The efficient utilization of the electromagnetic spectrum is crucial for the performance and coexistence of emerging 5G-Advanced and 6G/Next-G wireless communication networks operating in 3D space such as unmanned aerial vehicle (UAV)-assisted and high altitude platform station (HAPS)-based systems. This paper introduces a comprehensive analytical framework for evaluating spectrum utilization efficiency (SUE) and link spectral efficiency (SE) in aerial communication systems, focusing on the general scenario of partially loaded networks. Leveraging a frequency reuse architecture to model the impact of co-channel interference, closed-form expressions for the upper and lower bounds of SE and SUE under best-case and worst-case scenarios are derived. The proposed framework incorporates probabilistic modeling of user distribution and channel utilization in analyzing multi-UAV next generation node B (UAV-gNB) and multi-interference scenarios under different propagation models, including free-space path loss, log-normal fading, and Nakagami-m fading. Simulations validate the analytical results and the proposed framework, demonstrating its potential utility in optimizing spectrum usage and interference management in 3D communication networks. The findings provide actionable insights for designing and deploying efficient and scalable aerial communication networks with improved spectrum utilization and SE performance.

Advanced Air Mobility (AAM) is envisioned to bring a new mode of air transportation with promising benefits to the society, at large. AAM services such as air taxis and air ambulances are expected to bridge the digital divide between urban and rural/tribal regions. For AAM services to be deployed at large-scale, there are several technical challenges that need to be addressed. Reliable communications to support autonomous airspace operations is one such challenge. This paper presents a comprehensive architecture for ground-based communication support for AAM vehicles. The architecture includes: (1) air corridor design, (2) unit air cell that serves as a building block for air corridor, and (3) novel antenna designs for the base station and the AAM vehicle. Detailed analysis is presented for these architectural elements to demonstrate their viability for practical implementation.

This paper addresses the problem of frequency reuse planning in aerial communications systems and proposes a 3D frequency reuse architecture for spectrum management in unmanned aerial vehicle (UAV) swarms. A 3D geometric shape is introduced for modeling an aerial frequency reuse cell and the corresponding frequency reuse factor is derived for efficient spectrum utilization. An algorithm based on the covariance matrix of the distribution of the UAV user equipment (UAV-UE) locations is also presented to determine the required 3D volume coverage of UAV base stations (UAV-BSs). The algorithm facilitates the placement of UAV•BSs according to the frequency reuse plan, enabling seamless spectrum management for UAV-BSs. Simulation results show that indeed the proposed algorithm is a reasonable approach for UAV-BS deployment for UAV-UE swarms having arbitrary location distributions. The study extends the well-established terrestrial cellular frequency reuse concepts to the aerial domain and provides insights into the 3D frequency reuse, including the cell shape for tessellating the 3D space, frequency reuse distances and cluster sizes.

This paper designs Artificial Intelligence (AI) method, to determine an optimal trajectory for an Unmanned Aerial Vehicle (UAV) mounted mobile base station to maximize its coverage of distinct users. Determining such an optimal trajectory for arbitrarily distributed users over an area is, in general, difficult and there is no closed-form solution. Since the users are arbitrarily located the method must adapt accordingly. To accomplish this, the problem is formulated in a way that is compatible with Reinforcement Learning (RL) and two AI approaches are designed to learn an optimal trajectory. The first uses Deep Reinforcement Learning (DRL) implemented with a Deep Q-Network (DQN) while the second is a reward-based greedy algorithm. It is shown that these new algorithms significantly outperform the state-of-the-art previously proposed deep learning based approaches. Moreover, the simple AI-based greedy approach is shown to perform close to the DQN-aided DRL algorithm at a much lower computational complexity at least in the type of scenarios considered in this paper.

This work proposes a cognitive engine design that enables a radio to find transmission opportunities in non-contiguous wideband spectrum to avoid interference. The radio’s objective is to apply both frequency hopping and transmit power adjustment to maintain a required level of quality-of-service (QoS). The spectrum is partitioned into sub-bands each made of a number of narrowband channels. A multi-task deep Q-network (DQN) is utilized to solve the underlying problem where communications over each sub-band represents a single task. The proposed technique exploits transfer learning between tasks to speed up learning operation for new tasks. The proposed multi-task transfer DQN is proved to be converged. It is shown through simulations that the radio is able to learn an efficient strategy to evade interference signals in a partially observable environment. The experimental results indicate that the proposed approach offers up to 24% improvement to the percentage of successful communications when compared to other RL-based approaches found in existing literature.

This paper introduces a novel deep learning (DL) based successive interference cancellation (SIC) scheme for an uplink multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) system. The proposed scheme is aimed at mitigating the problems of error propagation and high computational complexity encountered with traditional SIC schemes. A separate deep neural network (DNN) is used to directly decode each user's signal at every SIC step. In particular, the DNN simulates the following operations: channel estimation, signal detection and canceling of decoded signals from the received combined signal. Results from simulation show superior performance and the effectiveness of the proposed approach. It outperforms traditional SIC schemes including the DL based approaches in terms of the bit error rate while maintaining low computational complexity.

In this study, the authors review various jamming and anti‐jamming strategies in the context of cognitive radios (CRs). The study explores different jamming models and classifies them according to their functionality. Furthermore, a study of jamming detection techniques is provided to enable a CR to identify different jamming signals. Finally, anti‐jamming communications are discussed in detail to encounter different types of jamming attacks. The focus of the study is on advanced anti‐jamming approaches that are based on learning strategies including, for example, game theoretic learning, reinforcement learning and deep learning. They show differences and similarities between these approaches and the conditions under which each of the algorithms can be useful.

Spectrum awareness is one of the most challenging problems in wideband autonomous cognitive radio (WACR) design. Detection and classification of low-SNR signals is important for proper WACR functionality as it enables the radio to adapt to the user needs and surrounding RF environment. In this context, identification of radar and communications signals is critical in various applications, especially, electronic warfare. This paper introduces a classification framework for radar and communications signals based on their cyclostationary features, specifically, the cyclic profile. Two classification algorithms are used: an artificial neural network (ANN) and a convolutional neural network (CNN). The simulation results show that cyclic profile is a good candidate compared with other features to distinguish between radar and communications signals even at very low SNRs. Furthermore, from a complexity perspective, the ANN is shown to be more effective than the CNN in the proposed classification framework.

Cyclostationary detection was shown to be a promising technique for signal detection and identification in cognitive radios (CRs). In the presence of multiple transmitted signals, however, the corresponding cyclic frequencies become superposed in the cyclic profile, making it impractical to identify the cyclic features of each signal. To address this issue, we propose a spectrum sensing technique based on blind source separation (BSS) which separates the active sources using independent component analysis (ICA) before estimating the cyclic frequencies. The source separation performance is enhanced by applying an adaptive noise cancelling (ANC) filter to the received signals. Cyclic profiles with improved estimation accuracy are then obtained by computing cyclic profiles of each source signal separately. We evaluate the source separation performance of the combined ANC-ICA technique and show its superior performance, compared to other alternative source separation techniques.

This paper proposes a reinforcement learning based approach to anti-jamming communications with wideband autonomous cognitive radios (WACRs) in a multi-agent environment. Assumed system model allows multiple WACRs to simultaneously operate over the same (wide) spectrum band. Each radio attempts to evade the transmissions of other WACRs as well as avoiding a jammer signal that sweeps across the whole spectrum band of interest. The WACR makes use of its spectrum knowledge acquisition ability to detect and identify the location (in frequency) of this sweeping jammer and the signals of other WACRs. This information and reinforcement learning is used to successfully learn a sub-band selection policy to avoid both the jammer signal as well as interference from other radios. It is shown, through simulations, that the proposed learning-based sub-band selection policy has low computational complexity and significantly outperforms the random sub-band selection policy.

In this paper, we propose a robust compressive sampling approach for wideband spectrum sensing in cognitive radios in the presence of non-Gaussian noise. Wideband cognitive radios can be subjected to heterogeneous spectral activities from various sources rendering the resultant noise non- Gaussian. While conventional detectors (e.g. least-squares estimates) are known to be sensitive to the Non-Gaussian nature of noise, the proposed compressive sampling based robust detector is shown to overcome that limitation leading to better signal activity detection under such conditions. The proposed robust detector combines the Huber cost (loss) function with an l1-norm constraint for wideband spectrum sensing with a smaller number of samples (compared to Nyquist rate sampling). Note that, while the Huber cost function robustify our approach against non- Gaussian noise, the l1-norm regularization term ensures the sparsity in the signal reconstruction. It is shown that our proposed robust method outperforms the conventional Periodogram when applied to noisy signals with a smaller number of samples.

In this paper, we develop a comprehensive real-time interactive framework for the utility and customers in a smart grid while ensuring grid-stability and quality-of-service (QoS). First, we propose a hierarchical architecture for the utility-customer interaction consisting of sub-components of customer load prediction, renewable generation integration, power-load balancing and demand response (DR). Within this hierarchical architecture, we focus on the problem of real-time scheduling in an abstract grid model consisting of one controller and multiple customer units. A scalable solution to the real-time scheduling problem is proposed by combining solutions to two sub-problems: (1) centralized sequential decision making at the controller to maximize an accumulated reward for the whole micro-grid and (2) distributed auctioning among all customers based on the optimal load profile obtained by solving the first problem to coordinate their interactions. We formulate the centralized sequential decision making at the controller as a hidden mode Markov decision process (HM-MDP). Next, a Vikrey auctioning game is designed to coordinate the actions of the individual smart-homes to actually achieve the optimal solution derived by the controller under realistic gird interaction assumptions. We show that though truthful bidding is a weakly dominant strategy for all smart-homes in the auctioning game, collusive equilibria do exist and can jeopardize the effectiveness and efficiency of the trading opportunity allocation. Analysis on the structure of the Bayesian Nash equilibrium solution set shows that the Vickrey auctioning game can be made more robust against collusion by customers (anticipating distributed smart-homes) by introducing a positive reserve price. The corresponding auctioning game is then shown to converge to the unique incentive compatible truthful bidding Bayesian Nash equilibrium, without jeopardizing the auctioneer's (microgrid controller's) profit. The paper also explicitly discusses how this two-step solution approach can be scaled to be suitable for more complicated smart grid architectures beyond the assumed abstract model.

In this paper, we propose detectors (both parametric and robust) for wideband spectrum sensing in cognitive radios (CR's). The proposed detectors are able to detect spectral activity over a wide frequency range, while assuming little knowledge about the signals of interest. The parametric detector is based on a locally optimal (LO) Neyman-Pearson (NP) test and assumes a known non-Gaussian noise distribution. The corresponding decision statistic of the LO NP detector is expressed in frequency domain, allowing to identify the active channels within the wide frequency band of interest. On the other hand, for situations in which the noise distribution is only approximately known, we propose a robust signal detector that is immune to deviations of the noise model from a certain nominal distribution. The proposed wideband robust detector is based on a robust spectral estimator and is formulated as a non-linear regression. This regression problem can be solved using a fixed-point iteration algorithm at a quadratic computational complexity, in contrast with the Newton's method which would have cubic complexity order. The simulation results show that the proposed detectors can achieve better detection performance in the presence of non-Gaussian noise, compared to existing detectors under the same conditions.

We propose wide-band spectrum sensing scheduling solutions for cognitive radios that are equipped with reconfigurable RF front-ends. The wide frequency spectrum of interest is segmented into frequency sub-bands due to software and hardware limitations. These sub-bands can be non-contiguous, and each may contain an arbitrary number of channels from an arbitrary number of systems. It is assumed that the CR can only sense one sub-band at a time. Three sub-band selection policies are proposed to find spectrum opportunities taking into account realistic hardware reconfiguration energy consumptions and time delays. Two of the proposed policies rely on the individual channel Markov properties and the sub-band Markov properties, respectively. Although these two policies may achieve good performance, they rely on complete knowledge of RF environment dynamics and thus may become computationally demanding. The third sub-band selection policy based on Q-learning is proposed to circumvent this. Performance of the three policies are compared and discussed against a performance upper-bound of the optimal solution to the corresponding partially observable Markov decision process formulation. The suitability of the Q-learning technique is validated by showing that it achieves good performance through numerical results in both simulated and real measured RF environments.

Spectrum sensing scheduling policies are proposed to find spectrum opportunities for wide-band cognitive radios by taking into account realistic reconfiguration energy consumptions and time delays. The first policy relies on the RF environment Markov properties. Thus, it may become computationally demanding. The second sub-band selection policy based on Q-learning is proposed to circumvent this. Performance of the two policies are compared and discussed against a performance upper-bound of the optimal solution to the corresponding partially observable Markov decision process formulation. The suitability of the Q-learning technique is validated by showing that it achieves good performance in simulation.

In this paper, we propose a Bayesian non-parametric signal classification approach for spectrum sensing in cognitive radios (CR's). The proposed classification approach is based on the Dirichlet process mixture model (DPMM) that allows inferring the number and types of signals from their spectral and cyclic properties. The proposed algorithm is completely autonomous and does not require any prior knowledge of the existing signals or the number of distinct signal classes. We assume that the cluster parameters are drawn from a mixture model, where each mixture component parameterizes a specific observation model, including both Gaussian and non-Gaussian models. By using the Gibbs sampling, we estimate the observation model and cluster parameters that best fit the observed data. Given N data points, under certain regularity conditions, we derive an upper bound for the mean-squared error (MSE) in estimating the clusters means. A Bayesian prediction method is also developed to estimate the probability distribution of the data points. The proposed algorithm is applied to detect and classify WiFi and Bluetooth signals in the ISM band. Simulation results validate the proposed classification approach and show its robustness against channel impairments such as Rayleigh channel fading.

A novel non-parametric, multi-variate quickest detection method is proposed for cognitive radios (CRs) using both energy and cyclostationary features. The proposed approach can be used to track state dynamics of communication channels. This capability can be useful for both dynamic spectrum sharing (DSS) and future CRs, as in practice, centralized channel synchronization is unrealistic and the prior information of the statistics of channel usage is, in general, hard to obtain. The proposed multi-variate non-parametric average sample power and cyclostationarity-based quickest detection scheme is shown to achieve better performance compared to traditional energy-based schemes. We also develop a parallel on-line quickest detection/off-line change-point detection algorithm to achieve self-awareness of detection delays and false alarms for future automation. Compared to traditional energy-based quickest detection schemes, the proposed multi-variate non-parametric quickest detection scheme has comparable computational complexity. The simulated performance shows improvements in terms of small detection delays and significantly higher percentage of spectrum utilization.

In this paper, we present an autonomous cognitive radio (CR) architecture that incorporates the main features of cognition. This model, referred to as the Radiobot, is capable of self-learning and self-reconfiguration to match its RF environment. The proposed CR architecture assumes a joint blind energy and cyclostationary detection methods to classify the communication systems in its vicinity, without any prior knowledge of the sensed signals. We derive the receiver operating characteristic (ROC) of the energy detector and show, analytically, the impact of the sliding window length on the energy detection. A learning algorithm is proposed, allowing the Radiobot to independently learn from its past experience in order to optimize its operating parameters. By applying the learning algorithm to the sensing module, we verify, through simulations, the convergence of the proposed algorithm to the optimal solution.

This paper presents an autonomous cognitive radio (CR) architecture, referred to as the Radiobot. This model goes beyond adaptive radio systems to exploit the main ingredients of cognition which, in this context, are mainly self-learning and self-reconfiguration. Without any prior knowledge of the RF environment, the Radiobot applies a sequence of increasingly sophisticated processing steps to detect and identify the sensed signals. In particular, in this paper, it applies a blind energy detection followed by a cyclostationary detection method to detect the active signals and extract their underlying periodic properties as reflected in cyclic frequencies. These extracted signal features are classified based on the Chinese restaurant process (CRP) and a learning algorithm is applied to achieve autonomous self-reconfiguration of the sensing module. We analyze the impact of fading and Doppler frequency shift on both the energy and cyclostationary detections, and show the receiver operating characteristic (ROC) of the carrier frequency detector. We show the robustness of the cyclostationary detection against channel fading and wide-sense stationary noise. Simulation results are presented to verify the multi-band operability and the reconfiguration ability of the Radiobot and to verify the convergence of the proposed learning algorithm.

Cognitive radio techniques allow secondary users (SU's) to opportunistically access underutilized primary channels that are licensed to primary users. We consider a group of SU's with limited spectrum sensing capabilities working cooperatively to find primary channel spectrum holes. The objective is to design the optimal sensing and access policies that maximize the total secondary throughput on primary channels accrued over time. Although the problem can be formulated as a Partially Observable Markov Decision Process (POMDP), the optimal solutions are intractable. Instead, we find the optimal sensing policy within the class of myopic policies. Compared to other existing approaches, our policy is more realistic because it explicitly assigns SU's to sense specific primary channels by taking into account spatial and temporal variations of primary channels. Contributions: (1) formulation of a centralized spectrum sensing/access architecture that allows exploitation of all available primary spectrum holes; and (2) proposing sub-optimal myopic sensing policies with low-complexity implementations and performance close to the myopic policy. We show that our proposed sensing/access policy is close to the optimal POMDP solution and outperforms other proposed strategies. We also propose a Hidden Markov Model based algorithm to estimate the parameters of primary channel Markov models with a linear complexity.

Managing the power resource in battery operated wireless devices is very crucial for extending the lifetime, here we propose the concept of power trading in wireless communications. We present a business model using sealed bid procurement auction based game theory for power-trading in cooperative wireless communication with quality of service (QoS) constraints. We formulate the problem as an auction in a buyer's market sequentially/repeatedly played with a single source and a multiple relay network. The source, in-need of cooperation of a relay due to lack of battery power to communicate with the destination, broadcasts a cooperation-request specifying its QoS requirements. The QoS that we consider here are the bit error rate and the total delay associated with relaying the source data. The relays respond with their bids in terms of Euros/bit, and the source selects the best relay based on the bids. The relays compete with each other to win the game and profit from power trading. Each relay updates its pricing index via reinforcement learning to win the game during successive bidding intervals of the repeated game. Based on this model our results show that the relay node with the best features such as a better wireless channel and a better geographical position with respect to the source and destination nodes has a better chance of winning the game, and hence giving rise to a dominant strategy. More importantly, we show that the gains from the wireless channels can be converted into economic profits which is an attractive feature of the proposed business model for power trading.

In this paper, we develop a centralized spectrum sensing and Dynamic Spectrum Access (DSA) scheme for secondary users (SUs) in a Cognitive Radio (CR) network. Assuming that the primary channel occupancy follows a Markovian evolution, the channel sensing problem is modeled as a Partially Observable Markov Decision Process (POMDP). We assume that each SU can sense only one channel at a time by using energy detection, and the sensing outcomes are then reported to a central unit, called the secondary system decision center (SSDC), that determines the channel sensing/accessing policies. We derive both the optimal channel assignment policy for secondary users to sense the primary channels, and the optimal channel access rule. Our proposed optimal sensing and accessing policies alleviate many shortcomings and limitations of existing proposals: (a) ours allows fully utilizing all available primary spectrum white spaces, (b) our model, and thus the proposed solution, exploits the temporal and spatial diversity across different primary channels, and (c) is based on realistic local sensing decisions rather than complete knowledge of primary signalling structure. As an alternative to the high complexity of the optimal channel sensing policy, a suboptimal sensing policy is obtained by using the Hungarian algorithm iteratively, which reduces the complexity of the channel assignment from an exponential to a polynomial order. We also propose a heuristic algorithm that reduces the complexity of the sensing policy further to a linear order. The simulation results show that the proposed algorithms achieve a near-optimal performance with a significant reduction in computational time.

Cognitive radio (CR) techniques allow unlicensed secondary users (SUs) to opportunistically access underutilized primary channels that are licensed to primary users (PUs). We consider a multi-primary channel scenario in which the SUs cooperatively try to find these primary channel spectrum holes by limited spectrum sensing. The objective is to design the optimal sensing and accessing policy that maximizes the total secondary system throughput on the primary channels accrued over time, while satisfying a constraint on the probability of colliding with licensed transmissions. Although the problem can be formulated as a Partially Observable Markov Decision Process (POMDP), the optimal solutions are often intractable. As a result, we find the optimal myopic channel sensing policy that maximizes instantaneous total secondary system throughput on the primary channels at each time. The contributions of this paper include: 1) developing a universal optimal myopic channel sensing policy that is applicable for any number of primary channels, any number of SUs and any channel coefficients (assumed known); 2) formulation of a centralized spectrum sensing and decisionmaking architecture for cognitive secondary systems that allow exploitation of all available spectrum white spaces across the whole primary spectrum. We compare our combined sensing and accessing strategies with other proposed strategies and show that our proposed strategy outperforms them in terms of the resulting total secondary system throughput under the same constraints on collision with primary users.

Recent advances in the physical layer have enabled the simultaneous reception of multiple packets by a node in wireless networks. We address the throughput optimization problem in wireless networks that support multipacket reception (MPR) capability. The problem is modeled as a joint routing and scheduling problem, which is known to be NP-hard. The scheduling subproblem deals with finding the optimal schedulable sets, which are defined as subsets of links that can be scheduled or activated simultaneously. We demonstrate that any solution of the scheduling subproblem can be built with \vert E\vert + 1 or fewer schedulable sets, where \vert E\vert is the number of links of the network. This result is in contrast with previous works that stated that a solution of the scheduling subproblem is composed of an exponential number of schedulable sets. Due to the hardness of the problem, we propose a polynomial time scheme based on a combination of linear programming and approximation algorithm paradigms. We illustrate the use of the scheme to study the impact of design parameters on the performance of MPR-capable networks, including the number of transmit interfaces, the beamwidth, and the receiver range of the antennas.

We propose a new architecture for dynamic spectrum sharing called the distributed dynamic spectrum leasing (D-DSL) and a game-theoretic framework for its implementation on a cognitive radio network. In D-DSL, each available frequency channel is assigned to a primary user who may independently lease the channel to secondary users. Secondary users are allowed to transmit in multiple channels simultaneously. We establish conditions for the system to reach an equilibrium and analyze the robustness of the proposed game theoretic D-DSL in time-varying channels. For the same amount of available primary spectrum, D-DSL leads to better overall performance compared to previously proposed Centralized-DSL.

Dynamic spectrum leasing (DSL) was proposed recently as a new paradigm for dynamic spectrum sharing (DSS) in cognitive radio networks (CRN's). In this paper, we propose a new way to encourage primary users to lease their spectrum: The secondary users (SU's) place bids indicating how much power they are willing to spend for relaying the primary signals to their destinations. In this formulation, the primary users achieve power savings due to asymmetric cooperation. We propose and analyze both a centralized and a distributed decision-making architecture for the secondary CRN. In the centralized architecture, a Secondary System Decision Center (SSDC) selects a bid for each primary channel based on optimal channel assignment for SU's. In the decentralized cognitive network architecture, we formulate an auction game-based protocol in which each SU independently places bids for each primary channel and receivers of each primary link pick the bid that will lead to the most power savings. A simple and robust distributed reinforcement learning mechanism is developed to allow the users to revise their bids and to increase their rewards. The performance results show the significant impact of reinforcement learning in both improving spectrum utilization and meeting individual SU performance requirements.

In this paper, we propose a novel mobility protocol for mobile node navigation in a hybrid sensor network consisting of both static and mobile nodes to improve the dynamic coverage. Use of mobile nodes in sensor networks for coverage improvement is suggested in recent research. However, most of the existing literature on hybrid sensor networks considered the use of node mobility at the deployment stage in which nodes do not move after the initial deployment. In this paper, our focus is on efficiently managing the node mobility to provide dynamic coverage in hybrid sensor networks compensating the lack of coverage provided by static nodes. The key feature of the proposed mobility protocol is that, mobile nodes are directed to move to maximize the coverage-time of the uncovered area by static nodes. The proposed mobility protocol can be implemented distributively by collaborating among mobile and static nodes locally. The effectiveness of the proposed mobility protocol is shown in terms of the presence probability matrix and coverage-time.

Dynamic Spectrum Leasing (DSL) is a new paradigm for efficient spectrum sharing in cognitive radio networks that was recently proposed in. Unlike in a hierarchical dynamic spectrum access (DSA) network, DSL allows active participation of both the primary and the secondary users in the spectrum sharing process. In this paper we further generalize the DSL game introduced in by allowing for linear multiuser detectors, in particular the matched filter (MF) and linear minimum mean squared error (LMMSE) receivers, at the secondary base stations. We establish the conditions so that the DSL game has desired equilibrium properties. Performance of the proposed system at the equilibrium is compared through simulations under different linear receivers at the secondary base station.

In this paper, we propose a new mobility assisted tracking (MAT) algorithm for tracking a single target in a hybrid sensor network consisting of both static and mobile nodes. The network is assumed to be partitioned into clusters and cluster heads are formed from a set of high capacity static nodes. One cluster head is selected to perform the tracking task using particle filters at a given time based on the observations received from the nodes belonging to the corresponding cluster. We exploit the node mobility in the hybrid sensor network to dynamically maintain a certain coverage level at the predicted target location at each time. In the proposed MAT algorithm mobile nodes are directed to move towards the predicted target position at each time step if the predicted target position is not covered to the desired coverage level by static nodes. Simulation results show that with the proposed MAT algorithm, an improved performance closer to the PCRLB is achieved with a relatively small number of mobile nodes in the network compared to the scenario when all nodes are static. The proposed scheme is also robust against static node as well as cluster head failures.

Recent advances in the physical layer have enabled the simultaneous reception of multiple packets by a node in wireless networks. In this paper, we present a generalized model for the throughput optimization problem in multi-hop wireless networks that support multi-packet reception (MPR) capability. The model incorporates the multi-access channel, which accurately accounts for the achievable capacity of links used by simultaneous packet transmissions. The problem is modeled as a joint routing and scheduling problem. The scheduling subproblem deals with finding the optimal schedulable sets, which are defined as subsets of links that can be scheduled or activated simultaneously. We demonstrate that any solution of the scheduling subproblem can be built with |E| + 1 or fewer schedulable sets, where |E| is the number of links of the network. This result contrasts with a conjecture that states that a solution of the scheduling subproblem, in general, is composed of an exponential number of schedulable sets. Due to the hardness of the problem, we propose a polynomial time scheme based on a combination of linear programming and greedy paradigms. The scheme guarantees the operation of links at maximum aggregate capacity, where the sum of the capacity of the links is maximized and the multi-access channel is fully exploited.

Starting from local observations, iterative consensus algorithms attempt to drive a sensor network to a common estimate in a decentralized, incremental manner. When additive noise perturbs the sensor exchanges, a decreasing stepsize guarantees convergence under certain conditions, although the design of such stepsize sequence for fastest convergence is an unsettled issue. We present a greedy approach to stepsize sequence design, which minimizes the mean squared error at each iteration. This design requires knowledge of the network topology; in order to overcome this drawback, a modified design based only on average descriptors of the network is also developed.

In this paper, we propose the concept of centralized dynamic spectrum leasing (C-DSL), in which multiple primary users belonging to the same primary system participate in the spectrum leasing process with secondary users (potential bidder for spectrum) under centralized control. We develop a new game-theoretic user interaction model suitable for C-DSL in a cognitive radio network. Dynamic Spectrum Leasing (DSL), proposed in allows active participation of both primary and secondary users in the spectrum sharing process. Motivated by network spectrum utilization considerations, we propose generalizations to the primary system utility function defined in and a new utility function for the secondary users. We also generalize the proposed non-cooperative C-DSL game to allow for linear multiuser detectors at the secondary base stations. We formulate the conditions on the primary system and the secondary user utility functions so that the proposed C-DSL game has desired equilibrium properties. We prove that the proposed C-DSL game has unique Nash equilibria (NE) under both matched filter (MF) and linear minimum mean-squared error (LMMSE) receivers. Equilibrium performance of the system and robustness of the proposed game theoretic adaptive implementation are investigated through simulations.

Dynamic spectrum leasing (DSL) is one of the schemes proposed for dynamic spectrum sharing (DSS) in cognitive radio networks. In DSL, spectrum owners, denoted as primary users, dynamically adjust the amount of secondary interference they are willing to tolerate in response to the demand from secondary transmitters. In this correspondence we investigate how much can be gained by primary users if this limited interaction with secondary system is allowed, compared to a scheme in which the interference cap allowed by primary users is fixed {a priori} by a regulatory authority. To that end, we define performance metrics for both primary and secondary systems based on the theoretically achievable multiuser sum-rate of the secondary system and analyze both schemes' behavior with respect to different system parameters. This analysis shows that (i) in dynamic environments DSL based schemes may present an important advantage over other schemes with fixed interference constraints, and (ii) DSL schemes are robust against inaccurate a priori information that may degrade system performance.

We investigate the impact of mobile node density on several detection performance measures for stationary target detection by a hybrid sensor network consisting of both static and mobile nodes. Such hybrid sensor networks are becoming attractive with the recent advances in sensor nodes equipped with mobile platforms. However, adding a large number of mobile nodes to a sensor network for continuous coverage improvement might be expensive due to mobile node's higher energy consumptions compared to that with static nodes. Motivated by these, we investigate the trade-off between the density of mobile nodes and the network performance in a hybrid sensor network with respect to several performance measures of interest, when mobile nodes perform random mobility. We derive analytical (exact and/or approximate) formulae for detection probability, detection latency and mean first contact distance, by applying the theory of coverage processes and use them to evaluate the tradeoff between the fraction of mobile nodes and these performance measures. Analytical results presented in this paper give insights on how to select optimal network parameters in designing hybrid sensor networks to achieve desired performance requirements. Validity of the derived analytical results is verified via Monte-Carlo simulations.

This paper proposes a framework for distributed sequential parameter estimation in wireless sensor networks. In the proposed scheme, the estimator is updated sequentially at the current node with its new measurement and the noisy corrupted local estimator from the previous node. Since all nodes in the network may not carry useful information, methodologies to find the best set of nodes and the corresponding node ordering for the sequential estimation process are investigated. It is shown that the determining the optimal set of nodes that leads to the globally optimal performance is computationally complex when the network size is large. We develop two distributed greedy type node selection algorithms with reduced computational and communication complexities. In these algorithms, the next best node is selected at the current node such that it optimizes a certain reward function. It is shown that the performance of both proposed greed type schemes leads to exact, or close to exact, results to the optimal scheme computed via forward dynamic programming, under certain conditions. Moreover, contrast to existing methodologies, our work considers the node selection and inter-node communication noise jointly in the sequential estimation process.

Hierarchical dynamic spectrum access (DSA) has received the most attention in recent years as the solution for better spectrum utilization. In this paper, on the other hand, we develop a framework for dynamic spectrum leasing (DSL). Power control in hierarchical DSA networks only involves that of controlling secondary user transmissions. Thus, in game theoretic formulations of power control in cognitive DSA networks only secondary users are considered as players of the game. In proposed dynamic spectrum leasing, on the other hand, the primary users are rewarded for allowing secondary users to operate in their licensed spectrum. Thus, in the proposed DSL networks the primary users have an incentive to allow secondary users to access the spectrum whenever possible to the maximum extent. We develop a game theoretic framework for such dynamic spectrum leasing in which primary users actively participate in a non-cooperative game with secondary users by selecting an interference cap on the total interference they willing to tolerate. We establish that the proposed primary-secondary user power control game has a unique Nash equilibrium. Performance of a DSL system based on the proposed game model is compared through simulations under different linear receivers at the secondary base station.

The problem of distributed sequential estimation of a nonrandom parameter over noisy communication links is considered, with observations that are correlated spatially across the sensor field. A recursive algorithm for updating the sequential estimator is derived assuming wide-sense stationary observations. It is shown that the performance of the sequential estimator is a trade-off between the quality of node observations and communication channels. A sufficient condition for the convergence of the estimator variance is derived, and asymptotic expressions for variance when this condition is met are obtained for both iid and correlated observations.

Optimal power scheduling for distributed detection in a Gaussian sensor network is addressed for both independent and correlated observations. We assume amplify-and-forward local processing at each node. The wireless link between sensors and the fusion center is assumed to undergo fading and coefficients are assumed to be available at the transmitting sensors. The objective is to minimize the total network power to achieve a desired fusion error probability at the fusion center. For i.i.d. observations, the optimal power allocation is derived analytically in closed form. When observations are correlated, first, an easy to optimize upper bound is derived for sufficiently small correlations and the power allocation scheme is derived accordingly. Next, an evolutionary computation technique based on particle swarm optimization is developed to find the optimal power allocation for arbitrary correlations. The optimal power scheduling scheme suggests that the sensors with poor observation quality and bad channels should be inactive to save the total power expenditure of the system. It is shown that the probability of fusion error performance based on the optimal power allocation scheme outperforms the uniform power allocation scheme especially when either the number of sensors is large or the local observation quality is good.

We consider the problem of optimal power scheduling for decentralized detection of a deterministic signal in a wireless sensor network with correlated observations. Each distributed sensor node independently performs amplify-and-forward (AF) processing of its observation. The fading coefficients of wireless links from distributed sensors to the fusion center (FC) are assumed to be available at transmitting nodes. When sensor observations are correlated it is difficult to derive a closed form solution for optimal power values to achieve a required fusion error performance. In this work, we develop an evolutionary computation technique based on Particle Swarm Optimization (PSO) to obtain the optimal power allocation under a required fusion error probability threshold constraint. It is shown that the optimal power allocation scheme turns off the nodes with poor channels and provides significant system power savings compared to that of uniform power allocation scheme especially when either the number of sensors in the system is large or the local observation quality is good.

In this paper we consider the problem of optimal power allocation for fusion of a deterministic signal in an inhomogeneous wireless sensor network (WSN) with correlated observations. We assume that each distributed node performs analog-relay amplifier local processing on its observation and transmits locally processed data to the fusion center over a wireless channel. We also assume that the channel between the fusion center and sensors undergoes fading and the fading coefficients are assumed to be known to the transmitter. We derive exact fusion error probability and an easy to optimize upper bound for the fusion error probability that is valid for sufficiently small correlations. The transmit power is allocated to sensor nodes to keep the fusion error probability bound under a required threshold while minimizing the total power spent by the network. It is shown that the optimal scheme inactivates the sensor nodes with poor observation quality and low fading coefficients. For the remaining active sensors the transmit power is determined by the individual channel gains, local observation quality, required fusion error probability bound and the correlation coefficient. From numerical results we see that this optimal scheme has a significant gain over the uniform power allocation scheme when either local observations are good or the number of sensors is large and the correlation coefficient is sufficiently small. It is also shown that the optimal power allocation scheme can be implemented distributively with a minimal feedback from the fusion center.

A virtual multiple-input multiple-output (MIMO) communications architecture based on vertical Bell Laboratories layered space-time (V-BLAST) receiver processing is proposed for wireless sensor networks (WSNs). The proposed scheme does not require transmitter-side node cooperation unlike previously proposed virtual MIMO schemes. The energy and delay efficiencies of the proposed virtual MIMO scheme are derived for networks with both single-and multiple-antenna data gathering nodes (DGNs). Numerical results show the significant energy savings offered by the proposed method. These results also indicate that rate optimization over transmission distance is not essential as in virtual MIMO systems based on Alamouti scheme. In most scenarios, a fixed-rate virtual MIMO system with 4-quadrature amplitude modulation can achieve performance very close to that of an optimized, variable-rate system. In the case of single-antenna DGNs, the proposed scheme typically incur larger delay values compared to traditional single-input single-output communication, making it a good candidate for energy-starved but delay-tolerant WSNs.

The problem of decision fusion in a large wireless sensor system with many power-constrained distributed nodes is considered. The sensor network is assumed to be inhomogeneous. i.e. the channel statistics are not identical among the sensors. Assuming identical, binary local quantizing schemes at all the nodes, the optimal fusion scheme is derived. The large system performance of this optimal decision fusion detector is next analyzed. The asymptotic fusion performance is derived via the Lindberg-Feller central limit theorem for non-identical large samples. Numerical examples are used to show that the derived large system closed-form fusion error probability expressions provide a very close approximation to the exact finite-size sensor system fusion performance even for a relatively smaller number of nodes.

We consider the problem of optimal power scheduling for the decentralized detection of a deterministic signal in an inhomogeneous wireless sensor network. The observation noise at local sensors is assumed to be independently and identically distributed (i.i.d.) and at the local sensors each node performs analog-relay amplifier processing for its observation independently. The communication between the local sensors and the fusion center is assumed to be through an inhomogeneous channel. The optimal power scheduling scheme suggests that the sensors with poor observation quality and channels should be inactive in order to save total power expenditure of the system. For the remaining active sensors the optimal transmit power is determined jointly by the individual channel gains, total number of active sensors, local observation signal to noise ratio (SNR) and the required probability of error at the fusion center. We show that the optimal scheme can provide significant system power savings compared to the uniform power allocation scheme when the number of sensors in the system is large.

In this paper, we propose asynchronous non-orthogonal communication between distributed sensors and a data fusion center via asynchronous direct-sequence code-division multiple access (DS-CDMA). Furthermore, we evaluate the performance of such a system in the presence of local-sensor and channel errors due to multiple-access interference (MAI) and noise. We derive the optimal Bayesian receiver for this system operating over both a simple AWGN channel and a Rayleigh-fading channel, assuming full knowledge of the channel fading coefficients. The optimal receiver is shown to have exponential complexity as a function of the number of sensors. A set of low-complexity partitioned receivers are then proposed and analyzed in relation to the optimal one. These partitioned receivers are based on well-known linear and nonlinear multiuser detectors for DS-CDMA. The synchronous DS-CDMA case was studied previously, and it is shown here that the degradation induced by moving to an asynchronous model is relatively small for the optimal receiver. Moreover, we provide compelling evidence that the quality of the local-sensor decisions is the limiting factor in fusion performance for fading channels with high signal-to-noise ratio.

Recently there has been a significant interest in distributed detection and data fusion with analog-relay amplifier local processing under a global power constraint. In particular, it was shown in S.K. Jayaweera (2005) that the optimal fusion performance for a distributed stochastic signal detection is achieved by a finite number of sensors. In this paper, we propose a sensor system optimization method based on the Bhattachrya error exponent. In addition to the global power constraint we also consider the case in which the total available bandwidth may also be limited. Assuming an equi-correlated signalling model we derive the error exponents to the Bayesian fusion performance for asymptotically large systems. Again we optimize the sensor system size based on the Bhattacharya error exponent and provide simple rules that are valid for either the low or high observation SNR regimes

This paper derives the optimum non-uniform quantization scheme for a distributed estimation problem based on noisy observations in a wireless sensor network. The optimal quantization scheme minimizes the mean squared estimation error subjected simultaneously to both a total system power as well as individual node power constraints. According to the derived optimal scheme, sensors with poor signal-to-noise ratios (SNR) refrain from transmitting their observations. The rest of the sensors transmit their quantized observations. The number of quantized bits depends on the SNR and the maximum power constraint at each node. The numerical results show that compared to a uniform power allocation scheme, the proposed scheme achieves significantly smaller Mean Squared Error (MSE) for the same average power consumption.

An energy-efficient virtual multiple-input multiple-output (MIMO)-based communication architecture is proposed for distributed and cooperative wireless sensor networks. Assuming a space-time block coding (STBC) based MIMO system, the energy and delay efficiencies of the proposed scheme are derived using semi-analytic techniques. The dependence of these efficiency values on physical channel propagation parameters, fading coherence time and the amount of required training is also investigated. The results show that with judicious choice of design parameters the virtual MIMO technique can be made to provide significant energy and delay efficiencies, even after allowing for additional training overheads.

This paper proposes an effective sensor data transmission approach where virtual implementation of multiple input multiple output (V-MIMO) cooperative communication is judiciously integrated with joint distributed compression and recursive least squares (RLS) based adaptive signal processing. Both space-time block codes (STBC) and Vertical Bell Laboratories layered space time (V-BLAST) architecture are considered for energy efficient virtual MIMO implementations. The energy efficiency of the integrated system is analyzed for different transmission distances and channel conditions. The numerical results show that, beyond certain transmission distance thresholds, the proposed integrated scheme offers significant energy efficiency compared to previously proposed systems. We also show that this integrated approach is very effective in highly correlated multimedia data based wireless sensor networks. In particular, it can be ideal for networks based on prioritized transmission of wavelet transformed image and video data.

An energy aware, collaborative, tracking algorithm is proposed for ad-hoc wireless sensor networks consisting of randomly distributed low-end sensors and a high-end data gathering node which is assumed to be located at the center of each cluster. The collaborative tracking algorithm is implemented distributively by passing sensing and computation operations from one cluster to another based on an energy-based cost function that attempts to maximize the whole network lifetime. The performance of the proposed collaborative algorithm is evaluated in terms of both tracking error and energy consumption of the whole network. Simulation based performance results for both single and multiple simultaneously active sensors are also presented.

We address the problem of signal design for non-coherent, binary pulse-position modulated (PPM) UWB communications in the presence of fading. By deriving the probability of error in closed form, it is shown that the optimal value for the time shift parameter, /spl delta/, used in pulse position modulation is fundamentally different from that of coherent reception. In particular, we show that the optimal choice of /spl delta/ is one that results in zero correlation between the two waveforms used for binary pulse position modulation. By examining the performance sensitivity around several possible optimal /spl delta/ solutions for a commonly used UWB mono-pulse, we also show that it is still possible to employ /spl delta/

The effect of Rician-ness on the capacity of multiple-antenna systems is investigated under the assumption that channel state information (CSI) is available only at the receiver. The average-power-constrained capacity of such systems is considered under two different assumptions on the available knowledge about fading at the transmitter: the case in which the transmitter has no knowledge of fading at all, and the case in which the transmitter has knowledge of the distribution of the fading process but not the instantaneous CSI. The exact capacity is given for the former case, while capacity bounds are derived for the latter case. A new signaling scheme is also proposed for the latter case and it is shown that by exploiting the knowledge of Rician-ness at the transmitter via this signaling scheme, significant capacity gain can be achieved. The derived capacity bounds are evaluated explicitly to provide numerical results in some representative situations.

An energy-efficient virtual multiple-input multiple-output (MIMO) communication architecture based on V-BLAST receiver processing is proposed for energy-constrained, distributed wireless sensor networks. The proposed scheme does not require transmitter-side sensor cooperation unlike previously proposed virtual MlMO schemes for wireless sensor networks. In sensor networks with single-antenna data gathering nodes, the virtual MIMO operation is realized via the receiver-side local communication assuming node cooperation. Numerical results show that the significant energy savings are offered by the proposed virtual MIMO architecture in distributed wireless sensor networks. These results also indicate that while rate optimization over transmission distance may offer improved energy efficiencies in some cases, this is not essential in achieving energy savings as opposed to previously proposed Alamouti scheme-based virtual MIMO implementations. In fact, in most scenarios a fixed-rate virtual MIMO system with binary phase-shift-keying (BPSK) can achieve performance very close to that of a variable-rate system with optimized rates. However, these results also indicate that the proposed scheme can lead to larger delay penalties compared to a traditional SISO communication based sensor network as the order of the virtual MIMO architecture grows. This results in a trade-off between the achievable energy efficiency and the delay incurred, making the proposed virtual V-BLAST based MIMO scheme an especially good candidate communication architecture for energy-starved and delay-tolerant wireless sensor networks having no inter-sensor communication.

An energy-aware, collaborative tracking algorithm is proposed for ad-hoc wireless sensor networks. The algorithm is implemented distributively by passing sensing and computation operations from one sensor to another. In order to maximize the life-time of the network, this active sensor is chosen by a high end sensor in order to minimize the cost function based on the total energy spent by the network at each time instant. Assuming a wireless ad-hoc sensor network in which low end sensors are randomly distributed with data gathering node as a high end sensor, a state-space model for a target motion and sensor measurement and a cost function based on energy requirements for communication and sensing has been derived. Simulation results for the performance of the proposed scheme have also been presented.

The capacity of a multiple antenna system in Rician fading is considered under the assumption that channel state information (CSI) is available only at the receiver and that the transmitter has knowledge of the statistical properties of the fading process though not the instantaneous CSI. Tight upper and lower bounds for the capacity of such a system are derived and a new input signalling scheme that significantly outperforms the existing choices is also proposed. It is shown that by exploiting the knowledge of Rician-ness at the transmitter, a much higher capacity can be achieved for a multiple antenna system in Rician fading. The derived capacity bounds have been evaluated explicitly to provide numerical results in some representative situations.

The capacity of multiple-antenna systems operating in Rayleigh flat fading is considered under the assumptions that channel state information (CSI) is available at both transmitter and receiver, and that the transmitter is subjected to an average power constraint. First, the capacity of such systems is derived for the special case of multiple transmit antennas and a single receive antenna. The optimal power-allocation scheme for such a system is shown to be a water-filling algorithm, and the corresponding capacity is seen to be the same as that of a system having multiple receive antennas (with a single transmitter antenna) whose outputs are combined via maximal ratio combining. A suboptimal adaptive transmission technique that transmits only over the antenna having the best channel is also proposed for this special case. It is shown that the capacity of such a system under the proposed suboptimal adaptive transmission scheme is the same as the capacity of a system having multiple receiver antennas (with a single transmitter antenna) combined via selection combining. Next, the capacity of a general system of multiple transmitter and receiver antennas is derived together with an equation that determines the cutoff value for such a system. The optimal power allocation scheme for such a multiple-antenna system is given by a matrix water-filling algorithm. In order to eliminate the need for cumbersome numerical techniques in solving the cutoff equation, approximate expressions for the cutoff transmission value are also provided. It is shown that, compared to the case in which there is only receiver CSI, large capacity gains are available with optimal power and rate adaptation schemes. The increased capacity is shown to come at the price of channel outage, and bounds are derived for this outage probability.

An iterative receiver is proposed for the downlink of CDMA communication systems that employ both a convolutional channel code and a space-time trellis code to achieve transmit diversity in the presence of channel fading. The proposed iterative receiver operates by exchanging extrinsic information between the channel and space-time decoders in each iteration. Comparison of performance results, obtained via simulations, with a similar system using Alamouti space-time codes (space-time block codes) shows that the proposed turbo decoder outperforms a concatenated system consisting of channel and Alamouti codes and that the performance gap increases with fast fading.

A turbo decoding scheme for a noncoherent, unitary space-time modulated communication system employing an FEC convolutional code is proposed. The channel is assumed to be Rayleigh fading with neither the transmitter nor the receiver knowing the channel coefficients. The convolutional encoder is applied to the information symbols before performing unitary spacetime coding. Treating the combination of convolutional code and the unitary space-time code as a serially concatenated turbo code, it is shown that the performance can be improved by iterative decoding. Simulation results suggest that the iterative decoder can offer significant performance gain over conventional, non-iterative methods and also can help close the performance gap between coherent (e.g. Alamouti type) and non-coherent techniques by a significant margin.

Multiuser detectors for trellis space-time coded synchronous CDMA, operating in the presence of independent Rayleigh fading are considered. The joint optimal maximum likelihood (ML) multiuser detector, which can be implemented as a Viterbi algorithm, is derived for such space-time coded systems. A suboptimal, low complexity iterative space-time multiuser detector based on interference cancellation and the turbo principle is also derived. Simulations show that this latter method achieves performance competitive with the single user bound for space-time coded systems.