Reliable, ultra-low-latency communication in Vehicular Ad-hoc Networks (VANETs) is critical for autonomous vehicle safety, yet existing routing protocols struggle with high overhead and frequent link disruptions in dynamic environments. A key limitation is their failure to leverage the rich sensor data already available in modern vehicles for network-level coordination. This paper introduces a novel segmentation-based routing framework that directly addresses these challenges by transforming in-vehicle sensor data into a shared, high-resolution network topology. Our core contribution is a dynamic road occupancy matrix, created by dividing roadways into velocity-adaptive, single-vehicle segments, which provides all nodes with a unified and near real-time view of the traffic environment. This matrix is disseminated efficiently using a Time Division Integrated Sensing and Communication (TD-ISAC) framework inspired by 5G Sidelink. We propose a hybrid Lagrangian Relaxation and Branch-and-Bound algorithm to dynamically optimize the TD-ISAC frame, balancing the trade-off between sensing accuracy and communication throughput. To further enhance topological precision and mitigate vehicle mobility effects, the framework integrates a Kalman Filter and Transformer model for predictive position estimation and employs a reserve relay mechanism to ensure multi-hop link stability. Comprehensive simulations demonstrate that our framework significantly outperforms state-of-the-art protocols. Notably, it reduces end-to-end delay by up to 68% compared to ZRP and 41% against the TD-ISAC-based Starling Flocks, while simultaneously decreasing routing overhead. This dual improvement enables a more efficient and robust network for safety-critical applications.

Vehicular edge computing (VEC) is a critical enabler of low-latency and computation-intensive vehicular applications by offloading tasks from vehicles to edge servers. However, the dynamic nature of vehicular networks introduces significant uncertainty in task characteristics and network conditions. This paper proposes a robust task offloading scheme for VEC within the open radio access network (O-RAN) architecture. The proposed scheme integrates large-timescale computational resource allocation (CRA) with small-timescale task partitioning and radio resource allocation (RRA) using O-RAN’s hierarchical control framework. Our scheme minimizes the network-wide resources under latency constraints that are subjected to demand uncertainty. We employ the cutting-set method to address the demand uncertainty in the large-timescale CRA. We obtain a closed-form solution to the optimal task partitioning problem and provide a heuristic approach for the small-timescale RRA. Simulation results show that the small-timescale RRA succeeds to counteract the demand uncertainty at the large-timescale CRA, that is, no outage occurs when demands are in the assumed uncertainty set, whereas the non-robust scheme exhibits as high as 50% outage probability. Moreover, our slotted scheme consumes about 50% less bandwidth than the conventional non-slotted robust solution.

This paper proposes an efficient path-planning scheme for unmanned aerial vehicles (UAVs) aimed at addressing coverage holes in wireless networks. Coverage holes can undermine the quality of service (QoS) of terrestrial cellular networks where they cause outage times longer than a threshold value dictated by the different application requirements. The proposed approach leverages the self-organizing map (SOM), an unsupervised machine learning technique, to design a UAV trajectory that minimizes the flight path length, while ensuring a coverage hole-free cell or guaranteeing a maximum outage time across the existing holes. The designed path also satisfies constraints on minimum and maximum UAV velocity. Simulation results show that for realistic scenarios, we can practically eliminate all coverage holes when one UAV travels over the designed path. For more extreme scenarios, we show that we need to deploy multiple UAVs to satisfy the QoS requirements where each UAV covers a partition of the holes. To achieve optimal partitioning, we utilize the ant colony algorithm.

This article introduces a new approach to address the spectrum scarcity challenge in 6G networks by implementing the enhanced licensed shared access (ELSA) framework. Our proposed auction mechanism aims to ensure fairness in spectrum allocation to mobile network operators (MNOs) through a novel weighted auction called the fair Vickery-Clarke-Groves (FVCG) mechanism. Through comparison with traditional methods, the study demonstrates that the proposed auction method improves fairness significantly. The enhancement of the efficiency of the LSA system is suggested through the utilization of spectrum sensing and the integration of UAV-based networks. This research employs two methods to solve the problem. Firstly, a novel greedy algorithm, named Market Share-Based Weighted Greedy Algorithm (MSWGA), is proposed to achieve better fairness compared to traditional auction methods. Secondly, Deep Reinforcement Learning (DRL) algorithms are exploited to optimize the auction policy and demonstrate its superiority over other methods. Simulation results show that the deep deterministic policy gradient (DDPG) method performs superior to soft actor critic (SAC), MSWGA, and greedy methods. Moreover, a significant improvement is observed in fairness index compared to the traditional greedy auction methods. This improvement is as high as about 27% and 35% when deploying the MSWGA and DDPG methods, respectively.

Coverage holes directly affect the quality of service (QoS) and reliability of wireless networks and should be avoided as much as possible. In this paper we address this issue through the deployment of unmanned aerial vehicles (UAVs) as mobile base stations and we propose proper UAV path planning. While most of the works in the literature define holes based on statistical sense, i.e., when the coverage probability for a point on cell is below a certain threshold, e.g., 95%, in this paper, we target applications that allow only for short time disconnections, and define a location that is not covered for a certain amount of time to be in a coverage hole. To minimize such holes, we use optimal UAV path planning based on the two families of trajectories, namely, spiral and oval curves. We show that the proposed oval curves will result in a better performance in addressing the coverage holes and can guarantee a minimum signal to noise ratio over the whole coverage area, and over a guaranteed amount of time.

In this paper, we propose various path-planning scenarios for unmanned aerial vehicles (UAV) surveillance applications, aiming to provide uniform coverage over the region of interest while minimizing mechanical energy consumption. We demonstrate that depending on the specific nature of the application, the optimal path, as well as the preferred UAV type (fixed-wing versus rotarywing), can vary. We subsequently provide recommendations about the choice of UAV type and optimal paths for surveillance applications such as fire outbreak detection or intrusion detection. Generally, it is commonly perceived that, for a given application and path, rotarywing UAVs consume significantly more energy than their fixed-wing counterparts. However, to our surprise, we identify scenarios where the rotarywing UAV outperforms its fixed-wing counterpart in terms of energy consumption.

Mutual random phase injection can be considered an appropriate solution for tackling the low key generation rate issue in physical layer key generation schemes. In this paper, the confidentiality of a phase-based group secret key generation scheme has been analyzed by employing a geometric secrecy approach in a static environment. Accordingly, we exploit this approach to achieve some common randomness quantities in our group model. For this purpose, the process of key generation is defined for a star topology. Next, both vulnerability and secrecy regions are identified. To address the challenge of security vulnerability in non-proximity areas due to the spatial correlation in static environments with dominant line-of-sight (LoS), multiple carrier frequencies have been employed at the channel probing stage to enhance the secrecy regions. Finally, the key error probability is provided not only to establish an appropriate evaluation of the quality of the generated keys but also to show the efficiency of our proposed scheme in comparison with previous works.

In this paper, we present a quality of service (QoS)-aware priority-based spectrum management scheme to guarantee the minimum required bit rate of vertical sector players (VSPs) in the 5G and beyond generation, including the 6th generation (6G). VSPs are considered as spectrum leasers to optimize the overall spectrum efficiency of the network from the perspective of the mobile network operator (MNO) as the spectrum licensee and auctioneer. We exploit a modified Vickrey-Clarke-Groves (VCG) auction mechanism to allocate the spectrum to them where the QoS and the truthfulness of bidders are considered as two important parameters for prioritization of VSPs. The simulation is done with the help of deep deterministic policy gradient (DDPG) as a deep reinforcement learning (DRL)-based algorithm. Simulation results demonstrate that deploying the DDPG algorithm results in significant advantages. In particular, the efficiency of the proposed spectrum management scheme is about %85 compared to the %35 efficiency in traditional auction methods.

This paper presents an analysis of the Age of Information (AoI) in a wireless sensor network consisting of multiple IoT sensors. This network consists of three nodes: a wireless power source (WPS), sensors, and an access point (AP). We exploit hybrid visible light communication/radio frequency (VLC/RF) for the sensors with the orthogonal frequency bands. Power domain non-orthogonal multiple access (PD-NOMA) and successive interference cancellation (SIC) are also adopted for the sensors and the AP. Finally, we present two main optimization problems for average AoI with the sensor's transmit power constraint. We analyze the average AoI in a hybrid VLC/RF network, and we obtain the global optimum value for the average AoI of each sensor by considering the probability of sensors being charged, the probability of choosing the links, calculating the probability of successful decoding. According to the optimization results based on the Particle Swarm Optimization (PSO) method, the average AoI for each sensor and the total average AoI in the proposed system are reduced by 10% and 15%, respectively.

Attention deficit hyperactivity disorder (ADHD) is one of the most common mental disorders. This disease includes a combination of disorders in maintaining attention, hyperactivity, and impulsive behaviors. Diagnosis of ADHD is primarily clinical and based on history and examination. This study aims to provide a method for a more accurate diagnosis of adult ADHD using electroencephalography (EEG) signals. EEG signals recorded from 37 ADHD and 42 healthy adults were used as a control group in the age range of 20–68 years. We designed a convolutional neural network with three convolutional layers, three max-pooling layers, and one fully connected layer and trained it using spectrogram images obtained from EEG signals. The Cz channel was used for the diagnosis ADHD in four different states. To evaluate the performance of the proposed method, metrics such as accuracy, sensitivity, specificity, and precision were calculated. The results showed that using only one Cz channel has a good performance in diagnosing of ADHD. The highest accuracy of classification was related to the classification of two groups in the state when their eyes-open and eyes-closed spectrogram images were subtracted from each other. The results showed that proposed method based on deep learning can be a suitable method for diagnosing ADHD.

In this paper, fresh Basic Safety Messages (BSM) (e.g., vehicle’s position and speed) are used to control the Connected Automated Vehicles (CAVs) to reduce Time to Collision (TTC) error which leads to decrease in Collision Risk (CR). In contrast to exiting works, a novel Safety-aware Age of Information (S-AoI) metric is proposed that in addition to AoI, takes into account the risk assessment of CAVs to design an efficient transmission protocol for BSMs. We also deploy user-centric Cell-free-massive-MIMO (CFmMIMO) to improve the communication coverage, accessibility, and reliability, where each CAV is served by a cluster of nearby Access Points (APs). Unlike previous works, a two time-scale distributed deterministic policy gradients algorithm is adopted which greatly reduces the signal processing complexity, system load as well as signaling overhead while maintaining the performance. Simulation results show that the proposed framework, i.e, user-centric CFmMIMO technology together with S-AoI metric, can reduce average TTC error between 24%-35% across different lane change probabilities compared to the baseline scenario in which we use small cell mMIMO with AoI metric. Such a reduction in TTC error results in significant decrease (as high as 75%) in CR ratio.

Utilization of moving unmanned aerial vehicles (UAV) has attracted a lot of attention in recent years. Accordingly, path planning to optimize a given utility function, such as mechanical energy, has been the subject of many works. In a prior work, we have proposed path panning schemes to uniformly cover an area for communication coverage and surveillance applications with minimum mechanical energy. As far as energy and power minimization is concerned, an important issue that is sometimes being overlooked is the peak-power that the UAV has to afford to provide the path planning of interest. In this paper, we address this issue and find paths that provide a uniform coverage with minimum peak power. We then compare the results with the case where mechanical energy was minimized. This is done for fixed-wing as well as rotary-wing UAVs. It is observed that depending on the UAV specs, we can expect a mild increase (7% in our case) in peak power in some cases when going from peak power optimized scenario to energy optimized scenario. There are also cases where there is no major difference between the 2 scenarios.

Unmanned aerial vehicles (UAVs) have become important in many applications including last-mile deliveries, surveillance and monitoring, and wireless networks. This paper aims to design UAV trajectories that simultaneously perform multiple tasks. We aim to design UAV trajectories that efficiently perform some transportation operation (e.g., package delivery), and at the same time provide uniform coverage over a neighborhood area which is needed for applications such as network coverage, Internet of Things (IoT) devices data collection, wireless power transfer, and surveillance. We first consider multi-task UAVs for a simplified scenario where the neighborhood area is a circular region where UAV missions start from the center and the destinations are assumed to be uniformly distributed on the circle boundary. We propose a trajectory process such that if according to which the UAV's move, a uniform coverage can be achieved while the transport (delivery) efficiency is still preserved. We then consider a more practical scenario in which the transport destinations are arbitrarily distributed in an arbitrarily-shaped region. We show that simultaneous uniform coverage and efficient transport trajectory (e.g. package delivery) is possible for such realistic scenarios. This is shown using both rigorous analysis as well as simulations.

Ultra-low latency is the most important requirement of the Tactile Internet (TI), which is one of the proposed services for the next-generation wireless network (NGWN), e.g., fifthgeneration (5G) network. In this paper, a new queuing model for the TI is proposed for the cloud radio access network (CRAN) architecture of the NGWN by applying power domain non-orthogonal multiple access (PD-NOMA) technology. In this model, we consider both the radio remote head (RRH) and baseband processing unit (BBU) queuing delays for each endto-end (E2E) connection between a pair of tactile users. In our setup, to minimize the transmit power of users subject to guaranteeing an acceptable delay of users, and fronthaul and access constraints, we formulate a resource allocation (RA) problem. Furthermore, we dynamically set the fronthaul and access links to minimize the total transmit power. Given that the proposed RA problem is highly non-convex, in order to solve it, we utilize diverse transformation techniques such as successive convex approximation (SCA) and difference of two convex functions (DC). Numerical results show that by dynamic adjustment of the access and fronthaul delays, transmit power reduces in comparison with the fixed approach per each connection. Also, energy efficiency of orthogonal frequency division multiple access (OFDMA) and PD-NOMA are compared for our setup.

The Tactile Internet (TI) is one of the next generation wireless network services with end to end (E2E) delay as low as 1 ms. Since this ultra low E2E delay cannot be met in the current 4G network architecture, it is necessary to investigate this service in the next generation wireless network by considering new technologies such as networks function virtualization (NFV). On the other hand, given the importance of E2E delay in the TI service, it is crucial to consider the delay of all parts of the network, including the radio access part and the NFV core part. In this paper, for the first time, we investigate the joint radio resource allocation (R-RA) and NFV resource allocation (NFV-RA) in a heterogeneous network where queuing delays, transmission delays, and delays resulting from virtual network function (VNF) execution are jointly considered. For this setup, we formulate a new resource allocation (RA) problem to minimize the total cost function subject to guaranteeing E2E delay of each connection. Since the proposed optimization problem is highly non-convex, we exploit alternative search method (ASM), successive convex approximation (SCA), and heuristic algorithms to solve it. Besides, for the NFV-RA, we propose an online heuristic algorithm, and analyze its performance for the TI service. Simulation results reveal that the proposed scheme can significantly reduce the network costs compared to the case where the two problems are optimized separately. Moreover, we compare the online algorithm with its offline counterpart as well as a baseline approach and it is shown that the online algorithm outperforms both of them.

Recently, the utilization of aerial base stations (ABSs) has attracted a lot of attention. For the static implementation of ABSs, it has been shown that if the ABSs are statistically distributed in a given height over a cell, according to a binomial point process (BPP), a fairly uniform coverage across the cell is achievable. However, such a static deployment exhibits poor performance in terms of average fade duration (AFD) for the static or low speed moving users and power consumption. Therefore, considering a network of moving ABSs is of practical importance. On the other hand, once such a moving ABS network is considered, the coverage probability may not necessarily remain at an acceptable level. This paper is concerned with the design of stochastic trajectory processes such that if according to which the ABSs move, in addition to improving the AFD, an acceptable coverage profile can be obtained. We propose two families of such processes, namely, spiral and oval processes, and analytically demonstrate that the same coverage as the static case is achievable. We then focus on two special cases of such processes, namely, radial and ring processes, and show that the AFD is reduced about two orders of magnitude with respect to the static case. To obtain a more practical scenario, we also consider deterministic counterparts of the proposed radial and ring processes and show that similar coverage and AFD as the stochastic case can be obtained.

Stochastic geometry has been successfully applied for performance analysis of wireless networks. Performance of some emerging applications such as unmanned aircraft systems (UAS) relies heavily on unique mobility characteristics that are sometimes not fully captured by the current stochastic geometry results. This paper focuses on this issue by introducing families of trajectory processes that preserve uniformity within a cell in the framework of aerial base stations (ABS) networks. This means if the ABS move according to such trajectory processes, they will be distributed according to a binary point process (BPP) at any time snapshot. We propose two families of such processes, namely spiral and oval processes, and analytically prove our claim. We then focus on 2 special cases of such processes, namely, radial and ring processes and demonstrate their attractive properties as far as implementation is concerned.

In this paper, we propose a stochastic geometry based pricing for infrastructure sharing in Internet of Things (IoT) networks. We consider a game consisting of a Network Operator (NO) as the seller and an IoT Device Owner (DO) as the buyer in which the seller owns an infrastructure that can address the communication needs of the DO. Using the proposed scheme, we show that DO and NO can reach a win-win deal in which a reasonable cost is imposed to DO in exchange of providing an acceptable coverage by the NO. In particular, we show that the DO can achieve a coverage probability of interest at a lower cost compared to the case in which the proposed pricing model is absent. The proposed idea provides a transparent pricing model between NOs and DOs and paves the road for IoT applications to become more widespread.

This study is concerned with the design of ensembles of systematic low‐density parity‐check (LDPC) codes to increase the lifetime of wireless sensors by taking advantage of the inherent correlation between the transmitted data of the sensors. The authors consider two correlated sensors where the data is encoded independently at each sensor through a systematic LDPC encoder and sent over two independent channels. At the receiver, a joint source‐channel decoder consisting of two component LDPC decoders is considered where the encoded bits at the output of each component decoder are used as the a priori information at the other decoder. The authors first perform asymptotic performance analysis using the concept of extrinsic information transfer (EXIT) charts. Then, the developed modified EXIT charts are used to design ensembles for different values of correlation. Our results show that as the average check node degree of the designed ensembles grow, the decoding thresholds corresponding to the designed ensembles approach the theoretical limit. Finite block‐length performance evaluation indicates that for larger values of correlation, deploying the designed ensembles through the joint decoder can almost double the sensor's lifetime without increasing the complexity of the encoder.

This paper is concerned with the design of capacity approaching ensembles of Low-Density Parity-Check (LDPC) codes for correlated sources. We consider correlated binary sources where the data is encoded independently at each source through a systematic LDPC encoder and sent over two independent Gaussian channels. At the receiver, a joint iterative decoder consisting of two component LDPC decoders is considered where the encoded bits at the output of each component decoder are used at the other decoder as the a priori information. We first provide asymptotic performance analysis using the concept of extrinsic information transfer (EXIT) charts. Compared to the conventional EXIT charts devised to analyze LDPC codes for point to point communication, the proposed EXIT charts have been completely modified to be able to accommodate the systematic nature of the codes as well as the iterative behavior between the two component decoders. Then, the developed modified EXIT charts are deployed to design ensembles for different levels of correlation. Our results show that as the average degree of the designed ensembles grow, the thresholds corresponding to the designed ensembles approach the capacity. In particular, for ensembles with average degree of around 9, the gap to capacity is reduced to about 0.2 dB.

In this paper, a new approach for multiple access (MA) in fifth generation (5G) of cellular networks called power domain sparse code multiple access (PSMA) is proposed. In PSMA, we adopt both the power domain and the code domain to transmit multiple users' signals over a subcarrier simultaneously. In such a model, the same sparse code multiple access (SCMA) codebook can be used by multiple users where, for these users, power domain non-orthogonal multiple access (PD-NOMA) technique is used to send signals non-orthogonally. Although different SCMA codebooks are orthogonal and produce no interference over each other, the same codebook used by multiple users produces interference over these users. We investigate the signal model as well as the receiver and transmitter of the PSMA method. To evaluate the performance of PSMA, we consider a single cell with multiple users. In this case, our design objective is to maximize the system sum rate of the network subject to some system level and QoS constraints such as transmit power constraints. We formulate the proposed resource allocation problem as an optimization problem and solve it by successive convex approximation (SCA) techniques. Finally, the effectiveness of the proposed approach is investigated using numerical results.

We propose a robust resource allocation framework to improve the physical layer security in the presence of an active eavesdropper. In the considered system, we assume that both legitimate receiver and eavesdropper are full-duplex (FD) while most works in the literature concentrate on passive eavesdroppers and half-duplex (HD) legitimate receivers. In this paper, the adversary intends to optimize its transmit and jamming signal parameters so as to minimize the secrecy data rate of the legitimate transmission. In the literature, assuming that the receiver operates in HD mode, secrecy data rate maximization problems subject to the power transmission constraint have been considered in which cooperating nodes act as jammers to confound the eavesdropper. This paper investigates an alternative solution in which we take advantage of FD capability of the receiver to send jamming signals against the eavesdroppers. The proposed self-protection scheme eliminates the need for external helpers. Moreover, we consider the channel state information uncertainty on the links between the active eavesdropper and other legitimate nodes of the network. Optimal power allocation is then obtained based on the worst-case secrecy data rate maximization, under a legitimate transmitter power constraint in the presence of the active eavesdropper. Numerical results confirm the advantage of the proposed secrecy design and in certain conditions, demonstrate substantial performance gain over the conventional approaches.

We investigate the optimal power allocation problem for a femtocell network where the utilized air interface is single carrier frequency division multiple access (SC-FDMA). To overcome the interference, the receivers are equipped by frequency domain equalizers designed based on the zero forcing criterion. We derive the optimal power allocation when we protect the data rate of macro users by imposing a temperature limit on the interference arising from femto users to the macro base station. This constraint confines the cross-tier interference. We show that the optimization problem is convex and solve it using dual decomposition technique to obtain the optimal power of each femto user.

The ergodic resource allocation (ERA) problem for uplink transmission in underlay cognitive radio networks (CRNs) is investigated. The objective is to maximize the ergodic sum-rate of secondary users (SUs) considering the unavailability of perfect channel state information (CSI), and subject to transmit power limitations of SUs, and the interference threshold constraint to guarantee the quality of service of primary users. Since with average-based formulation of ERA, the interference threshold constraint and transmit power limitations of SUs do not hold instantaneously, one can replace the average-based constraints in ERA with their outage-based counterparts. For the uncertainty on the CSI values, we utilize the robust optimization theory where the uncertain parameters are modeled as a sum of the estimated value and error which is assumed to be bounded. We then map the considered ERA problems to their robust counterparts. Generally, the robust approaches degrade the performance (e.g., sum rate of SU), as they conservatively consider the error to be in the maximum extent and try to preserve the constrains under any condition of error (worst-case scenario). We aim to moderate this effect by using appropriate models for uncertain parameters, relaxing the worst-case scenario, and stochastically preserving the constraints. Moreover, robust problems are in general non-convex and suffer from high computational complexity due to the existence of uncertain system parameters. Therefore, we use effective suboptimal approaches to solve them with a reasonable complexity. This includes methods based on chance constraint approach as well as an iterative scheme. The proposed solutions provide a trade-off between robustness, performance, and complexity. Simulation results reveal that by using the proposed schemes, stable sum-rate of SUs in the presence of CSI uncertainties can be achieved while the instantaneous power and interference constraints are met with a desired probability.

We propose a robust resource allocation framework to provide physical layer security for a multiple input single output (MISO) communication system. In the considered system, we assume that the both legitimate receiver and eavesdropper are in full-duplex (FD) mode and compare the corresponding performance to conventional cooperative jamming frameworks where a half-duplex (HD) receiver is at hand. In the present paper, the adversary intends to optimize its transmit and jamming signal parameters so as to minimize the MISO secrecy rate between the legitimate transmitter and receivers. The proposed self-protection scheme eliminates the need for external helpers and provides system robustness. Moreover, we investigate robustness against channel state information uncertainty. Optimal power allocation is obtained based on worst-case secrecy rate maximization, under legitimate transmitter power constraint in the presence of an active eavesdropper. Numerical results are then provided to confirm the advantages of using FD receivers.

This paper analyzes the bit error rate (BER) performance of grouped frequency division multiple access (GFDMA) using a tool known as finite length analytical tool (FLAT). We derive the equations of the FLAT method including the variance of the equalizer output signal for different iterations. We use this value to calculate the BER of the equalizer output when a specific set of sub-carriers are assigned to more than one user. Through comparative performance evaluation, we show that the proposed FLAT method can accurately evaluate the BER of the GFDMA system and the difference between the theoretical and simulated BER curves is at most 0.1 dB. Thus, we can efficiently utilize the FLAT method to design the GFDMA system and avoid extensive simulations required to determine the number of users transmitting over the same sub-carriers simultaneously.

In this study, the authors consider a cooperative communication framework based on one‐way and two‐way relays to provide secure communications for secondary users (SUs) within an orthogonal frequency‐division multiple access‐based underlay cognitive network. By proposing a radio resource allocation problem with the aim of maximising the secrecy sum‐rate of SUs and solving it, they show that the deployment of relays is vital to achieve a non‐zero secrecy sum‐rate. Also, the impact of two‐way relay in system performance improvement is clearly visible in comparison with one‐way relay such that it can roughly double the resulting system secrecy sum‐rate. The impact of different system parameters on the achievable secrecy sum‐rate for both one‐way and two‐way relays is also investigated and compared through simulations.

A message passing (MP) algorithm for resource allocation (RA) in an Orthogonal Frequency Division Multiple Access (OFDMA) based spectrum sharing system is developed in this paper. We derive optimal power and subcarrier allocations. We further shed light on advantages of the MP algorithm particularly against the dual solution and highlight its suitability in reducing the computational burdens for practical configurations. It is observed that MP algorithm converges only after two iterations compared to 400 required iterations in the dual case. This considerably reduces the corresponding computational time, which in turn results in much lower consumed processing energy. The saved processing energy and time can then be exploited for enhancing the scheduled data rate especially when the number of users is high.

In this paper, the effect of channel distribution information (CDI) imperfectness on the performance of wireless networks is investigated. In the literature, ergodic resource allocation problems are solved assuming that perfect CDI is available which might not be a practical assumption. Therefore, we adopt the nonparametric density estimation methods for estimating the channel gain distribution. The estimation is first carried out through the well-known kernel density estimation (KDE) method. Since KDE is too sensitive to contaminated data, we adopt the robust kernel density estimation (RKDE) method. The analysis is performed over an ergodic resource allocation problem framework in the uplink of an orthogonal frequency division multiple access based network. In the proposed problem, the objective is to maximize the average total rate subject to total power constraint for each user. Simulation results indicate that for large enough number of nominal data and a reasonable number of outlier data, RKDE can provide a sum rate very close to the one obtained based on the actual CDI.

A robust Ergodic Resource Allocation (ERA) scheme is proposed in this paper in the framework of an orthogonal frequency division multiple access (OFDMA) based underlay heterogeneous network in which the allocations are made so as to maximize the average network sum-rate while guaranteeing the macro network interference requirements with any desired high probability. In previously proposed ERA schemes, both in conventional and heterogeneous networks, the optimal solution is obtained assuming that average of constraints are satisfied. In a heterogeneous network, this is translated into the fact that instantaneous level of interference on macro users can not be guaranteed, i.e., there is an uncontrolled probability of collision which is not acceptable by macro network. In this paper, we reformulate the ERA problem by replacing the average based constraints, in our case, femto total power constraint and macro interference threshold constraint, with their probabilistic counterparts so that both constraints are satisfied instantaneously with any desired high probability. We consider both cases of continuous and quantized channel state information. The proposed problems are then solved based on three methods, namely, iterative, analytical, and hybrid approaches. The optimality of the proposed methods is also assessed and the iterative approach is shown to have a performance quite close to that of the optimal solution. Simulation results confirm the effectiveness of the proposed scheme to provide a robust instantaneous imposed interference on macro network and a robust femto total transmit power. We also investigate the convergence properties of the proposed iterative approach.

In this paper, we introduce a new cooperative paradigm for secure communication in cognitive radio networks (CRNs) where secondary users (SUs) are allowed to access the spectrum of primary users (PUs) as long as they preserve the secure communication of PUs in the presence of malicious eavesdroppers. To do so, the SU transmission is divided into two hops: at first hop, the SU transmitter sends the information to a relay set and the SU receiver acts as a friendly jammer to disturb the overhearing of eavesdroppers and at the second hop, one of the relays is selected to pass the information to the SU receiver and the SU transmitter acts as a friendly jammer for the PU. In this new setup, the time duration for each hop, the power transmissions of all nodes in CRN, and relay selection at the second hop are allocated in such a way that the secrecy rate of the SU is maximized subject to the minimum required PU's secrecy rate. From primary service perspective, this transforms the possibly disturbing secondary service activities into a beneficial network element. We investigate instantaneous and ergodic resource allocation problems for perfect and imperfect channel state information (CSI). Since these problems are non-convex, we propose a solution based on decomposition of main optimization problem into three subproblems related to the power allocation, time allocation, and relay selection. We show that the power allocation problem can be transformed into a generalized geometric programming (GGP) model via the so-called scaled algorithm and it can be solved very efficiently. Simulation results indicate that in terms of the secondary secrecy rate, the proposed setup outperforms the conventional setup in which the secrecy rate of the PU is not guaranteed.

In this study, the authors investigate the performance of optical code‐division multiple access (OCDMA) systems with belief propagation (BP)‐based receivers. They propose three receivers for the optical fibre channel that provide a trade‐off between detecting complexity and system performance. The first proposed receiver achieves a performance very close to the so‐called known interference lower bound. The second receiver exhibits a considerably less complexity at the expense of a slight degradation in performance. They show that the third BP‐based receiver, which is a simplified version of the second receiver, is surprisingly the same as the so‐called multistage detector in OCDMA systems. They then study the problem of finding proper spreading codes for the proposed receivers. BP‐based receivers perform well if the graph corresponding to the spreading matrix has no short cycles. The probability of existence of short cycles directly depends on the sparsity of the spreading matrix. Therefore they look for sparse spreading matrices that are also uniquely detectable, that is, the corresponding input data vectors and the output spread vectors are in one‐to‐one correspondence. The existence of random uniquely detectable matrices (for which the elements are binary with equal probability) has already been proved by Edrös and Rényi when the dimensions of matrix tend to infinity. In this study, they prove the existence of sparse uniquely detectable spreading matrices in the large system limit, when the number of users and the number of chips approach infinity and their ratio is kept constant. For finite length systems, they propose to use optical codes with one chip interference between codes and show that they exhibit a better performance than random sparse codes.

In this paper, the downlink ergodic resource allocation (ERA) in a relay-assisted OFDMA-based cognitive network is considered with the objective of maximizing the average secondary service sum-rate. This is subject to the average total transmission power constraint and the collision probability constraint on each subcarrier at each hop of transmission to guarantee the primary quality of service with any arbitrarily high probability. In the proposed scheme, no interaction between secondary and primary networks is necessary as opposed to previously proposed frameworks. To reduce the signaling overhead between secondary base station and secondary users, which is considerably higher in relay-assisted networks compared to ordinary networks, we propose to use channel quantization. In channel quantization instead of channel gain values, the index of the fading region corresponding to that value is fed back. Due to the probabilistic nature of the collision probability constraint, the proposed ERA problem cannot be solved by conventional methods such as the dual decomposition method. Hence, we propose two novel sub-optimal solutions called Iterative Approach and Analytical Approach. Simulations results indicate the efficiency of the proposed solutions with the iterative approach slightly outperforming the analytical approach at the expense of higher complexity. We also compare continuous and quantized ERA. Simulation results demonstrate a trade-off between the volume of required feedback information and performance.

In this paper, we consider an underlay spectrum sharing system and propose utilizing discrete rate Trellis Coded Modulation Orthogonal Frequency Division Multiple Access (TCM-OFDMA) technique in the secondary system. Downlink radio resource allocation schemes are then proposed for such system. Simulation results indicate that using the proposed scheme, we can significantly increase the sum rate compared to conventional uncoded OFDMA schemes with only a moderate increase in system complexity. It is also shown that in cases where the secondary network is unable to achieve higher rates by increasing the total power and/or interference threshold, utilizing TCM-OFDMA acts as a smart alternative.

In this paper, we prove the existence of capacity achieving linear codes with random binary sparse generating matrices over the Binary Symmetric Channel (BSC). The results on the existence of capacity achieving linear codes in the literature are limited to the random binary codes with equal probability generating matrix elements and sparse parity-check matrices. Moreover, the codes with sparse generating matrices reported in the literature are not proved to be capacity achieving for channels other than Binary Erasure Channel. As opposed to the existing results in the literature, which are based on optimal maximum a posteriori decoders, the proposed approach is based on a different decoder and consequently is suboptimal. We also demonstrate an interesting trade-off between the sparsity of the generating matrix and the error exponent (a constant which determines how exponentially fast the probability of error decays as block length tends to infinity). Based on our results, we also propose a channel coding rate achievable by linear codes at a given block length and error probability. Moreover, we prove the existence of capacity achieving linear codes with a given (arbitrarily low) density of ones on rows of the generating matrix. In addition to proving the existence of capacity achieving sparse codes, an important conclusion of our paper is to prove that any arbitrarily selected sequence of sparse generating matrices is capacity achieving with high probability.

Generalized Extrinsic Information Transfer (GEXIT) charts were introduced as an extension of EXIT charts which have an extensive use in analysis and design of many iterative schemes including Low-Density Parity-Check (LDPC) codes. While a powerful as well as an insightful concept, their full potential as a designing tool for LDPC code ensembles has not been realized due to some missing steps. This papers aims at filling these gaps by proving some important properties of GEXIT charts and using them to design capacity-approaching LDPC code ensembles. The primary results on GEXIT charts are limited to regular variable and check node degrees. Moreover, variable node GEXIT curves have only been derived for the case where no physical channel is present. In a recent paper, GEXIT curves for irregular variable node and check node degree distributions have been derived. In this paper, we derive GEXIT charts of LDPC code ensembles over binary-input output-symmetric memoryless channels with any channel parameter. For the case of binary symmetric channel, we derive closed form expression for the GEXIT curve of variable nodes. We also propose to use an alternative representation of GEXIT charts in which we plot the inverse of variable node GEXIT curve together with dual GEXIT curve of the check node. We prove that the area theorem still holds in this case. Using these results, we analyze and design capacity-approaching LDPC codes using GEXIT charts.

A new approach for the approximation of the channel log-likelihood ratio (LLR) for wireless channels based on Taylor series is proposed. The approximation is applied to uncorrelated flat fading channels with unknown channel state information at the receiver. It is shown that the proposed approximation greatly simplifies the calculation of channel LLRs, and yet provides results almost identical to those based on the exact calculation of channel LLRs. The results are obtained in the context of bit-interleaved coded modulation (BICM) schemes with low-density parity-check (LDPC) codes, and include threshold calculations and error rate performance of finite-length codes. Compared to the existing approximations, the proposed method is either significantly less complex, or considerably more accurate.

A new approach for the approximation of the channel log-likelihood ratio (LLR) for wireless channels based on Taylor series is proposed. The approximation is applied to the uncorrelated flat Rayleigh fading channel with unknown channel state information at the receiver. It is shown that the proposed approximation greatly simplifies the calculation of channel LLRs, and yet provides results almost identical to those based on the exact calculation of channel LLRs. The results are obtained in the context of bit-interleaved coded modulation (BICM) schemes with low-density parity-check (LDPC) codes, and include threshold calculations and error rate performance of finite-length codes. Compared to the existing approximations, the proposed method is either significantly less complex, or considerably more accurate.

A systematic construction of capacity achieving low-density parity-check (LDPC) code ensemble sequences over the Binary Erasure Channel (BEC) has been proposed by Saeedi et al. based on a method, here referred to as Successive Maximization (SM). In SM, the fraction of degree-i nodes are successively maximized starting from i = 2 with the constraint that the ensemble remains convergent over the channel. In this paper, we propose SM to design universally capacity approaching rate-compatible LDPC code ensemble sequences over the general class of Binary-Input Output-Symmetric Memoryless (BIOSM) channels. This is achieved by first generalizing the SM method to other BIOSM channels to design a sequence of capacity approaching ensembles called the parent sequence. The SM principle is then applied to each ensemble within the parent sequence, this time to design rate-compatible puncturing schemes. As part of our results, we extend the stability condition which was previously derived for degree-2 variable nodes to other variable node degrees as well as to the case of rate-compatible codes. Consequently, we prove that using the SM principle, one is able to design universally capacity achieving rate-compatible LDPC code ensemble sequences over the BEC. Unlike the previous results in the literature, the proposed SM approach is naturally extendable to other BIOSM channels. The performance of the rate-compatible schemes designed based on our method is comparable to those designed by optimization.

We propose a systematic method to design irregular low-density parity-check (LDPC) codes for binary erasure channels (BEC). Compared to the existing methods, which are based on the application of asymptotic analysis tools such as density evolution or Extrinsic Information Transfer (EXIT) charts in an optimization process, the proposed method is much simpler and faster. Through a number of examples, we demonstrate that the codes designed by the proposed method perform very closely to the best codes designed by optimization. An important property of the proposed designs is the flexibility to select the number of constituent variable node degrees P. The proposed designs include existing systematic designs as a special case with P = N - 1, where N is the maximum variable node degree. Compared to the existing systematic designs, for a given rate and a given ¿ > 0, the designed ensembles can have a threshold in ¿-neighborhood of the capacity upper bound with smaller values of P and N. They can also achieve the capacity of the BEC as N, and correspondingly P and the maximum check node degree tend to infinity.

Existing design methods for irregular Low-Density Parity-Check (LDPC) codes over the additive white Gaussian noise channel are based on using asymptotic analysis tools such as density evolution in an optimization process. Such a process is computationally expensive particularly when a large number of constituent variable node degrees are involved in the design. In this paper, we propose a systematic approach for the design of irregular LDPC codes. The proposed method, which is based on a pre-computed upper bound on the fraction of edges connected to variable nodes of degree 3, is considerably less complex than the conventional optimization approach. Through a number of examples, we demonstrate that using our method, ensembles with performance very close to those devised based on optimization, can be designed. In addition to having very good performance, the number of constituent variable node degrees in the designed ensembles is only three or four. This, in some cases, is much smaller than the corresponding number for optimization-based designs with similar performance.

In this paper, new sequences$(\lambda ^{n},\rho ^{n})$of capacity achieving low-density parity-check (LDPC) code ensembles over the binary erasure channel (BEC) is introduced. These sequences include the existing sequences by Shokrollahias a special case. For a fixed code rate$R$, in the set of proposed sequences, Shokrollahi's sequences are superior to the rest of the set in that for any given value of$n$, their threshold is closer to the capacity upper bound$1- R$. For any given$\delta $,$0 < \delta < 1-R$, however, there are infinitely many sequences in the set that are superior to Shokrollahi's sequences in that for each of them, there exists an integer number$n_{0}$, such that for any$n > n_{0}$, the sequence$(\lambda ^{n},\rho ^{n})$requires a smaller maximum variable node degree as well as a smaller number of constituent variable node degrees to achieve a threshold within$\delta $-neighborhood of the capacity upper bound$1-R$. Moreover, it is proven that the check-regular subset of the proposed sequences are asymptotically quasi-optimal, i.e., their decoding complexity increases only logarithmically with the relative increase of the threshold. A stronger result on asymptotic optimality of some of the proposed sequences is also established.

In the past decade, there has been tremendous amount of research on the analysis and design of the Low-Density Parity-Check (LDPC) codes with belief propagation decoding over different types of Binary-Input Output-Symmetric Memoryless (BIOSM) channels. However, with the exception of the Binary Erasure Channel (BEC), analytical results on LDPC codes over such channels are limited and most results are based on numerical methods and optimization. In particular, systematic design of provably capacity achieving sequences of LDPC code ensembles over general class of BIOSM channels has remained a fundamental open problem. Such sequences have been designed only for the binary erasure channel (BEC). In this paper, we first prove some novel analytical properties of the LDPC code ensembles over BIOSM channels. In particular, we prove a result that generalizes the previously known stability condition over symmetric channels. This suggests that a modified version of the flatness condition, a property which has been shown to be critical for capacity achieving sequences over the BEC, can be used to devise capacity achieving sequences for general class of BIOSM channels. Based on this assumption, we propose a method which could result in the systematic design of such sequences over BIOSM channels. Numerical evidence is promising and provides a consistent convergence behavior to capacity over the considered BIOSM channels as the average check node degree increases.

Belief propagation (BP) algorithm for decoding low-density parity-check (LDPC) codes over a binary input additive white Gaussian noise (BIAWGN) channel requires the knowledge of the signal-to-noise ratio (SNR) at the receiver to achieve its ultimate performance. An erroneous estimation or the absence of a perfect knowledge of the SNR at the decoder is referred to as "SNR mismatch". SNR mismatch can significantly degrade the performance of LDPC codes decoded by the BP algorithm. In this paper, using extrinsic information transfer (EXIT) charts, we design irregular LDPC codes that perform better (have a lower SNR threshold) in the presence of mismatch compared to the conventionally designed irregular LDPC codes that are optimized for zero mismatch. Considering that min-sum (MS) algorithm is the limit of BP with infinite SNR over-estimation, the EXIT functions generated in this work can also be used for the efficient analysis and design of LDPC codes under the MS algorithm.

In this paper, we introduce new sequences (lambdan, rhon) of capacity achieving low-density parity-check (LDPC) code ensembles over the binary erasure channel (BEC). These sequences include the existing sequences by Shokrollahi as a special case. For a fixed code rate R, in the set of proposed sequences, Shokrollahipsilas sequences are superior to the rest of the set in that for any given value of n, their threshold is closer to the capacity upper bound 1 - R. For any given delta, 00, such that for any n > n0, the sequence (lambdan, rhon) requires a smaller maximum variable node degree as well as a smaller number of constituent variable node degrees to achieve a threshold within delta-neighborhood of the capacity upper bound 1 - R. Moreover, we prove that the check-regular subset of the proposed sequences are asymptotically quasi-optimal, i.e., their decoding complexity per iteration increases only logarithmically with the relative increase of the threshold. A stronger result on asymptotic optimality of some of the proposed sequences is also established.

We propose a deterministic method to design irregular Low-Density Parity-Check (LDPC) codes for binary erasure channels. Compared to the existing methods, which are based on the application of asymptomatic analysis tools such as density evolution or Extrinsic Information Transfer (EXIT) charts in an optimization process, the proposed method is much simpler and faster. Through a number of examples, we demonstrate that the codes designed by the proposed method perform very closely to the best codes designed by optimization. It can also be proved that, the proposed code ensembles are capacity-achieving and are thus asymptotically optimal.

In this paper, we investigate the performance of the belief propagation (BP) algorithm for decoding low-density parity-check codes over the additive white Gaussian noise channel when there is an incorrect estimate of the channel signal-to-noise ratio (SNR) (referred to as "SNR mismatch") at the decoder. At the extremes for over- and underestimation of SNR, the performance of BP tends to that of min-sum algorithm and the channel bit-error rate, respectively. Our results for regular codes indicate that the sensitivity to mismatch increases by increasing the variable-node degree and by decreasing the check-node degree. The effect of variable-node degree, however, appears to be more profound, such that at a given rate, the codes with the smallest variable and check degrees are more robust against SNR mismatch. For irregular codes, by comparing the thresholds of a few ensembles, we demonstrate that the ensemble which performs better in the absence of mismatch can perform worse in the presence of it. To obtain our asymptotic results, we propose a computationally efficient method based on the Gaussian approximation of density evolution in the presence of SNR mismatch. We also show that the asymptotic results are consistent with simulation results for codes with finite block lengths

In this paper we analyze the performance of a turbo frequency domain equalizer using the BER transfer chart. This tool evaluates the signal to noise ratio improvement at the decoder input during iterations. We derive a formula for the variance of the equalizer output at each iteration as a function of the error probability of the previous iteration. By defining an equivalent SNR based on the equalizer output mean and variance, and knowing the decoder bit error rate curve, we are able to evaluate the bit error rate of the decoder output in each iteration. Compared to the initially proposed BER transfer charts, this method gives us a more accurate curve for the equalizer and follows the dynamic of the process. Simulation results show that this method can predict the performance of the system with reasonable accuracy.

It is well known that clipping the OFDM signals in digital part of the transmitter is one of the simplest methods to reduce the peak to mean envelop power ratio. However, it suffers from additional clipping distortion, peak regrowth after digital to analog conversion, and out-of-band distortion. Recently, to combat the effect of in-band distortion and peak regrowth, it is proposed that before clipping, oversampling is performed by padding the modulating sequence with zeros. In this paper, we propose a robust DFT-based method (DBM) to reconstruct the clipped samples and mitigate the clipping distortion in the presence of channel noise at the expense of bandwidth expansion. We show through extensive simulations that by slightly increasing the bandwidth of the system, we can significantly improve the performance while limiting the maximum of the analog signal. Furthermore, we compare the performance of the DBM and the channel coding methods. It can be seen that for lower bandwidth expansions, the DBM outperforms the channel coding methods at moderate SNR values while for higher bandwidth expansions, the channel coding methods seem to be more efficient. Furthermore, we introduce a hybrid system which outperforms both the DBM and the channel coding methods at most SNR values.