This paper proposes a distributed transmission slot selection and transmit power control algorithm based on hierarchical Q-learning to reduce the probability of packet collision and maximize spatial reuse in aerial broadcasting networks based on slotted ALOHA protocol where unmanned aerial vehicles (UAVs) send their location information to avoid collision among UAVs. In the proposed algorithm, each UAV operates as an independent agent, the inner loop learns the optimal transmission slot and the outer loop learns the optimal transmit power under a given slot selection, enabling autonomous parameter selection according to the environment. The simulation results show that the proposed algorithm significantly reduces the packet collision rate compared to existing methods and improves both slot utilization and communication stability.

Unmanned aerial vehicle (UAV)-based air-to-ground (A2G) Wi-Fi network have attracted considerable attention due to their potential to overcome the limitations of ground access points (APs) and the flexibility in network deployment. In this paper, we propose a hierarchical multi-agent Q-learning (HiMAQ) framework that jointly optimizes transmit power, clear channel assessment (CCA) threshold, and UAV-AP position to enhance sum throughput, fairness, and energy efficiency in A2G Wi-Fi network. Simulation results demonstrate that the proposed HiMAQ framework outperforms existing baseline models in terms of the proposed reward considering various performance measures such as sum throughput, number of outage users, Jain’s fairness index, and energy consumption.

In massive random access-based tactical flying adhoc networks (FANETs), the rapid mobility of unmanned aerial vehicles (UAVs) leads to highly dynamic topological changes that frequently cause collisions of control and data packets and ultimately degrade network performance. To address the collision problem, this study proposes an online safe multi-agent reinforcement learning (OSMAR)-based massive random access method, incorporating an artificial Q-adjustment (AQA) mechanism to optimally allocate transmission slots to multiple UAVs within each frame. Specifically, a safe reinforcement learning (RL) technique is employed to design the action selection policy, which balances exploration and exploitation by considering long-term rewards and risks based on a Boltzmann distribution. With this, a blacklist mechanism is incorporated to prevent UAVs from choosing the time slots with high collision risk. Also, the AQA mechanism, composed of an artificial Q-decrement (AQD) that lowers the Q-values of slots already used by other UAVs and an artificial Q-initialization (AQI) that resets the Q-values of idle slots, leverages overheard channel status information to accelerate convergence and enhance adaptation under dynamic network conditions. Extensive simulations demonstrate that the proposed OSMAR method outperforms existing approaches in terms of collision probability, while also enhancing fairness and maintaining robustness under various network conditions.

Ubiquitous connectivity is the most significant goal of sixth-generation (6G) wireless networks. To this end, integrating non-terrestrial networks (NTNs) with terrestrial networks (TN) can be an inevitable path to 6G. To achieve cost-effectiveness and bandwidth efficiency in NTN design, the open radio access network (O-RAN) and integrated access and backhaul (IAB) approaches, which prevail in TN, are adopted in the proposed NTN architecture. To maximize the uplink rate under bandwidth and power constraints, we jointly optimize i) the power allocation to pilot and data symbols, ii) the compression rates for inter-satellite link (ISL) transmission of pilot and data, and iii) the bandwidth partitioning between the access link and the xHaul link. Because the original optimization problem is challenging to solve directly, it is decomposed into three subproblems: power, compression rate, and bandwidth optimization, while the other parameters being kept fixed. The convergent solution is obtained via an iterative optimization approach that alternates between optimization and a gradient-based search. The effectiveness of the proposed approach is verified by numerical simulations.

The rapid proliferation of Internet of Things (IoT) devices across domains such as smart homes, industrial control systems, and healthcare networks has significantly expanded the attack surface for cyber threats, including botnet-driven distributed denial-of-service (DDoS), malware injection, and data exfiltration. Conventional intrusion detection systems (IDS) face critical challenges like privacy, scalability, and robustness when applied in such heterogeneous IoT environments. To address these issues, we propose SecureDyn-FL, a comprehensive and robust privacy-preserving federated learning (FL) framework tailored for intrusion detection in IoT networks. SecureDyn-FL is designed to simultaneously address multiple security dimensions in FL-based IDS: (1) poisoning detection through dynamic temporal gradient auditing, (2) privacy protection against inference and eavesdropping attacks through secure aggregation, and (3) adaptation to heterogeneous non-independent-and-identically-distributed (non-IID) data via personalized learning. The framework introduces three core contributions: (i) a dynamic temporal gradient auditing mechanism that leverages Gaussian mixture models (GMMs) and Mahalanobis distance (MD) to detect stealthy and adaptive poisoning attacks, (ii) an optimized privacy-preserving aggregation scheme based on transformed additive ElGamal encryption with adaptive pruning and quantization for secure and efficient communication, and (iii) a dual-objective personalized learning strategy that improves model adaptation under non-IID data using logit-adjusted loss. Extensive experiments on the N-BaIoT dataset under both IID and non-IID settings, including scenarios with up to 50% adversarial clients, demonstrate that SecureDyn-FL consistently outperforms state-of-the-art FL-based IDS defenses. It achieves up to 99.01% detection accuracy, a 98.9% F1-score, and significantly reduced attack success rates across diverse poisoning attacks, while maintaining strong privacy guarantees and computational efficiency for resource-constrained IoT devices.

Operating in flying ad-hoc networks (FANETs) is challenging due to the highly dynamic nature of the network environment. Energy efficiency is crucial in these networks, particularly because unmanned aerial vehicles (UAVs) have limited battery capacity. In slotted-ALOHA (S-ALOHA)-based FANETs, frequent packet collisions, driven by changes in the network environment, can significantly degrade energy efficiency. Therefore, accurately estimating the number of active UAVs is essential for improving the performance and energy efficiency of S-ALOHA-based networks. Several estimation methods, such as low-bound, Schoute, max-probability, and Bayesian estimation, have been explored and perform well in static network environments. However, their estimation accuracy decreases significantly in dynamic environments. To address this issue, this study proposes a hybrid pseudo-Bayesian estimation method designed to improve estimation accuracy in highly dynamic environments. Specifically, this method combines the strengths of pure-Bayesian and pseudo-Bayesian estimation methods to overcome limitations such as the pure-Bayesian method's inadequacy in dynamic environments and the lower estimation accuracy of the pseudo-Bayesian method. This paper compares the performance of the proposed method with that of benchmark methods in terms of estimation error and the number of successful packets, considering variation periods and step sizes. The results demonstrate that the proposed method is more adaptable to dynamic changes in network environments.

Recently, interest in communication services using unmanned aerial vehicles (UAVs) and integrated sensing and communication (ISAC) systems that utilize radar and communication simultaneously has been increasing rapidly. Wireless communications through UAVs have the advantage of easily securing a line-of-sight (LoS) channel with ground users due to their flexibility in deployment. ISAC systems can use bandwidth efficiently by combining radar and communication into a single system. In this study, communication and radar performance were optimized in a UAV-relay-based ISAC system using a single communication waveform through multi-agent Q-learning. The convergence and performance of the proposed Q-learning algorithm were confirmed through simulations.

Wireless-powered communications (WPCs) are increasingly crucial for extending the lifespan of low-power Internet of Things (IoT) devices. Furthermore, reconfigurable intelligent surfaces (RISs) can create favorable electromagnetic environments by providing alternative signal paths to counteract blockages. The strategic integration of WPC and RIS technologies can significantly enhance energy transfer and data transmission efficiency. However, passive RISs suffer from double-fading attenuation over RIS-aided cascaded links. In this article, we propose the application of an active RIS within WPC-enabled IoT networks. The enhanced flexibility of the active RIS in terms of energy transfer and information transmission is investigated using adjustable parameters. We derive novel closed-form expressions for the ergodic rate and outage probability by incorporating key parameters, including signal amplification, active noise, power consumption, and phase quantization errors. Additionally, we explore the optimization of WPC scenarios, focusing on the time-switching factor and power consumption of the active RIS. The results validate our analysis, demonstrating that an active RIS significantly enhances WPC performance compared to a passive RIS.

Reconfigurable intelligent surfaces (RISs) are emerging as a transformative technology for sixth-generation (6G) wireless networks. They enable dynamic manipulation of the propagation environment to enhance signal coverage, mitigate interference, and improve spectral and energy efficiencies. However, this flexibility introduces significant security vulnerabilities when RISs are maliciously controlled. This study explores the threats posed by such RISs, focusing on their potential to compromise the security and integrity of 6G networks. From an adversarial perspective, we analyze key attack vectors, including sophisticated jamming attacks that disrupt communication, eavesdropping attacks that intercept communications, and pilot contamination attacks that impair channel estimation accuracy, all contributing to severe performance degradation. For each attack, we detail the underlying mechanisms and adversarial optimization strategies designed to maximize impact. A case study quantifies the practical effects of these malicious RIS-based attacks in a simulated 6G network scenario. This research emphasizes the critical need for robust defense mechanisms and proposes essential research directions to address the evolving threats from malicious RISs, ensuring the security of 6G networks.

In the sixth-generation (6G) Internet of Things (IoT) networks, unmanned aerial vehicle (UAV)-mounted base stations and reconfigurable intelligent surfaces (RISs) have been considered key technologies to enhance coverage, flexibility, and security in nonterrestrial networks (NTNs). In addition to aerial networks enabled by NTN technologies, the integration of underwater networks with 6G IoT can be considered one of the most innovative challenges in future IoT. Along with such trends in IoT, this study investigates the secrecy performance of IoT networks that integrate radio frequency (RF) UAV-based NTNs and underwater optical wireless communication (UOWC) with RISs. Under three potential eavesdropping scenarios, i.e., overhearing of RF signal, UOWC signal, and both signals, we derive closed-form expressions for secrecy performance metrics, including average secrecy capacity, secrecy outage probability, probability of strictly positive secrecy capacity, and effective secrecy throughput. Extensive numerical analyses and Monte Carlo simulations elucidate the impact of system parameters, such as fading severity, the number of RIS reflecting elements, underwater turbulence, pointing errors, and detection techniques on system security. The findings offer comprehensive design guidelines for network deployment aiming to enhance secrecy performance and ensure secure communication in diverse and challenging environments.

Sixth-generation (6G) networks pose substantial security risks because confidential information is transmitted over wireless channels with a broadcast nature, and various attack vectors emerge. Physical layer security (PLS) exploits the dynamic characteristics of wireless environments to provide secure communications, while reconfigurable intelligent surfaces (RISs) can facilitate PLS by controlling wireless transmissions. With RIS-aided PLS, a lightweight security solution can be designed for low-end Internet of Things (IoT) devices, depending on the design scenario and communication objective. This article discusses RIS-aided PLS designs for 6G-IoT networks against eavesdropping and jamming attacks. The theoretical background and literature review of RIS-aided PLS are discussed, and design solutions related to resource allocation, beamforming, artificial noise, and cooperative communication are presented. We provide simulation results to show the effectiveness of RIS in terms of PLS. In addition, we examine the research issues and possible solutions for RIS modeling, channel modeling and estimation, optimization, and machine learning. Finally, we discuss recent advances, including simultaneous transmitting and reflecting-RIS and malicious RIS.

This article introduces an innovative cell-free massive MIMO (CF-mMIMO) architecture integrating a low-Earth orbit satellite with unmanned aerial vehicles (UAVs) as flying access points (FAPs), referred to as a CF-mMIMO satellite-UAV network, aimed at enhancing uplink spectrum efficiency (SE) for ground users (GUs). Expressions are derived to estimate both direct and cascaded channels involving satellites, UAVs, and GUs, while proposing local combining strategies for the network. A straightforward dynamic clustering framework is developed to mitigate interference among GUs effectively, leveraging the inherent characteristics of UAV-based networks to optimize user service quality. An optimization model for FAP trajectory and energy consumption is proposed to effectively manage energy in limited-resource scenarios and enhance network performance. Additionally, max-min fairness power control issue is explored, with a closed-form solution being presented that ensures the equitable SE distribution among GUs. We also tackle a nonconvex optimization challenge to maximize the network’s sum-rate by refining GU power levels, incorporating large-scale fading decoding (LSFD) to further elevate SE. Extensive simulations validate the effectiveness of our proposed methods, illustrating significant improvements over traditional configurations. These approaches not only advance CF-mMIMO satellite-UAV network performance but also lay foundational strategies for future aerial communication systems.

Reconfigurable intelligent surface (RIS) has recently gained significant interest as an emerging technology for future wireless networks thanks to its potential for improving the coverage in challenging propagation environments. This paper studies an RIS-assisted communication system, where a source transmits data to a destination in the presence of a weak direct link. We analyze and compare RIS designs based on long-term and short-term channel statistics in terms of coverage probability and ergodic rate. For the considered optimization designs, we derive closed-form expressions for the coverage probability and ergodic rate, which explicitly unveil the impact of the propagation environment and the RIS on the system performance. Besides the optimization of the RIS phase profile, we formulate an RIS placement optimization problem with the aim of maximizing the coverage probability by relying only on partial channel state information. An efficient algorithm is proposed based on the gradient ascent method. Simulation results are illustrated in order to corroborate the analytical framework and findings. The proposed RIS phase profile is shown to outperform several heuristic benchmark schemes in terms of outage probability and ergodic rate. In addition, the proposed RIS placement strategy provides an extra degree of freedom that remarkably improves the system performance.

In wireless communication networks including unmanned aerial vehicles (UAVs), joint communication and radar (JCR) using a single waveform for both communication and sensing functions has been considered. In the JCR, the power allocation to the pilot and data parts can be optimized in terms of communication and sensing performance metrics. Furthermore, to serve ground users effectively, the location of UAVs, which receive the transmit signal from a base-station (BS) and forward to ground users, should be optimized. In multi-UAV environments, the optimization of signal power and UAV's position becomes too complicated to solve with a conventional optimization framework. Therefore, a reinforcement learning approach, i.e., multi-agent Q-learning, is adopted to optimize the UAV-assisted JCR networks.

Reconfigurable intelligent surface (RIS)-assisted millimeter wave (mmWave) communication is a promising technology for enlarging the coverage area of millimeter wave systems. Unfortunately, realizing the full potential of these systems requires addressing numerous challenges in channel estimation. In this paper, channel estimation for RIS-assisted mmWave communications is considered. Under the assumption that the array manifolds of the base station antennas and the RIS reflecting elements are given by uniform arrays, an efficient two-stage channel estimation method based on array signal processing techniques is proposed. In the proposed algorithm, the direct and indirect channels are jointly estimated by space-time processing that exploits the sparsity in RIS-assisted mmWave channels and the features associated with uniform arrays. Then, several practical issues, including detection of the number of channel paths, imperfect RIS hardware, and complexity, are addressed. Extensions to the cases of uniform planar array-based RIS, wideband communication, and multiple users are also discussed. Numerical results validate the effectiveness of the proposed method.

This paper investigates the wireless communications assisted by both decode-forward (DF)-relay and reconfigurable intelligent surface (RIS). For assessment of outage performance, the statistical characteristics of signal-to-noise ratios (SNRs) are investigated. For RIS-aided systems with practical design constraints, e.g., total power and the number of reflecting elements, the deployment of a relay can improve the performance. The numerical results are presented to demonstrate the performance comparison of the hybrid relay-RIS (R-RIS) and RIS-only systems under various design parameters.

In air-to-ground communications, the network lifetime depends on the operation time of unmanned aerial vehicle-base stations (UAV-BSs) owing to the restricted battery capacity. Therefore, the maximization of energy efficiency and the minimization of outage ground users are important metrics of network performance. To achieve these two objectives, the location and transmit power of the UAV-BSs in the network must be optimized. This optimization problem may not be tractable in the conventional optimization framework because multiple UAV-BSs interact in a complicated manner. Hence, we formulate the problem as a Markov decision process and develop an algorithm to obtain a solution in a reinforcement learning framework. To avoid a central controller and high computational complexity, we employ a multiagent distributed${Q}$-learning algorithm to obtain a solution. Specifically, we propose a multiagent${Q}$-learning-based UAV-BS deployment and power control strategy to maximize energy efficiency and minimize the number of outage users in multi-UAV wireless networks. Through intensive simulations, it is demonstrated that the proposed algorithm can outperform benchmark algorithms in terms of average energy efficiency and number of average outage users in multi-UAV wireless networks.

An unmanned aerial vehicle-mounted base station (UAV-BS) is a promising technology for the forthcoming sixth-generation wireless networks, owing to its flexibility and cost effectiveness. Besides, the limited network operation time of UAV-BS networks can be overcome with the concept of tethered unmanned aerial vehicles (TUAVs), which are powered from an energy source in the ground. Along with this trend, the optimal deployment (i.e., trajectory control) of TUAVs to maximize throughput in multicell environments has been studied. As the problem is modeled by a Markov decision process, a multiagent$Q$-learning (QL) algorithm was developed to obtain a solution. When considering the limited inter-UAV link capacity and computing power of each UAV, the proposed multiagent QL algorithm can be a practical approach. Intensive simulations were conducted to evaluate the performance of the proposed algorithm with respect to various metrics, such as sum or individual rates, fairness, and computational complexity in multicell air-to-ground (A2G) networks. Our proposed algorithm achieves superior performance compared to conventional algorithms, such as random action, QL-based altitude control algorithms (QAC), and centralized QL algorithm.

In this paper, the problem of training signal design for intelligent reflecting surface (IRS)-assisted millimeter-wave (mmWave) communication under a sparse channel model is considered. The problem is approached based on the Cramér-Rao lower bound (CRB) on the mean-square error (MSE) of channel estimation. By exploiting the sparse structure of mmWave channels, the CRB for the channel parameter composed of path gains and path angles is derived in closed form under Bayesian and hybrid parameter assumptions. Based on the derivation and analysis, an IRS reflection pattern design method is proposed by minimizing the CRB as a function of design variables under constant modulus constraint on reflection coefficients. Extensions of the proposed design to a multi-antenna transceiver, a uniform planar array (UPA)-based IRS, and multi-user case are discussed. Numerical results validate the effectiveness of the proposed design method for sparse mmWave channel estimation.

In this article, we consider physical-layer security (PLS) of the Internet-of-Things (IoT) communications based on hybrid automatic repeat request (HARQ) protocols in multiple-input single-output single-eavesdropper (MISOSE) channels. Owing to the reciprocity of wireless channels, a negative acknowledge frame can be used to obtain the channel state information (CSI) from a source with multiple antennas to an IoT destination device, and this CSI of a legitimate link is used for beamforming for the next transmission round (TR). Therefore, a higher secrecy rate can be expected from retransmissions with beamforming based on the CSI as compared to the first TR without CSI. To evaluate the secrecy throughput under time-invariant and time-varying channels, connection and secrecy outage probabilities (OPs) are derived. By exploiting the characteristics of the OPs, we suggest an approximated secrecy throughput. We then consider the optimization of the approximated secrecy throughput considering the rate parameters for legitimate and eavesdropping links. With an alternating optimization approach, an algorithm to find convergent system parameters, i.e., the rates for legitimate and eavesdropping links, is proposed. Moreover, an asymptotic analysis with the large number of transmit antennas indicates an interesting behavior of the secrecy rate with regard to the number of TRs. The analytical results are verified by rigorous numerical simulations and they can serve as a useful guideline to those involved in the design of secure IoT communication systems with HARQ in MISOSE channels.

In this paper, the dimension of artificial noise (AN) and corresponding transmit power in multiple-input multiple-output single-eavesdropper channels are investigated. It is widely known that AN is transmitted in the null space of the channel matrix of a legitimate link between a source and a destination. Here, the secrecy outage probabilities are derived for various dimensions of AN. Recognizing the fact that the secrecy outage performance depends on the dimension of the AN, an optimization problem is formulated to maximize the secrecy rate under a secrecy outage constraint. The optimal power splitting factor between the information and AN signals is then derived. Furthermore, the asymptotic behaviors of the optimal power splitting factor and the resulting maximum secrecy rate are examined. Through asymptotic analyses and numerical verification, it is formally shown that the full-dimension AN maximizes the secrecy rate under a secrecy outage constraint. Because the complexity of AN design increases as the dimension of the AN increases, we can achieve a tradeoff between secrecy performance and AN design complexity by determining the dimension of the AN. This study can provide a guideline of AN design specifically pertaining to the dimension of the AN.

Required throughput for a fronthaul in a cloud-radio access network tremendously increases and becomes a bottleneck in a communication network with wide bandwidth and a large number of antennas. To reduce the fronthaul capacity requirement, more digital function blocks are shifted from a central unit to a distributed unit. In this study, we consider separated delivery of data and pilot signals with different compression rates under time-varying channels to reduce fronthaul burden. By analyzing channel prediction error based on a Kalman filter, an achievable rate for uplink data is derived. The fronthaul rates for data and pilots are derived using rate-distortion theory. Both uplink rate maximization and fronthaul rate minimization are simultaneously designed by considering the data and pilot signal distortions as well as the frame structure. First, the optimization of signal distortion with a fixed pilot interval is investigated. Next, a sub-optimal algorithm to find the pilot interval with the fixed signal distortion is developed. Finally, an iterative algorithm to obtain the sub-optimal solution, which is the joint solution of signal distortion and pilot interval, is proposed. The numerical results demonstrate that the proposed algorithm can provide convergent solutions.

Relay stations (RSs), as key components of next‐generation heterogenous networks, can be readily deployed between base station (BS) and users to extend cell coverage and improve throughput. Since RSs are connected with a donor BS through wireless backhaul‐links, i.e. BS–RS links, additional radio resources should be allocated to these links. For in‐band relaying, access‐links and backhaul‐links should be jointly optimised, which make the resource allocation problem for a relay network more difficult and challenging. In this study, the authors develop an analytical framework for resource allocation in relay networks with a time‐division duplexing mode for access‐link and backhaul‐link transmissions. The solution of the proposed analytical framework maximises the total utility of the relay network, and interference coordination among the BS and RSs is also considered. The developed framework is thoroughly evaluated by comparison with exhaustive search methods in different case studies. The comparison results show that the proposed framework provides optimal solutions with low computational complexity.

In this paper, an optimization problem to maximize the energy efficiency of a hybrid automatic repeat request with incremental redundancy (HARQ-IR) is considered for massive multiple-input multiple-output (MIMO) systems. Because a sum rate for multiple HARQ-IR rounds is approximated by a Gaussian distribution, a problem maximizing energy efficiency becomes tractable. In particular, a simplified form of energy efficiency for massive MIMO transmissions with an HARQ-IR protocol is derived using an outage probability, which is given by a tail probability of the Gaussian distribution. By exploiting the proposed simplified form, we show that the energy efficiency of the system is approximated by a piecewise quasi-concave function. Using these properties, an algorithm finding the optimal transmission power and number of transmission rounds for maximizing energy efficiency is proposed. Moreover, we show that the optimal number of HARQ transmission rounds can be less than the maximum allowable retransmissions when considering the constant power consumption of the massive MIMO system with an HARQ-IR protocol. Simulation results verify the theoretical analysis and show the performance of the proposed algorithm.

For physical layer security with multiple antennas over wireless channels, we consider an artificial noise-aided secure beamforming system. A transmitter can send the confidential information to the legitimate user more securely without eavesdropping when an artificial jamming signal interfering eavesdroppers is transmitted with the confidential information signal. The transmitter splits its transmit power for both the information and jamming signals with a power splitting factor. Under such a system model, we investigate the impacts on secrecy performance of a power splitting factor, the numbers of antennas and eavesdroppers, and noise variance at the legitimate receivers and eavesdroppers by analyzing the expected secrecy rate. The optimal power splitting factor and limiting secrecy rate with large number of antennas are also derived. Moreover, we examine the expected secrecy rate loss caused by channel variation over time. It is shown that the secrecy rate loss is independent of system parameters like a power splitting factor, the numbers of antennas and eavesdroppers in a high signal to interference plus noise ratio (SINR) region. In a low SINR region, on the other hand, the secrecy rate loss changes with system parameters. Simulation results verify these observations on ergodic secrecy rate and its loss due to time-varying channels.

For a mobile multihop relay (MMR) system, a centralised packet scheduling method is compared with a distributed packet scheduling method. The performance of the MMR system considering the joint effect of a finite‐length queue and an adaptive modulation and coding scheme for base station and relay station is analysed. A finite state Markov chain (FSMC) describing the queue and channel state pairs’ transitions for the MMR system is built. Moreover, a method of reducing the state space of the FSMC, which enables a hop‐by‐hop performance analysis of the MMR system without explosive growth in the number of states, is presented. Numerical and simulation results show that the proposed FSMC modelling is accurate and the performance of centralised scheduling becomes degraded because of increased mismatches between a scheduled modulation and coding scheme (MCS) level and the actual MCS level.

Channel sensing in cognitive radio systems is examined with the objective to optimise primary user's power allocation in pilot signals in order to maximise the sum rate of both primary and secondary links. The pilot signal of the primary transmitter can be used not only for channel estimation at the primary receiver, but also for channel sensing at the secondary transmitter. A tradeoff exists between channel‐sensing performance and the sum rate of primary and secondary links. At first, the sum rate of primary and secondary networks can be increased by selecting the optimal operating point of channel sensing when a pre‐determined pilot is given. When pilot power can be controlled, the problem is formulated as a joint optimisation problem for the channel‐sensing performance and power allocation at the pilot signal. Using the optimal solutions, the maximum sum rate can be achieved. This joint optimisation problem has a unique solution that can be found numerically with an iterative manner and it has fast convergence characteristic. Channel sensing of the secondary user as well as an achievable rate of the primary user is considered in primary pilot power allocation.

A new transceiver architecture for overloaded MIMO interference channels is proposed to fix the rate saturation problem of purely linear beamforming in this case. The proposed scheme is based on a mixture of linear beamforming and multi-user detection. It is shown that non-trivial degrees of freedom can be achieved by the proposed mixed scheme properly dividing the interference signals for linear processing and multiuser detection, and the achievable degrees of freedom of the proposed scheme are obtained. Numerical results show that the proposed scheme outperforms linear beamforming in overloaded MIMO interference channels.

Based on a linear formulation to interference alignment, an adaptive algorithm for interference-aligning beam tracking in time-varying MIMO interference channels is proposed. It is shown that obtaining the interference-aligning beam vector is equivalent to minimizing a certain Rayleigh quotient, and the conjugate gradient approach is adopted to construct an adaptive algorithm. The convergence and stability of the proposed algorithm are established in static channel case, and numerical results show that the proposed algorithm performs well compared with other existing methods with much less complexity.

The problem of interference alignment in time-varying MIMO interference channels is considered. To reduce complexity, an adaptive algorithm for beam vector design is proposed based on our previous work of least squares approach to beam design for interference alignment and matrix perturbation theory. The proposed algorithm calculates interference-aligning beam vectors by additive update of the previous value and reduces complexity significantly. Numerical results are provided to validate the proposed algorithm. It is shown that the proposed adaptive algorithm yields almost the same performance as a non-adaptive method that calculates interference-aligning beam vectors at every time step.

In this paper, a new amplify-and-forward (AF) relay systems using superimposed pilot signals is proposed for frequency selective fading channel environments. In the proposed scheme, the relay superimposes an additional pilot sequence on the received signal from the source, and forwards the combined signal to the destination. The superimposed pilot signal in this way enables the destination node to estimate the channel between the relay and destination (R-D), and this channel information is used to process the received signal at the destination optimally; the noise correlation due to the frequency selective fading in the R-D channel is whitened. The optimal power ratio of the superimposed pilot to the incoming signal at the relay is obtained via simulations, and it is shown under frequency selective channel environments that the proposed scheme yields better bit-error- rate (BER) performance than the conventional method where all relay power is allocated only to the incoming signal.

New large-deviations results that characterize the asymptotic information rates for general d-dimensional (d -D) stationary Gaussian fields are obtained. By applying the general results to sensor nodes on a two-dimensional (2-D) lattice, the asymptotic behavior of ad hoc sensor networks deployed over correlated random fields for statistical inference is investigated. Under a 2-D hidden Gauss-Markov random field model with symmetric first-order conditional autoregression and the assumption of no in-network data fusion, the behavior of the total obtainable information [nats] and energy efficiency [nats/J] defined as the ratio of total gathered information to the required energy is obtained as the coverage area, node density, and energy vary. When the sensor node density is fixed, the energy efficiency decreases to zero with rate Theta(area-1/2) and the per-node information under fixed per-node energy also diminishes to zero with rate O(Nt-1/3) as the number Ntof network nodes increases by increasing the coverage area. As the sensor spacing dnincreases, the per-node information converges to its limit D with rate D-radic(dn)e-alphadnfor a given diffusion rate alpha. When the coverage area is fixed and the node density increases, the per-node information is inversely proportional to the node density. As the total energy Et consumed in the network increases, the total information obtainable from the network is given by O(logEt) for the fixed node density and fixed coverage case and by Theta(Et2/3) for the fixed per-node sensing energy and fixed density and increasing coverage case.

The detection of hidden two-dimensional Gauss-Markov random fields using sensor networks is considered. Under a conditional autoregressive model, the error exponent for the Neyman-Pearson detector satisfying a fixed level constraint is obtained using the large deviations principle. For a symmetric first order autoregressive model, the error exponent is given explicitly in terms of the SNR and an edge dependence factor (field correlation). The behavior of the error exponent as a function of correlation strength is seen to divide into two regions depending on the value of the SNR. At high SNR, uncorrected observations maximize the error exponent for a given SNR, whereas there is non-zero optimal correlation at low SNR. Based on the error exponent, the energy efficiency (defined as the ratio of the total information gathered to the total energy required) of ad hoc sensor network for detection is examined for two sensor deployment models: an infinite area model and and infinite density model. For a fixed sensor density, the energy efficiency diminishes to zero at rate 0(area -1/2) as the area is increased. On the other hand, non-zero efficiency is possible for increasing density depending on the behavior of the physical correlation as a function of the link length.

The problem of optimal sensing and training in a cognitive radio system is considered when the training signal of the primary transmitter is used for both channel estimation at the primary receiver and sensing for the secondary transmitter. First, the optimal operating characteristics of sensing that maximizes the overall system rate for given training is investigated. It is shown that the optimal false alarm probability at the secondary sensor is monotone increasing as the activity of the primary user increases if the sensing ROC curve is concave. When the primary activity factor is unknown, the max-min criterion is applied to optimal sensing strategy and the resulting max-min optimal solution is given by an equalizer rule for any type of sensing ROC curve. The joint optimization of sensing and training has a unique solution and it can be easily found numerically using a gradient ascent algorithm. By optimal design of sensing and training in such a way, the overall system rate can be improved.

Using large deviations results that characterize the amount of information per node on a two-dimensional (2-D) lattice, asymptotic behavior of a sensor network deployed over a correlated random field for statistical inference is investigated. Under a 2-D hidden Gauss-Markov random field model with symmetric first order conditional autoregression, the behavior of the total information [nats] and energy efficiency [nats/J] defined as the ratio of total gathered information to the required energy is obtained as the coverage area, node density and energy vary.

In this paper, the robust time and frequency offset tracking algorithms and architecture for high throughput wireless local area network (WLAN) systems are presented. The designed architecture has feedback and feed forward compensation loop with well defined loop filter coefficients obtained by various experiments for practical implementation. Considered functional blocks include residual CFO estimator, carrier phase estimator, and timing error estimator. In addition to the algorithm descriptions of these blocks, the implementation method is presented, as they are modeled in the floating/cycle and bit true fixed point C model. These C models have been used as a reference for RTL coding and its verification. The design has been implemented successfully on an FPGA device. The proposed tracking method produces results very close to that of the offset-free system.

For higher data rate wireless communication, multiple antenna and orthogonal frequency division multiplexing (OFDM) schemes are considered as a key technology. In IEEE 802.11 TGn (Task Group n), multiple input multiple output-OFDM (MIMO-OFDM) methods, including spatial division multiplexing (SDM), are adopted for a higher throughput solution in the next generation wireless local area network (WLAN) physical (PHY) layer. In this paper, we design a transmission architecture based on dual-band and MIMO-OFDM schemes and media access control (MAC) layer using the enhanced distributed channel access (EDCA) including a block acknowledgement (ACK) method. Moreover, test results of this prototype system implemented with field programmable gate arrays (FPGA) are introduced.

Multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) is regarded as a promising technology enabling higher data rate wireless communications in frequency selective fading channels. This paper proposes the next generation wireless local area network (WLAN) physical (PHY) layer transmission technology using dual-band and MIMO-OFDM schemes and media access control (MAC) layer based on the enhanced distributed channel access (EDCA) scheme. Also it shows test results of MIMO-OFDM prototype system implemented with field programmable gate arrays (FPGA). This design targets over 200 Mbps of the maximum PHY data rate in 40 MHz

Multiple input multiple output-orthogonal frequency division multiplexing (MIMO-OFDM) is regarded as a promising technology enabling higher data rate wireless communications in frequency selective fading channels. This paper proposes the next generation wireless local area network (WLAN) physical (PHY) layer transmission technology using dual-band and MIMO-OFDM schemes. It describes a design of the higher data rate WLAN system targeting the maximum speed of 144 Mbps in 20 MHz bandwidth channels and 288 Mbps in 40 MHz. Additionally, the design suggests a new frame structure based on the 802.11a frame format for guaranteeing the compatibility with IEEE 802.11a legacy-OFDM systems and the MIMO channel estimation capability. For a cost-effective implementation and improved error performance, 2 transmit and 3 receive antennas are used in dual-band.

A decision feedback equalizer (DFE) type of equalization scheme is proposed to mitigate the effect of delay spread for orthogonal frequency division multiplexing (OFDM) systems. The performance degradation is severe in case that the delay spread of wireless channels is longer than a cyclic prefix (CP) due to the effect of inter-symbol interference (ISI) and inter-carrier interference (ICI). The effect of ICI and ISI is analytically described in matrix equations which includes signal-to-noise ratio (SNR) degradation for a given channel condition. The proposed scheme improves the performance using decision feedback mechanism without increasing the length of CP, which preserves the system efficiency for the OFDM.

In this paper, we propose a new residual carrier frequency and timing offset tracking scheme for a SC-FDE (single carrier - frequency domain equalizer) with frequency-domain pilots. The SC-FDE is a field that gathers more and more interest as a modulation scheme for the broadband wireless communications. For simple channel equalization SC-FDE systems perform equalization in frequency-domain instead of time-domain using FFT, IFFT and CP (cyclic prefix). We analyze the effect of frequency and timing offset in a frequency-domain and propose a new method of frequency and timing synchronization. Also, we describe a method to assign frequency-domain pilots and tracking algorithms for residual carrier frequency and timing offset using these pilots. Moreover, we verify the algorithm with a numerical analysis and computer simulation.