the problem into three subproblems and design algorithms for each of them. In the first step, we divide the users into groups using a vertex coloring algorithm. In the second step, we solve the power optimization problem using the interior-point method for each group and conduct mode selection between the cellular mode and D2D mode for D2D users, and we assign channel resources to these groups in the final step. Numerical results show that our algorithm achieves higher sum rate and serves more users with relatively small time consumption compared with other algorithms. Also, the influence of system parameters and the tradeoff between sum rate and the number of served users are studied through simulation results.

Cloud Radio Access Networks (RANs) have become a key enabling technique for the next generation (5G) wireless communications, which can meet requirements of massively growing wireless data traffic. However, resource allocation in cloud RANs still needs to be further improved in order to reach the objective of minimizing power consumption and meeting demands of wireless users over a long operational period. Inspired by the success of Deep Reinforcement Learning (DRL) on solving complicated control problems, we present a novel DRL-based framework for power-efficient resource allocation in cloud RANs. Specifically, we define the state space, action space and reward function for the DRL agent, apply a Deep Neural Network (DNN) to approximate the action-value function, and formally formulate the resource allocation problem (in each decision epoch) as a convex optimization problem. We evaluate the performance of the proposed framework by comparing it with two widely-used baselines via simulation. The simulation results show it can achieve significant power savings while meeting user demands, and it can well handle highly dynamic cases.

In this paper, throughput of hybrid automatic repeat request (HARQ) schemes with finite blocklength codes is studied for both constant-rate and ON-OFF discrete-time Markov arrivals under statistical queuing constraints and deadline limits. After analyzing the decoding error probability and outage probability, the distribution of transmission period is characterized, and the throughput expressions are obtained for both arrival models. Analytical results are verified via Monte Carlo simulations. In the numerical results, the impact of deadline constraints, fixed transmission rate, coding blocklength, and queuing constraints on the throughput is analyzed.

In this work, we focus on a low-latency multiuser broadcast network operating in the finite blocklength regime and employing a non-orthogonal multiple-access (NOMA) scheme. By letting the user with the stronger channel from the source act as a relay, we propose two relay-assisted transmission schemes, namely relaying and NOMA-relay. We study the finite blocklength performance of the proposed schemes in comparison with the NOMA scheme. Both the average performance of and fairness between users are considered. Our results show that the NOMA scheme is not preferred in the low-latency scenario in comparison to the proposed schemes. In particular, the relaying scheme generally provides the best fairness between users, while the NOMA-relay scheme is able to achieve a higher average throughput by setting the packet size relatively aggressively.

A K-tier heterogeneous downlink millimeter wave (mmWave) cellular network with user-centric small cell deployments is studied in this paper. In particular, we consider a heterogeneous network model with user equipments (UEs) being deployed according to a Poisson Cluster Process (PCP), i.e., Thomas cluster process, where the UEs are clustered around the base stations (BSs) and the distances between UEs and the BS are modeled as Gaussian distributed. In addition, distinguishing features of mmWave communications including directional beamforming and a sophisticated path loss model incorporating both line-of-sight (LOS) and non-line-of-sight (NLOS) transmissions, are taken into account. In this paper, the complementary cumulative distribution function (CCDF) and probability density function (PDF) of the path loss are provided. Also, using tools from stochastic geometry, we derive a general expression of the signal-to-interference-plus-noise ratio (SINR) coverage probability. Our results demonstrate that coverage probability can be improved by decreasing the size of UE clusters around BSs, and interference has noticeable influence on the coverage performance of our model.

Cloud radio access network (C-RAN) architecture is a new mobile network architecture that enables cooperative baseband processing and information sharing among multiple cells and achieves high adaptability to nonuniform traffic by centralizing the baseband processing resources in a virtualized baseband unit (BBU) pool. In this work, we formulate the utility of each user using a convex delay cost function, and design a two-step scheduling algorithm with good delay performance for the C-RAN architecture. In the first step, all users in multiple cells are grouped into small user groups, according to their interference levels and estimated utilities. In the second step, channels are matched to the user groups to maximize the system utility. The performance of our algorithm is further studied via simulations, and the advantages of C-RAN architecture is verified.

In this paper, we provide an analytical framework to analyze the uplink performance of device-to- device (D2D)-enabled millimeter wave (mmWave) cellular networks. Signal-to-interference-plus- noise ratio (SINR) outage probabilities are derived for both cellular and D2D links using tools from stochastic geometry. The distinguishing features of mmWave communications such as directional beamforming and having different path loss laws for line-of-sight (LOS) and non-line-of- sight (NLOS) links are incorporated into the outage analysis by employing a flexible mode selection scheme and Nakagami fading.

In this paper, we study the performance of non-orthogonal multiple access (NOMA) scheme in wireless-powered communication networks (WPCN) focusing on the system energy efficiency. We consider multiple energy-harvesting user equipments (UEs) that operate based on harvest-then-transmit protocol. The uplink information transfer is carried out by using power-domain multiplexing, and the receiver decodes each UE's data in such a way that the UE with the best channel gain is decoded without interference. In order to determine optimal resource allocation strategies, we formulate optimization problems considering two models, namely half-duplex operation and asynchronous transmission, based on how downlink and uplink operations are coordinated. In both cases, we have concave-linear fractional problems, and hence Dinkelbach's method can be applied to obtain the globally optimal solutions. Thus, we first derive analytical expressions for the harvesting interval, and then we provide an algorithm to describe the complete procedure. Simulation results are included to justify the theoretical characterizations. In fact, we observe that broadcasting at higher power level is more energy efficient for WPCN with uplink NOMA.

In this paper, wireless video transmission over full-duplex channels is studied. In order to provide the desired performance levels to the end-users in real-time video transmissions, quality of service (QoS) requirements such as statistical delay constraints are also considered. Effective capacity (EC) is used as the throughput metric in the presence of such statistical delay constraints since deterministic delay bounds are difficult to guarantee due to the time-varying nature of wireless fading channels. A communication scenario with a pair of users and multiple subchannels in which users can have different delay requirements is addressed. Following characterizations from the rate-distortion (R-D) theory, a logarithmic model of the quality-rate relation is used for predicting the quality of the reconstructed video in terms of the peak signal-to-noise ratio (PSNR) at the receiver side. Since the optimization problem is not concave or convex, the optimal power allocation policy that maximizes the weighted sum video quality subject to total transmission power constraint is derived by using monotonic optimization (MO) theory. The optimal scheme is compared with two suboptimal strategies.

In this paper, we consider a wireless scenario in which multiple-nodes operating under delay constraints transmit finite alphabet input signals for simultaneous wireless information and power transfer (SWIPT). These nodes communicate through time-division multiple access channels, and the receiving node harvests energy from the received signal while decoding information by applying power splitting scheme. In addition, the transmitting nodes are subject to limitations on the buffer overflow probability, specified by the quality of service (QoS) exponent. Due to harvested energy constraint, we have introduced a novel approach that assigns probabilities non-uniformly to different signals in the constellation which improves the overall performance in terms of throughput and energy efficiency (EE). We formulate optimization problems to maximize the effective capacity and effective EE while taking input signal probabilities, operating intervals, and splitting factor into account. Since obtaining closed-form expressions for the optimization parameters is unlikely, we develop an algorithm to determine the solutions numerically. In the numerical results, we observe that QoS constraints primarily affect achievable rate distribution among the users, and override the channel conditions. In addition, having static slope characteristics for non-uniform probability assignment is more energy efficient than having dynamic characteristics.

This paper studies energy efficiency (EE) and average throughput maximization for cognitive radio systems in the presence of unslotted primary users. It is assumed that primary user activity follows an ON-OFF alternating renewal process. Secondary users first sense the channel possibly with errors in the form of miss detections and false alarms, and then, start the data transmission only if no primary user activity is detected. The secondary user transmission is subject to constraints on collision duration ratio, which is defined as the ratio of average collision duration to transmission duration. In this setting, the optimal power control policy which maximizes the EE of the secondary users or maximizes the average throughput while satisfying a minimum required EE under average/peak transmit power and average interference power constraints is derived. Subsequently, low-complexity algorithms for jointly determining the optimal power level and frame duration are proposed. The impact of probabilities of detection and false alarm, transmit and interference power constraints on the EE, average throughput of the secondary users, optimal transmission power, and the collisions with primary user transmissions are evaluated. In addition, some important properties of the collision duration ratio are investigated. The tradeoff between the EE and average throughput under imperfect sensing decisions and different primary user traffic are further analyzed.

In this paper, throughput and energy efficiency of secure wireless transmission of delay sensitive data generated by random sources is studied. A fading broadcast model in which the transmitter sends confidential and common messages to two receivers is considered. It is assumed that the common and confidential data, generated from Markovian sources, are stored in buffers prior to transmission, and the transmitter operates under constraints on buffer/delay violation probability. Under such statistical quality-of-service constraints, effective capacity of time-varying wireless transmissions and effective bandwidth of Markovian sources are employed to determine the throughput. In particular, secrecy capacity is used to describe the service rate of buffers containing confidential messages. Moreover, energy per bit is used as the energy efficiency metric and energy efficiency is studied in the low signal-to-noise regime. Specifically, minimum energy per bit required for the reliable communication of common and confidential messages is determined and wideband slope expressions are identified. The impact of buffer/delay constraints, correlation between channels, source characteristics/burstiness, channel knowledge at the transmitter, power allocation, and secrecy requirements on the throughput and energy efficiency of common and confidential message transmissions is identified.

This paper studies energy-efficiency (EE) and throughput optimization in the presence of randomly arriving data and quality of service (QoS) constraints. For this purpose, maximum average arrival rates supported by transmitting signals with arbitrary input distributions are initially characterized in closed form by employing the effective bandwidth of time-varying sources (e.g., discrete-time Markov and Markov fluid sources, and discrete-time and continuous-time Markov modulated Poisson sources) and effective capacity of the time-varying wireless channel. Subsequently, EE is formulated as the ratio of the maximum average arrival rate to the total power consumption, in which circuit power is also taken into account. Following these characterizations, the optimal power control policies maximizing the EE or maximizing the throughput under a minimum EE constraint are obtained. Through numerical results, the performance of the optimal power control policies is evaluated for different signal constellations and is also compared with that of constant power transmission. The impact of QoS constraints, source characteristics, circuit power, input distributions on the EE, and the throughput is analyzed.

We envision that in the near future, just as Infrastructure-as-a-Service, radios, and radio resources in a wireless network can also be provisioned as a service to mobile virtual network operators (MVNOs), which we refer to as Radio-as-a-Service (RaaS). A major obstacle for wide adoption of RaaS is the lack of incentives and fairness for allocating radio resources among MVNOs. In this paper, we present a novel auction-based model to enable fair pricing and fair resource allocation according to real-time needs of MVNOs for RaaS. Based on the proposed model, we study the auction mechanism design with the objective of maximizing social welfare. First, we present an integer linear programming and Vickrey-Clarke-Groves-based auction mechanism for obtaining optimal social welfare. To reduce time complexity, we present a polynomial-time greedy mechanism for the RaaS auction. Both methods have been formally shown to be truthful and individually rational. Extensive simulation results show that the proposed greedy auction mechanism can quickly produce close-to-optimal solutions. Furthermore, to prevent winning bidders from making 0 payment, we introduce reserve prices, and present auction mechanisms with reserve prices, which are shown to be truthful and individually rational too.

In this paper, throughput of hybrid automatic repeat request (HARQ) schemes is studied in the presence of Markovian data arrivals and statistical queuing constraints. In particular, two queuing models are considered. Specifically, when outage occurs, the transmitter keeps the packet, lowers its priority, and attempts to retransmit it later in the first queue model while the packet is discarded and removed from the buffer in the second queue model. The throughput is investigated when outage constraints, statistical queuing constraints and deadline constraints are imposed. The deadline constraint provides a limitation on the number of retransmissions. Under these assumptions, throughput characterizations are obtained for HARQ chase combining (CC) scheme with three types of Markovian sources, namely the ON-OFF discrete-time and fluid Markov sources and Markov modulated Poisson source (MMPS). Our analytical results are verified via Monte Carlo simulations. In the numerical results, the impact of source randomness, deadline constraints, outage probability and queuing constraints on the throughput is analyzed.

In this paper, we provide an analytical framework to analyze heterogeneous downlink millimeter-wave (mm-wave) cellular networks consisting of K tiers of randomly located base stations (BSs), where each tier operates in an mm-wave frequency band. Signal-to-interference-plus-noise ratio (SINR) coverage probability is derived for the entire network using tools from stochastic geometry. The distinguishing features of mm-wave communications, such as directional beamforming, and having different path loss laws for line-of-sight and non-line-of-sight links are incorporated into the coverage analysis by assuming averaged biased-received power association and Nakagami fading. By using the noise-limited assumption for mmwave networks, a simpler expression requiring the computation of only one numerical integral for coverage probability is obtained. Also, the effect of beamforming alignment errors on the coverage probability analysis is investigated to get insight on the performance in practical scenarios. Downlink rate coverage probability is derived as well to get more insights on the performance of the network. Moreover, the effect of deploying low-power smaller cells and the impact of biasing factor on energy efficiency is analyzed. Finally, a hybrid cellular network operating in both mm-wave and μ-wave frequency bands is addressed.

This paper details novel methods to accurately estimate the achievable rates of channels with additive Gaussian mixture (GM) noise. Attention is paid to a Gaussian input and discrete inputs. Such discrete inputs represent a wide range of signaling strategies and include the capacity-achieving input as a special case. At first, we propose a simple technique to calculate the GM noise entropy. Specifically, when the noise level is high, a lower bound on the integrand of the noise entropy is established and the noise entropy can be estimated in closed-form. In the low noise region, the piecewise-linear curve fitting (PWLCF) method is applied to calculate the noise entropy. It is then demonstrated this can be estimated in both regions with a predetermined accuracy. We then extend this result to calculate the output entropy and the achievable rate when the input is Gaussian distributed, which is shown to be asymptotically optimal. Next, we propose a simple PWLCF-based method to estimate the output entropy for a given discrete input. In particular, the output entropy is evaluated by examining the output in high and low regions of amplitude using a lower bound on the integrand of the output entropy and PWLCF, respectively. It is demonstrated that the output entropy, and consequently, the achievable rates, can be computed to achieve any desired accuracy level.

Device-to-device (D2D) communication underlaid with cellular networks is a new paradigm, proposed to enhance the performance of cellular networks. By allowing a pair of D2D users to communicate directly and share the same spectral resources with the cellular users, D2D communication can achieve higher spectral efficiency, improve the energy efficiency, and lower the traffic delay. In this paper, transmission mode selection and resource allocation in a time-division multiplexed (TDM) cellular network with one cellular user, one base station, and a pair of D2D users is investigated under rate and queueing constraints. In particular, four possible modes are considered, namely the cellular mode, dedicated mode, uplink reuse mode, and downlink reuse mode. Using tools from stochastic network calculus, the system throughput under statistical queueing constraints is formulated, efficient resource allocation algorithms for all possible modes are proposed, and the influence of the positions of each node and the queueing constraints is analyzed via numerical results. Scenarios and conditions for different modes to be optimal in the sense of maximizing the sum-throughput are identified.

Power control policies in the fading multiple-access channels (MAC) with quality of service (QoS) constraints and random arrivals are studied. Perfect channel side information (CSI) is assumed to be available at both the transmitters and the receiver. Two types of Markovian sources, namely discrete Markov source and Markov fluid source, are considered. The maximum average arrival rates that can be supported in the fading multiple-access channel under QoS constraints are identified by incorporating the effective capacity of time-varying wireless transmission channels and effective bandwidth of random arrivals. The average throughput region is shown to be convex. Power control policies that maximize the weighted summation of the average arrival rates are characterized in the two-user case. Specifically, given the decoding order strategy, the conditions that the optimal power control policies must satisfy are determined, and an algorithm for the optimal power control policies is proposed for different source arrival models.

This paper investigates the energy efficiency of communication channels subject to additive Gaussian mixture interference in the low-power regime. This channel is widely used to capture the asynchronism in heterogeneous cellular networks. In particular, we characterize the low-signal-to-noise-ratio (low-SNR) metrics of minimum energy per bit or capacity per unit cost and the wideband slope of the spectral-efficiency curve. Instead of directly maximizing the mutual information or determining the optimal input, we make use of the characterization that the first derivative of the capacity with respect to SNR at SNR= 0 can be determined from the Kullback-Leibler divergence without identifying the optimal input. We also identify the wideband slope of spectral efficiency in closed-form by relying on the discreteness in amplitude of the capacity-achieving input. The characterization of these fundamental energy efficiency metrics allows us to find the signaling strategies that are optimally efficient in the low-SNR regime and obtain an analytical confirmation of the earlier information-theoretic findings.

In this paper, we study the performance of wireless information and power transfer in the presence of statistical queuing constraints. We consider harvest-then-transmit protocol in which users first harvest energy from a dedicated source and then transmit information through an uplink multiple access channel (MAC). Each user is subject to limitations on the buffer overflow probability, specified by the quality of service (QoS) exponent θ, and the optimal time allocation for energy harvesting and information decoding operations depends on these constraints in addition to the channel characteristics. We formulate optimization problems to maximize the throughput with and without QoS constraints. In both cases, the problems are convex, and hence Karush-Kuhn-Tucker (KKT) conditions are necessary and sufficient for global optimality. However, it is difficult to obtain closed-form expressions for optimal time interval since we assume that operating intervals are independent of each fading state realization. Hence, we develop an algorithm to obtain optimal solutions numerically. Simulation results justify that QoS constraints primarily affect achievable rate distribution among the users, and override the channel conditions.

This paper studies the performance of hierarchical modulation-based image transmission in device-to-device (D2D) cellular wireless networks under constraints on both transmit and interference power levels. Hierarchical quadrature amplitude modulation (HQAM) is considered in which high priority (HP) data is protected more than low priority (LP) data. In this setting, closed-form bit error rate (BER) expressions for HP data and LP data are derived over multiple Rayleigh fading subchannels in 3 different transmission modes. The optimal power control that minimizes weighted sum of average BERs of HP bits and LP bits or its upper bound subject to average transmit power and average interference power constraints is derived. Performance comparisons of image transmission in 3 different modes are carried out, and the proposed power control strategies are evaluated in terms of the BERs and received data quality.

Inspired by the success of use of Virtual Machines (VMs) in cloud computing, virtualization has been introduced to wireless networking recently, enabling support for multiple Mobile Virtual Network Operators (MVNOs) via isolated slices over a shared wireless substrate. In this paper, we present design, implementation and evaluation of a novel cloud framework, R-Cloud, to enable radio resources at Base Stations (BSs) to be effectively allocated to multiple MVNOs as a service, which is referred to as Radio-as-a-Service (RaaS). R-Cloud employs a hybrid two-level control framework to enable coarse-grained and fine-grained resource allocation at the cloud and BS levels respectively. Specifically, R-Cloud not only coordinates resource allocation among BSs, MVNOs and mobile users across a Radio Access Network (RAN) and enables performance isolation by optimizing resource sharing and user association using an LP-rounding based algorithm at the cloud level; but also effectively schedule transmissions among multiple users at a BS using an optimal scheduling policy. We implemented R-Cloud over a wireless network testbed with software defined radios. It has been shown by extensive experimental and simulation results that R-Cloud can achieve effective RaaS over wireless networks and the proposed resource allocation algorithms outperform widely-used baseline solutions.

In this paper, throughput of two-hop wireless relay channels is studied in the finite blocklength regime. Half-duplex relay operation, in which the source node initially sends information to the intermediate relay node and the relay node subsequently forwards the messages to the destination, is considered. It is assumed that all messages are stored in buffers before being sent through the channel, and both the source node and the relay operate under statistical queueing constraints. After characterizing the transmission rates in the finite blocklength regime, the system throughput is formulated via queueing analysis. Subsequently, several properties of the throughput function in terms of system parameters are identified, and an efficient algorithm is proposed to maximize the throughput. Interplay between throughput, queueing constraints, relay location, time allocation, and code blocklength is investigated through numerical results.

This paper proposes optimal power adaptation schemes that maximize the energy efficiency (EE) in the presence of Markovian sources and finite-alphabet inputs subject to quality of service (QoS) constraints. First, maximum average arrival rates supported by transmitting signals with arbitrary input distributions are characterized in closed-form by employing the effective bandwidth of time-varying sources (e.g., discrete-time Markov and Markov fluid sources) and effective capacity of the time-varying wireless channel. Subsequently, EE is defined as the ratio of the maximum average arrival rate to the total power consumption, in which circuit power is also taken into account. Following these characterizations, an optimization problem is formulated to maximize the EE of the system, and optimal power control schemes are determined. Through numerical results, the performance of the optimal power control policies is evaluated for different signal constellations and is also compared with that of constant power transmission. The impact of QoS constraints, source characteristics, input distributions on the maximum achievable EE and the throughput is analyzed.

In this paper, we develop a scheduling algorithm for device-to-device (D2D) cellular networks with deadline constraints via the convex delay cost approach. At the beginning of each time slot, the algorithm allocates all available channels to the users, and each user can choose to transmit in different modes. After characterizing the transmission rates and defining the utility for each possible scheduling decision, we propose power optimization algorithms to maximize the utility for each type of decision. Our scheduling algorithm allocates each channel according to the decision that provides the maximum utility value, and it manages mode selection, channel allocation and power optimization. Via simulation results, we discuss the parameter selection for our algorithm and verify the performance improvements by allowing D2D users to share channels with other users.

In this paper, energy efficiency of relay-assisted millimeter wave (mmWave) cellular networks with Poisson Point Process (PPP) distributed base stations (BSs) and relay stations (RSs) is analyzed using tools from stochastic geometry. The distin- guishing features of mmWave communications such as directional beamforming and having different path loss laws for line-of-sight (LOS) and non-line- of-sight (NLOS) links are incorporated into the energy efficiency analysis. Following the description of the system model for mmWave cellular networks, coverage probabilities are computed for each link. Subsequently, average power consumption of BSs and RSs are modeled and energy efficiency is determined in terms of system parameters. Energy efficiency in the presence of beamforming alignment errors is also investigated to get insight on the performance in practical scenarios. Finally, the impact of BS and RS densities, antenna gains, main lobe beam widths, LOS interference range, and alignment errors on the energy efficiency is analyzed via numerical results.

In this paper, we study optimal resource allocation strategies focusing on the system energy efficiency with simultaneous power transfer and information decoding operation at the access point (AP). We consider a three node wireless system that consists of the AP, energy harvesting user (EHU) and non-energy harvesting user (N-EHU), and EHU opportunistically harvests energy from N-EHU and the AP. Using this model, we propose introducing an energy- bearing signal in addition to the information- bearing signal at the N-EHU, and investigate their significance to improve the overall energy efficiency. We formulate an optimization problem that maximizes the system energy efficiency by taking the harvested energy constraint at the EHU into account. Based on this, we provide analytical expressions for the optimal transmit power level when the constraint is satisfied with strict inequality by the energy-efficiency-maximizing input. In such a case, system energy efficiency improves along with the energy demand, and using only information-bearing signal is optimal. On the other hand, when the constraint needs to be satisfied with equality, including an energy- bearing signal leads to better performance. However, explicit expressions to determine power allocation strategies are not immediately available, and hence we develop an algorithm using the subgradient method to solve the problem numerically. To justify these theoretical framework, we provide simulation results. In fact, we observe that having an energy-bearing signal together with the information-bearing signal improves the system energy efficiency and leads to higher levels of harvested energy. We also demonstrate the impact of peak power on the resource allocation policies.

This paper studies energy-efficient power allocation schemes for secondary users in sensing-based spectrum sharing cognitive radio systems. It is assumed that secondary users first perform channel sensing possibly with errors and then initiate data transmission with different power levels based on sensing decisions. In this setting, the optimization problem is to maximize energy efficiency (EE) subject to peak/average transmission power constraints and peak/average interference constraints. By exploiting the quasi-concave property of the EE maximization problem, the original problem is transformed into an equivalent parameterized concave problem, and an iterative power allocation algorithm based on Dinkelbach's method is proposed. The optimal power levels are identified in the presence of different levels of channel side information (CSI) regarding the transmission and interference links at the secondary transmitter, namely, perfect CSI of both transmission and interference links, perfect CSI of the transmission link, imperfect CSI of the interference link, imperfect CSI of both links, or only statistical CSI of both links. Through numerical results, the impact of sensing performance, different types of CSI availability, and transmit and interference power constraints on the EE of the secondary users is analyzed.

In this paper, we detail effective methods to approximate the achievable rates of channels with additive Gaussian mixture (GM) noise for both real and complex channels to achieve any desired level of accuracy. Attention is paid to a Gaussian input, a discrete real input, and a complex input with discrete amplitude and independent uniform phase. Such discrete inputs represent a wide range of input distributions and they include the capacity-achieving inputs as special cases. At first, we propose a simple technique to accurately calculate the noise entropy. Specifically, when the noise level is high, a lower bound on the integrand of the entropy is established and the noise entropy can be estimated using a closed-form solution. In the low noise region, the piecewise-linear curve fitting (PWLCF) method is applied. We then extend this result to calculate the achievable rate when the input is Gaussian distributed, which is shown to be asymptotically optimal. Next, we propose a simple PWLCF-based method to approximate the output entropy for a real GM channel when the input is discrete, and for a complex GM channel when the input is discrete in amplitude with independent uniform phase. In particular, for the real channel, the output entropy is evaluated by examining the output in high and low regions of amplitude using a lower bound on the integrand of the output entropy and PWLCF, respectively. For the complex channel, the output entropy is approximated a similar manner but using polar coordinates and the Kernel function. It is demonstrated that the output entropy, and consequently, the achievable rates, can be computed to achieve any given accuracy level.

In this paper, energy efficiency of hybrid automatic repeat request (HARQ) schemes with statistical queuing constraints is studied for both constant-rate and random Markov arrivals by characterizing the minimum energy per bit and wideband slope. In particular, two queuing models are considered. Specifically, when outage occurs, the transmitter keeps the packet, lowers its priority, and attempts to retransmit it later in the first queue model, while the packet is discarded and removed from the buffer in the second queue model. For both models, energy efficiency is investigated when outage constraints, statistical queuing constraints, and deadline constraints are imposed. The deadline constraint provides a limitation on the number of retransmissions or equivalently the number of HARQ rounds. Under these assumptions, closed-form expressions are obtained for the minimum energy per bit and wideband slope for HARQ with chase combining, and comparisons among different arrival models are made. For instance, it is shown that stricter queuing constraints and more bursty sources degrade the energy efficiency by lowering the wideband slope. In the numerical results, analytical characterizations are verified through simulations. Moreover, the impact of source variations/burstiness, deadline constraints, outage probability, and queuing constraints on the energy efficiency is analyzed.

In this paper, energy-efficient power adaptation policies in fading channels are analyzed when data arrivals are modeled as Markovian processes (namely, discrete Markov, Markov fluid, and discrete and fluid Markov modulated Poisson processes) and statistical quality of service (QoS) constraints are imposed on buffer overflow probabilities. In the analysis, both transmission and circuit power consumptions are considered. After formulating energy efficiency (EE) as maximum throughput normalized by the total power consumption, optimal power control policies that maximize EE are obtained for different source models. The impact of source randomness on EE is determined. Optimal power control schemes maximizing the throughput under EE or average power constraints are also investigated. With this, tradeoff between throughput and EE is studied. Finally, the analysis is extended to multichannel scenarios. Overall, the influence of source statistics, QoS constraints, and number of subchannels on the optimal power control policies, throughput, and EE performance is identified.

This paper analyzes the impact of statistical delay constraints on the achievable rate of a two-hop wireless communication link in which the communication between a source and a destination is accomplished via a buffer-aided relay node. It is assumed that there is no direct link between the source and the destination, and the buffer-aided relay forwards the information to the destination by employing the decode-and-forward scheme. Given statistical delay constraints specified via maximum delay and delay violation probability, the tradeoff between the statistical delay constraints imposed on any two concatenated queues is identified. With this characterization, the maximum constant arrival rates that can be supported by this two-hop link are obtained by determining the effective capacity of such links as a function of the statistical delay constraints, signal-to-noise ratios at the source and relay, and the fading distributions of the links. It is shown that asymmetric statistical delay constraints at the buffers of the source and relay node can improve the achievable rate. Overall, the impact of the statistical delay tradeoff on the achievable throughput is provided.

Throughput and energy efficiency in fading channels are studied in the presence of randomly arriving data and statistical queuing constraints. In particular, Markovian arrival models, including discrete-time Markov, Markov fluid, and Markov-modulated Poisson sources, are considered. Employing the effective bandwidth of time-varying sources and the effective capacity of time-varying wireless transmissions, maximum average arrival rates in the presence of statistical queuing constraints are characterized. For the two-state (ON/OFF) source models, throughput is determined in a closed form as a function of the source statistics, channel characteristics, and quality of service (QoS) constraints. Throughput is further studied in certain asymptotic regimes. Furthermore, energy efficiency is analyzed by determining the minimum energy per bit and a wideband slope in the low signal-to-noise ratio regime. Overall, the impact of source characteristics, QoS requirements, and channel fading correlations on the throughput and energy efficiency of wireless systems is identified.

In this paper, the throughput of relay networks with multiple source-destination pairs under queueing constraints has been investigated for both variable-rate and fixed-rate schemes. When channel side information (CSI) is available at the transmitter side, transmitters can adapt their transmission rates according to the channel conditions, and achieve the instantaneous channel capacities. In this case, the departure rates at each node have been characterized for different system parameters, which control the power allocation, time allocation, and decoding order. In the other case of no CSI at the transmitters, a simple automatic repeat request (ARQ) protocol with fixed rate transmission is used to provide reliable communication. Under this ARQ assumption, the instantaneous departure rates at each node can be modeled as an ON-OFF process, and the probabilities of ON and OFF states are identified. With the characterization of the arrival and departure rates at each buffer, stability conditions are identified, and an effective capacity analysis is conducted for both cases to determine the system throughput under statistical queueing constraints. In addition, for the variable-rate scheme, the concavity of the sum rate is shown for certain parameters, helping to improve the efficiency of parameter optimization. Finally, through numerical results, the influence of system parameters and the behavior of the system throughput are identified.

In this paper, the throughput of and mode selection between half-duplex and full-duplex modes are studied in two-way multiple-input multiple-output (MIMO) systems operating under statistical queuing constraints. In particular, the effective capacity of these systems is determined in order to identify the throughput under constraints on the buffer overflow probability. In the low signal-to-noise ratio (SNR) regime, the optimal input covariance matrices that achieve the minimum energy per bit of the system are investigated. Full-duplex mode is found to have better performance at low SNRs and short distances, while half-duplex mode outperforms full-duplex operation at high SNR levels and long distances. Additionally, in the numerical results, the influence of the self-interference cancelation parameter and QoS exponent on the throughput is analyzed.

This paper studies the optimal power control policy and frame duration that maximize the throughput of secondary users operating under transmit power, interference power, and collision constraints in the presence of unslotted primary users. It is assumed that primary user activity follows an ON-OFF alternating renewal process. Secondary users first sense the channel albeit with errors in the form of miss detections and false alarms, and then start the data transmission only if no primary user activity is detected. Under these assumptions, we determine the optimal power control policy subject to peak transmit power and average interference power constraints and propose a lowcomplexity algorithm for the joint optimization of the power level and frame duration under collision constraints.We further analyze some important properties of the collision duration ratio, which is defined as the ratio of average collision duration to transmission duration, and investigate the impact of the probabilities of detection and false alarm on the throughput, optimal transmission power, and the collisions with primary user transmissions.

This paper studies the characterization of the optimal input for impulsive ambient noise channels under average power constraint. Our focus is on the two-term Gaussian mixture complex noise model, which has been widely used to model impulsive noise arising in various communication channels. We first demonstrate that there exists a unique input distribution that achieves the channel capacity and the capacity-achieving input distribution has a uniformly distributed phase. By examining the Kuhn-Tucker conditions (KTC), we further show that if the optimal amplitude input distribution contains an infinite number of mass points on a bounded interval, the channel output must be Gaussian distributed. However, by using Bernstein's theorem to examine the completely monotonic condition, it is shown that the assumption of a Gaussian distributed output is not valid. As a result, there is always a finite number of mass points on any bounded interval in the optimal amplitude distribution. In addition, by applying a novel bounding technique on the KTC and using the Envelop Theorem, we demonstrate that the optimal amplitude distribution cannot have an infinite number of mass points. That gives us a unique solution of the optimal input having discrete amplitude with a finite number of mass points. Given such interesting results, we also develop an efficient way to compute the discrete optimal input and the corresponding capacity.

We envision that in the near future, just as Infrastructure-as-a-Service (IaaS), radios and radio resources in a wireless network can also be provisioned as a service to Mobile Virtual Network Operators (MVNOs), which we refer to as Radio-as-a-Service (RaaS). In this paper, we present a novel auction-based model to enable fair pricing and fair resource allocation according to real-time needs of MVNOs for RaaS. Based on the proposed model, we study the auction mechanism design with the objective of maximizing social welfare. First, we present an Integer Linear Programming (ILP) based auction mechanism for obtaining optimal social welfare. To reduce time complexity, we present a polynomial-time greedy mechanism for the RaaS auction. Both methods have been formally shown to be truthful and individually rational. Extensive simulation results show that the proposed greedy auction mechanism can quickly produce close-to-optimal solutions.

Energy efficiency in multiple-antenna fading channels is analyzed in the presence of Markov sources and queueing constraints, which are imposed as limitations on buffer overflow probabilities. Two random arrival models, namely discrete Markov and Markov fluid processes, are considered. Employing the notions effective capacity of time-varying channels and effective bandwidth of time-varying sources, maximum average arrival rates of these sources that can be supported by multiple-antenna wireless systems under statistical queueing constraints are determined and the throughput levels are identified. In the low signal-to-noise ratio (SNR) regime, minimum energy per bit and wideband slope expressions are obtained. Performance with both uniform power allocation and low-SNR optimal power allocation across transmit antennas is investigated. It is shown that the minimum energy per bit does not depend on the queueing constraints and source burstiness. On the other hand, wideband slope is shown to decrease as queueing constraints get stricter and/or sources become more bursty, thus resulting in degraded energy efficiency.

In this paper, a mathematical framework for the analysis of average symbol error probability (ASEP) in millimeter wave (mmWave) cellular networks with Poisson Point Process (PPP) distributed base stations (BSs) is developed using tools from stochastic geometry. The distinguishing features of mmWave communications such as directional beamforming and having different path loss laws for line-of-sight (LOS) and non-line-of-sight (NLOS) links are incorporated in the average error probability analysis. First, average pairwise error probability (APEP) expression is obtained by averaging pairwise error probability (PEP) over fading and random shortest distance from mobile user (MU) to its serving BS. Subsequently, average symbol error probability is approximated from APEP using the nearest neighbor (NN) approximation. ASEP is analyzed for different antenna gains and base station densities. Finally, the effect of beamforming alignment errors on ASEP is investigated to get insight on more realistic cases.

Simultaneous wireless information and power transfer (SWIPT) has emerged as a promising technology, enabling the transmission of both data and energy to a receiver equipped with an RF energy-harvesting circuitry. In this paper, we consider a point-to-point communication system in which a source transmits finite-alphabet signals. The receiver has information-decoding (ID) and energy-harvesting (EH) components, and power-splitting scheme is applied to carry out these operations concurrently. In order to improve the rate-energy tradeoff characteristics, we have introduced a novel approach that assigns probabilities non-uniformly to different signals in the constellation. According to the relationship between signal probabilities and energy consumption, these signal probabilities can be dynamically adjusted using two techniques, namely static slope characteristics and dynamic slope characteristics, given the minimum harvested energy constraint. Intuitively, advantage of one approach over the other depends on the improvement of the power-splitting factor when high energy input signals become more likely to be transmitted. In order to determine the optimal solution, we formulate an optimization problem and develop an algorithm taking into account the key parameters, e.g., splitting factor and signal probabilities. Numerical results are provided to justify the theoretical characterizations, considering 16-QAM.

In this paper, the throughput under queuing constraints in multiple-access relay fading channels achieved by fixed-rate transmissions and a simple automatic repeat request (ARQ) protocol is investigated in the absence of channel side information (CSI) at the transmitters. Transmission is considered to be either in the ON or OFF state, depending on the reliability of the reception, and retransmissions are triggered by the ARQ protocol in the OFF state. The probabilities of these transmission states are identified. Through stability and queuing analysis, feasible set of system parameters is determined, and the maximum constant arrival rates at source nodes, which can be supported by the system under queuing constraints, are characterized in terms of the fixed transmission rates, state probabilities, and quality of service parameters.

This paper studies the performance of hierarchical-modulation-based image and video transmission in cognitive radio systems with imperfect channel sensing results under constraints on both transmit and interference power. Data intended for transmission is first compressed via source coding techniques and then divided into two priority classes, namely high priority (HP) data and low priority (LP) data, by taking into consideration the unequal importance of bits in the output codestream. After dividing the compressed data into packets of equal size, turbo coding is applied. Finally, the resulting packets are modulated using hierarchical quadrature amplitude modulation (HQAM). In this setting, closed-form bit error probability expressions for HP data and LP data are derived over Nakagami-m fading channels in the presence of sensing errors. Subsequently, the effects of probabilities of detection and false alarm on error rate performance of cognitive transmissions are evaluated. In addition, tradeoffs between the number of retransmissions and peak signal-to-noise ratio (PSNR) quality are analyzed numerically. Moreover, performance comparisons of multimedia transmission with conventional QAM and hierarchical QAM are carried out in terms of the received data quality and number of retransmissions.

In cognitive radio systems, employing sensing-based spectrum access strategies, secondary users are required to perform channel sensing to detect the activities of licensed primary users in a channel, and in realistic scenarios, channel sensing occurs with possible errors due to miss-detections and false alarms. As another challenge, time-varying fading conditions in the channel between the secondary transmitter and the secondary receiver have to be learned via channel estimation. In this paper, performance of causal channel estimation methods in correlated cognitive radio channels under imperfect channel sensing results is analyzed, and achievable rates for reliable communication under both channel and sensing uncertainty are investigated by considering the input-output mutual information. Initially, cognitive radio channel model with channel sensing error and channel estimation is described. Then, using pilot symbols, minimum mean square error (MMSE) and linear-MMSE (L-MMSE) estimation methods are employed at the secondary receiver to learn the channel fading coefficients. Expressions for the channel estimates and mean-squared errors (MSE) are determined, and their dependencies on channel sensing results, and pilot symbol period and energy are investigated. Since sensing uncertainty leads to uncertainty in the variance of the additive disturbance, channel estimation strategies and performance are interestingly shown to depend on the sensing reliability. It is further shown that the L-MMSE estimation method, which is in general suboptimal, performs very close to MMSE estimation. Furthermore, assuming the channel estimation errors and the interference introduced by the primary users as zero-mean and Gaussian distributed, achievable rate expressions of linear modulation schemes and Gaussian signaling are determined. Subsequently, the training period, and data and pilot symbol energy allocations are jointly optimized to maximize the achievable rates for both signaling schemes.

This paper studies the characterization of the optimal input and the computation of the capacity of additive quadrature Gaussian mixture (GM) noise channels under an average power constraint. The considered model can be used to represent a wide variety of channels with impulsive interference, such as the well-known Bernoulli-Gaussian and Middleton class-A impulsive noise channels, as well as multiple-access interference channels and cognitive radio channels under imperfect sensing. At first, we demonstrate that there exists a unique input distribution that achieves the channel capacity, and the capacity-achieving input distribution has a uniformly distributed phase. By examining the Kuhn-Tucker alignment conditions (KTCs), we further show that, if the optimal input amplitude distribution contains an infinite number of mass points on a bounded interval, the channel output must be Gaussian-distributed. However, by using Bernstein's theorem to examine the completely monotonic condition, it is shown that the assumption of a Gaussian-distributed output is not valid. As a result, there are always a finite number of mass points on any bounded interval in the optimal amplitude distribution. In addition, by applying a novel bounding technique on the KTC and using the envelop theorem, we demonstrate that the optimal amplitude distribution cannot have an infinite number of mass points. This gives us the unique solution of the optimal input having discrete amplitude with a finite number of mass points. Given this discrete nature of the optimal input, we then develop a simple method to compute the discrete optimal input and the corresponding capacity. Our numerical examples show that, in many cases, the capacity-achieving distribution consists of only one or two mass points.

This paper studies optimal power control policies that maximize the achievable rates of underlay cognitive radio systems with arbitrary input distributions under both peak/average transmit power and peak/average interference power constraints for general fading distributions. In particular, optimal power adaptation schemes are formulated and low-complexity optimal power control algorithms are proposed. Additionally, simpler approximations of optimal power control policies in the low-power regime are determined. By considering gamma distributed channel power gains of the interference link between the secondary transmitter and the primary receiver and of the transmission link between the secondary transmitter and the secondary receiver, closed-form expressions for the maximum achievable rate attained with optimal power control in the low-power regime are provided. Through numerical results, the impact of the fading severity of both interference and transmission links and transmit power and interference power constraints on the maximum achievable rate of the cognitive user for different practical constellations and Gaussian signals are investigated.

Wireless embedded smart cameras provide significant advantages for surveillance applications, due to their mobility and flexibility in deployment. Many surveillance applications rely on data exchange and image transmission through the network. Moreover, with the widespread use of smart phones and social media, image exchange through crowdsourcing can provide invaluable information for surveillance and investigation purposes. However, transmission of images consumes significant amount of energy, and energy is a limited resource for wireless embedded devices. So far, not much attention has been paid to the energy consumption of a static or mobile node during image transmission for different scenarios. In this paper, we present a new approach for image transmission, which employs a relay node and decreases the overall energy consumption of the sender and the relay nodes. We have performed the experiments with actual wireless embedded smart cameras (CITRIC), with TelosB motes, for a static as well as a mobile scenario. We have compared the energy consumption of the proposed method with that of a traditional relay approach, and that of not using a relay node at all. Experimental results show that, with the proposed relay approach, the energy consumption of image transmission can be reduced by up to 16% in a mobile scenario.

This paper studies optimal power control policies that maximize the achievable rates of underlay cognitive radio systems with arbitrary input distributions under both peak transmit/peak interference power constraints and peak transmit/average interference power constraints for general fading distributions. A low-complexity optimal power control algorithm is proposed. Also, the optimal power control policy in the low-power regime is analyzed. In particular, by considering gamma distributed channel power gains of the interference link between the secondary transmitter and the primary receiver and the transmission link between the secondary transmitter and the secondary receiver, closed-form expressions for the maximum achievable rate attained with the optimal power control strategy in the low-power regime are provided. Through numerical results, the impact of the fading severity of both interference and transmission links and transmit power and interference power constraints on the maximum achievable rate of the cognitive user for different constellations and Gaussian signals are investigated.

Throughput regions of fading broadcast channels with random data arrivals in the presence of quality-of-service (QoS) requirements is studied when power control is employed at the transmitter. Two source models with random arrivals, namely discrete Markov source and Markov fluid source, are considered. It is assumed that superposition coding with power control is performed at the transmitter with interference cancellation at the receivers. By utilizing the effective capacity of time-varying wireless transmission channels and effective bandwidth of random arrivals, the maximum average arrival rates that can be supported in the fading broadcast channel with QoS guarantees are identified and the throughput is characterized. Optimal power control policies that maximize the weighted combination of the average arrival rates are investigated in the two-user case. In particular, optimality conditions are determined and an algorithm for optimal power control is proposed for different source arrival models. Impact of source burstiness, power adaptation, the use of different transmission strategies (e.g., superposition coding and time-division multiplexing) on the throughput regions and sum-rates are investigated.

In this paper, the optimal power control policy that maximizes the effective capacity for arbitrary input distributions in fading channels subject to an average power constraint is studied. A low-complexity power control algorithm is proposed. In addition, energy efficiency is investigated by characterizing both the minimum energy per bit and wideband slope for arbitrary signaling in the low-power regime when channel side information (CSI) is available only at the receiver. With perfect CSI at both the transmitter and receiver, the optimal power adaptation strategy in this regime is also determined. Through numerical results, performance comparison with constant power scheme and optimal power adaptation strategy for different signal constellations and Gaussian signals is given. The impact of QoS constraints, input distributions, and average transmit power level on the proposed power control policy, maximum achievable effective capacity and energy efficiency is analyzed.

Transmission over a half-duplex relay channel with secrecy and quality-of-service (QoS) constraints is studied. It is assumed that data is stored in buffers prior to transmission and transmitters operate with a constraint on the buffer overflow probability. Using the effective capacity formulation, secrecy throughput is derived for the half duplex two-hop fading relay system operating in the presence of an eavesdropper. The scenario without security considerations is also addressed. For both scenarios, minimum energy per bit is obtained. The impact of QoS exponents and channel correlation on the throughput is investigated via numerical analysis. Taking into account the path loss effect, the effect of the relay and eavesdropper locations on the throughput and energy efficiency is studied in a simple linear network.

This paper studies the symbol error rate performance of cognitive radio transmissions in the presence of imperfect sensing decisions. Two different transmission schemes, namely sensing-based spectrum sharing (SSS) and opportunistic spectrum access (OSA), are considered. In both schemes, secondary users first perform channel sensing, albeit with possible errors. In SSS, depending on the sensing decisions, they adapt the transmission power level and coexist with primary users in the channel. On the other hand, in OSA, secondary users are allowed to transmit only when the primary user activity is not detected. Initially, for both transmission schemes, general formulations for the optimal decision rule and error probabilities are provided for arbitrary modulation schemes under the assumptions that the receiver is equipped with the sensing decision and perfect knowledge of the channel fading, and the primary user's received faded signals at the secondary receiver has a Gaussian mixture distribution. Subsequently, the general approach is specialized to rectangular quadrature amplitude modulation (QAM). More specifically, the optimal decision rule is characterized for rectangular QAM, and closed-form expressions for the average symbol error probability attained with the optimal detector are derived under both transmit power and interference constraints. The effects of imperfect channel sensing decisions, interference from the primary user and its Gaussian mixture model, and the transmit power and interference constraints on the error rate performance of cognitive transmissions are analyzed.

The estimation of the fundamental matrix is an important problem in epipolar geometry. Many estimation methods have been proposed before, including the eight-point algorithm, Simple Evolutionary Agent (SEA) and RANSAC. In this paper, we investigate the evolutionary agent-based algorithm for fundamental matrix estimation, and present a new algorithm that improves the existing evolutionary algorithm both accuracy- and efficiency-wise. The model focuses on selecting a best combination of input points to compute the fundamental matrix via the eight-point algorithm. To improve the existing algorithm, our new model holds competition over all agents for population control and evolutionary experience accumulation. In addition to a larger competition scope, we add the outlier elimination mechanism, which greatly accelerates the algorithm. New parameters are introduced to control the convergence more efficiently. The improved algorithm achieves lower computation load and more accurate results. A general analysis about parameter selection is also provided.

Multi-camera multi-object tracking problem can be regarded as a multi-player game by adopting a game theoretical approach. In embedded vision sensor networks, energy, processing power and bandwidth are limited, and should be efficiently used. In this paper, in addition to dynamic grouping of the camera nodes, we focus on the (re)assignment of object tracking tasks by simultaneously considering energy levels, processing loads and accuracy/reliability of nodes in utility calculation. Instead of using a predetermined period to perform auctions, nodes trigger the reassignment process in an event-driven manner. Four scenarios are used for triggering reassignment, namely (i) new-object entry, (ii) object lost or exit, (iii) critical energy level or energy decrease rate, and (iv) critical target location and resolution. We also analyze the communication cost in terms of the number of messages sent between the cameras. We have performed experiments with different number of cameras and targets, and varying target trajectories and camera topology. We have computed the lifetime of the network with and without consideration of the energy levels in the task (re)assignment. We have also compared the number of messages sent with periodic reassignment and with the proposed event-driven triggering mechanism. The simulation results show a significant increase in the lifetime of the network as well as a decrease in the number of messages that are sent when the proposed approach is employed.

In this paper, throughput of two-way relaying under buffer constraints is studied. In the two-way relay system, source nodes initially send their messages to the relay in the multiple-access phase. Relay decodes and stores the messages from different sources in different buffers and subsequently broadcasts a superimposed signal. It is assumed that both source nodes and the relay operate in the presence of statistical queueing constraints. Under these assumptions, arrival rates that can be supported in this system are investigated through the logarithmic moment generating functions of the arrival and service processes. In particular, after identifying the service rates in the multiple-access and broadcast phases and addressing the stability conditions, characterizations of the maximum arrival rates are provided in terms of system resource allocation parameters, signal-to-noise ratios, and quality-of-service exponents. Impact of different parameters on the performance is investigated through numerical results.

In this paper, we consider a cognitive multiple access channel (MAC) in which the secondary users seek to communicate with the secondary base station in a spectrum-sharing environment. The base station selects only one secondary user, which maximizes the weighted difference between the channel power gains of the interference link and the cognitive transmission link. This selection strategy can also be specialized to selecting the secondary user that has either the least interference channel gain or the highest data channel gain. Consequently, the selected secondary user is subject to an interference power constraint in order to avoid harmful interference inflicted on the primary user. It is assumed that the primary user and secondary users operate under statistical quality of service (QoS) constraints imposed as limitations on the buffer size. In this setting, we characterize the effective capacity for both primary user and the secondary users under different selection methods. The impacts of the secondary user selection method, the number of secondary users, QoS constraints on the performance of both the primary user and secondary users are investigated. We interestingly show that for a large number of secondary users, the performance of the primary user is not affected by the selection method in the interference-limited regime.

In this paper, a cognitive multiple access channel in which the secondary transmitters seek to communicate with a common secondary receiver is considered. Before data transmission, it is assumed that cooperative spectrum sensing is performed with possible errors. For this channel, achievable rate regions are initially derived under two scenarios depending on how secondary transmitters access the channel. In the first scenario, secondary users can send data under both busy and idle sensing decisions by adapting the energy level of the transmitted signals according to the sensing result. In the second scenario, the secondary transmitters are not allowed to perform data transmission if the channel is sensed as busy. Subsequently, the performance in the low-power regime is analyzed by characterizing minimum energy per bit and slope regions in these scenarios. The impact of channel sensing performance (e.g., the probabilities of detection and false alarm, channel sensing duration) on achievable rate region, energy efficiency and slope region considering both scenarios are investigated.

In this paper, effective capacity of a two-way amplify and forward (AF) relay channel, in which two source nodes exchange their signals via a relay node, is analyzed. Depending on the availability of the channel side information (CSI), the relay, which operates in half-duplex mode, has a fixed-gain or variable-gain factor. It is assumed that source nodes are subject to individual statistical quality of service (QoS) constraints in the form of limitations on buffer size. Under these assumptions and constraints, a characterization of the throughput for source nodes is provided by obtaining closed-form expressions for lower bounds on the effective capacity. Performances of two-way fixed- gain and variable-gain AF relaying are compared. The relations between buffer constraints, transmission power, the location of the relay node and effective capacity are also studied.

In this paper, error rate performance of cognitive radio transmissions is studied in the presence of imperfect channel sensing decisions. It is assumed that cognitive users first perform channel sensing, albeit with possible errors. Then, depending on the sensing decisions, they select the transmission energy level and employ MI× MQrectangular quadrature amplitude modulation (QAM) for data transmission over a fading channel. In this setting, the optimal decision rule is formulated under the assumptions that the receiver is equipped with the sensing decision and perfect knowledge of the channel fading. It is shown that the thresholds for optimal detection at the receiver are the midpoints between the signals under any sensing decision. Subsequently, minimum average error probability expressions for M-ary pulse amplitude modulation (M-PAM) and MI× MQrectangular QAM transmissions attained with the optimal detector are derived. The effects of imperfect channel sensing decisions on the average symbol error probability are analyzed.

In this paper, throughput achieved in cognitive radio channels with finite blocklength codes under buffer limitations is studied. Cognitive users first determine the activity of the primary users' through channel sensing and then initiate data transmission at a power level that depends on the channel sensing decisions. It is assumed that finite blocklength codes are employed in the data transmission phase. Hence, errors can occur in reception and retransmissions can be required. Primary users' activities are modeled as a two-state Markov chain and an eight-state Markov chain is constructed in order to model the cognitive radio channel. Channel state information (CSI) is assumed to be perfectly known by either the secondary receiver only or both the secondary transmitter and receiver. In the absence of CSI at the transmitter, fixed-rate transmission is performed whereas under perfect CSI knowledge, for a given target error probability, the transmitter varies the rate according to the channel conditions. Under these assumptions, throughput in the presence of buffer constraints is determined by characterizing the maximum constant arrival rates that can be supported by the cognitive radio channel while satisfying certain limits on buffer violation probabilities. Tradeoffs between throughput, buffer constraints, coding blocklength, and sensing duration for both fixed-rate and variable-rate transmissions are analyzed numerically. The relations between average error probability, sensing threshold and sensing duration are studied in the case of variable-rate transmissions.

Transmission over fading broadcast and interference channels in the presence of quality of service (QoS) constraints is studied. Effective capacity, which provides the maximum constant arrival rate that a given service process can support while satisfying statistical QoS constraints, is employed as the performance metric. In the broadcast scenario, the effective capacity region achieved with superposition coding and successive interference cancellation is identified and is shown to be convex. Subsequently, optimal power control policies that achieve the boundary points of the effective capacity region are investigated, and an algorithm for the numerical computation of the optimal power adaptation schemes for the two-user case is provided. In the interference channel model, achievable throughput regions are determined for three different strategies, namely treating interference as noise, time division with power control and simultaneous decoding. It is demonstrated that as in Gaussian interference channels, simultaneous decoding expectedly performs better (i.e., supports higher arrival rates) when interfering links are strong, and treating interference as noise leads to improved performance when the interfering cross links are weak while time-division strategy should be preferred in between. When the QoS constraints become more stringent, it is observed that the sum-rates achieved by different schemes all diminish and approach each other, and time division with power control interestingly starts outperforming others over a wider range of cross-link strengths.

A two-hop wireless communication link in which a source sends data to a destination with the aid of an intermediate relay node is studied. It is assumed that there is no direct link between the source and the destination, and the relay forwards the information to the destination by employing the decode-and-forward scheme. Both the source and intermediate relay nodes are assumed to operate under statistical quality of service (QoS) constraints imposed as limitations on the buffer overflow probabilities. The maximum constant arrival rates that can be supported by this two-hop link in the presence of QoS constraints are characterized by determining the effective capacity of such links as a function of the QoS parameters and signal-to-noise ratios at the source and relay, and the fading distributions of the links. The analysis is performed for both full-duplex and half-duplex relaying. Through this study, the impact upon the throughput of having buffer constraints at the source and intermediate relay nodes is identified. The interactions between the buffer constraints in different nodes and how they affect the performance are studied. The optimal time-sharing parameter in half-duplex relaying is determined, and performance with half-duplex relaying is investigated.

Communication over fading interference channels in the presence of statistical quality of service (QoS) constraints is considered. Effective capacity, which provides the maximum constant arrival rate that a given service process can support while satisfying statistical queueing constraints, is employed as the performance metric. In a two-user and buffer constrained setting, arrival rate regions that can be supported in the fading interference channel are studied. More specifically, for three different strategies, namely treating interference as noise, time division with power control and simultaneous decoding, achievable throughput regions are determined. It is demonstrated that as in Gaussian interference channels, simultaneous decoding expectedly performs better (i.e., supports higher arrival rates) when interfering links are strong, and treating interference as noise leads to improved performance when the interfering cross links are weak while time-division strategy should be preferred in between. When the QoS constraints become more stringent, it is observed that the sum-rates achieved by different schemes all diminish and approach each other, and time division with power control interestingly starts outperforming others over a wider range of cross-link strengths.

In this paper, transmission over multiaccess fading channels under QoS constraints is studied in the low power regime. QoS constraints are imposed as limitations on the buffer violation probability. The effective capacity, which characterizes the maximum constant arrival rates in the presence of such statistical QoS constraints, is employed as the performance metric. The minimum received bit energy levels and wideband slope regions are characterized for different transmission and reception strategies, namely time-division multiple-access (TDMA), superposition coding with fixed decoding order, and superposition coding with variable decoding order. It is shown that the minimum received bit energies achieved by these different strategies are the same and independent of the QoS constraints. When wideband slope regions are considered, the suboptimality of TDMA with respect to superposition schemes is shown. For the case of superposition coding, it is proven that varying the decoding order at the receiver with the fading realizations does not enlarge the wideband slope region.

Energy efficiency in wireless fading channels is studied in the presence of statistical quality of service (QoS) constraints. Energy per bit is employed as the performance metric. Minimum energy per bit and wideband slope expressions are determined to quantify the energy efficiency in the low signal-to-noise ratio (SNR) regime. In particular, the impact of channel and source variations is investigated. A block-fading model in which fading coefficients are arbitrarily correlated in one block before assuming independent realizations in the next one, is considered. Initially, the effect of fading correlation on energy efficiency is analyzed. It is shown that while minimum energy per bit does not depend on the fading correlation, wideband slope decreases as positive correlation increases, resulting in increased energy requirements at low but nonzero SNR levels. Subsequently, an ON-OFF source arrival model is considered. It is shown that while the minimum energy per bit does not get affected by source variations, wideband slope diminishes with increasing source burstiness. (”THIS PAPER IS ELIGIBLE FOR THE STUDENT PAPER AWARD”).

Secrecy capacity of a multiple-antenna wiretap channel is studied in the low signal-to-noise ratio (SNR) regime. Expressions for the first and second derivatives of the secrecy capacity with respect to SNR at SNR = 0 are derived. Transmission strategies required to achieve these derivatives are identified. In particular, it is shown that it is optimal in the low-SNR regime to transmit in the maximal-eigenvalue eigenspace of φ = Hm†Hm- Nm/NeHe†Hewhere Hmand Hedenote the channel matrices associated with the legitimate receiver and eavesdropper, respectively, and Nmand Neare the noise variances at the receiver and eavesdropper, respectively. Energy efficiency is analyzed by finding the minimum bit energy required for secure and reliable communications, and the wideband slope. Increased bit energy requirements under secrecy constraints are quantified. Finally, the impact of fading is investigated, and the benefits of fading in terms of energy efficiency are shown.

Effective capacity, which provides the maximum constant arrival rate that a given service process can support while satisfying statistical queueing constraints, is analyzed in a multiuser scenario. In particular, the effective capacity region of fading multiple-access channels in the presence of quality of service (QoS) constraints is studied. Perfect channel side information is assumed to be available at both the transmitters and the receiver. It is initially assumed that the transmitters send the information at a fixed power level and, hence, do not employ power control policies. Under this assumption, the performance achieved by superposition coding with successive decoding techniques is investigated. It is shown that varying the decoding order with respect to the channel states can significantly increase the achievable throughput region. In the two-user case, the optimal decoding strategy is determined for the scenario in which the users have the same QoS constraints. The performance of orthogonal transmission strategies is also analyzed. It is shown that for certain QoS constraints, time-division multiple access can achieve better performance than superposition coding if fixed successive decoding order is used at the receiver side. In the subsequent analysis, power control policies are incorporated into the transmission strategies. The optimal power allocation policies for any fixed decoding order over all channel states are identified. For a given variable decoding-order strategy, the conditions that the optimal power control policies must satisfy are determined, and an algorithm that can be used to compute these optimal policies is provided.

Cognitive radio transmissions in the presence of channel uncertainty are considered. In practical scenarios, cognitive secondary users need to perform both channel sensing in order to identify whether the channel is being occupied by the primary users or not, and also channel estimation in order to learn the channel fading coefficients. Generally, errors occur in both channel sensing and estimation, and this leads to a coupling between the two. More specifically, imperfect sensing affects both the structure and the performance of channel estimation schemes. With this motivation, the interactions between channel sensing and estimation are studied in this paper. In particular, different channel estimation schemes including minimum mean-square error (MMSE), linear MMSE, and mismatched MMSE estimations are analyzed, and their dependence on sensing decisions and their performances are investigated.

In this paper, wireless systems operating under queueing constraints in the form of limitations on the buffer violation probabilities are considered. The throughput of wireless systems operating under such constraints is captured by the effective capacity formulation. It is assumed that finite blocklength codes are employed for transmission. Under this assumption, a recent result on the channel coding rate in the finite blocklength regime is incorporated into the analysis and the throughput achieved with such codes in the presence of queueing constraints is identified. Interactions between the effective rate, queueing constraints, error probabilities, and blocklength values are investigated. In particular, it is shown that for given signal-to-noise ratio, blocklength, and quality of service exponent, the effective rate is maximized at a unique decoding error probability value.

This paper investigates the performance of wireless systems that employ finite-blocklength channel codes for transmission and operate under queueing constraints in the form of limitations on buffer overflow probabilities. A block fading model, in which fading stays constant in each coherence block and change independently between blocks, is considered. It is assumed that channel coding is performed over multiple coherence blocks. An approximate lower bound on the transmission rate is obtained from Feintein's Lemma. This lower bound is considered as the service rate and is incorporated into the effective capacity formulation, which characterizes the maximum constant arrival rate that can be supported under statistical queuing constraints. Performances of variable-rate and fixed-rate transmissions are studied. The optimum error probability for variable rate transmission and the optimum coding rate for fixed rate transmission are shown to be unique. Moreover, the tradeoff between the throughput and the number of blocks over which channel coding is performed is identified.

In this paper, two-hop communication between a source and a destination with the aid of an intermediate relay node is considered. Both the source and intermediate relay node are assumed to operate under statistical quality of service (QoS) constraints imposed as limitations on the buffer overflow probabilities. It is further assumed that the nodes send the information at fixed power levels and have perfect channel side information. In this scenario, the maximum constant arrival rates that can be supported by this two-hop link are characterized by finding the effective capacity. Through this analysis, the impact upon the throughput of having buffer constraints at the source and intermediate-hop nodes is identified.

In this paper, we study two-way relay networks (TWRNs) in which two source nodes exchange their information via a relay node indirectly in Rayleigh fading channels. Both Amplify-and-Forward (AF) and Decode-and-Forward (DF) techniques have been analyzed in the TWRN employing a Markov chain model through which the network operation is described and investigated in depth. Automatic Repeat-reQuest (ARQ) retransmission has been applied to guarantee the successful packet delivery. The bit energy consumption and goodput expressions have been derived as functions of transmission rate in a given AF or DF TWRN. Numerical results are used to identify the optimal transmission rates of which the bit energy consumption is minimized or the goodput is maximized. The network performances are compared in terms of energy and transmission efficiency in AF and DF modes.

We present a system for wide-area multi-object tracking across disjoint camera views. We employ a probabilistic Petri Net-based approach to account for the uncertainties of the vision algorithms (such as unreliable background subtraction, and tracking failure) and to incorporate the available domain knowledge. We combine appearance features of objects as well as the travel-time evidence for target matching and consistent labeling across disjoint camera views. 3D color histogram, Histogram of Oriented Gradients, object size and aspect ratio are used as the appearance features. The distribution of the travel time is modeled by a Gaussian Mixture Model. By incorporating the domain knowledge about the camera configurations and the information about the received packets from other cameras, certain transitions are fired in the probabilistic Petri net. The system is trained to learn different parameters of the matching process. We present wide-area tracking of vehicles as an example where we used three non-overlapping cameras. The first and the third cameras are approximately 150 meters apart from each other with two intersections in the blind region. The results show the success of the proposed method.

1Transmission over fading broadcast channels in the presence of quality of service (QoS) constraints is studied. Effective capacity, which provides the maximum constant arrival rate that a given service process can support while satisfying statistical QoS constraints, is employed as the performance metric. The effective capacity region achieved with superposition coding and successive interference cancellation is identified and is shown to be convex. Subsequently, optimal power control policies that achieve the boundary points of the effective capacity region are investigated, and an algorithm for the numerical computation of the optimal power adaptation schemes for the two-user case is provided. Additionally, performance attained with time-division multiplexing (TDM) of messages is studied for comparison with the optimal schemes.

In this paper, achievable rates and resource allocation strategies for imperfectly known two-way relay fading channels are studied. Decode-and-forward (DF) relaying is considered. It is assumed that communication starts with the network training phase in which the users and the relay estimate the fading coefficients, albeit imperfectly. Subsequently, data transmission is performed in multiple-access and broadcast phases. In both phases, achievable rate regions are identified by treating the terms that arise due to channel estimation errors and imperfect interference cancellation as Gaussian distributed noise components. The achievable rate region of the two-way relay channel is given by the intersection of the achievable rate regions of multiple-access and broadcast phases. The impact of several training and transmission parameters (such as training power levels, time/bandwidth allocated to the multiple access and broadcast phases, and relay power allocation parameter) on the achievable rate regions and sum rates is investigated.

In this paper, throughput of cognitive multiple-input multiple-output (MIMO) systems operating under quality-of-service (QoS) constraints is studied. It is assumed that transmission power and the covariance of the input signal vector are varied depending on the sensed activities of primary users in the system. Considering the reliability of the transmission and channel sensing results, a state-transition model is provided. Effective capacity is determined, and expressions for the first and second derivatives of the effective capacity are obtained at SNR =0. The minimum bit energy requirements in the presence of QoS limitations are identified. Numerical results are provided.

Wireless embedded smart cameras provide flexibility in camera deployment in terms of the locations and number of the cameras. However, these battery-powered embedded vision sensors have very limited energy, memory, and processing power. Energy consumption and latency are two major concerns in wireless embedded camera networks. In multi-camera tracking applications, the amount of data exchanged between cameras has an effect on the tracking accuracy, the energy consumption of the camera nodes and the latency. In this paper, we provide a detailed quantitative analysis of the accuracy-latency-energy tradeoff for overlapping and non-overlapping camera setups when different-sized data packets are transferred in a wireless manner. The experiments have been performed with an actual wireless embedded smart camera network employing CITRIC motes, and performing tracking of objects.

Energy efficiency of fixed-rate transmissions is studied in the presence of queueing constraints and channel uncertainty. It is assumed that neither the transmitter nor the receiver has channel side information prior to transmission. The channel coefficients are estimated at the receiver via minimum mean-square-error (MMSE) estimation with the aid of training symbols. It is further assumed that the system operates under statistical queueing constraints in the form of limitations on buffer violation probabilities. The optimal fraction of power allocated to training is identified. Spectral efficiency-bit energy tradeoff is analyzed in the low-power and wideband regimes by employing the effective capacity formulation. In particular, it is shown that the bit energy increases without bound in the low-power regime as the average power vanishes. A similar conclusion is reached in the wideband regime if the number of noninteracting subchannels grow without bound with increasing bandwidth. On the other hand, it is proven that if the number of resolvable independent paths and hence the number of noninteracting subchannels remain bounded as the available bandwidth increases, the bit energy diminishes to its minimum value in the wideband regime. For this case, expressions for the minimum bit energy and wideband slope are derived. Overall, energy costs of channel uncertainty and queueing constraints are identified, and the impact of multipath richness and sparsity is determined.

In this paper, secure transmission of information over fading broadcast channels is studied in the presence of statistical queueing constraints. Effective capacity is employed as a performance metric to identify the secure throughput of the system, i.e., effective secure throughput. It is assumed that perfect channel side information (CSI) is available at both the transmitter and the receivers. Initially, the scenario in which the transmitter sends common messages to two receivers and confidential messages to one receiver is considered. For this case, the effective secure throughput region, which is the region of constant arrival rates of common and confidential messages that can be supported by the buffer-constrained transmitter and fading broadcast channel, is defined. It is proven that this effective throughput region is convex implying that time-sharing between any two viable transmission and power control strategies results in effective throughput values inside the region. Then, the optimal power control policies that achieve the boundary points of the effective secure throughput region are investigated and an algorithm for the numerical computation of the optimal power adaptation schemes is provided. Additionally, the throughput region achieved by time-division multiplexing of common and confidential messages is explored. Subsequently, the special case in which the transmitter sends only confidential messages to one receiver is addressed in more detail. For this case, effective secure throughput is formulated and two different power adaptation policies are studied. These power adaptation policies are compared with the opportunistic ones that are optimal in the absence of quality of service (QoS) constraints. It is shown that opportunistic schemes, in which data transmission with high rates and high power occurs only when the main channel is much better than the eavesdropper channel, are no longer optimal under buffer constraints, and the transmitter should send the data at a certain moderate rate and power even when the main channel strength is comparable to that of the eavesdropper channel to avoid buffer overflows.

In this paper, performance of cognitive transmission over time-selective flat fading channels is studied under quality of service (QoS) constraints and channel uncertainty. Cognitive secondary users (SUs) are assumed to initially perform channel sensing to detect the activities of the primary users, and then attempt to estimate the channel fading coefficients through training. Energy detection is employed for channel sensing, and different minimum mean-square-error (MMSE) estimation methods are considered for channel estimation. In both channel sensing and estimation, erroneous decisions can be made, and hence, channel uncertainty is not completely eliminated. In this setting, performance is studied and interactions between channel sensing and estimation are investigated. Following the channel sensing and estimation tasks, SUs engage in data transmission. Transmitter, being unaware of the channel fading coefficients, is assumed to send the data at fixed power and rate levels that depend on the channel sensing results. Under these assumptions, a state-transition model is constructed by considering the reliability of the transmissions, channel sensing decisions and their correctness, and the evolution of primary user activity which is modeled as a two-state Markov process. In the data transmission phase, an average power constraint on the secondary users is considered to limit the interference to the primary users, and statistical limitations on the buffer lengths are imposed to take into account the QoS constraints of the secondary traffic. The maximum throughput under these statistical QoS constraints is identified by finding the effective capacity of the cognitive radio channel. Numerical results are provided for the power and rate policies.

In this paper, performance of transmission in cognitive radio systems over time-selective flat fading channels is studied under quality of service (QoS) constraints and channel uncertainty. Cognitive secondary users are assumed to initially perform sensing over the transmission channel to detect the activities of the primary users. Then, depending on the channel sensing result, they choose their transmission power policies and perform channel estimation. Following the training phase, they transmit data through the channel. The activities of the primary users are modeled as a two-state Markov process. A state transition model is constructed to model the cognitive transmissions. Statistical limitations on the buffer lengths are imposed to take into account the QoS constraints, and an average power constraint on the secondary users is considered to limit the interference to the primary users. The maximum throughput under these statistical QoS constraints is identified by finding the effective capacity of the cognitive radio channel. Numerical results are provided for the power and rate policies.

Energy and bandwidth are limited resources in wireless sensor networks, and communication consumes significant amount of energy. When wireless vision sensors are used to capture and transfer image and video data, the problems of limited energy and bandwidth become even more pronounced. Thus, message traffic should be decreased to reduce the communication cost. In many applications, the interest is to detect composite and semantically higher-level events based on information from multiple sensors. Rather than sending all the information to the sinks and performing composite event detection at the sinks or control-center, it is much more efficient to push the detection of semantically high-level events within the network, and perform composite event detection in a peer-to-peer and energy-efficient manner across embedded smart cameras. In this paper, three different operation scenarios are analyzed for a wireless vision sensor network. A detailed quantitative comparison of these operation scenarios are presented in terms of energy consumption and latency. This quantitative analysis provides the motivation for, and emphasizes (1) the importance of performing high-level local processing and decision making at the embedded sensor level and (2) need for peer-to-peer communication solutions for wireless multimedia sensor networks.

In this paper, the fading broadcast channel with confidential messages is studied in the presence of statistical quality of service (QoS) constraints in the form of limitations on the buffer length. We employ the effective capacity formulation to measure the throughput of the confidential and common messages. We assume that the channel side information (CSI) is available at both the transmitter and the receivers. Considering average power constraints at the transmitter side, we first define the effective secure throughput region, and prove that the throughput region is convex. Then, we obtain the optimal power control policies that achieve the boundary points of the effective secure throughput region.

In this paper, cognitive transmission under quality of service (QoS) constraints is studied. In the cognitive radio channel model, it is assumed that the secondary transmitter sends the data with two different transmission and power policies, depending on the activity of the primary users and the probability of detection, which is determined by channel sensing performed by the secondary users. A state transition model with three states is constructed to model this cognitive transmission channel. Statistical limitations on the buffer lengths are imposed to take into account the QoS constraints. The transmission and power policies are determined through the constraint to provide a minimum-rate transmission to the primary users for a certain percentage of the time. The maximum throughput under these statistical QoS constraints is identified by finding the effective capacity of the cognitive radio channel. The impact upon the effective capacity of several system parameters, including the channel sensing duration, detection threshold, detection and false alarm probabilities, QoS parameters, and data transmission and power policies are investigated.

Effective capacity, which provides the maximum constant arrival rate that a given service process can support while satisfying statistical delay constraints, is analyzed in a multiuser scenario. In particular, we study the achievable effective capacity region of the users in multiaccess fading channels (MAC) in the presence of quality of service (QoS) constraints. We assume that channel side information (CSI) is available at both the transmitters and the receiver, and superposition coding technique with successive decoding is used. When the power is fixed at the transmitters, we show that varying the decoding order with respect to the channel state can significantly increase the achievable throughput region. For a two-user case, we obtain the optimal decoding strategy when the users have the same QoS constraints. Meanwhile, it is shown that time-division multiple-access (TDMA) can achieve better performance than superposition coding with fixed successive decoding order at the receiver side for certain QoS constraints. For power and rate adaptation, we determine the optimal power allocation policy with fixed decoding order at the receiver side. Numerical results are provided to demonstrate our results.

In this paper, collaborative use of relays to form a beamforming system with the aid of perfect channel state information (CSI) and to provide physical-layer security is investigated. In particular, a decode-and-forward-based relay beamforming design subject to total and individual relay power constraints is studied with the goal of maximizing the secrecy rate. The total power constraint leads to a closed-form solution. The design under individual relay power constraints is formulated as an optimization problem which is shown to be easily solved using two different approaches, namely semideflnite programming and second-order cone programming. Furthermore, a simplified and suboptimal technique which reduces the computation complexity under individual power constraints is presented.

In this paper, secure transmission of information over an ergodic fading channel is studied in the presence of statistical quality of service (QoS) constraints.We employ effective capacity to measure the secure throughput of the system, i.e., effective secure throughput. We assume that the channel side information (CSI) of the main and the eavesdropper channels is available at the transmitter side. Under this assumption, we investigate the optimal power control policies that maximize the effective secure throughput. In particular, it is noted that opportunistic transmission is no longer optimal and the transmitter should not wait to send the data at a high rate until the main channel is much better than the eavesdropper channel. Moreover, it is shown that the benefits of adapting the power with respect to the CSI of both the eavesdropper and main channels rather than the CSI of only the main channel diminish as QoS constraints become more stringent.

In this paper, cognitive transmission under quality of service (QoS) constraints is studied. In the cognitive radio channel model, it is assumed that both the secondary receiver and the secondary transmitter know the channel fading coefficients perfectly and optimize the power adaptation policy under given constraints, depending on the channel activity of the primary users, which is determined by channel sensing performed by the secondary users. The transmission rates are equal to the instantaneous channel capacity values. A state transition model with four states is constructed to model this cognitive transmission channel. Statistical limitations on the buffer lengths are imposed to take into account the QoS constraints. The maximum throughput under these statistical QoS constraints is identified by finding the effective capacity of the cognitive radio channel. The impact upon the effective capacity of several system parameters, including the channel sensing duration, detection threshold, detection and false alarm probabilities, and QoS parameters, is investigated.

In this paper, we have studied the energy efficiency of cooperative networks operating in either the fixed Amplify-and-Forward (AF) or the selective Decode-and-Forward (DF) mode. We consider the optimization of the M-ary quadrature amplitude modulation (MQAM) constellation size to minimize the bit energy consumption under given bit error rate (BER) constraints. In the computation of the energy expenditure, the circuit, transmission, and retransmission energies are taken into account. The link reliabilities and retransmission probabilities are determined through the outage probabilities under the Rayleigh fading assumption. Several interesting observations with practical implications are made. For instance, it is seen that while large constellations are preferred at small transmission distances, constellation size should be decreased as the distance increases. Moreover, the cooperative gain is computed to compare direct transmission and cooperative transmission.

In this paper, a game-theoretic analysis for the resource allocation policies in fading multiple-access channels (MAC) in the presence of quality of service (QoS) constraints is performed. Effective capacity, which provides the maximum constant arrival rate, or throughput, that a given service process can support while satisfying statistical delay constraints, is considered in a multiuser scenario. We assume that the channel side information (CSI) is available at both the receiver and transmitters, and the transmitters are selfish, rational with certain QoS constraints and average power limitations. Without the aid of the receiver, we prove that there is always a unique admissible Nash equilibrium of the noncooperative power control game. The Nash equilibrium of the power control game is proved to be always inside the rate region where successive decoding techniques are used at the receiver.

In this paper, collaborative use of relays to form a beamforming system with the aid of perfect channel state information (CSI) and to provide communication in physical-layer security between a transmitter and two receivers is investigated. In particular, we describe decode-and-forward based null space beamforming schemes and optimize the relay weights jointly to obtain the largest secrecy rate region. Furthermore, the optimality of the proposed schemes is investigated by comparing them with the outer bound secrecy rate region.

In this paper, the performance of cognitive radio systems is studied when the secondary users operate under statistical quality of service (QoS) constraints. In the cognitive radio channel model, secondary users initially perform channel sensing, and then engage in data transmission at two different average power levels depending on the channel sensing results. A state transition model is constructed to model this cognitive transmission channel. Statistical QoS constraints are imposed as limitations on buffer violation probabilities. Effective capacity of the cognitive radio channel, which provides the maximum throughput under such QoS constraints, is determined. This analysis is conducted for fixed-power/fixed-rate, fixed-power/variable-rate, and variable-power/variable-rate transmission schemes under different assumptions on the availability of channel side information (CSI) at the transmitter. The interactions and tradeoffs between the throughput, QoS constraints, and channel sensing parameters (e.g., sensing duration and threshold, and detection and false alarm probabilities) are investigated. The performances of fixed-rate and variable-rate transmission methods are compared in the presence of QoS limitations. It is shown that variable schemes outperform fixed-rate transmission techniques if the detection probabilities are high. Performance gains through adapting the power and rate are quantified and it is shown that these gains diminish as the QoS limitations become more stringent.

In this paper, cognitive transmission under quality-of-service (QoS) constraints is studied. In the cognitive radio channel model, it is assumed that the secondary transmitter sends the data at two different fixed rates and power levels, depending on the activity of the primary users, which is determined by channel sensing performed by the secondary users. A state transition model with eight states is constructed to model this cognitive transmission channel. Statistical limitations on the buffer lengths are imposed to take into account the QoS constraints. The maximum throughput under these statistical QoS constraints is identified by finding the effective capacity of the cognitive radio channel. The impact upon the effective capacity of several system parameters, including channel sensing duration, detection threshold, detection and false alarm probabilities, QoS parameters, and fixed transmission rates is investigated. It is noted that the optimal transmission rates are independent of the primary users' activity in the case of perfect channel sensing.

Energy efficiency of fixed-rate transmissions is studied in the presence of queueing constraints and channel uncertainty. It is assumed that neither the transmitter nor the receiver has channel side information prior to transmission. The channel coefficients are estimated at the receiver via minimum mean-square-error (MMSE) estimation with the aid of training symbols. It is further assumed that the system operates under statistical queueing constraints in the form of limitations on buffer violation probabilities. The optimal fraction of power allocated to training is identified. Spectral efficiency-bit energy tradeoff is analyzed in the low-power and wideband regimes by employing the effective capacity formulation. In particular, it is shown that the bit energy increases without bound in the low-power regime as the average power vanishes. On the other hand, it is proven that if sparse multipath fading with bounded number of independent resolvable paths is experienced, the bit energy diminishes to its minimum value in the wideband regime as the available bandwidth increases. For this case, expressions for the minimum bit energy and wideband slope are derived. Overall, energy costs of channel uncertainty and queueing constraints are identified.

Transmission over wireless fading channels under quality of service (QoS) constraints is studied when only the receiver has perfect channel side information. Being unaware of the channel conditions, transmitter is assumed to send the information at a fixed rate. Under these assumptions, a two-state (ON-OFF) transmission model is adopted, where information is transmitted reliably at a fixed rate in the ON state while no reliable transmission occurs in the OFF state. QoS limitations are imposed as constraints on buffer violation probabilities, and effective capacity formulation is used to identify the maximum arrival rate that a wireless channel can sustain while satisfying statistical QoS constraints. Energy efficiency is investigated by obtaining the minimum bit energy and wideband slope expressions in both low-power and wideband regimes. The increased energy requirements due to the presence of QoS constraints are quantified. Comparisons with variable-rate/fixed-power and variable-rate/variable-power cases are given. Overall, an energy-delay tradeoff for fixed-rate transmission systems is provided.

Secrecy capacity of a multiple-antenna wiretap channel is studied in the low signal-to-noise ratio (SNR) regime. Expressions for the first and second derivatives of the secrecy capacity with respect to SNR at SNR = 0 are derived. Transmission strategies required to achieve these derivatives are identified. In particular, it is shown that it is optimal in the low-SNR regime to transmit in the maximum-eigenvalue eigenspace of Phi = HmdaggerHm- Nm/NeHedaggerHewhere Hmand Hedenote the channel matrices associated with the legitimate receiver and eavesdropper, respectively, and Nmand Neare the noise variances at the receiver and eavesdropper, respectively. Energy efficiency is analyzed by finding the minimum bit energy required for secure and reliable communications, and the wideband slope. Increased bit energy requirements under secrecy constraints are quantified. Finally, the impact of fading is investigated.

In this paper, the performance of signaling strategies with high peak-to-average power ratio is analyzed in both coherent and noncoherent fading channels. Two modulation schemes, namely on-off phase-shift keying (OOPSK) and on-off frequency-shift keying (OOFSK), are considered. The optimal detector structures are identified and analytical expressions for the error probabilities are obtained for arbitrary constellation sizes. Numerical techniques are employed to compute the error rates. It is concluded that increasing the peakedness of the signals results in reduced error rates for a given power level and hence equivalently improves the energy efficiency for fixed error probabilities.

In this paper, the capacity and energy efficiency of training-based communication schemes employed for transmission overaprioriunknown Rayleigh block-fading channels are studied. Initially, the case in which the product of the estimate error and transmitted signal is assumed to be Gaussian noise is considered. In this case, it is shown that bit energy requirements grow without bound as the signal-to-noise ratio (SNR) goes to zero, and the minimum bit energy is achieved at a nonzero SNR value below which one should not operate. The effect of the block length on both the minimum bit energy and the SNR value at which the minimum is achieved is investigated. Flash training and transmission schemes are analyzed and shown to improve the energy efficiency in the low-SNR regime. In the second part of this paper, the capacity and energy efficiency of training-based schemes are investigated when the channel input vector in each coherence block is subject to peak power constraints. The capacity-achieving input structure is characterized and the magnitude distribution of the optimal input is shown to be discrete with a finite number of mass points. The capacity, bit energy requirements, and optimal resource allocation strategies are obtained through numerical analysis. The improvements in energy efficiency whenon-offkeying (OOK) with fixed peak power and vanishing duty cycle is employed are studied.

The central design challenge in next generation wireless systems is to have these systems operate at high bandwidths and provide high data rates while being cognizant of the energy consumption levels especially in mobile applications. Since communicating at very high data rates prohibits obtaining high bit resolutions from the analog-to-digital (A/D) converters, analysis of the energy efficiency under the assumption of hard-decision detection is called for to accurately predict the performance levels. In this paper, transmission over the additive white Gaussian noise (AWGN) channel, and coherent and noncoherent fading channels is considered, and the impact of hard-decision detection on the energy efficiency of phase and frequency modulations is investigated. Energy efficiency is analyzed by studying the capacity of these modulation schemes and the energy required to send one bit of information reliably in the low signal-to-noise ratio (SNR) regime. The capacity of hard-decision-detected phase and frequency modulations is characterized at low SNR levels through closed-form expressions for the first and second derivatives of the capacity at zero SNR. Subsequently, bit energy requirements in the low-SNR regime are identified. The increases in the bit energy incurred by hard-decision detection and channel fading are quantified. Moreover, practical design guidelines for the selection of the constellation size are drawn from the analysis of the spectral efficiency-bit energy tradeoff.

Energy efficiency in fading channels in the presence of Quality of Service (QoS) constraints is studied. Effective capacity, which provides the maximum arrival rate that a wireless channel can sustain while satisfying statistical QoS constraints, is considered. Spectral efficiency-bit energy tradeoff is analyzed in the low-power and wideband regimes by employing the effective capacity formulation, rather than the Shannon capacity. Through this analysis, energy requirements under QoS constraints are identified. The analysis is conducted under two assumptions: perfect channel side information (CSI) available only at the receiver and perfect CSI available at both the receiver and transmitter. In particular, it is shown in the low-power regime that the minimum bit energy required under QoS constraints is the same as that attained when there are no such limitations. However, this performance is achieved as the transmitted power vanishes. Through the wideband slope analysis, the increased energy requirements at low but nonzero power levels in the presence of QoS constraints are determined. A similar analysis is also conducted in the wideband regime. The minimum bit energy and wideband slope expressions are obtained. In this regime, the required bit energy levels are found to be strictly greater than those achieved when Shannon capacity is considered. Overall, a characterization of the energy-bandwidth-delay tradeoff is provided.

Error performance of noncoherent detection of on-off frequency shift keying (OOFSK) modulation over fading channels is analyzed when the receiver is equipped with multiple antennas. The analysis is conducted for two cases: 1) the case in which the receiver has the channel distribution knowledge only; and 2) the case in which the receiver perfectly knows the fading magnitudes. For both cases, the maximum a posteriori probability (MAP) detection rule is derived and analytical probability of error expressions are obtained. Numerical and simulation results indicate that for sufficiently low duty cycle values, lower error probabilities with respect to FSK signaling are achieved. Equivalently, when compared to FSK modulation, OOFSK with low duty cycle requires less energy to achieve the same probability of error, which renders this modulation a more energy efficient transmission technique. Also, through numerical results, the impact of number of antennas, antenna correlation, duty cycle values, and unknown channel fading on the performance are investigated.

Transmission over wireless fading channels under quality of service (QoS) constraints is studied when only the receiver has channel side information. Being unaware of the channel conditions, transmitter is assumed to send the information at a fixed rate. Under these assumptions, a two-state (ON-OFF) transmission model is adopted, where information is transmitted reliably at a fixed rate in the ON state while no reliable transmission occurs in the OFF state. QoS limitations are imposed as constraints on buffer violation probabilities, and effective capacity formulation is used to identify the maximum throughput that a wireless channel can sustain while satisfying statistical QoS constraints. Energy efficiency is investigated by obtaining the bit energy required at zero spectral efficiency and the wideband slope in both wideband and low-power regimes assuming that the receiver has perfect channel side information (CSI). Initially, the wideband regime with multipath sparsity is investigated, and the minimum bit energy and wideband slope expressions are found. It is shown that the minimum bit energy requirements increase as the QoS constraints become more stringent. Subsequently, the low-power regime, which is also equivalent to the wideband regime with rich multipath fading, is analyzed. In this case, bit energy requirements are quantified through the expressions of bit energy required at zero spectral efficiency and wideband slope. It is shown for a certain class of fading distributions that the bit energy required at zero spectral efficiency is indeed the minimum bit energy for reliable communications. Moreover, it is proven that this minimum bit energy is attained in all cases regardless of the strictness of the QoS limitations. The impact upon the energy efficiency of multipath sparsity and richness is quantified, and comparisons with variable-rate/fixed-power and variable-rate/variable-power cases are provided.

Energy efficiency in fading channels in the presence of QoS constraints is studied. Effective capacity, which provides the maximum constant arrival rate that a given process can support while satisfying statistical delay constraints, is considered. Spectral efficiency-bit energy tradeoff is analyzed in the low-power and wideband regimes by employing the effective capacity formulation, rather than the Shannon capacity, and energy requirements under QoS constraints are identified. The analysis is conducted for the case in which perfect channel side information (CSI) is available at the receiver and also for the case in which perfect CSI is available at both the receiver and transmitter. In particular, it is shown in the low-power regime that the minimum bit energy required in the presence of QoS constraints is the same as that attained when there are no such limitations. However, this performance is achieved as the transmitted power vanishes. Through the wideband slope analysis, the increased energy requirements at low but nonzero power levels are determined. A similar analysis is also conducted in the wideband regime, and minimum bit energy and wideband slope expressions are obtained. In this regime, the required bit energy levels are found to be strictly greater than those achieved when Shannon capacity is considered. Overall, an energy-delay tradeoff is characterized.

In this paper, training-based transmissions over a priori unknown Rayleigh block fading channels are considered. The input signals are assumed to be subject to peak power constraints. Prior to data transmission, channel fading coefficients are estimated in the training phase with the aid of pilot symbols. In this setting, the capacity and capacity-achieving input distribution are studied. The magnitude distribution of the optimal input is shown to be discrete with a finite number of mass points. The capacity, bit energy requirements, and optimal resource allocation strategies are obtained through numerical analysis. The bit energy is shown to grow without bound as SNR decreases to zero due to the presence of peakedness constraints. Capacity and energy-per-bit are also analyzed under the assumptions that the transmitter interleaves the data symbols before transmission over the channel, and per-symbol peak power constraints are imposed. Comparisons of the performances of training-based and noncoherent transmission schemes are provided.

In this paper, transmission over the additive white Gaussian noise (AWGN) channel, and coherent and noncoherent fading channels using M-ary orthogonal frequency-shift keying (FSK) or on-off frequency-shift keying (OOFSK) is considered. The receiver is assumed to perform hard-decision detection. In this setting, energy required to reliably send one bit of information is investigated. It is shown that for fixed M and duty cycle, bit energy requirements grow without bound as the signal-to-noise ratio (SNR) vanishes. The minimum bit energy values are numerically obtained for different values of M and the duty cycle. The impact of fading on the energy efficiency is identified. Requirements to approach the minimum bit energy of -1.59 dB are determined.

In this paper, pilot-assisted transmission over Gauss-Markov Rayleigh fading channels is considered. A simple scenario, where a single pilot signal is transmitted every T symbols and T - 1 data symbols are transmitted in between the pilots, is studied. First, it is assumed that binary phase-shift keying (BPSK) modulation is employed at the transmitter. With this assumption, the training period, and data and training power allocation are jointly optimized by maximizing an achievable rate expression. Achievable rates and energy-per-bit requirements are computed using the optimal training parameters. Secondly, a capacity lower bound is obtained by considering the error in the estimate as another source of additive Gaussian noise, and the training parameters are optimized by maximizing this lower bound.

The low-SNR capacity of M-ary PSK transmission over both the additive white Gaussian noise (AWGN) and fading channels is analyzed when hard-decision detection is employed at the receiver. Closed-form expressions for the first and second derivatives of the capacity at zero SNR are obtained. The spectral-efficiency/bit-energy tradeoff in the low-SNR regime is analyzed by finding the wideband slope and the bit energy required at zero spectral efficiency. Practical design guidelines are drawn from the information-theoretic analysis. The fading channel analysis is conducted for both coherent and noncoherent cases, and the performance penalty in the low-power regime for not knowing the channel is identified.

In this paper, the bit energy requirements of training-based transmission over block Rayleigh fading channels are studied. Pilot signals are employed to obtain the minimum mean-square-error (MMSE) estimate of the channel fading coefficients. Energy efficiency is analyzed in the worst case scenario where the channel estimate is assumed to be perfect and the error in the estimate is considered as another source of additive Gaussian noise. It is shown that bit energy requirement grows without bound as the SNR goes to zero, and the minimum bit energy is achieved at a nonzero SNR value below which one should not operate. The effect of the block length on both the minimum bit energy and the SNR value at which the minimum is achieved is investigated. Flash training schemes are analyzed and shown to improve the energy efficiency in the low-SNR regime. Energy efficiency analysis is also carried out when peak power constraints are imposed on pilot signals.

In this paper, the error performance of on-off frequency shift keying (OOFSK) modulation over fading channels is analyzed when the receiver is equipped with multiple antennas. The analysis is conducted in two cases: the coherent scenario where the fading is perfectly known at the receivers and the noncoherent scenario where neither the receiver nor the transmitter knows the fading coefficients. For both cases, the maximum a posteriori probability (MAP) detection rule is derived and analytical probabilities of error expressions are obtained. The effect of fading correlation among the receiver antennas is also studied. Simulation results indicate that for sufficiently low duty cycle values, lower probabilities of error values with respect to FSK signaling are achieved. Equivalently, when compared to FSK modulation, OOFSK with low duty cycle requires less energy to achieve the same probability of error, which renders this modulation a more energy efficient transmission technique.

In this paper, random coding error exponents and cutoff rate are studied for noncoherent Rician fading channels, where neither the receiver nor the transmitter has channel side information. First, it is assumed that the input is subject only to an average power constraint. In this case, a lower bound to the random coding error exponent is considered and the optimal input achieving this lower bound is shown to have a discrete amplitude and uniform phase. If the input is subject to both average and peak power constraints, it is proven that the optimal input achieving the random coding error exponent has again a discrete nature. Finally, the cutoff rate is analyzed, and the optimality of the single-mass input amplitude distribution in the low-power regime is discussed.

Transmission of information over a discrete-time memoryless Rician fading channel is considered, where neither the receiver nor the transmitter knows the fading coefficients. First, the structure of the capacity-achieving input signals is investigated when the input is constrained to have limited peakedness by imposing either a fourth moment or a peak constraint. When the input is subject to second and fourth moment limitations, it is shown that the capacity-achieving input amplitude distribution is discrete with a finite number of mass points in the low-power regime. A similar discrete structure for the optimal amplitude is proven over the entire signal-to-noise ratio (SNR) range when there is only a peak-power constraint. The Rician fading with the phase-noise channel model, where there is phase uncertainty in the specular component, is analyzed. For this model, it is shown that, with only an average power constraint, the capacity-achieving input amplitude is discrete with a finite number of levels. For the classical average-power-limited Rician fading channel, it is proven that the optimal input amplitude distribution has bounded support.

Transmission of information over a discrete-time memoryless Rician fading channel is considered, where neither the receiver nor the transmitter knows the fading coefficients. The spectral-efficiency/bit-energy tradeoff in the low-power regime is examined when the input has limited peakedness. It is shown that if a fourth-moment input constraint is imposed, or the input peak-to-average power ratio is limited, then in contrast to the behavior observed in average-power-limited channels, the minimum bit energy is not always achieved at zero spectral efficiency. The low-power performance is also characterized when there is a fixed peak limit that does not vary with the average power. A new signaling scheme that overlays phase-shift keying on ON-OFF keying (OOK) is proposed and shown to be optimally efficient in the low-power regime.

Transmission of information over wideband fading channels using M-ary orthogonal on/off FSK (OOFSK) signaling, in which M-ary FSK signaling is overlaid on on/off keying, is considered. It is assumed that the receiver uses energy detection for the reception of OOFSK signals. Capacity expressions are obtained when the receiver has perfect and imperfect fading side information. Power efficiency is investigated when the transmitter is subject to a peak-to-average power ratio (PAR) limitation or a peak power limitation. It is shown that under PAR limitation, it is extremely power inefficient to operate in the very low SNR regime. On the other hand, if there is only a peak power limitation, it is demonstrated that power efficiency improves as one operates with smaller SNR and vanishing duty factor.

In this paper, the capacity and spectral efficiency of peak power limited Rician block-fading channels when neither the receiver nor the transmitter knows the fading coefficients is studied. The capacity-achieving input amplitude distribution of the average power limited memoryless unknown Rayleigh fading channel is discrete with a finite number of mass points is proved. The spectral-efficiency and the bit energy tradeoff in the low power regime is studied.