Low Earth Orbit (LEO) nano-satellites can provide uplink connectivity for large numbers of distributed Internet of Things (IoT) sensing devices. To achieve a target Quality-of-Service (QoS), devices must send packets multiple times, due to collisions. This paper characterises the achievable set of terminal QoS targets, and determines the optimal uplink packet attempt rates. We show that QoS target feasibility is determined by the solution of a linear program (LP), and that the solution gives the optimal packet attempt rates. We show that the QoS targets can be modified using the shadow prices from the LP, to obtain feasibility. We show that our LP based approach can support greater than 30% more ground sensor terminals, compared to existing schemes.

This paper analyses the data message success rate for Internet of Things (IoT) sensing devices communicating over Low Earth Orbit (LEO) satellite links. We present an analytical framework for optimizing multi-objective multi-packet reception on the uplink. We present an analytical result for the probability of message success for a given ground terminal, and present an analytical result for the overall probability of message success, averaged across all terminals.

This paper considers millimeter-wave (mmWave) networks with hybrid beamforming communications, where base stations have a limited number of radio frequency (RF) chains. The base stations have overlapping coverage to overcome blockage issues in both downlink and uplink transmission. We propose a user association (UA) scheme that minimizes the time required for clearing data traffic of users in the coverage area. We formulate the UA problem as a time allocation problem, allocating time to user-base station links. We provide an innovative two-stage approach to solve this problem. Stage one optimizes a time fraction allocation for user-base station links. Then these time fractions are distributed across the RF chains at each base station using a fully distributed algorithm. Stage two then schedules the user-base station links, provably solving the UA minimum clearing time problem. We then characterize the achievability of any set of target user rates. Numerical results show that our proposed UA scheme achieves significantly reduced clearing times in comparison to baseline schemes.

This paper presents an efficient practical Zak-OTFS modulation implementation using time and frequency windowing methods. We present two general classes of delay-Doppler (DD) twisted convolution (TC) filters (Type-1 and Type-2), and show that they can be realized by time and frequency windowing functions. We then propose practical methods to generate time domain Zak-OTFS signals, for actual transmission, using the windowing functions. For Type-1, the signals are generated using an interpolation filter. For Type-2, they are generated using a form of precoded OFDM. We show that this allows a wide variety of pulse shapes to be implemented in practice for Zak-OTFS modulation. This was not previously possible. We also show that the Type-2 signals are more spectrally efficient than their Type-1 counterparts. Finally, we compare the channel predictability of the two implementations.

This paper designs energy-efficient trajectories for unmanned aerial vehicles (UAVs) harvesting data sequentially from distributed ground nodes. We propose a novel optimization framework for path planning, based on dynamic programming. We develop an optimum backward-forward algorithm that jointly optimizes the hovering locations for each ground node, and the visiting order to those locations. Our algorithm minimizes the total energy consumption of the UAV over its trajectory. Our framework is compatible with various probabilistic wireless communication channel models, and can also be applied to different cost functions, including minimising the total flying time, and allowing for bi-directional communications. We also develop a lower complexity algorithm that approximates the optimum UAV trajectory by decomposing the original problem into two sub-problems, and iterating back and forth between the two. This alternating algorithm has polynomial time complexity, and we show that it produces a near-optimum UAV trajectory, with as little deviation as 5% to 15% from the average energy consumption of the optimum algorithm.

This paper provides capacity results for multi-user mm-wave hybrid-beamforming, and presents optimal joint beam allocation and user scheduling algorithms. We characterize the downlink capacity of a practical system with quantized analog beamforming code-books under the constraint that users cannot be scheduled at the same time if they are closer together than a beam width in angle. We show that the capacity region is determined by a small number of linear inequality constraints. We also present capacity-achieving scheduling algorithms that provide beam allocations guaranteeing that user rate requirements are met within each resource block. In particular, we propose “sand-filling” algorithms that are provably optimal and which have linear complexity. Intuitively, our schemes can be viewed in terms of filling containers with coloured sand, in such a way that the colours at any given height do not conflict with the colours in the other containers at the same height, where the containers represent the RF chains (i.e. the beamforming resources), and the coloured sand represents the users (and their rate requirements). We show a numerical example where the capacity of our scheme is 82% higher than a traditional resource partitioning scheme.

In virtual reality (VR), users' headpose, that is, the location and the orientation of users' viewport, determines the view of the virtual world that is shown to the users. The importance of the viewport pose to VR experiences calls for the development of VR viewport pose models. However, no study has obtained a full pose (the position and the orientation) model applicable to modeling the viewport pose in VR experiences. In this paper, informed by our experimental measurements of viewport trajectories across 4 different types of VR interfaces, we first develop a statistical model of viewport poses in VR environments. Based on the developed model, we examine the correlations between pixels in VR frames that correspond to different viewport poses, and obtain an analytical expression for the visibility similarity (ViS) of the pixels across different VR frames. We then propose a lightweight ViS-based algorithm (ALG-ViS) that adaptively splits VR frames into the background and the foreground, reusing the background across different frames. Our implementation of ALG-ViS in two Oculus Quest 2 rendering systems demonstrates ALG-ViS running in real time, supporting the full VR frame rate, and outperforming baselines on measures of frame quality and bandwidth consumption.

This paper develops a beam direction optimization framework for next-generation GEO satellite networks. The objective is to meet traffic demands at user locations. Given beam-pointing directions, the downlink of the GEO satellite is a vector broadcast channel that consists of a single transmitter and multiple distributed ground users. We characterize the downlink channel matrix for the multibeam satellite network by using an array factor formula for uniform planar arrays. We obtain a necessary and sufficient condition dependent on the downlink channel matrix to provision traffic demands by meeting given SINR targets at user locations. Utilizing the necessary and sufficient conditions, we formulate a joint beam direction and power optimization problem to attain target SINRs which uses minimum total power. Our results demonstrate that analog beamforming with optimized beam shifts can achieve an SINR gain of 8 dB when compared to analog beamforming without beam direction optimization. It also offers a spatial multiplexing advantage of 90 km by enabling simultaneous provisioning of user locations in close proximity within the same frequency band. When compared to hybrid beamforming, our scheme can achieve an SINR gain of 2 dB.

Edge computing is increasingly proposed as a solution for reducing resource consumption of mobile devices running simultaneous localization and mapping (SLAM) algorithms, with most edge-assisted SLAM systems assuming the communication resources between the mobile device and the edge server to be unlimited, or relying on heuristics to choose the information to be transmitted to the edge. This paper presents AdaptSLAM, an edge-assisted visual (V) and visual-inertial (VI) SLAM system that adapts to the available communication and computation resources, based on a theoretically grounded method we developed to select the subset of keyframes (the representative frames) for constructing the best local and global maps in the mobile device and the edge server under resource constraints. We implemented AdaptSLAM to work with the state-of-the-art open-source V-and VI-SLAM ORB-SLAM3 framework, and demonstrated that, under constrained network bandwidth, AdaptSLAM reduces the tracking error by 62% compared to the best baseline method.

This paper derives the optimum detection rule for communication systems with n-bit phase quantization when data is differentially encoded at the transmitter. The proposed approach avoids the channel estimation problem at the receiver. First, a maximum likelihood detection rule for block-2 detectors utilizing only two consecutive quantized observations at the channel output is obtained. Second, it is shown that the derived maximum likelihood detection rule continues to be optimum for the class of block-L detectors for L ≥ 3 when n = log2M, where M is the input alphabet size. Finally, utilizing the structure of the derived optimum detector, a message error probability expression is obtained for Rayleigh fading wireless channels. A simulation study is performed to illustrate the performance of the optimum detectors as well as the performance loss due to the lack of channel knowledge at the receiver. The proposed approach and the solutions presented in this paper provide an initial step to communicate with low-resolution ADCs without requiring receiver-side channel knowledge.

The importance of the dynamics of the viewport pose, i.e., the location and the orientation of users’ points of view, for virtual reality (VR) experiences calls for the development of VR viewport pose models. In this paper, informed by our experimental measurements of viewport trajectories across 3 different types of VR interfaces, we first develop a statistical model of viewport poses in VR environments. Based on the developed model, we examine the correlations between pixels in VR frames that correspond to different viewport poses, and obtain an analytical expression for the visibility similarity (ViS) of the pixels across different VR frames. We then propose a lightweight ViS-based ALG-ViS algorithm that adaptively splits VR frames into the background and the foreground, reusing the background across different frames. Our implementation of ALG-ViS in two Oculus Quest 2 rendering systems demonstrates ALG-ViS running in real time, supporting the full VR frame rate, and outperforming baselines on measures of frame quality and bandwidth consumption.

The reconfigurable intelligent surface (RIS) is a promising technology that is anticipated to enable high spectrum and energy efficiencies in future wireless communication networks. This paper investigates optimum location-based RIS selection policies in RIS-aided wireless networks to maximize the end-to-end signal-to-noise ratio for product-scaling and sum-scaling path-loss models where the received power scales with theproductandsumof the transmitter-to-RIS and RIS-to-receiver distances, respectively. These scaling laws cover the important cases of end-to-end path-loss models in RIS-aided wireless systems. The random locations of all available RISs are modeled as a Poisson point process. To quantify the network performance, the outage probabilities and average rates attained by the proposed RIS selection policies are evaluated by deriving the distance distribution of the chosen RIS node as per the selection policies for both product-scaling and sum-scaling path-loss models. We also propose a limited-feedback RIS selection framework to achieve distributed network operation. The outage probabilities and average rates obtained by the limited-feedback RIS selection policies are derived for both path-loss models as well. The numerical results show notable performance gains obtained by the proposed RIS selection policies.

In this paper, we investigate antenna selection strategies for multi-antenna wireless communication systems with low-resolution quantizers. We propose three sub-optimum but low-complexity antenna selection strategies and characterize their symbol error probability performance. We show that the strategy that selects the diversity branch with channel rotated constellation points being furthest away from the decision boundary is the best strategy in terms of symbol error probability. Using numerical analysis, we provide evidence to suggest that this selection strategy achieves the same diversity order as the optimum maximum likelihood (ML) detector under the same operating conditions. An extensive simulation study is performed to illustrate the accuracy of the derived results.

This paper studies the performance and key structural properties of the optimum location-based relay selection policy for wireless networks consisting of homogeneous Poisson distributed relays. The distribution of the channel quality indicator at the optimum relay location is obtained. A threshold-based distributed selective feedback policy is proposed for the discovery of the optimum relay location with finite average feedback load. It is established that the total number of relays feeding back obeys a Poisson distribution and an analytical expression for the average feedback load is derived. The analytical expressions for the average rate and outage probability with and without selective feedback are obtained for general path-loss models. It is shown that the optimum location-based relay selection policy outperforms other common relay selection strategies notably. It is also shown that utilizing location information from five relays on average is enough to achieve almost the same performance with the infinite feedback load case. As generalizations, isotropic Poisson point processes and heterogeneous source-to-relay and relay-to-destination links are also studied.

In cognitive radio networks, compressive sensing has the potential to allow a secondary user to efficiently monitor a wideband spectrum at a sub-Nyquist sampling rate without complex hardware. In general, compressive sensing techniques leverage the assumption of sparsity of the wideband spectrum to recover the spectrum by solving a set of ill-posed linear equations. In this paper, we adopt the framework of a generative adversarial neural network (GAN) in deep learning and propose a deep compressive spectrum sensing GAN (DCSS-GAN), where two neural networks are trained to compete with each other to recover the spectrum from undersampled samples in the time domain. The proposed DCSS-GAN is a data-driven learning approach that does not require a priori statistics about the radio environment. In addition, it is an end-to-end algorithm that directly recovers the information of spectrum occupancy from raw samples and without the need of energy detection. Various simulations show that the proposed DCSS-GAN has a 12.3% to 16.2% performance gain on prediction accuracy at a 1/8th compression ratio compared to the conventional LASSO approach.

This paper investigates a three-layer IoT-fog-cloud computing system to determine the optimum workload and power allocation at each layer. The objective is to minimize maximum per-layer latency (including both data processing and transmission delays) with individual power constraints. The resulting optimum resource allocation problem is a mixed-integer optimization problem with exponential complexity. Hence, the problem is first relaxed under appropriate modeling assumptions, and then an efficient iterative method is proposed to solve the relaxed but still non-convex problem. The proposed algorithm is based on an alternating optimization approach, which yields close-to-optimum results with significantly reduced complexity. Numerical results are provided to illustrate the performance of the proposed algorithm compared to the exhaustive search method. The latency gain of three-layer distributed IoT-fog-cloud computing is quantified with respect to fog-only and cloud-only computing systems.

This paper considers a location-based optimal relay selection scheme for a relay-assisted wireless network where available decode-and- forward relays are distributed as a homogeneous Poisson point process. To solve an optimum relay selection problem, a central entity or the source requires information pertaining to all relay locations. Since the task of feeding this information back is impractical, we investigate a threshold-based limited feedback distributed relay selection policy. We show that the total number of relays feeding back is a Poisson distributed random variable. For a given threshold-based limited feedback distributed relay selection policy, we obtain analytical expressions for the average rate and the outage probability over the fading and no-fading communication scenarios. The derived analytical expressions are verified and the performance achieved by the proposed relay selection policy is illustrated through extensive simulations. It is observed that the limited feedback distributed relay selection policy can achieve almost the same performance with the optimum relay selection policy by only utilizing location information from a few number of relays.

Wireless sensor networks (WSNs) are the foundation of the Internet of Things (IoT), and in the era of the fifth generation of wireless communication networks, they are envisioned to be truly biquitous, reliable, scalable, and energy efficient. To this end, topology control is an important mechanism to realize self-organized WSNs that are capable of adapting to the dynamics of the environment. Topology optimization is combinatorial in nature, and generally is NP-hard to solve. Most existing algorithms leverage heuristic rules to reduce the number of search candidates so as to obtain a suboptimal solution in a certain sense. In this paper, we propose a deep reinforcement learning-based topology optimization algorithm, a unified search framework, for self-organized energy-efficient WSNs. Specifically, the proposed algorithm uses a deep neural network to guide a Monte Carlo tree search to roll out simulations, and the results from the tree search reinforce the learning of the neural network. In addition, the proposed algorithm is an anytime algorithm that keeps improving the solution with an increasing amount of computing resources. Various simulations show that the proposed algorithm achieves better performance as compared to heuristic solutions, and is capable of adapting to environment and network changes without restarting the algorithm from scratch.

This paper investigates the location-based relay selection problem, where the source node chooses its relay from a set of spatially deployed decode-and-forward relays. The advantages of location-based relay selection are the elimination of excessive relay switching rate and the feedback reduction avoiding the requirement of having full channel state information at the source node. For a homogeneous Poisson point process of candidate relays, we first derive the distribution for the distance of the relay (relative to the source and destination nodes) selected by the optimum location-based relay selection policy. This result is independent of the functional form of the path-loss function as long as it is a non-increasing function of the transmitter-receiver separation. By utilizing the derived optimum relay distance distribution, we then obtain analytical expressions for the average rate and outage probability by considering the power-law decaying path-loss function for the no-fading and Rayleigh fading communication scenarios. It is observed that the optimum relay selection policy outperforms the other common selection strategies notably, including the ones choosing the relay closest to the source, the relay closest to the destination and the relay closest to the mid-point between source and destination.

This paper considers a low-resolution wireless communication system in which transmitted signals are corrupted by fading and additive noise. First, a universal lower bound on the average symbol error probability (SEP), correct for all M-ary modulation schemes, is obtained when the number of quantization bits is not enough to resolve M signal points. Second, in the special case of M-ary phase shift keying (M-PSK), the optimum maximum likelihood detector for equi-probable signal points is derived. Third, utilizing the structure of the derived optimum receiver, a general average SEP expression for the M-PSK modulation with n-bit quantization is obtained when the wireless channel is subject to fading with a circularly-symmetric distribution. Finally, an extensive simulation study of the derived analytical results is presented for general Nakagami-m fading channels. It is observed that a transceiver architecture with n-bit quantization is asymptotically optimum in terms of communication reliability if n ≥ log2M + 1. That is, the decay exponent for the average SEP is the same and equal to m with infinite-bit and n-bit quantizers for n ≥ log2M + 1. On the other hand, it is only equal to 1/2 and 0 for n = log2M and n ≥ log2M, respectively. Hence, for fading environments with a large value of m, using an extra quantization bit improves communication reliability significantly.

Despite promising capacity gains, small cell densifications in ultra-dense HetNets can lead to frequent handovers (HOs), which may cause significant network overheads and decline in user experience. With the aim of modelling handovers in the context of ultra-dense HetNets, we propose a novel approach to model coverages of overlapping small cells. Based on this model, we derive the cumulative distribution function (CDF) of the user equipment's (UE) time-of-stay in small cells using boundary length and chord length distributions of small cell coverage. Our model is comprehensive enough to capture the nature of both inter-tier and intra-tier HOs in small cell networks, the latter of which is a major challenge in ultra-dense HetNets. Our analytical results can provide guidance for optimisations of HO parameters based on user velocity and small cell density to reduce network overheads and improve user experience.

This paper considers the use of full-duplex technology in cognitive radio networks to allow secondary users to sense the presence of primary users and transmit data simultaneously. This is the main advantage over half-duplex radios. In such networks, the so-called sensing-throughput trade-off exists due to the fact that while a higher transmit power results in higher secondary network throughput, sensing performance is degraded by the self-interference at the full-duplex transceiver. This paper presents a novel deep reinforcement learning-based joint spectrum sensing and power control algorithm for downlink communications in a cognitive small cell. The proposed algorithm can adapt to the unknown radio environment to transmit data opportunistically to the secondary users while avoiding interference to the primary network. Simulation results show that our algorithm achieves better performance than the traditional energy detection-based sensing method and performs close to a genie-aided method with the optimal spectrum utilization, especially in the high-SNR regime.

This paper introduces an analytical framework to investigate optimal design choices for the placement of virtual controllers along the cloud-to-things continuum. The main application scenarios include low-latency cyber-physical systems in which real-time control actions are required in response to the changes in states of an Internet of Things (IoT) node. In such cases, deploying controller software on a cloud server is often not tolerable due to delay from the network edge to the cloud. Hence, it is desirable to trade reliability with latency by moving controller logic closer to the network edge. Modeling the IoT node as a dynamical system that evolves linearly in time with quadratic penalty for state deviations, recursive expressions for the optimum control policy and the resulting minimum cost value are obtained by taking virtual fog controller reliability and response time latency into account. Our results indicate that latency is more critical than reliability in provisioning virtualized control services over fog endpoints, as it determines the swiftness of the fog control system as well as the timeliness of state measurements. Based on a drone trajectory tracking model, an extensive simulation study is also performed to illustrate the influence of reliability and latency on the control of autonomous vehicles over fog.

This paper investigates the downlink performance of dense K-tier heterogeneous cellular networks (HCNs) under general settings. First, Gaussian approximation bounds for the standardized aggregate wireless interference (AWI) in dense K-tier HCNs are obtained for when base stations (BSs) in each tier are distributed over the plane according to a spatial and general Poisson point process. The Kolmogorov-Smirnov (KS) distance is used to measure deviations of the distribution of the standardized AWI from the standard normal distribution. An explicit and analytical expression bounding the KS distance between these two distributions is obtained as a function of a broad range of network parameters, such as per-tier transmission power levels, per-tier BS intensity, BS locations, general fading statistics, and general bounded path-loss models. Bounds achieve a good statistical match between the standardized AWI distribution and its normal approximation even for moderately dense HCNs. Second, various spatial performance metrics of interest, such as outage capacity, ergodic capacity, and area spectral efficiency in the downlink of K-tier HCNs for general signal propagation models are investigated by making use of the derived distribution approximation results. Considering two specific BS association policies, it is shown that the derived performance bounds track the actual performance metrics reasonably well for a wide range of BS intensities, with the gap among them becoming negligibly small for denser HCN deployments. Finally, both analytical and numerical results on the area spectral efficiency reveal a non-linear growth trend with diminishing returns of HCN performance. Hence, the SIR invariance property does not hold under bounded path-loss models, which is a critical finding from the HCN design perspective. In particular, it points out a critical BS density beyond which the HCN performance starts to decline due to excessive wireless interference.

Social and communication networks interact with each other in multifaceted ways, yet these interactions are often considered to be secondary in throughput, privacy and security analysis for communication networks. In this paper, full-duplex (FD) and half-duplex (HD) multi-antenna cooperative communication systems are studied by taking both physical links and social connections into account. An optimal beamformer for maximizing communication rate in the proposed socio-technological setting aims to balance between the direct link and the cooperating link as well as respecting the trust degree between the users. The resulting optimization problems are nontrivial to solve, even numerically, as they are not convex. The complexity of the problems is significantly reduced by showing that a linear combination of the direct and cooperating links' channel vectors maximizes the achievable rate. Then, a computationally efficient numerical solution is used to maximize the rates both in the FD and HD modes. Numerical results demonstrate that significant gains in communication rates can be obtained with the proposed optimal beamforming design.

This paper studies the stability and dynamic control of underlay mobile edge networks. First, the stability region for a multiuser edge network is obtained under the assumption of full channel state information. This result provides a benchmark figure for comparing performance of the proposed algorithms. Second, a centralized joint flow control and scheduling algorithm is proposed to stabilize the queues of edge devices while respecting the average and instantaneous interference power constraints at the core access point. This algorithm is proven to converge to a utility point arbitrarily close to the maximum achievable utility within the stability region. Finally, more practical implementation issues such as distributed scheduling are examined by designing efficient scheduling algorithms taking advantage of communication diversity. The proposed distributed solutions utilize mini-slots for contention resolution and achieve a certain fraction of the utility optimal point. The performance lower bounds for distributed algorithms are determined analytically. The detailed simulation study is performed to pinpoint the cost of distributed control for mobile edge networks with respect to centralized control.

In this paper, beamforming design is investigated for a multi-antenna cooperative communication system in which both physical links and social connections (trust degrees) between nodes are taken into account. An optimal beamformer aims to balance between the direct link and the cooperating link as well as respecting the trust degree. The resulting optimization problem is nontrivial to solve, even numerically, as it is not convex. The complexity of the problem is largely reduced by showing that a linear combination of the direct and cooperating links' channel vectors maximizes the achievable rate. Then, a computationally efficient numerical solution is used to maximize the rate. Numerical results demonstrate that significant gains in communication rates can be obtained with the proposed optimal beamforming design.

This paper derives Gaussian approximation bounds for the standardized aggregate wireless interference (AWI) in the downlink of dense K-tier heterogenous cellular networks when base stations in each tier are distributed over the plane according to a (possibly non-homogeneous) Poisson process. The proposed methodology is general enough to account for general bounded path-loss models and fading statistics. The deviations of the distribution of the standardized AWI from the standard normal distribution are measured in terms of the Kolmogorov-Smirnov distance. An explicit expression bounding the Kolmogorov-Smirnov distance between these two distributions is obtained as a function of a broad range of network parameters such as per-tier transmission power levels, base station locations, fading statistics and the path-loss model. A simulation study is performed to corroborate the analytical results. In particular, a good statistical match between the standardized AWI distribution and its normal approximation occurs even for moderately dense heterogenous cellular networks. These results are expected to have important ramifications for the characterization of performance upper and lower bounds for emerging 5G network architectures.

This paper derives tight performance upper and lower bounds on the downlink outage efficiency of K-tier heterogeneous cellular networks (HCNs) for general signal propagation models with Poisson distributed base stations in each tier. In particular, the proposed approach to analyze the outage metrics in a K-tier HCN allows for the use of general bounded path-loss functions and random fading processes of general distributions. Considering two specific base station (BS) association policies, it is shown that the derived performance bounds track the actual outage metrics reasonably well for a wide range of BS densities, with the gap among them becoming negligibly small for denser HCN deployments. A simulation study is also performed for 2-tier and 3-tier HCN scenarios to illustrate the closeness of the derived bounds to the actual outage performance with various selections of the HCN parameters.

This paper studies the achievable throughput performance of the cognitive uplink under a limited primary cooperation scenario wherein the primary base station cannot feed back all interference channel gains to the secondary base station. To cope with the limited primary cooperation, we propose a feedback protocol called K-out-of-N feedback protocol, in which the primary base station feeds back only the KN smallest interference channel gains, out of N of them, to the secondary base station. We characterize the throughput performance under the K-out-of-N feedback protocol by analyzing the achievable multiuser diversity gains (MDGs) in cognitive uplinks for three different network types. Our results show that the proposed feedback mechanism is asymptotically optimum for interference-limited (IL) and individual-power-and-interference-limited (IPIL) networks for a fixed positive KN. It is further shown that the secondary network throughput in the IL and IPIL networks (under both the full and limited cooperation scenarios) logarithmically scales with the number of users in the network. In total-power-and-interference-limited (TPIL) networks, on the other hand, the K-out-of-N feedback protocol is asymptotically optimum for KN= Nδ, where δ ∈ (0, 1). We also show that, in TPIL networks, the secondary network throughput under both the limited and full cooperation scales logarithmically double with the number of users in the network. These results indicate that the cognitive uplink can achieve the optimum MDG even with limited cooperation from the primary network. They also establish the dependence of pre-log throughput scaling factors on the distribution of fading channel gains for different network types.

This paper introduces a model to study both single tier and multitier wireless communication systems consisting of a multitude of wireless access points (AP), and operating according to the classical opportunistic beamforming framework. The AP locations in the proposed network model are determined by using planar Poisson point processes. The extreme value distribution of signal-to-interference-plus-noise-ratio (SINR) on a beam is of fundamental importance for obtaining performance bounds for such an opportunistic communication system. Two tight distribution approximation results are provided for the distribution of maximum SINR on a beam, which is hard to obtain due to correlation structure of the underlying inter-AP interference field, using key tools from stochastic geometry. These approximations hold for general path loss models that satisfy some mild conditions. Simulations and numerical evaluations are presented to validate the results, to provide further insights into the derived approximate maximum beam SINR distributions, and to illustrate the utility of these approximations in obtaining performance bounds for opportunistic communication systems having multiple interfering APs. In particular, key performance measures such as beam outage probability and ergodic aggregate data rate of an AP are derived by utilizing the approximated distributions.

In this study, an optimal channel switching strategy is proposed for average capacity maximization in the presence of average and peak power constraints. Necessary and sufficient conditions are derived to determine when the proposed optimal channel switching strategy can or cannot outperform the optimal single channel strategy, which performs no channel switching. Also, it is obtained that the optimal channel switching strategy can be realized by channel switching between, at most, two different channels. In addition, a low-complexity optimization problem is derived to obtain the optimal channel switching strategy. Furthermore, based on some necessary conditions that need to be satisfied by the optimal channel switching solution, an alternative approach is proposed for calculating the optimal channel switching strategy. Numerical examples are provided to exemplify the derived theoretical results and to provide intuitive explanations.

Optimal channel switching is proposed for average capacity maximization in the presence of average and peak power constraints. A necessary and sufficient condition is derived in order to determine when the proposed optimal channel switching approach can or cannot outperform the optimal single channel approach, which performs no channel switching. Also, it is stated that the optimal channel switching solution can be realized by channel switching between at most two different channels. In addition, a low-complexity optimization problem is derived in order to obtain the optimal channel switching solution. Numerical examples are provided to exemplify the derived theoretical results.

Playback delay control is an important mechanism to avoid jitter in video streaming systems. This paper introduces a playback delay minimization problem for multiuser video streaming systems providing a jitter-free video streaming service to end users in the system. In particular, a necessary condition on the playback delay for jitter-free streaming is obtained. Then, based on the derived necessary condition, an optimum rate splitting algorithm that splits available rate to all users is proposed. The proposed algorithm is optimum in the sense that it achieves the minimum system delay, which is defined as the maximum of all initial playback delays, while ensuring jitter-free streaming service to all users. Finally, using these results, an expression for the minimum system delay as a function of system parameters such as total rate and playback curves of requested video files is also derived.

This paper studies optimum power control and sum-rate scaling laws for the distributed cognitive uplink. It is first shown that the optimum distributed power control policy is in the form of a threshold based water-filling power control. Each secondary user executes the derived power control policy in a distributed fashion by using local knowledge of its direct and interference channel gains such that the resulting aggregate (average) interference does not disrupt primary's communication. Then, the tight sum-rate scaling laws are derived as a function of the number of secondary users N under the optimum distributed power control policy. The fading models considered to derive sum-rate scaling laws are general enough to include Rayleigh, Rician and Nakagami fading models as special cases. When transmissions of secondary users are limited by both transmission and interference power constraints, it is shown that the secondary network sum-rate scales according to 1/enhlog log (N), where n_h is a parameter obtained from the distribution of direct channel power gains. For the case of transmissions limited only by interference constraints, on the other hand, the secondary network sum-rate scales according to 1/eγglog (N), where γgis a parameter obtained from the distribution of interference channel power gains. These results indicate that the distributed cognitive uplink is able to achieve throughput scaling behavior similar to that of the centralized cognitive uplink up to a pre-log multiplier 1/e, whilst primary's quality-of-service requirements are met. The factor 1/e can be interpreted as the cost of distributed implementation of the cognitive uplink.

This paper studies downlink coverage maximization for cellular networks in which base station (BS) locations are modeled using a spatial Poisson point process, considering three different coverage models, and under constraints on transmit power, BS density and transmit power density. Firstly, the coverage optimization problem is solved analytically for the first coverage model that focuses on noise-limited communication by ignoring interference and random fading effects. This model provides useful insights into the significance of bounded path loss models to obtain meaningful solutions for this problem. The other two coverage models are based on the users' received signal-to-interference-plus-noise-ratio (\sinr) from their associated BSs. For these models, it is shown that the coverage optimization problem can be reduced to a constrained single dimensional optimization problem without any loss of optimality. The related solutions can be obtained with limited computational complexity by resorting to a numerical search over a compact subset of candidate values. Bounds on the optimum BS density are also provided to further truncate the search space. All results are derived for general bounded path loss models. Specific applications are also illustrated to provide further design insights and to highlight the importance of using bounded path loss models for coverage analysis.

This paper studies the outage capacity of a network consisting of a multitude of heterogeneous mobile users and operating according to the classical opportunistic beamforming framework. The base station is located at the center of the cell, which is modeled as a disk of finite radius. The random user locations are modeled using a homogeneous spatial Poisson point process. The received signals are impaired by both fading and location dependent path loss. For this system, we first derive an expression for the beam outage probability. This expression holds for all path loss models that satisfy some mild conditions. Then, we focus on two specific path loss models (i.e., an unbounded model and a more realistic bounded one) to illustrate the applications of our results. In the large system limit, where the cell radius tends to infinity, the beam outage capacity and its scaling behavior are derived for the selected specific path loss models. This paper also studies opportunistic schemes that achieve fairness among the heterogeneous users. Numerical evaluations are performed to give further insights and to illustrate the applicability of the outage capacity results even to a cell having a small finite radius.

This paper studies the outage capacity of a network consisting of a multitude of heterogenous mobile users, and operating according to the classical opportunistic beamforming framework. The base station is located at the center of the cell, which is modeled as a disk of finite radius. The random user locations are modeled using a homogenous spatial Poisson point process. The received signals are impaired by both fading and location dependent path loss. For this system, we first derive an expression for the beam outage probability. This expression holds for all path loss models that satisfy some mild conditions. Then, we focus on two specific path loss models (i.e., an unbounded model and a more realistic bounded one) to illustrate the applications of our results. In the large system limit where the cell radius tends to infinity, the beam outage capacity and its scaling behavior are derived for the selected specific path loss models. It is shown that the beam outage capacity scales logarithmically for the unbounded model. On the other hand, this scaling behavior becomes double logarithmic for the bounded model. Intuitive explanations are provided as to why we observe different scaling behavior for different path loss models. Numerical evaluations are performed to give further insights, and to illustrate the applicability of the outage capacity results even to a cell having a small finite radius.

This paper studies optimal distributed power allocation and scheduling policies (DPASPs) for distributed total power and interference limited (DTPIL) cognitive multiple access networks in which secondary users (SU) independently perform power allocation and scheduling tasks using their local knowledge of secondary transmitter secondary base-station (STSB) and secondary transmitter primary base-station (STPB) channel gains. In such networks, transmission powers of SUs are limited by an average total transmission power constraint and by a constraint on the average interference power that SUs cause to the primary base-station. We first establish the joint optimality of water-filling power allocation and threshold-based scheduling policies for DTPIL networks. We then show that the secondary network throughput under the optimal DPASP scales according to 1/enhlog log (N), where nhis a parameter obtained from the distribution of STSB channel power gains and N is the total number of SUs. From a practical point of view, our results signify the fact that distributed cognitive multiple access networks are capable of harvesting multiuser diversity gains without employing centralized schedulers and feedback links as well as without disrupting primary's quality-of-service (QoS).

This paper studies coverage maximization for cellular networks in which base station (BS) locations are modeled using a homogenous spatial Poisson point process, and user locations are arbitrary. A user is covered for communication if its received signal-to-interference-plus-noise-ratio (SINR) is above a given threshold value. Two coverage models are considered. In the first model, the coverage of a user is determined based on the received SINR only from the nearest BS. The nearest BS happens to be the BS maximizing the received SINR without fading. In the second model, on the other hand, the coverage of a user is determined based on the maximum SINR from all BSs in the network. The objective is to maximize the coverage probability under the constraints on transmit power density (per unit area). Using stochastic geometry, coverage probability expressions for both coverage models are obtained. Using these expressions, bounds on the coverage maximizing power per BS and BS density are obtained. These bounds truncate the search space of the optimization problem, and thereby simplify the numerical evaluation of optimum BS power and density values considerably. All results are derived for general bounded path loss models satisfying some mild conditions. Specific applications are also illustrated to provide further insights into the optimization problem of interest.

This paper considers optimum allocation of transmission powers in next generation networks. The proposed framework is general enough to cover both emerging heterogeneous network (HetNet) architectures and cognitive radio (CR) networks. There are two types of users in our model. Type 1 users (T1U) represent either femtocell users in a HetNet, or secondary users in a CR network. Type 2 users (T2U) represent either macrocell users in a HetNet, or primary users in a CR network. T1Us share the same frequency band with T2Us, and they form an uplink to their intended base station (i.e., either the femtocell base station or the secondary base station) while causing interference to T2Us. The optimum power allocation strategy maximizing the aggregate communication rate of T1Us is found under individual transmission power constraints and a total interference power constraint at T2Us. It is shown that the optimum power allocation exhibits a binary structure, which means links are either quot;onquot; or quot;offquot;, up to at most one exceptional fractional power level. Further, it is shown that T1Us transmitting at positive power correspond to the ones having better quot;jointquot; power and interference channel gains. Applications of these results are illustrated for well-known fading models such as Rayleigh, Rician-K, and Nakagami-m fading.

This paper studies the structure of downlink sum-rate maximizing selective decentralized feedback policies for opportunistic beamforming under finite feedback constraints on the average number of mobile users feeding back. First, it is shown that any sum-rate maximizing selective decentralized feedback policy must be a threshold feedback policy. This result holds for all fading channel models with continuous distribution functions. Second, the resulting optimum threshold selection problem is analyzed in detail. This is a nonconvex optimization problem over finite-dimensional Euclidean spaces. By utilizing the theory of majorization, an underlying Schur-concave structure in the sum-rate function is identified, and the sufficient conditions for the optimality of homogenous threshold feedback policies are obtained. Applications of these results are illustrated for well-known fading channel models such as Rayleigh, Nakagami, and Rician fading channels. Rather surprisingly, it is shown that using the same threshold value at all mobile users is not always a rate-wise optimal feedback strategy, even for a network in which mobile users experience statistically the same channel conditions. For the Rayleigh fading channel model, on the other hand, homogenous threshold feedback policies are proven to be rate-wise optimal if multiple orthonormal data carrying beams are used to communicate with multiple mobile users simultaneously.

In cognitive multiple access networks, primary-secondary feedback links are needed to convey secondary transmitter primary base station (STPB) channel gains from the primary base station (PBS) to the secondary base station (SBS). To reduce the amount of feedback exchange between PBS and SBS, this paper proposes a feedback control protocol called K-smallest channel gains (K-SCG) feedback protocol in which the PBS feeds back the KNsmallest STPB channel gains, out of N of them, to the SBS. We study the performance of K-SCG feedback protocol for total power and interference limited (TPIL) networks when transmit powers of secondary users (SUs) are optimally allocated. In TPIL networks, transmit powers of SUs are limited by an average total power constraint as well as a constraint on the average total interference power that they cause to the PBS. It is shown that for KN= Nδwith δ ∈ (0, 1), K-SCG feedback protocol is asymptotically optimal, i.e., secondary network throughput under K-SCG and full feedback protocols scales according to 1/nhlog log (N) where nhis a parameter obtained from the distribution of secondary transmitter secondary base station (STSB) channel power gains, and N is the number of SUs. It is also shown that for KN= o(N), the interference power at the PBS converges to zero almost surely and in mean as N becomes large. This result implies that for N large enough, the secondary network just requires the indices of SUs corresponding to the KNsmallest STPB channel gains for performing jointly optimal user scheduling and power allocation rather than the actual realizations of STPB channel gains.

Opportunistic beamforming (OBF) is a well-known communication technique that utilizes partial channel state information (CSI) to obtain multiuser diversity gains in the downlink. We focus on the structure of the optimal homogenous threshold feedback policy that maximizes the ergodic downlink sum-rate for OBF under a peak feedback load constraint, which we model by using a multi-packet reception model for the uplink. We solve the resulting quasi-convex optimization problem by obtaining a formula for the sum-rate maximizing feedback probability. While providing insights on the implications of our results in practical systems, we also illustrate the tradeoff between feedback and rate by obtaining the Pareto optimal boundary between feasible and infeasible feedback-rate pairs.

This paper analyzes layer 2 contention resolution strategies for wireless networks with multipacket reception by using noncooperative game theory. Necessary and sufficient conditions are obtained for a strategy profile to be a Nash equilibrium. Applications of the derived equilibrium conditions to predict selfish behavior and the resulting equilibrium performance are illustrated in specific communication scenarios along with various design insights. The collective equilibrium behavior of wireless networks with large user populations is also studied, and a Poisson-Bernoulli type approximation is obtained for the total number of packet arrivals. Finally, random access control with imperfect information structure is considered, the form of equilibrium strategies as well as uniqueness and existence results for general wireless channel models are obtained, and the best-response learning dynamics achieving an equilibrium are illustrated in specific instances.

This paper considers the optimum single cell power control maximizing the aggregate (uplink) communication rate of the cell when there are peak power constraints at mobile users, and a low-complexity data decoder (without successive decoding) at the base station. It is shown that the optimum power allocation is binary, which means that links are either “on” or “off.” By exploiting further structure of the optimum binary power allocation, a simple polynomial-time algorithm for finding the optimum transmission power allocation is proposed, together with a reduced complexity near-optimal heuristic algorithm. Sufficient conditions under which channel-state aware time division multiple access (TDMA) maximizes the aggregate communication rate are established. In a numerical study, we compare and contrast the performance achieved by the optimum binary power-control policy with other suboptimum policies and the throughput capacity achievable via successive decoding. It is observed that two dominant modes of communication arise, wideband or TDMA, and that successive decoding achieves better sum-rates only under near perfect interference cancellation efficiency. In this paper, we exploit the theory of majorization to obtain the aforementioned results. In the final part of this paper, we do so to solve power-control problems in the areas of femtocells and cognitive radio and find that, again, optimal solutions have a binary (or almost binary) character.

Abstract-This paper focuses on the secondary network throughput scaling in cognitive radio networks when secondary users' transmission powers are optimally allocated. Throughput scaling laws are obtained for two different cognitive radio networks under two different communication scenarios. In the first network type called power-interference limited networks, secondary users' transmission powers are limited by both average total power constraint and the constraint on the average interference that they cause to primary users. In the second network type called interference limited networks, secondary users' transmission powers are only limited by average interference constraint. For both network types, an asymmetric communication scenario, in which the channels between secondary users and the secondary base station experience Rayleigh fading and those between secondary users and the primary base station experience Rician fading, and a symmetric communication scenario, in which both types of channels experience Rayleigh fading, are considered. It is shown that the secondary network throughput scales like log log ((K+1/eK)N) and log ((K+1/eK)N) for power-interference limited and interference limited networks, respectively, under the asymmetric communication scenario, where N is the number of secondary users and K >; 0 is the Rician factor. For the symmetric communication scenario, these scaling laws are given by log log (N) and log(N) for power-interference limited and interference limited networks, respectively.

Optimum capacity scaling in the downlink of a single-cell multiple-input multiple-output communication system can be achieved by a communication strategy called opportunistic beamforming in which information carrying beams are randomly formed and users are opportunistically scheduled based on their partial channel state information. Even though opportunistic beamforming reduces the amount of feedback required to achieve optimum capacity scaling laws, the number of users feeding back in its plain implementations still grows linearly with the total number of users in the system, which is an onerous requirement on the feedback channel. In this paper, we focus on a more stringent but realistic O(1) feedback constraint on the feedback channel, and obtain the tradeoff curve tracing the Pareto optimal boundary between feasible and infeasible feedback-rate pairs. We show that any point on this tradeoff curve can be obtained by means of homogenous decentralized thresholding policies, in which a user feeds back only if the received signal quality at her best link is good enough, and derive the form of these optimum policies. We further show that if the O(1) feedback constraint is relaxed, we can achieve the optimum capacity scaling by a feedback amount growing like O((log n)ϵ) for any e ∈ (0, 1), where n is the number of users in the system.

This paper considers the optimum single cell power-control maximizing the aggregate (uplink) communication rate of the cell when there are peak power constraints at mobile users, and a low-complexity data decoder (without successive decoding) at the base station. It is shown, via the theory of majorization, that the optimum power allocation is binary, which means links are either “on” or “off”. By exploiting further structure of the optimum binary power allocation, a simple polynomial-time algorithm for finding the optimum transmission power allocation is proposed. Sufficient conditions under which channel-state aware time-division-multiple-access (TDMA) maximizes the aggregate communication rate are established. Finally, a simulation study is performed and it is observed that two dominant modes of communication arise, wideband or TDMA. The heuristic algorithm that chooses the best of these two modes is observed to be extremely close to optimal.

Beamforming techniques utilizing only partial channel state information (CSI) has gained popularity over other communication strategies requiring perfect CSI thanks to their lower feedback requirements. The amount of feedback in beamforming based communication systems can be further reduced through selective feedback techniques in which only the users with channels good enough are allowed to feed back by means of a decentralized feedback policy. In this paper, we prove that thresholding at the receiver is the rate-wise optimal decentralized feedback policy for feedback limited systems with prescribed feedback constraints. This result is highly adaptable due to its distribution independent nature, provides an analytical justification for the use of threshold feedback policies in practical systems, and reinforces previous work analyzing threshold feedback policies as a selective feedback technique without proving its optimality. It is robust to selfish unilateral deviations. Finally, it reduces the search for rate-wise optimal feedback policies subject to feedback constraints from function spaces to a finite dimensional Euclidean space.

Threshold feedback policies are well known and provably rate-wise optimal selective feedback techniques for communication systems requiring partial channel state information (CSI). However, optimal selection of thresholds at mobile users to maximize information theoretic data rates subject to feedback constraints is an open problem. In this paper, we focus on the optimal threshold selection problem, and provide a solution for this problem for finite feedback systems. Rather surprisingly, we show that using the same threshold values at all mobile users is not always a rate-wise optimal feedback strategy, even for a system with identical users experiencing statistically the same channel conditions. By utilizing the theory of majorization, we identify an underlying Schur-concave structure in the rate function and obtain sufficient conditions for a homogenous threshold feedback policy to be optimal. Our results hold for most fading channel models, and we illustrate an application of our results to familiar Rayleigh fading channels.

This paper introduces and solves the sum rate maximization problem at the multiple-input multiple-output (MIMO) downlink as a function optimization problem subject to feedback constraints at the uplink. It is first shown that this optimization problem can be reduced to a finite dimensional non-convex optimization problem. Then, the resulting problem can be solved by investigating Schur-concavity of the aggregate communication rate across multiple traffic flows. Necessary and sufficient conditions for the rate optimality of homogenous threshold feedback policies are established. With some surprise, it is shown that homogenous thresholding is not always rate-wise optimal even if mobile users experience the same channel conditions statistically. Applications of these results are illustrated for Rayleigh fading channels.

This paper introduces a new model, the octopus model, for studying small-world networks. The model is proposed for general measure-metric spaces and parametrizes the generation of complex networks in terms of the distribution of long-range connections per node. This model allows for the generation of a wide spectrum of complex networks including the ones possessing the clustering features of the Watts-Strogatz model and those possessing the scale-free features of the Barabási model. Analytical expressions for the average message delivery time in small-world networks as a function of source-target separation are derived. These analytical formulas show that nodes tend to communicate with one another only through their short-range contacts, and the average message delivery time rises linearly when the separation between source and target is small. On the other hand, as this separation increases, long-range connections are more commonly used, and the average message delivery time rapidly saturates to a constant value and stays almost the same for large values of the separation. These results are consistent with previous experimental observations made by Travers and Milgram in 1969, as well as by others. Other somewhat surprising conclusions of the paper are that hubs have a limited effect in reducing the average message delivery time and that the variance of connectivity in small-world networks adversely affects this time.

This paper addresses the following question: how reliable is it to use the unbounded path-loss model G(d) = d-α, where α is the path-loss exponent, to model the decay of transmitted signal power in wireless networks? G(d) is a good approximation for the path-loss in wireless communications for large values of d but is not valid for small values of d due to the singularity at 0. This model is often used along with a random uniform node distribution, even though in a group of uniformly distributed nodes some may be arbitrarily close to one another. The unbounded path-loss model is compared to a more realistic bounded path-loss model, and it is shown that the effect of the singularity on the total network interference level is significant and cannot be disregarded when nodes are uniformly distributed. A phase transition phenomenon occurring in the interference behavior is analyzed in detail. Several performance metrics are also examined by using the computed interference distributions. In particular, the effects of the singularity at 0 on bit error rate, packet success probability and wireless channel capacity are analyzed.

Small-world networks are networks in which the graphical diameter of the network is as small as the diameter of random graphs but whose nodes are highly clustered when compared with the ones in a random graph. Examples of small-world networks abound in sociology, biology, neuroscience and physics as well as in human-made networks. This paper analyzes the average delivery time of messages in dense small-world networks constructed on a plane. Iterative equations for the average message delivery time in these networks are provided for the situation in which nodes employ a simple greedy geographic routing algorithm. It is shown that two network nodes communicate with each other only through their short-range contacts, and that the average message delivery time rises linearly if the separation between them is small. On the other hand, if their separation increases, the average message delivery time rapidly saturates to a constant value and stays almost the same for all large values of their separation.

This paper analyzes the asymptotic behavior of a multiple-access network comprising a large number of selfish transmitters competing for access to a common wireless communication channel, and having different utility functions for determining their strategies. A necessary and sufficient condition is given for the total number of packet arrivals from selfish transmitters to converge in distribution. The asymptotic packet arrival distribution at Nash equilibrium is shown to be a mixture of a Poisson distribution and finitely many Bernoulli distributions.

A new technique based on first passage processes is presented as a means for calculating the detection time of an event by wireless mobile sensors located in Rd, d = 1,2 and 3. Our proposed method is valid for any type of mobility model, and is amenable to a variety of extensions. We consider heterogenous networks in which sensor platforms have different mobility patterns. The second extension considers unreliable sensors with randomly distributed operational lifetimes. The second generalization is of paramount importance if the network is deployed in a hostile environment. Our main results are illustrated for random linear motion and Brownian motion. An approach for applying our theorems when sensors move according to a time- homogenous Markov process is also considered.

This paper analyzes the behavior of selfish transmitters when they contend for the access of a noise impaired wireless channel with and without capture effect. We first focus on nodes with single packet detection capability contending for the access of a noisy wireless channel. We identify the effect of noise on selfish nodes' transmission probabilities. We show the existence of a critical threshold c*, which only depends on the noise distribution, such that if the cost of a packet failure is greater than c*, then backing-off is in transmitters' best-interest regardless of strategies of others. Otherwise, they may transmit, or back-off, or randomize among these two actions depending on their opponents' strategies. We finally turn our attention to channels with capture effect where it is possible to receive up to k packets simultaneously. We analyze the impact of such a capture effect on transmission probabilities of selfish nodes.

We give a rigorous mathematical analysis of a game-theoretic MAC protocol for wireless networks. We begin with a wireless communication network in which n selfish nodes (agents), which might have different perceived utilities, contend for access on a common wireless communication channel. We first formulate this distributed multiple access problem in terms of a one-shot random access game, and characterize the Nash equilibria of the game. We then look at the asymptotic properties of the system as n rarr infin. When all nodes are identical, the best possible bounds on the rate of convergence of the asymptotic packet arrivals and the channel throughput are given. The analysis of the asymptotic packet arrivals in the heterogeneous case concludes the paper