The massive surge in device connectivity demands higher data rates, increased capacity with low latency and high throughput. Hence, to provide ultra-reliable, low-latency communication with ubiquitous connectivity for Internet-of-Things (IoT) devices, next-generation wireless communication leverages the incorporation of machine learning tools. However, standard data-driven models often need large datasets and lack interpretability. To overcome this, model-driven deep learning (DL) approaches combine domain knowledge with learning to improve accuracy and efficiency. Deep unfolding is a model-driven method that turns iterative algorithms into deep neural network (DNN) layers. It keeps the structure of traditional algorithms while allowing end-to-end learning. This makes deep unfolding both interpretable and effective for solving complex signal processing problems in wireless systems. We first present a brief overview of the general architecture of deep unfolding to provide a solid foundation. We also provide an example to outline the steps involved in unfolding a conventional iterative algorithm. We then explore the application of deep unfolding in key areas, including signal detection, channel estimation, beamforming design, decoding for error-correcting codes, integrated sensing and communication, power allocation, and physical layer security. Each section focuses on a specific task, highlighting its significance in emerging 6G technologies and reviewing recent advancements in deep unfolding-based solutions. Finally, we discuss the challenges associated with developing deep unfolding techniques and propose potential improvements to enhance their applicability across diverse wireless communication scenarios.

Reconfigurable intelligent surface (RIS) is considered a groundbreaking technology and an essential component of the 6th generation communications, owing to its inherent ability to control and optimize wireless channels. Recently, both active and passive RIS have been used together in hybrid form to achieve better performance with better power optimization. This paper analyzes the performance of a wireless communication system utilizing non-orthogonal multiple access (NOMA) for ultra-dense networks by a hybrid reconfigurable intelligent surface (HRIS), which leverages the hybrid RIS architecture that includes both the active and the passive reflecting elements (REs). For both the near-user and the far-user, a closed-form expression of the outage probability in Nakagami-$m$fading channels has been derived. Furthermore, performance of the considered network is compared with the traditional orthogonal multiple access (OMA) technique to showcase the advantage of NOMA over the OMA. The impact of severity, data rate, number of REs, and other system parameters on the outage performance of the considered networks are illustrated. Finally, Monte-Carlo simulations are conducted to corroborate the validity of derived closed-form expressions.

Emerging massive machine-type communications service class needs to support many devices while ensuring that scarce radio resources are utilized efficiently. Nonorthogonal multiple access is proposed to minimize the signaling overhead and optimize resource allocation. However, during the initial access, the base station (BS) is presented with the challenge of identifying sparsely active devices in the absence of knowledge about the sparsity and channel state information. The user channels in most practical scenarios have common reflection paths, making them partially correlated, which can be exploited to improve the detection performance at the BS. In this context, we formulate a novel multiuser detection (MUD) problem in spatially correlated Rician channels, which we reformulate as a multilabel classification problem utilizing deep learning techniques. We propose two diverse approaches to tackle this problem: 1) ViT-Net, a vision transformer-based architecture, and 2) FAR-Net, a fully activated deep neural network featuring residual connections. Our analysis highlights the significance of spatial correlation for MUD, which can accord around 13% higher overloading ratio compared to the noncorrelated scenario. Numerical evaluations demonstrate the effectiveness of the proposed model in addressing spatial correlation compared to the existing deep-learning models.

The large untapped spectrum in sub-THz allows for ultra-high throughput communication to realize many seemingly impossible applications in 6G. Phase noise (PN) is one key hardware impairment, which is accentuated as we increase the frequency and bandwidth. Furthermore, the modest output power of the power amplifier demands limits on peak to average power ratio (PAPR) signal design. In this work, we design a PN-robust, low PAPR single-carrier (SC) waveform by geometrically shaping the constellation and adapting the pulse shaping filter pair under practical PN modelling and adjacent channel leakage ratio (ACLR) constraints for a given excess bandwidth. We optimize the waveforms under conventional and state-of-the-art PN-aware demappers. Moreover, we introduce a neural-network (NN) demapper enhancing transceiver adaptability.We formulate the waveform optimization problem in its augmented Lagrangian form and use a back-propagation-inspired technique to obtain a design that is numerically robust to PN, while adhering to PAPR and ACLR constraints. The results substantiate the efficacy of the method, yielding up to 2.5 dB in the requiredEb/N0under stronger PN along with a PAPR reduction of 0.5 dB. Moreover, PAPR reductions up to 1.2 dB are possible with competitive BLER and SE performance in both low and high PN conditions.

Reconfigurable intelligent surfaces (RIS)-based communications with reflection modulation (RM) is a novel area of research that opens up a range of unconventional modulation techniques. Existing literature primarily focuses on specific applications where the RIS encodes its own information onto its reflection pattern. Quadrature reflection modulation (QRM) and reflection pattern modulation (RPM) are two promising reflection pattern designs that effectively deliver local data available at the RIS. This paper explores a more general application of RIS-based information transfer for a single-user downlink system via jointly mapped RM (JRM), where the RIS and the access point (AP) jointly deliver the information available at the AP. The data symbols are mapped to a constellation of tuples, each tuple containing a transmit signal and a reflection pattern. Two JRM constellation designs are proposed, namely jointly-mapped QRM (JQRM) and jointly-mapped RPM (JRPM). The proposed constellation design employs a smaller transmit signal set size compared to a generic modulation scheme, increasing the separation among adjacent constellation points. A jointly active and passive beamforming design is adopted for a multiple-input-single-output (MISO) downlink system. The simulation results analyze and compare the bit-error-rate (BER) performance of the proposed JQRM and JRPM schemes, with their respective separately-mapped counterparts and theoretical upper bounds as benchmarks.

The large untapped spectrum in the sub-THz allows for ultra-high throughput communication to realize many seemingly impossible applications in 6G. One of the challenges in radio communications in sub-THz is the hardware impairments. Specifically, phase noise is one key hardware impairment, which is accentuated as we increase the frequency and bandwidth. Furthermore, the moderate output power of the sub-THz power amplifier demands limits on peak to average power ratio (PAPR) signal design. Single carrier frequency domain equalization (SC-FDE) has been identified as a suitable candidate for sub-THz, although some challenges such as phase noise and PAPR still remain to be tackled. In this work, we design a phase noise robust, modest PAPR SC waveform by geometrically shaping the constellation under practical conditions. We formulate the waveform optimization problem in its augmented Lagrangian form and use a back-propagation-inspired technique to obtain a constellation design that is numerically robust to phase noise, while maintaining a relatively low PAPR compared to the conventional waveforms.

This work investigates the applicability of transformer neural networks (NNs) for behavioral modeling and predistortion of wideband power amplifiers (PAs). We propose an augmented real-valued time-delay transformer NN (RTDTNN) model that outperforms state-of-the-art models in terms of performance while utilizing fewer model coefficients. To the best of our knowledge, the transformer NN has not been utilized in behavioral modeling and digital predistortion (DPD) modeling in the literature. Therefore, this study introduces a new approach based on the transformer architecture to fill this void. This study introduces architectural modifications to the vanilla transformer structure to achieve high performance with a simplified transformer model for behavioral and DPD modeling of the PA. The RTDTNN model is more suited for wideband PA linearization where memory depth is high, indicating potential applicability in high-bandwidth requirements posed by beyond 5G communication networks. The experimental results based on a $100 \mathbf{M H z} 5 \mathbf{G}$-like OFDM signal with LDMOS Doherty PA show that the RTDTNN model exhibits high linearization performance, improving mean ACPR from $-28.85 \mathbf{d B}$ to $-42.77 \mathbf{d B}$, NMSE from -21.51 dB to -42.10 dB, and EVM from 6.62% to 0.35%, while utilizing 42.15% fewer model coefficients compared to the state-of-the-art ARVTDNN approach.

A deep learning (DL)-based joint pilot and data power control algorithm that solves the sum rate maximization problem in a cell-free massive multiple-input multiple-output (MIMO) system is proposed. The sum rate optimization problem for the uplink is formulated subject to per-user total transmit energy budget constraints, where user pilot and data power allocations are optimized to maximize the system sum rate. Instead of solving the non-convex problem using mathematical optimization theory, we utilize a data-driven solution approach to learn the optimal solutions. Specifically, we model a deep neural network (DNN) and train it via unsupervised learning using a custom loss function that captures the sum rate optimization objective and transmit energy constraints in the optimization problem. This unsupervised learning approach has a simpler and more flexible model training stage since it does not require labeled data for model training as in supervised learning. Simulation results show that the proposed DNN-based joint pilot and data power control algorithm improves the system sum rate compared to equal power and equal energy allocation heuristics and data power control-only approach. Furthermore, the joint power allocation results in significant energy savings (around 60 %) compared to fixed power allocation schemes.

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technology to enhance the performance of sixth-generation (6G) and beyond communication systems. The passive nature of RISs and their large number of reflecting elements pose challenges to the channel estimation process. The associated complexity further escalates when the channel coefficients are fast-varying as in scenarios with user mobility. In this paper, we propose an extended channel estimation framework for RIS-assisted multiple-input multiple-output (MIMO) systems based on a convolutional neural network (CNN) integrated with an autoregressive (AR) predictor. The implemented framework is designed for identifying the aging pattern and predicting enhanced estimates of the wireless channels in correlated fast-fading environments. Insightful simulation results demonstrate that our proposed CNN-AR approach is robust to channel aging, exhibiting a high-precision estimation accuracy. The results also show that our approach can achieve high spectral efficiency and low pilot overhead compared to traditional methods.

Over the past decade, polar codes have received significant attraction and have been selected as the coding method for the control channel in fifth-generation (5G) wireless communication systems. However, conventional polar codes are reliant solely on binary ($2 \times 2$) kernels, which restricts their block length to being only powers of 2. In response, multi-kernel (MK) polar codes have been proposed as a viable solution to achieve increased flexibility in code length. This article proposes unrolled and pipelined architectures for encoding both systematic and nonsystematic MK polar codes, capable of high-throughput encoding of codes constructed with binary, ternary ($3 \times 3$), or binary-ternary mixed kernels. Furthermore, two novel nonsystematic stage-folded encoders, designed to minimize resource usage, have been introduced for the encoding of pure-ternary and MK codes. The proposed MK encoders additionally provide the functionality of dynamic kernel assignment. The proposed architectures exhibit an unprecedented level of flexibility by supporting 83 different codes and offering various architectures that provide tradeoffs between throughput and resource consumption. The FPGA implementation results demonstrate that a partially pipelined polar encoder of size$N=4096$operating at a frequency of 270 MHz gives a throughput of 1080 Gb/s. In addition, a new compiler scripted in Python is introduced to automatically generate HDL modules for the desired encoders. By inserting the desired parameters, a designer can simply obtain all the necessary VHDL files for FPGA implementation.

Integrated Sensing and Communication (ISAC) is one new technology under development for Sixth Generation (6G) systems. This paper focuses on creating a simulation pipeline for dynamic vehicular traffic scenarios and a novel approach to reducing wireless communication overhead with a LiDAR-based system. The simulation pipeline can be used to generate data sets for numerous research. Additionally, developed error model for vehicle detection algorithms can be used to identify LiDAR performance with respect to different parameters like LiDAR height, range, and laser point density. A periodic beam index map is developed by capturing antenna azimuth and elevation angles which denote maximum Reference Signal Receive Power (RSRP) for a simulated receiver grid on the road and classifying areas using Support Vector Machine (SVM) algorithm to reduce the number of Synchronization Signal Blocks (SSBs) that needed to send in Vehicle to Infrastructure (V2I) communication.

Long short-term memory(LSTM) neural networks and convolutional neural networks (CNNs) have been used by modern researchers to compensate power amplifier (PA) distortions. In this research, two digital predistortion (DPD) models based on LSTM and CNN called augmented-LSTM and 1D-CNN-LSTM are proposed. The augmented-LSTM model effectively reduces the distortions in wideband PAs. The measurement results show that the augmented-LSTM model gives better linearization performance compared to other state-of-the-art models designed based on neural networks (NNs). On the other hand, the 1D-CNN-LSTM model is proposed to simplify the augmented-LSTM model by integrating a CNN layer prior the LSTM layer causing reduction in the number of input features to the LSTM layer. The measurement results show that the 1D-CNN-LSTM model offers low-complexity linearization for wideband PAs and provides comparable results to the augmented-LSTM model.

Semantic communication represents a promising roadmap toward achieving end-to-end communication with reduced communication overhead and an enhanced user experience. The integration of semantic concepts with wireless communications presents novel challenges. This paper proposes a flexible simulation software that automatically transmits semantic segmentation map images over a communication channel. An additive white Gaussian noise (AWGN) channel using binary phase-shift keying (BPSK) modulation is considered as the channel setup. The well-known polar codes are chosen as the channel coding scheme. The popular COCO-Stuff dataset is used as an example to generate semantic map images corresponding to different signal-to-noise ratios (SNRs). To evaluate the proposed software, we have generated four small datasets, each containing a thousand semantic map samples, accompanied by comprehensive information corresponding to each image, including the polar code specifications, detailed image attributes, bit error rate (BER), and frame error rate (FER). The capacity to generate an unlimited number of semantic maps utilizing desired channel coding parameters and preferred SNR, in conjunction with the flexibility of using alternative datasets, renders our simulation software highly adaptable and transferable to a broad range of use cases.

Polar codes have received growing attention in the past decade and have been selected as the coding scheme for the control channel in the fifth generation (5G) wireless communication systems. However, the conventional polar codes have only been constructed by binary ($2\times 2$) kernel, which poses block length limitation to powers of 2. To attain more flexible block lengths, multi-kernel polar codes are proposed. In this paper, a combinational architecture for multi-kernel polar codes with high throughput is proposed based on successive cancellation decoding algorithm. The proposed scheme can decode pure-binary, pure-ternary ($3\times 3$), and binary-ternary mixed polar codes. The decoder’s architecture is rate-flexible, meaning that a new code rate can be assigned to the decoder at every clock cycle. The proposed architecture is validated by FPGA implementation, and the results reveal that a code of size$N=81$achieves the coded throughput of 1664.5 Mbps. A Python-based polar compiler is also proposed to automatically generate the HDL modules for target decoders. A designer can input the target block length and kernel ordering of a polar code and get the required VHDL files automatically.

We propose a novel scheme that allows MIMO system to modulate a set of permutation matrices to send more information bits, extending our initial work on the topic. This system is called Permutation Matrix Modulation (PMM). The basic idea is to employ a permutation matrix as a precoder and treat it as a modulated symbol. We continue the evolution of index modulation in MIMO by adopting all-antenna activation and obtaining a set of unique symbols from altering the positions of the antenna transmit power. We provide the analysis of the achievable rate of PMM under Gaussian Mixture Model (GMM) distribution and finite cardinality input (FCI). Numerical results are evaluated by comparing PMM with the other existing systems. We also present a way to attain the optimal achievable rate of PMM by solving a maximization problem via interior-point method. A low complexity detection scheme based on zero-forcing (ZF) is proposed, and maximum likelihood (ML) detection is discussed. We demonstrate the trade-off between simulation of the symbol error rate (SER) and the computational complexity where ZF performs worse in the SER simulation but requires much less computational complexity than ML.

An reconfigurable intelligent surface (RIS) can be used to establish line-of-sight (LoS) communication when the direct path is compromised, which is a common occurrence in a millimeter wave (mmWave) network. In this paper, we focus on the uplink channel estimation of a such network. We formu-late this as a sparse signal recovery problem, by discretizing the angle of arrivals (AoAs) at the base station (BS). On-grid and off-grid AoAs are considered separately. In the on-grid case, we propose an algorithm to estimate the direct and RIS channels. Neural networks trained based on supervised learning is used to estimate the residual angles in the off-grid case, and the AoAs in both cases. Numerical results show the performance gains of the proposed algorithms in both cases.

This paper introduces a new interference-aware user association (UA) scheme for a multi-base station (BS) wireless network in which intelligent reflecting surfaces (IRSs) are leveraged to improve each multi-antenna BS’s coverage region and mitigate the vulnerability to non-line of sight paths. We aim to maximize the total network downlink achievable rate by jointly optimizing the reflective phase shifters at IRSs while associating mobile users (MUs) to BSs, which is an intractable non-convex problem. An alternating optimization-based algorithm based on solving two sub-problems, i.e., one for UA and one for reflective phase shift optimization, is proposed to tackle the non-convex problem. The proposed algorithm optimizes phase shifters at IRS through fractional programming techniques, and the UA is solved by successive convex approximation (SCA). Simulation results show that the proposed algorithm significantly improves the total network achievable rate compared to heuristic methods, e.g., matching game.

The mmWave communication up to 71 GHz is already specified in 3rd generation partnership project (3GPP)5G New Radio (NR), and communication in sub-THz bands is being studied for 6G widely in the academia and industry. Operation with very narrow beamwidths and much higher bandwidths in contrast to Frequency Range 1 (sub-6 GHz) can cater to the high data rate requirements at the expense of extra signal processing burden to overcome the unfavourable conditions such as high attenuation and scattering in the presence of obstacles. Such severe signal power attenuation caused by an obstacle may degrade the network performance due to link failures occurring as a result of line-of-sight (LoS) to non-LoS (NLoS) transitions. These limitations raise the necessity of a sensing system to collect situational awareness data to assist the wireless communication network. This work proposes a method to improve the LoS detection and user localization accuracy using multiple light detection and ranging (LiDAR) sensors co-located in access points (APs). We also propose an approach to predict the LoS transitions based on static LiDAR maps and the proposed method detected the LoS transition 400ms before its occurrence.

5G New Radio (NR) mmWave operates with narrow beams. Beam-based connections require careful management of beams to ensure a reliable connection, specially when the user has mobility. 5G NR defines beam management procedures to achieve this, at the expense of periodic reporting with increased overheads and resource usage. Concurrently, recent interest in sensing for assisting wireless systems provides an opportunity to extract situational awareness information which can aid in proactive decisions for the network. In this work, we utilize an infrastructure-mounted light detection and ranging (LiDAR) sensor system simultaneously operating with the wireless system to predict future beam decisions. A recurrent neural network (RNN) based learning model is proposed for the beam prediction, employing tracking information of users facilitated by the LiDARs and beam sequence information from the wireless system. Furthermore, a method for predictive beam management with increased periodicity of the reporting mechanism and aperiodic reporting is analyzed. The results for the considered scenario reveal 86.8% of the resources can be saved compared to the conventional beam reporting procedure, while achieving an 88.7% accuracy for optimal beam decisions.

Automating processes with the increased use of robots is one of the key vertical applications enabled by 6G. Sensing the surrounding environment, localization and communication become crucial factors for these robots to operate. Light detection and ranging (LiDAR) has emerged as an appropriate method for sensing due to its capability generating detail-rich positional information with high accuracy. However, LiDARs are power-hungry devices that generate bulk amounts of data, limiting their use as on-board sensors in robots. In this paper, we present a novel approach to the methodology of generating an enhanced 3D map with improved field-of-view using multiple LiDAR sensors. This offloads the sensing burden from robots to the infrastructure where a centralized communication network will establish localization. We utilize an inherent property of LiDAR point clouds; point rings with Inertial Measurement Unit (IMU) data embedded in the sensor for point cloud registration. The generated 3D point cloud map has an accuracy of 10 cm compared to the real-world measurements. We also carry out a proof of concept design of the proposed method using two LiDAR sensors fixed in the infrastructure at elevated positions. This extends to an application where a robot is navigated through the mapped environment using a wireless link with minimal support from the on-board sensors. Our results further validate the idea of using multiple elevated LiDARs as a part of the infrastructure for various localization applications.

A graph neural network (GNN) based access point (AP) selection algorithm for cell-free mas-sive multiple-input multiple-output (MIMO) systems is proposed. Two graphs, a homogeneous graph which includes only AP nodes representing the structure of the APs in the network, and a heterogeneous graph which includes both AP nodes and user equipment (UE) nodes are constructed to represent a cell-free massive MIMO network. A GNN based on the inductive graph learning framework GraphSAGE is used to obtain the embed-dings which are then used to predict the links between the nodes. The numerical results show that compared to the proximity-based AP selection algorithms, the proposed GNN based algorithm predicts the potential APs with more accuracy. Compared to the large scale fading coefficient based AP selection algorithms, the proposed algorithm does not require measured and sorted signal strengths of all the neighbouring APs. Furthermore, the proposed algorithm is scalable in terms of the number of users in the cell-free system.

A deep learning (DL)-based power control algorithm that solves the max-min user fairness problem in a cell-free massive multiple-input multiple-output (MIMO) system is proposed. Max-min rate optimization problem in a cell-free massive MIMO uplink setup is formulated, where user power allocations are optimized in order to maximize the minimum user rate. Instead of modeling the problem using mathematical optimization theory, and solving it with iterative algorithms, our proposed solution approach is using DL. Specifically, we model a deep neural network (DNN) and train it in an unsupervised manner to learn the optimum user power allocations which maximize the minimum user rate. This novel unsupervised learning-based approach does not require optimal power allocations to be known during model training as in previously used supervised learning techniques, hence it has a simpler and flexible model training stage. Numerical results show that the proposed DNN achieves a performance-complexity trade-off with around 400 times faster implementation and comparable performance to the optimization-based algorithm. An online learning stage is also introduced, which results in near-optimal performance with 4-6 times faster processing.

Reconfigurable intelligent surface (RIS) is an emerging technology for improving performance in fifth-generation (5G) and beyond networks. Practically channel estimation of RIS-assisted systems is challenging due to the passive nature of the RIS. The purpose of this paper is to introduce a deep learning-based, low complexity channel estimator for the RIS-assisted multi-user single-input-multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) system with hardware impairments. We propose an untrained deep neural network (DNN) based on the deep image prior (DIP) network to denoise the effective channel of the system obtained from the conventional pilot-based least-square (LS) estimation and acquire a more accurate estimation. We have shown that our proposed method has high performance in terms of accuracy and low complexity compared to conventional methods. Further, we have shown that the proposed estimator is robust to interference caused by the hardware impairments at the transceiver and RIS.

Multi-user shared access (MUSA) is introduced as advanced code domain non-orthogonal complex spreading sequences to support a massive number of machine-type communications (MTC) devices. In this paper, we propose a novel deep neural network (DNN)-based multiple user detection (MUD) for grant-free MUSA systems. The DNN-based MUD model determines the structure of the sensing matrix, randomly distributed noise, and inter-device interference during the training phase of the model by several hidden nodes, neuron activation units, and a fit loss function. The thoroughly learned DNN model is capable of distinguishing the active devices of the received signal without any a priori knowledge of the device sparsity level and the channel state information. Our numerical evaluation shows that with a higher percentage of active devices, the DNN-MUD achieves a significantly increased probability of detection compared to the conventional approaches.

Grant-free random access and uplink non- orthogonal multiple access (NOMA) have been introduced to reduce transmission latency and signaling overhead in massive machine-type communication (mMTC). In this paper, we propose two novel group-based deep neural network active user detection (AUD) schemes for the grant-free sparse code multiple access (SCMA) system in mMTC uplink framework. The proposed AUD schemes learn the nonlinear mapping, i.e., multi-dimensional codebook structure and the channel characteristic. This is accomplished through the received signal which incorporates the sparse structure of device activity with the training dataset. Moreover, the offline pre-trained model is able to detect the active devices without any channel state information and prior knowledge of the device sparsity level. Simulation results show that with several active devices, the proposed schemes obtain more than twice the probability of detection compared to the conventional AUD schemes over the signal to noise ratio range of interest.

Millimeter wave (mmWave) based multiple-input multiple-output (MIMO) capable user-centric (UC) ultra-dense (UD) networks are suggested to facilitate high throughput requirements of future networks. Due to the high blockage susceptibility of mmWave, the connections may drop frequently. Hence efficient and fast beam management in initial access (IA) is essential. Current cellular systems use beam sweeping based IA mechanisms. UC UD concept requires all of its access points (APs) to perform IA. This leads to a shortage of orthogonal radio resources. Nonorthogonal resource allocation causes interference which leads to a higher misdetection probability. In this paper, we propose a novel deep contextual bandit (DCB) based approach to perform fast and efficient IA in mmWave based UC UD networks. The DCB model uses one reference signal from the user to predict the IA beam. The reduced use of reference signals improves beam discovery delay and relaxes the requirement for radio resources. Ray-tracing and stochastic channel model-based simulations show that the suggested system outperforms its beam sweeping counterpart in terms of probability of beam misdetection and beam discovery delay in mmWave based UC UD networks.

In this paper, we propose a novel index modulation mechanism called permutation channel modulation (PCM) by exploiting spatial resource of multiple input multiple output (MIMO). A set of permutation matrices is treated as indices to convey information bits by modulating a block of bits to a permutation matrix. Assuming that channel state information at transmitter (CSIT) is known, modulated permutation matrix is multiplied to the singular values of MIMO channel matrix obtained from decomposing the channel matrix using singular values decomposition (SVD). Besides to a permutation matrix, information bits are also modulated to constellation symbols. Therefore, the transmitted signals contain two sources of information: a permutation matrix and constellation symbols. At the receiver, transmitted symbols and a permutation matrix are detected using our proposed detection scheme. We derive the capacity expression and define the optimal capacity by finding the optimal power allocation at each transmission. As performance measure, capacity of PCM is compared with existing techniques. Using 4 × 4, we find that the capacity of PCM is doubled and remains superior with higher antenna settings. Our work creates a new paradigm of index modulation for multiple antenna transmissions by exploiting its spatial resource and significantly improves the capacity and outage probability performances.

In this paper, we introduce a new load balancing user association scheme for millimeter wave (mmWave) cellular networks in which intelligent reflecting surface (IRS) is applied in the cellular network to improve the coverage region of each cell and mitigate mmWave vulnerability to non-line of sight (N-LoS) paths. The user association scheme improves network performance significantly by adjusting the interference according to the association. We study the IRS-assisted mmWave cellular network where one IRS is deployed to assist in the communication from the base station (BS) to mobile users (MUs) in each cell. We balance BS loads and maximize a network utility by optimizing the user association with a matching game. Simulation results show that the proposed scheme significantly improves the throughput compared to conventional user association techniques.

In this paper, we devise a highly efficient machine learning-based channel estimation for orthogonal frequency division multiplexing (OFDM) systems, in which the training of the estimator is performed online. A simple learning module is employed for the proposed learning-based estimator. The training process is thus much faster and the required training data is reduced significantly. Besides, a training data construction approach utilizing least square (LS) estimation results is proposed so that the training data can be collected during the data transmission. The feasibility of this novel construction approach is verified by theoretical analysis and simulations. Based on this construction approach, two alternative training data generation schemes are proposed. One scheme transmits additional block pilot symbols to create training data, while the other scheme adopts a decision-directed method and does not require extra pilot overhead. Simulation results show the robustness of the proposed channel estimation method. Furthermore, the proposed method shows better adaptation to practical imperfections compared with the conventional minimum mean-square error (MMSE) channel estimation. It outperforms the existing machine learning-based channel estimation techniques under varying channel conditions.

Recently, machine learning-based channel estimation has attracted much attention. The performance of machine learning-based estimation has been validated by simulation experiments. However, little attention has been paid to the theoretical performance analysis. In this paper, we investigate the mean square error (MSE) performance of machine learning-based estimation. Hypothesis testing is employed to analyze its MSE upper bound. Furthermore, we build a statistical model for hypothesis testing, which holds when the linear learning module with a low input dimension is used in machine learning-based channel estimation, and derive a clear analytical relation between the size of the training data and performance. Then, we simulate the machine learning-based channel estimation in orthogonal frequency division multiplexing (OFDM) systems to verify our analysis results. Finally, the design considerations for the situation where only limited training data is available are discussed. In this situation, our analysis results can be applied to assess the performance and support the design of machine learning-based channel estimation.

The rapidly increasing demand in indoor small cell networks has given rise to the concept of local 5G operator (OP) for local service delivery. In this regard, we develop a novel game-theoretic framework with geometric programming to model and analyze cache-enabled small cell base stations (SBSs) with infrastructure sharing for local 5G OP networks. In such a network, the local 5G OP provides wireless network in indoor area and rent out the infrastructure which are RAN and cache storage to multiple mobile network operators (MNOs) while guarantee the quality-of-experience (QoE) at the users (UEs) of MNOs. We formulate a Stackelberg game model where the local 5G OP is the leader and the MNOs are the followers. The local 5G OP aims to maximize its profit by optimizing its infrastructure rental fee, and the MNOs aim to minimize their renting cost of infrastructure by minimizing the cache intensity subject to latency constraint at each UE. Here, the cache intensity is defined as the product of the number of SBSs per unit area and the number of popular files stored in each SBS. The optimization problems of the local 5G OP and the MNOs are transformed into geometric programming. Accordingly, the subgame perfect equilibrium of Stackelberg game is obtained through the succesive geometric programming (SGP) method. Since the MNOs share their rented infrastructure, for cost sharing, we apply the concept of Shapley value to divide the cost among the MNOs. We show that the cost sharing problem can be mapped into a simplified “airport runway cost sharing problem”, in which the Shapley value can be computed efficiently. Finally, we present an extensive performance evaluation that reveals interesting insights into designing resource sharing with edge caching in local 5G OP networks.

Source traffic prediction is one of the main challenges of enabling predictive resource allocation in machine-type communications (MTC). In this paper, a long short-term memory (LSTM) based deep learning approach is proposed for eventdriven source traffic prediction. The source traffic prediction problem can be formulated as a sequence generation task where the main focus is predicting the transmission states of machinetype devices (MTDs) based on their past transmission data. This is done by restructuring the transmission data in a way that the LSTM network can identify the causal relationship between the devices. Knowledge of such a causal relationship can enable event-driven traffic prediction. The performance of the proposed approach is studied using data regarding events from MTDs with different ranges of entropy. Our model outperforms existing baseline solutions in saving resources and accuracy with a margin of around 9%. Reduction in random access (RA) requests by our model is also analyzed to demonstrate the low amount of signaling required as a result of our proposed LSTM based source traffic prediction approach.

Belief propagation (BP) is an iterative decoding algorithm for polar codes which can be parallelized effectively to achieve higher throughput. However, because of the presence of error floor due to cycles and stopping sets in the factor graph, the performance of the BP decoder is far from the performance of state of the art cyclic redundancy check (CRC) aided successive cancellation list (CA-SCL) decoders. It has been shown that successive BP decoding on multiple permuted factor graphs, which is called the multi-trellis BP decoder, can improve the error performance. However, when permuting the entire factor graph, since the decoder dismisses the information from the previous permutation, the number of iterations required is significantly larger than that of the standard BP decoder. In this work, we propose a new variant of the multi-trellis BP decoder which permutes only a subgraph of the original factor graph. This enables the decoder to retain information of variable nodes in the subgraphs, which are not permuted, reducing the required number of iterations needed in-between the permutations. As a result, the proposed decoder can perform permutations more frequently, hence being more effective in mitigating the effect of cycles which cause oscillation errors. Experimental results show that for a polar code with block length 1024 and rate 0.5 the error performance gain of the proposed decoder at the frame error rate of $10^{-6}$ is 0.25 dB compared to multi-trellis decoder based on full permutations. This performance gain is achieved along with reduced latency in terms of the number of iterations.

In this paper a proposal to improve the reliability of a downlink multiuser (MU) MIMO transmission scheme is investigated with the use of a new approach in orthogonal space-time block codes (OSTBC) and network coding with a superposition modulated system and side information. With the new encoded OSTBC approach, diversity is offered where each user receives all other users' symbols, which allows the recovery of symbols in several ways. In addition, multiple users can be accommodated with the same resource, which is quite useful in a wireless system where resources are always restricted. By employing superposition modulation, the side information needed for error recovery can be transmitted over the same resource used for the normal information frame. In addition, the proposed system exploits diversity through a novel technique of sub-constellation alignment-based signal combining for efficient side information dissemination. A detailed analysis of the new OSTBC approach is carried out. It is shown that the performance of the MU-MIMO system can be improved significantly in terms of block and frame error rates (BLER, FER) considered as reliability measures. By accommodating a reasonable number of multiple users, high reliability is achieved at the expense of the rate. To compensate for the low rate, conventional OSTBC can be considered and simulation results are shown, where, as a penalty to pay, multiple orthogonal resources are required.

In this paper, we consider the problem of admission control in 5G networks where enhanced mobile broadband (eMBB) users and ultra-reliable low-latency communication (URLLC) users are coexisting. URLLC users require low latency and high reliability while eMBB users require high data rates. Thus, it is essential to control the admission of eMBB users while giving priority to all URLLC users in a network where both types of users are coexisting. Our aim is to maximize the number of admitted eMBB users to the system with a guaranteed data rate while allocating resources to all URLLC users. We formulated this as an l0minimization problem. Since it is an NP-hard problem we have used approximation methods and sequential convex programming to obtain a suboptimal solution. Numerically we have shown that the proposed algorithm achieves near-optimal performance. Our algorithm is able to maximize the number of admitted eMBB users with an optimal allocation of resources while giving priority to all URLLC users.

The millimeter wave (mmWave) communication uses directional antennas. Hence, achieving fine alignment of transmit and receive beams at the initial access phase is quite challenging and time-consuming. In this paper, we provide a dynamic-weight based beam sweeping direction and synchronization signal block (SSB) allocation algorithm to optimize the cell search of the initial access in mmWave 5G NR networks. The number of SSBs transmitted in each beam sweeping direction depends on previously learned experience which is based on the number of detected UEs (user equipment) per SSB for each sweeping direction. Overall, numerical simulation results indicate that the proposed algorithm is shown to be capable of detecting more users with a lower misdetection probability. Furthermore, it is possible to achieve the same performance with a smaller number of dynamic resource (i.e., SSB) allocation, compared to constant resource allocation.

End-to-end learning of a communications system using the deep learning-based autoencoder concept has drawn interest in recent research due to its simplicity, flexibility and its potential of adapting to complex channel models and practical system imperfections. In this paper, we have compared the bit error rate (BER) performance of autoencoder based systems and conventional channel coded systems with convolutional coding (CC), in order to understand the potential of deep learning-based systems as alternatives to conventional systems. From the simulations, autoencoder implementation was observed to have a better BER in 0-5 dB Eb/N0range than its equivalent half-rate convolutional coded BPSK with hard decision decoding, and to have only less than 1 dB gap at a BER of 10-5. Furthermore, we have also proposed a novel low complexity autoencoder architecture to implement end-to-end learning of coded systems in which we have shown better BER performance than the baseline implementation. The newly proposed low complexity autoencoder was capable of achieving a better BER performance than half-rate 16-QAM with hard decision decoding over the full 0-10 dB Eb/N0range and a better BER performance than the soft decision decoding in 0-4 dB Eb/N0range.

Unmanned aerial vehicle (UAV) base stations (BSs) are reliable and efficient alternative to full fill the coverage and capacity requirements when the backbone network fails to provide such requirements due to disasters. In this paper, we consider optimal UAV-deployment problem in 3D space for a mmWave network. The objective is to deploy multiple aerial BSs simultaneously to completely serve the ground users. We develop a novel algorithm to find the feasible positions for a set of UAV-BSs from a predefined set of locations, subject to a signal-to-interference-plus-noise ratio (SINR) constraint of every associated user, UAV-BS's limited hovering altitude constraint and restricted operating zone constraint. We cast this 3D positioning problem as an ℓ0minimization problem. This is a combinatorial, NP-hard problem. We approximate the ℓ0minimization problem as non-combinatorial ℓ1-norm problem. Therefore, we provide a suboptimal algorithm to find a set of feasible locations for the UAV-BSs to operate. The analysis shows that the proposed algorithm achieves a set of the location to deploy multiple UVABSs simultaneously while satisfying the constraints.

Ensuring an effective coexistence of conventional broadband cellular users with machine type communications (MTCs) is challenging due to the interference from MTCs to cellular users. This interference challenge stems from the fact that the acquisition of channel state information (CSI) from machine type devices (MTD) to cellular base stations (BS) is infeasible due to the small packet nature of MTC traffic. In this paper, a novel approach based on the concept of opportunistic spatial orthogonalization (OSO) is proposed for interference management between MTC and conventional cellular communications. In particular, a cellular system is considered with a multi-antenna BS in which a receive beamformer is designed to maximize the rate of a cellular user, and, a machine type aggregator (MTA) that receives data from a large set of MTDs. The BS and MTA share the same uplink resources, and, therefore, MTD transmissions create interference on the BS. However, if there is a large number of MTDs to chose from for transmission at each given time for each beamformer, one MTD can be selected such that it causes almost no interference on the BS. A comprehensive analytical study of the characteristics of such an interference from several MTDs on the same beamformer is carried out. It is proven that, for each beamformer, an MTD exists such that the interference on the BS is negligible. To further investigate such interference, the distribution of the signal-to-interference-plus-noise ratio (SINR) of the cellular user is derived, and, subsequently, the distribution of the outage probability is presented. However, the optimal implementation of OSO requires the CSI of all the links in the BS, which is not practical for MTC. To solve this problem, an online learning method based on the concept of contextual multi-armed bandits (MAB) learning is proposed. The receive beamformer is used as the context of the contextual MAB setting and Thompson sampling: a well-known method of solving contextual MAB problems is proposed. Since the number of contexts in this setting can be unlimited, approximating the posterior distributions of Thompson sampling is required. Two function approximation methods, a) linear full posterior sampling, and, b) neural networks are proposed for optimal selection of MTD for transmission for the given beamformer. Simulation results show that is possible to implement OSO with no CSI from MTDs to the BS. Linear full posterior sampling achieves almost 90% of the optimal allocation when the CSI from all the MTDs to the BS is known.

Scheduling fast uplink grant transmissions for machine type communications (MTCs) is one of the main challenges of future wireless systems. In this paper, a novel fast uplink grant scheduling method based on the theory of multi-armed bandits (MABs) is proposed. First, a single quality-of-service metric is defined as a combination of the value of data packets, maximum tolerable access delay, and data rate. Since full knowledge of these metrics for all machine type devices (MTDs) cannot be known in advance at the base station (BS) and the set of active MTDs changes over time, the problem is modeled as a sleeping MAB with stochastic availability and a stochastic reward function. In particular, given that, at each time step, the knowledge on the set of active MTDs is probabilistic, a novel probabilistic sleeping MAB algorithm is proposed to maximize the defined metric. Analysis of the regret is presented and the effect of the prediction error of the source traffic prediction algorithm on the performance of the proposed sleeping MAB algorithm is investigated. Moreover, to enable fast uplink allocation for multiple MTDs at each time, a novel method is proposed based on the concept of best arms ordering in the MAB setting. Simulation results show that the proposed framework yields a three-fold reduction in latency compared to a maximum probability scheduling policy since it prioritizes the scheduling of MTDs that have stricter latency requirements. Moreover, by properly balancing the exploration versus exploitation tradeoff, the proposed algorithm selects the most important MTDs more often by exploitation. During exploration, the sub-optimal MTDs will be selected, which increases the fairness in the system, and, also provides a better estimate of the reward of the sub-optimal MTD.

The current autonomous driving architecture places a heavy burden in signal processing for the graphics processing units (GPUs) in the car. This directly translates into battery drain and lower energy efficiency, crucial factors in electric vehicles. This is due to the high bit rate of the captured video and other sensing inputs, mainly due to Light Detection and Ranging (LiDAR) sensor at the top of the car which is an essential feature in autonomous vehicles. LiDAR is needed to obtain a high precision map for the vehicle AI to make relevant decisions. However, this is still a quite restricted view from the car. This is the same even in the case of cars without a LiDAR such as Tesla. The existing LiDARs and the cameras have limited horizontal and vertical fields of visions. In all cases it can be argued that precision is lower, given the smaller map generated. This also results in the accumulation of a large amount of data in the order of several TBs in a day, the storage of which becomes challenging. If we are to reduce the effort for the processing units inside the car, we need to uplink the data to edge or an appropriately placed cloud. However, the required data rates in the order of several Gbps are difficult to be met even with the advent of 5G. Therefore, we propose to have a coordinated set of LiDAR's outside at an elevation which can provide an integrated view with a much larger field of vision (FoV) to a centralized decision making body which then sends the required control actions to the vehicles with a lower bit rate in the downlink and with the required latency. The calculations we have based on industry standard equipment from several manufacturers show that this is not just a concept but a feasible system which can be implemented.The proposed system can play a supportive role with existing autonomous vehicle architecture and it is easily applicable in an urban area.

The unmanned aerial vehicles (UAVs) can be deployed as aerial base stations or wireless relays to increase the capacity of wireless networks. In this paper, the positioning of a full-duplex (FD) UAV as a relay to provide coverage for an FD vehicular network is investigated. In particular, given a configuration of predefined locations for the UAV, and the position of the vehicular users on the ground, a novel algorithm is proposed to find the position for the UAV to satisfy the quality of service (QoS) requirements of the vehicles in the network. The positioning problem is formulated as an ℓ0minimization which is non- combinatorial, NP-hard, and finding a globally optimal solution for this problem has exponential complexity. Hence, we have approximated the nonconvex problem using ℓ1norm and proposed a suboptimal algorithm to solve it. Simulation results show that by using the proposed approach the number of times that UAV can satisfy the SINR constraints increases by approximately 10% compared to a baseline scenario in which the UAV has a fixed location.

In order to reduce the latency of data delivery, one of techniques is to cache the popular contents at the base stations (BSs) i.e. edge caching. However, the technique of caching at edge can only reduce the backhaul delay, other techniques such as BS densification will also need to be considered to reduce the fronthaul delay. In this work, we study the trade-offs between BS densification and cache size under delay constraint at a typical user (UE). For this, we use the downlink SINR coverage probability and throughput obtained based on stochastic geometrical analysis. The network deployment of BS and cache storage is introduced as a minimization problem of the product of the BS intensity and cache size which we refer to the product of “cache intensity” under probabilistic delay constraint. We examine the cases when (i) either BS intensity or the cache size is held fixed, and (ii) when both BS intensity and the cache size are vary. For the case when both BS intensity and the cache size are variable, the problem become nonconvex and we convert into a geometric programing which we solve it analytically.

Selection of the most appropriate communications technology for a smart grid (SG) application is far from trivial. We propose such a feasibility assessment starting from identification of key performance indicators (KPIs) required for peer-to-peer (P2P) energy trading and grid control operations from a communications perspective. A set of cross-disciplinary KPIs, both quantitative and qualitative, are considered from communications, power, business, actor involvement, financial, and demand side management categories. They serve as a general baseline for use cases, as there have been few previous works attempting to capture the essential features of P2P SG operations. The KPIs are briefly identified along with their relations to P2P energy trading and grid control. A straightforward comparison of the quantitative and qualitative KPIs' impact on technology selection is not feasible. This paper addresses the comparison with: 1) a prioritization of the KPIs using the analytic hierarchy process; 2) a comparison of technology solutions evaluated in our previous works against the KPIs' requirements; and 3) a total feasibility evaluation of the solutions against selected KPIs. The prioritization shows latency, reliability, security, scalability, robustness, costs of information and communication technologies (ICT) devices, and costs of ICT deployment are the most important KPIs in enabling P2P energy trading and grid control. Further, the technology feasibility assessment enables identification of the most suitable candidates for an SG application.

The conflicting problems of growing mobile service demand and underutilization of dedicated spectrum has given rise to a paradigm where mobile network operators (MNOs) share their infrastructure among themselves in order to lower their operational costs, while at the same time increase the usage of their existing network resources. We model and analyze such an infrastructure sharing system considering a single buyer MNO and multiple seller MNOs. Assuming that the locations of the BSs can be modeled as a homogeneous Poisson point process, we find the downlink signal-to-interference-plus-noise ratio (SINR) coverage probability for a user served by the buyer MNO in an infrastructure sharing environment. We analyze the trade-off between increasing the transmit power of a base station (BS) and the intensity of BSs owned by the buyer MNO required to achieve a given quality-of-service (QoS) in terms of the SINR coverage probability. Also, for a seller MNO, we analyze the power consumption of the network per unit area (i.e., areal power consumption) which is shown to be a piecewise continuous function of BS intensity, composed of a linear and a convex function. Accordingly, the BS intensity of the seller MNO can be optimized to minimize the areal power consumption while achieving a minimum QoS for the buyer MNO. We then use these results to formulate a single-buyer multiple-seller BS infrastructure market. The buyer MNO is concerned with finding which seller MNO to purchase from and what fraction of BSs to purchase. On the sellers' side, the problem of pricing and determining the fraction of infrastructure to be sold is formulated as a Cournot oligopoly market. We prove that the iterative update of each seller's best response always converges to the Nash Equilibrium.

The problem of admission control in a multicell downlink multiple-input single-output system is considered. The objective is to maximize the number of admitted users subject to the signal-to-interference-plus-noise ratio constraint for each admitted user and a transmit power constraint at each base station. We cast the admission control problem as an l0minimization problem. This problem is known to be combinatorial NP-hard. Hence, we have to rely on suboptimal algorithms to solve it. We first approximate the l0minimization problem via a non-combinatorial one. Then, we propose centralized and distributed algorithms to solve the non-combinatorial problem. To develop the centralized algorithm, we use the sequential convex programming method. The distributed algorithm is derived by using the alternating direction method of multipliers in conjunction with sequential convex programming. We show numerically that the proposed admission control algorithms achieve a near-to-optimal performance. Next, we extend the admission control problem to provide fairness, where a long term fairness among the users is guaranteed. We focus on proportional and max-min fairness and propose dynamic control algorithms via Lyapunov optimization. It is shown numerically that the proposed fair admission control algorithms guarantee fairness among the users.

In this paper we present a simple method to exploit the diversity of interference in heterogenous wireless communication systems with large number of machine-type-devices (MTD). We consider a system with a machine-type-aggregator (MTA) as underlay to cellular network with a multi antenna base station (BS). Cellular users share uplink radio resources with MTDs. Handling the interference from MTDs on the BS is the focus of this article. Our method takes advantage of received interference diversity on BS at each time on each resource block and allocates the radio resources to the MTD with the minimum interference on the BS. In this method, BS does not need to take the interference from MTD into account in the design of the receive beamformer for uplink cellular user, hence, the degrees of freedom is not used for interference management. Our simulation results show that each resource block can be shared between a cellular user and an MTD, with almost no harmful interference on the cellular user.

The performance of a single carrier system employing a large number of antennas is explored in this paper, considering the realizability in the millimeter-Wave (mmWave) range in 5G standardization. A significant disadvantage of Orthogonal Frequency division Multiplexing (OFDM) systems, which are currently being used in 4G LTE downlink, is its large peak-to-average power patio (PAPR). In addition, considering massive multiple-input-multiple-output (MIMO) scenario, implementing Inverse fast Fourier transform(IFFT)/fast Fourier transform(FFT) blocks per antenna branch makes the transceivers susceptible to higher complexity in implementation. A single carrier system has therefore the advantage of being relatively simple at the receiver side. However, due to the precoding needed in the downlink, the PAPR value increases with the number of channel taps. Performance of the system is investigated through simulations for single and multiuser cases with different large antenna configurations. Various channel configurations were considered including channel correlation, a measured channel model, and errors in the channel estimate. It can be seen from the results that single carrier scheme with sufficiently higher number of antennas at the base station side provides good bit error rate (BER) performance.

One of the many problems faced by current cellular network technology is the underutilization of the dedicated licensed spectrum of network operators. An emerging paradigm to solve this issue is to allow multiple operators to share some parts of each other's spectrum. Previous works on spectrum sharing have failed to integrate the theoretical insights provided by recent developments in stochastic geometrical approaches to cellular network analysis with the objectives of network resource allocation problems. In this paper, we study the non-orthogonal spectrum assignment with the goal of maximizing the social welfare of the network, defined as the expected weighted sum rate of the operators. We adopt the many-to-one stable matching game framework to tackle this problem. Moreover, using the stochastic geometrical approach, we show that its solution can be both stable as well as socially optimal. To obtain the maxima of social welfare, the computation of the game theoretical solution using the generic Markov Chain Monte Carlo method is proposed. We also investigate the role of power allocation schemes using Q-learning, and we numerically show that the effect of resource allocation scheme is much more significant than the effect of power allocation for the social welfare of the system.

In this paper, we study a problem where multiple operators (OPs) need to share a common pool of spectrum with each other. Our objective is to maximize the social welfare, defined as the overall weighted sum rate of the OPs. The problem is decomposed into two parts: the first part is to allocate RBs to OPs, which we do so by extending the framework of many-to-one matching game with externalities. The second part is to allocate power of small cell base stations (SBSs) belonging to each OP, which is accomplished using reinforcement learning. Assuming that the SBSs associated with each OPs are spatially distributed according to Poisson point process (PPP), we show that pairwise stable matchings achieve local maximas of the social welfare function. We propose two algorithms to search for the stable matchings. Simulation results show that these algorithms are well behaved in terms of convergence and efficiency of the solutions.

Continuous evaluation in Vehicular Ad-hoc Networks (VANET) will contribute to spectrum scarcity problem in near future. Cognitive radio (CR) system aims to provide opportunistic access and adapt the available frequency resources instead of conventional static spectrum allocation. Spectrum sensing is one of the most demanding aspects in CR design and implementation. Low signal to noise ratio (SNR) and fading effects posed limitations to deploying CR in realistic propagation scenario with fast and fine sensing features. Energy detection(ED) based spectrum sensing is a viable choice for many vehicle-to-vehicle (V2V) and vehicle-to-roadside infrastructure (V2I) communications. In this paper, we consider the performance of ED over composite Generalized-K (KG) fading to deal with both small and large scale fading in VANET. Diversity combining using Maximal ratio Combining (MRC) and Selective Combining (SC) over KG fading channel are investigated. A novel tractable expressio for Energy detection based average detection probability for optimal MRC diversity combining scheme is derived and closed form possibility is analyzed for SC. Both numerical and simulation models are examined for practical low to moderate shadow fading conditions. The results highlight the notable impact of shadowing spread and fading severity on detection performance and meliorating effects of employing combining techniques.

This paper presents the analysis of outage probability for dual-hop orthogonal frequency division multiplexing (OFDM) relay system with ordered subcarrier mapping (OSM) in Nakagami-m fading. Accurate closed-form expressions are derived for the outage probability while considering a fixed gain amplify-and-forward (AF) relay system. Two special cases of Nakagami-m fading, i.e., Rayleigh (m = 1) and one-sided Gaussian (m = 1/2) are, specifically, focused with capacity-enhancement subcarrier pairing scheme and their performance is analyzed for balanced links, i.e., when SNR of each hop is same, as well as for unbalanced links, i.e., when the SNR of second hop is one-half of the first hop SNR, scenarios. The later which is known as the worst case of fading is, especially, focused for various analysis of the system. The variation of outage probability is shown for different values of fading severity (m) and number of resolvable paths (L) parameters. Numerical results validate analytical work in Nakagami-m fading channel model.

A multi-cell minimum power beamforming problem is considered. It is known that the inter-cell interference (ICI) terms couple the base stations (BS) and inter-cell coordination is required for global optimal solution. The cooperation can be realized by exchange of instantaneous channel state information (CSI) or terms related to the ICI values via a backhaul link. However, the limited backhaul capacity and delay constraints put a limit on achievable performance when the number of antennas and users grow large or when dealing with a fast fading scenario. In this work, we demonstrate that the ICI terms coupling the coordinating BSs can be approximated using the random matrix theory (RMT) tools when the problem dimensions grow large, and that the approximated ICI values depend only on channels statistics, i.e, spatial load and user specific path loss values. This leads to a significant reduction in the information exchange rate among BSs. Furthermore, processing is simplified because with a fixed approximated ICI values the beamforming vectors can be obtained locally at each BS. The proposed solution guarantees the feasibility of the target signal-to-interference-plus-noise ratios (SINR) without any major loss of performance as compared to the optimal centralized design.

This paper presents the analysis of BER and ergodic capacity for dual-hop orthogonal frequency division multiplexing (OFDM) Amplify-and-forward (AF) relay system with subcarrier mapping (SCM) in Nakagami-m fading channel for m ≤ 1. The performance of capacity-enhancement optimal pairing scheme, i.e., Best-to-Best (BTB) SCM scheme is analyzed for the two special cases of Nakagami-m fading, i.e., one sided Gaussian fading and Rayleigh fading. The exact and approximate closed-form expressions for the moment generating function (MGF) of end-to-end SNR are derived. The upper bound for ergodic capacity is also derived. The simulation results validate the analysis in Nakagami-m fading channel.

Filter Bank Multicarrier (FBMC) systems has drawn interest as an alternative to orthogonal frequency division multiplexing (OFDM), as FBMC offers higher spectral efficiency and less susceptibility to synchronization errors. One drawback in FBMC systems is the presence of inter-carrier-interference (ICI) and inter-symbol-interference (ISI), which degrades the system performance when operating under fading channels. In this work we consider the bit error rate (BER) performance of a multiple input multiple output (MIMO) FBMC system. We evaluate the performance of linear and non-linear transceiver processing techniques, which attempt to mitigate the effect of ICI and ISI in MIMO FBMC systems. Simulation results are presented to evaluate and compare the transceiver processing techniques discussed.

A theoretical framework is presented for the evaluation of sum ergodic rate of a full-duplex underlay device-to-device network, when it shares the uplink resources of a conventional cellular user. The sum-rate of the full-duplex network is compared with a half-duplex network with equivalent radio frequency hardware complexity. Closed-form approximations are derived for the sum ergodic rate of the systems. Furthermore, the sum-rate performances are investigated for the case when a transmit power constraint is imposed on the underlay network to minimize the interference on the cellular network. The analytical results presented can be used as a tool to identify when full-duplex transmissions are viable in underlay device-to-device networks.

By using large point-to-point multiple input multiple output (MIMO), spatial multiplexing of a large number of data streams in wireless communications using millimeter-waves (mm-waves) can be achieved. However, according to the antenna spacing and transmitter-receiver distance, the MIMO channel is likely to be ill-conditioned. In such conditions, highly complex schemes such as the singular value decomposition (SVD) are necessary. In this paper, we propose a new low complexity system called discrete Fourier transform based spatial multiplexing (DFT-SM) with maximum ratio transmission (DFT-SM-MRT). When the DFT-SM scheme alone is used, the data streams are either mapped onto different angles of departures in the case of aligned linear arrays, or mapped onto different orbital angular momentums in the case of aligned circular arrays. Maximum ratio transmission pre-equalizes the channel and compensates for arrays misalignments. Simulation results show that, although the DFT-SM-MRT scheme has a much lower complexity than the SVD scheme, it still achieves large spectral efficiencies and is robust to misalignment and reflection.

End-to-end link performance of an asymmetric and asynchronous dual-hop decode-and-forward (DF) relay system built up using a causal multirate filter bank multicarrier (FBMC) technique operated under Rayleigh fading is presented. Three main performance measures namely bit error rate (BER), outage probability and channel capacity are used for this evaluation and approximate closed-form expressions for them are also made available. FBMC setup is modeled in exact form without any approximations while customizing to one of the most efficient subcarrier filter. Simulations are carried out in quasi-static multipath fading channels under symmetric, asymmetric, synchronous and asynchronous conditions where a minimum distance based receiver is employed for the symbol recovery. Performance characteristics are identified under different noise, interference, channel and synchronization conditions.

This paper proposes linear precoder-decoder schemes for a multiple-input multiple-output (MIMO) underlay device-to-device (D2D) communication system by considering two D2D modes: two-way relaying based D2D and direct D2D. The D2D communication takes place in the same spectrum as the cellular communication. In the two-way relaying based D2D mode, the relay uses physical layer network coding (PNC). The precoder-decoder design is based on minimizing mean square errors (MSE), which is useful to mitigate interference and to improve the performance of both D2D and cellular communications. Distributed and centralized algorithms are proposed considering bi-directional communication in both D2D and cellular communications. In the direct D2D mode, a similar MSE procedure is adopted, and exact solutions are derived for precoder-decoder matrices. In the numerical results, the optimality and convergence properties of the proposed algorithms are analyzed. Additionally, the system performances are investigated with interference thresholds and maximum available power at the nodes. Two transmit mode selection schemes are considered as dynamic and static selection schemes. Finally, these selection schemes are investigated over an XY grid by varying the position of a given device. The results reveal that the PNC two-way relaying based D2D mode extends the coverage area of D2D communication.

We consider a single-macrocell heterogeneous multiple-input multiple-output network, where the macrocell shares the same frequency band with the femto network. The interference power to the macro users from the femto base stations is kept below a threshold to guarantee that the performance of the macro users does not degrade due to the femto network. We consider the problem of finding the set of all achievable power-rate tuples for this setting. We first formulate a two-dimensional vector optimization problem in which we consider maximizing the sum-rate and minimizing the sum-power, subject to maximum power and interference threshold constraints. The considered problem is NP-hard. We provide a method to solve the problem by using the relationship between the weighted sum-rate maximization and weighted-sum-mean-squared-error minimization problems. Furthermore, using the proposed algorithm, we evaluate the impact of imposing interference threshold constraints and the impact of co-channel deployment in heterogeneous networks. The proposed algorithm can be used to evaluate the performance of real heterogeneous networks via off-line numerical simulations.

Antenna correlation is usually viewed as a detrimental effect in a multiple input multiple output (MIMO) system. This paper investigates how this affects the performance of an amplify-and-forward (AF) relay network. We consider multiple antennas at all nodes with a general correlation matrix structure having an arbitrary eigenvalue distribution. We derive exact closed form expression for the ergodic capacity and simplify to special case of distinct eigenvalues. Further, we investigate the system in high signal-to-noise ratio (SNR) and derive a simple asymptotic expression. Our results provide a comprehensive analysis and useful insight about the ergodic capacity of the system.

Orthogonal frequency division multiplexing with offset QAM (OFDM/OQAM) modulation scheme, more commonly known as filter bank multicarrier (FBMC), has received greater attention recently, due to its spectral efficiency compared to standard OFDM. However, intrinsic interference in FBMC causes a negative effect on the system performance depending on the channel condition. Hence, in this paper, we analyze the interference in OFDM/OQAM systems and introduce a preceder based on signal-to-leakage-plus-noise ratio (SLNR) to overcome the effect of interference on the system. First we implement the system in a more efficient way using inverse fast Fourier transform (IFFT). Then based on that model, the precoding matrix is generated. Finally through simulation and analysis, reduction of interference and improvement of performance are investigated.

We propose a multiple-input multiple-output (MIMO) two-way relaying based underlay device-to-device (D2D) communication system, in which relay uses physical layer network coding (PNC). Both cellular and D2D communication take place in the same spectrum. D2D communication is based on PNC, and a joint transceiver design is required to facilitate PNC mapping with MIMO channels. A joint precoder-decoder scheme is also considered in the cellular communication to mitigate interference, and improve the error performance. Mean square errors at nodes are considered as the objective function, and interference threshold limits and power constraints are also used in joint precoder-decoder design problems. These turn out to be non-convex optimization problems, and we propose two algorithms in both phases of D2D and cellular communication. Distributed optimization methods are used in the proposed algorithms. We analyze the convergence of proposed algorithms, and the system performances are investigated with different D2D pair locations, interference constraint thresholds, and maximum available power at nodes.

In this paper, we investigate a robust joint precoder-decoder design scheme for a multiple-input multiple-output (MIMO) physical layer network coding (PNC) based two-way relay system. Precoders at the source nodes and decoder at the relay node are used to facilitate PNC operations during multiple access stage. Both channel estimation error and antenna correlations are used to formulate an optimization problem to minimize weighted mean square error (WMSE) under a total power constraint. The problem becomes non- convex, and we propose an algorithm to solve it optimally. The effect of estimation error, antenna correlation parameters, weighting parameters, and number of antennas at nodes are also considered in the numerical analysis.

he co-channel deployment of a femtocell with a macrocell is considered. We investigate the resource allocation in the downlink with co-channel interference. A novel utility function is introduced as an energy efficiency metric. Two competing objectives (energy minimization and capacity maximization) are included in the utility function in order to balance the network in possible energy savings and to maintain the quality of service of end users. Energy Efficiency = (Sum Energy consumption at the base station)/(Sum rate of the cell). In order to improve the energy efficiency of the entire network, tiers are separately addressed, so that we could avoid a centralized resource allocation scheme. The problem is then converted into a convex optimization formulation with an interference coordination scheme.

This paper presents the derivation for per-tier outage probability of a randomly deployed femtocell network over an existing macrocell network. The channel characteristics of macro user and femto user are addressed by considering different propagation modeling for outdoor and indoor links. Location based outage probability analysis and capacity of the system with outage constraints are used to analyze the system performance. To obtain the simplified expressions, approximations of ratios of Rayleigh random variables (RVs), Rayleigh to log normal RVs and their weighted summations, are derived with the verifications using simulations.

We analyze the performance of a dual-hop relay system with correlated antennas at the source and the destination. Orthogonal space time block coding (OSTBC) is employed at the source. Single-antenna relays with and without direct transmission between the source and the destination are stud- ied. Assuming non-identically distributed Nakagami-m fading channels, closed- form expressions of outage probability, average symbol error rate (SER), and moment generating function (MGF) of the instantaneous end-to-end signal-to-noise ratio (SNR) are derived for the single- relay case. Asymptotic analysis is also performed to obtain the diversity order for the single-relay system. Furthermore, the analysis is extended to obtain expressions for the outage probability and SER for multiple-relay systems. Analytical results are validated with Monte-Carlo simulations using Mathematica and MATLAB programs.

Filter bank multicarrier (FBMC) transmission techniques are being studied and developed as alternatives or extensions for family of orthogonal frequency division multiplexing (OFDM) techniques. Comparatively higher spectral efficiency and capability of having a continuous transmission without guard intervals or cyclic prefixes are among the key inherent attractions. It is attempted to conduct an analysis on self interference of a causal multirate FBMC system supported by one of most stable subcarrier filters. Two types of self interferences under different synchronization conditions are investigated. These are interference generated from a desired symbol to the other symbols and interference generated from any other symbol to a particular symbol where the knowledge of those is highly useful for transmitter and receiver developments leading to an interference free reliable wireless transmission. Probability distribution functions of them are also given. Numerical results for asynchronous mode with offsets in time, frequency, time-frequency and synchronous mode are discussed.

We propose an energy-efficient method to enable the data collection of wireless sensor networks (WSNs) assisted by a cellular network. They use the same radio resources in an underlay manner resulting in bandwidth efficiency improvements. During the sensor data collection (SDC) period, the cellular user functions as a mobile sink node while keeping the normal cellular traffic, and multicarrier code-division multiple-access (MC-CDMA) transmission is utilized in the sensor-to-sink. Since the energy of sensor symbols from MC-CDMA transceiver is small and spread in the frequency domain, it eventually gives only rise to noise-level of each sub-carrier. After formulating the SDC procedure, we analyze the bit error probability of both the cellular traffic and sensor transmission. From the simulation results, the proposed algorithm is proved to be efficient.

Energy efficient beamforming and power control problem is considered for a MISO (multiple input single output) network. We consider an active femtocell within the coverage area of a macrocell. The femto base station is equipped with multi antennas and the users are considered to be single antenna users. A beamforming and power control problem is formulated in order to minimize the energy consumption per bit in the downlink transmission in the femtocell. The objective function is introduced as “sum power/sum rate” which has the unit J/bit to measure the energy efficiency of the network. The problem is non-convex. We introduce a novel method to solve this problem with an approximation. We show that the problem can be solved with convex optimization techniques which has more practical interest even though the solution is suboptimal. In order to measure the energy efficiency we apply an existing power model which considers the total energy consumption of a base station. Thus we expect that our solution indicates realistic energy consumption measurements. Then we introduce a beamforming and power control algorithm which minimizes the energy consumption per bit transmission. Finally, the behavior of the objective function is observed in different antenna configurations by varying the user density in different channel environments.

We investigate a joint precoder decoder design scheme for multiple-input multiple-output (MIMO) channel physical layer network coding (PNC) based two-way relay system. Precoders at source nodes and decoder at relay node are designed to facilitate PNC operations at the relay node. We consider minimizing the weighted mean square error (MSE) at relay node to enhance the accuracy of the PNC. Formulated optimization problem is non-convex and we propose an algorithm to solve it optimally. Numerical results confirm that the joint precoder decoder algorithm provides the optimal solution to minimizing weighted MSE with the total available power. Effect of weighting parameters, relay location and number of antennas at nodes are considered in the system analysis.

This paper studies the effect of feedback delay on the performance of amplify-and-forward (AF) relay selection over Nakagami-m fading environment. Spatial diversity can be improved by employing multiple relays with selection, however, this diversity gain cannot be fully realized when the feedback delay is present in the decision metric. Hence, we try to quantify this detrimental effect by deriving the exact closed form solution to outage probability and ergodic capacity for channel state information (CSI)-assisted relay. Further, we derive simple asymptotic results for outage to provide insight of the system performance and diversity. Monte Carlo simulation is used to verify the analytical work.

In this paper, we propose a method to connect a group of wireless sensors to LTE/LTE-A system by using the cellular users as mobile relays, with the basic principle of maintaining the normal traffic between the cellular users and eNodeB during the sensor data collection. The cellular spectrum is re-used on sensor-to-mobile relay link without employing additional frequency resources. Multi- carrier CDMA methods are suspected to be utilized by sensor nodes, where data of each sensor is spread to the whole re-used cellular spectrum to create a low data rate transmission. In order to avoid additional complex receiver on cellular terminals, the simple amplify-and-forward relaying scheme is used at mobile relays. Since eNodeB can observe the cellular traffic components in the re-used spectrum exactly, it has the capability of canceling them in the received overlapped signals, and detecting the sensor data by using advanced multi-user detection methods. Finally, the end-to-end outage probability is analyzed and a closed-form approximation is derived. The numerical results show that the approximate closed-form equation is significant and that the proposed scheme can be used to collect sensor data in LTE/LTE-A networks.

We investigate error performance and an optimum power allocation scheme for multiple-input multiple-output (MIMO) channel physical layer network coding (PNC) based two-way relay system. Zero forcing precoding technique is used at source nodes to facilitate PNC operations at the relay node. First, system error performance is investigated by introducing upper and lower bounds for BPSK modulation system. They are validated with numerical results and bounds provide accurate results at high SNR regime. Then, we consider sum rate maximization under a total power constraint to obtain the optimum power allocation scheme. Analytical solutions are derived and compared with other possible schemes, which are suboptimal. Numerical results confirm that the power allocation scheme provides the optimal solution for the achievable sum rate with the total available power and the relay position.

This paper investigates the performance of amplify-and-forward (AF) two-way relay system with antenna correlation. Overall outage probability (OOP), average symbol error rate (SER) and ergodic capacity are analyzed. By considering general correlation structure with arbitrary eigenvalue distribution, we derive a tight lower bound for OOP and use it to evaluate average SER and ergodic capacity. Moreover, the system is studied in high signal-to-noise ratio (SNR) to obtain the insightful behavior of the performance and diversity gain. Finally Monte Carlo simulations are conducted to verify the accuracy of the results.

The concept of relay transmission is applied to Land Mobile Satellite (LMS) communication where a terrestrial relay node forwards information from the satellite to the mobile unit. Two hop Amplify and Forward (AF) and Decode and Forward (DF) cases are considered where source, relay and destination, all are equipped with multiple antennas, using Maximal Ratio Transmission (MRT)-Selection Combining (SC) diversity technique for the first hop and Maximal Ratio Combining (MRC) for the second hop. We present analysis of both AF and DF in terms of outage probability and Symbol Error Rate (SER) for which closed form expressions are derived and show that DF always outperforms AF. Results are illustrated through several examples in order to assess the impact of multiple antennas at the nodes and shadowing severity parameter m on the performance.

We present a unified performance analysis of beamforming, for the system in which the source and the destination are both equipped with multiple antennas while the relay is equipped with a single antenna. By assuming general correlation structures with arbitrary eigenvalue multiplicities at the source and the destination, the exact outage probability for fixed gain relaying is derived. As a result, our new results unify all previously reported cases as well as new additional ones. Furthermore we derive the exact outage probability for TAS(Transmitter Antenna Selection)/MRC system and provide a performance comparison of the two systems. To gain insights, we have also presented high signal-to-noise ratio expressions which clearly show the impact of number of transmit/receive antennas and correlation on the outage probability. Finally, Monte Carlo simulations are presented to verify the correctness of the analytical results.

In this paper, we consider an optimal power allocation scheme for a physical layer network coding relay based secondary user (SU) communication in cognitive radio networks. SUs are located on two different primary user (PU) coverage areas and we introduce an energy and spectrally efficient SU communication scheme. The sum rate of relay based two way communications for SUs is maximized under a total power constraint and interference power threshold (IPT) constraints to PUs. The optimal power allocation schemes are illustrated for different cases of SUs and relay powers. Numerical results confirm that the power allocation scheme provides the optimal solution for the achievable sum rate and the IPT constraints have an effect on the sum rate variation with the total available power.

Inter carrier interference (ICI) is deemed as a critical issue in orthogonal frequency division multiple access (OFDMA) systems. Though ICI caused by Doppler spread is negligible in femtocell systems, frequent reception of signals over misaligned subcarriers will introduce severe ICI. Successive interference cancellation and decision feedback equalizers are some of the techniques proposed to mitigate ICI. However, most of these methods are highly complex, and thus, not suitable for commercial femtocell systems. In this paper, a simple equalization technique to reduce ICI in OFDMA based femtocell networks is proposed. It has been designed particularly considering the femtocell system's operating environment and their limited computational capacity. This technique provides excellent ICI reduction performance for smaller delay spreads of the users.

This paper addresses the problem of power allocation of the downlink in cognitive radio networks (CRNs). In a CRN the channel availability for the secondary users (SUs) is a random process determined by primary user (PU) behavior. In an overlay CRN where the PU has the priority and CRN may use only the idle channels, CRN loses the symbols that are being transmitted during that time slot when the PU takes the channel and if the error correction code cannot handle the length of symbol loss. Further, in the next time slot, the CRN has to select again a free channel to continue the communication or in the event that all channels are occupied by PUs and other SUs it enters an indefinite waiting process. Thus it is intuitive that the time averaged data rate of the CRN does not only depend on the channel characteristics (governed by Shannon's law) but also on the channels' PU activity level. CRNs, with the spectrum sensing capabilities inherent to them, have the ability to characterize those activity statistics. In this paper, we propose two schemes named ENPA (equivalent noise of PU activity) and EBPA (effective bandwidth of PU activity), which take into account the PU activity in CRN downlink power allocation. These schemes are shown to achieve higher average rates than the water-filling algorithm which is optimal for a linear filter channel.

In our study we have analyzed the interference, modeled as a Gamma variable, at the primary receiver by a cognitive radio adhoc network distributed in a Poisson point process (PPP) with relay assisted primary communication link. We have shown through the density functions that the signal to interference ratio (SIR) concentrates at high values with an increased number of relays leading to better performance. Outage probability, symbol error rate (SER) and ergodic capacity are analyzed to take more insight of the system and it is further shown that the best performance increase can be obtained with a single relay.

This paper presents the performance analysis of multi-user scheduling for channel state information (CSI)-assisted and fixed gain AF relaying with antenna correlation. We consider a source equipped with multiple correlated antennas communicating with multiple users via a relay which has dual correlated antennas. The system performance degrades with the spatial correlation. Hence we derive the exact closed form solutions to outage probability, average symbol error rate (SER) and generalized higher moment of the signal-to-noise ratio (SNR) to quantify this detrimental effect. The ergodic capacity is also analyzed. To understand the insight system performance behavior and the diversity gain, we present a high SNR analysis. The analytical results are verified with the Monte Carlo simulations at the end.

This paper investigates orthogonal space-time block coded transmission for a multiple-input multiple-output (MIMO) channel with non-coherent amplify-and-forward relaying in a situation where the source-relay and relay-destination channels undergo Rayleigh and Rician fading respectively. Fading coefficients of the Rician channel are independent but not necessarily identically distributed. We derive exact expressions for the moment generating function, first and second moments of the instantaneous signal-to-noise ratio at the destination to statistically characterize the system behavior. We then analyze the system performance by deriving new analytical expressions for the bit error rate and amount of fading. These performance metrics reveal that strong line of sight components between relay-destination link always limit the performance promised by MIMO scattering environment when both nodes have multiple antennas.

A new system model is introduced for spectrum sharing in a multi-operator environment. The infrastructure based cognitive radio network operates in underlay/overlay (hybrid) transmission mode sharing the spectrum of licensed users. Primary user networks (PUNs) are benefitted by the relaying capability of the cognitive radio base station so that their service degraded users due to low coverage, can enhance the performance. The cognitive radio network (CRN) also helps itself by transmitting its own data along with the primary data on the same band, while agreeing to maintain the signal to interference plus noise ratios of primary users' at a predetermined level. CRN operates in an underlay mode with an imposed interference threshold. In addition, maximizing the secondary user capacity by strategically changing the available frequencies from time slot to time slot is discussed with the introduction of two primary data relaying schemes (asynchronous / synchronous). The objective of the secondary user capacity maximization is reformulated into a linear programming problem with interference cancellation techniques. Power allocation for all the active users and the capacity of the secondary user is analyzed in both of the schemes.

In this paper, we consider the energy detection of primary user in MIMO cognitive relay scenario. The system consists of a single relay which is assigned by the cognitive radio network to transmit the amplified version of primary user signal to a cognitive coordinator. The source has a single antenna, a MIMO system with transmit antenna selection is used at the relay. The cognitive coordinator is equipped with multiple antennas for maximal ratio combining receiver. The performance of energy detector is analyzed for independent Nakagami-m fading channels. We derive closed form expression of moment generating function of overall received SNR. False alarm and average detection probabilities are derived theoretically without direct link between the primary user and the cognitive coordinator. Our analytical results are confirmed through comparison with Monte Carlo simulations.

A comprehensive performance analysis of the energy detector over fading channels with single antenna reception or with antenna diversity reception is developed. For the no-diversity case and for the maximal ratio combining (MRC) diversity case, with either Nakagami-m or Rician fading, expressions for the probability of detection are derived by using the moment generating function (MGF) method and probability density function (PDF) method. The former, which avoids some difficulties of the latter, uses a contour integral representation of the Marcum-Q function. For the equal gain combining (EGC) diversity case, with Nakagami-m fading, expressions for the probability of detection are derived for the cases L =2,3,4 and L >; 4, where L is the number of diversity branches. For the selection combining (SC) diversity, with Nakagami-m fading, expressions for the probability of detection are derived for the cases L =2 and L >; 2. A discussion on the comparison between MGF and PDF methods is presented. We also derive several series truncation error bounds that allow series termination with a finite number of terms for a given figure of accuracy. These results help quantify and understand the achievable improvement in the energy detector's performance with diversity reception. Numerical and simulation results are also provided.

We present a unified performance analysis of a system in which the source and the destination are equipped with multiple antennas and communicating via a single antenna relay. Our studies can be divided into two parts. First we consider a system with maximal ratio transmission (MRT) at the source and maximal ratio combining (MRC) at the destination by assuming general correlation structures with arbitrary eigenvalue multiplicities at the source and the destination. Then a system with transmit antenna selection (TAS) at the source with uncorrelated antennas and MRC at the destination with correlated antennas is investigated. The exact closed form expressions for outage probability, average symbol error rate (SER), generalized higher moments of SNR for both channel state information (CSI)-assisted and fixed gain relaying are derived and an analysis of the ergodic capacity is provided. Hence, our new results cover several previously reported cases as well as new additional ones. Further, we present the asymptotic analysis which gives an insight of the system performance and the diversity gain in each case. To verify the analytical results we provide Monte Carlo simulations at the end.

The cooperation among cognitive users (CUs) is considered as an efficient means to improve the primary user (PU) sensing in cognitive radio environment. Almost all previous related works employ the cooperation among CUs for PU activity detection whereas in this paper, the notions of cooperation among PUs and CUs are applied. This is achieved by allocating the available PU power among OFDMA subcarriers with two objectives 1. Guaranteed spectral efficiency to PU 2. Maximize detection of PU presence to a CU. The dynamic nature of channel parameters significantly improves the detection probability of CU with a minor reduction in PU's spectral efficiency. Analysis shows that a reduction of 5% spectral efficiency could be utilized to gain more than 15% in terms of the correct detection probability. The convexity of the formulated problem is analyzed. Finally, we propose deploying our scheme with CU collaborative sensing schemes to improve the detection probability in the low signal-to-noise ratio regions.

In this paper, we study the optimal power allocation of wireless relay nodes which are used in the secondary user (SU) communication of a cognitive radio (CR) network. We consider the behavior of transmitting powers of SUs where those powers are limited to the tolerable interference as seen by the primary user (PU) communication. To improve the performance of the secondary communication based on minimizing the outage probability, we re-formulate the power allocation problem with a new set of constraints. These are obtained by considering the co-channel interference generated by the SU communication to the PU communication. The power allocation problem is solved for both regenerative and non regenerative relay models under Rayleigh fading conditions. SU communication with N number of relays is discussed and compared. The outage probability of SU communication is limited by the interference power threshold (IPT) constraints of PUs and is affected significantly by the IPT levels of PUs.

Coexistence of a cognitive radio network (CRN) with a primary user network (PUN) using spectrum underlay communication is modeled as a non-cooperative power and rate control game (NPRG). Main objective is to maximize the utility of each secondary user (SU) making them more energy efficient. Quality of Service (QoS) levels of SUs are guaranteed by max-min fair signal to interference plus noise ratio (SINR) concept while maintaining QoS level of the primary user (PU), thus resulting in reliable operation by reducing the sum interference. Further, the applicability of the proposed novel pricing scheme and the algorithm is discussed with respect to a heterogeneous network with proper power and rate controlling. By reducing the sum power of SUs we explore the survival of cognitive users in a lowered interference threshold imposed by the primary network. The existence and uniqueness of Nash equilibrium and its Pareto efficiency is discussed. A novel power and rate control algorithm with a new pricing scheme is introduced to reach the Nash Equilibrium (NE). By using a common platform for rates and utilities, existing algorithms are compared through numerical analysis.

In this paper the performance of bit-interleaved coded modulation with iterative decoding (BICM-ID) based multiple-relay cooperative system is analyzed over Nakagami-$m$ fading channels. The relays are operating in amplify-and-forward (AF) mode. The moment generating function (MGF) based approach is used to obtain the theoretical error-free feedback (EF) bounds. The results show that a significant gain is obtained in the performance of the system due to cooperative and code diversities by introducing cooperating relays and bit-interleaved coded modulation with iterative decoding. The performance of bit-interleaved low-density parity-check coded modulation with iterative decoding (BILDPCM-ID) based cooperative system, using low-density parity-check (LDPC) code instead of convolutional code, is also analyzed in term of bit-error rate (BER). The performance comparisons of BICM-ID and BILDPCM-ID based systems are shown using the set partitioning (SP) labelling for 8PSK modulation scheme. The BER curves are obtained by performing the Monte Carlo simulation.

In this paper, we derive exact closed form bit error rate (BER) or symbol error rate (SER) expressions for orthogonal frequency division multiplexing (OFDM) systems with carrier frequency offset (CFO). We consider the performance of an OFDM system subject to CFO error in additive white Gaussian noise (AWGN), frequency flat and frequency selective Rayleigh fading channels. The BER/ SER performances of BPSK and QPSK modulation schemes are analyzed for AWGN and frequency-flat Rayleigh fading channels while BPSK is considered for frequency-selective Rayleigh fading channels. Our results can easily be reduced to the respective analytical error rate expressions for the OFDM systems without CFO error. Furthermore, the simulation results are provided to verify the accuracy of the new error rate expressions.

This paper derives a new infinite series representation for the trivariate non-central chi-squared distribution when the underlying correlated Gaussian variables have a tridiagonal form of an inverse covariance matrix. The joint probability density function is derived using Miller's approach and Dougall's identity. Moreover, the trivariate cumulative distribution function (cdf) and characteristic function (chf) are also derived. Finally, the bivariate non-central chi-squared distribution and some known forms are shown to be special cases of the more general distribution. However, the derivation of non-central chi-squared distribution for an arbitrary covariance matrix seems intractable via Miller's approach. Two applications of the newly derived results are provided for performance analysis of multiple input multiple output (MIMO) systems with transmit antenna selection over a correlated Rician fading environment. Some numerical results are also presented to verify the accuracy of the analytical expressions.

Probability density functions (pdf's) are derived for the phase and amplitude (envelope) of the complex gain X +jY (j = radic-1), where X and Y are two correlated non zero-mean Gaussian random variables. The pdf of the amplitude is derived as an infinite series, but reduces to a closed-form expression when the means are zero. The classical Rayleigh and Rician pdf's turn out to be special cases of the derived pdf. This pdf is used to analyze the error performance of non-coherent binary frequency shift keying (BFSK) with in-phase/quadrature(I/Q) imbalance over an additive white Gaussian noise (AWGN) channel. The resulting bit error rate (BER) expression is derived as an infinite series. The analytical expressions are validated by simulation, and the I/Q imbalance related performance degradation is quantified. Convergence of the PDF series and the BER series is established.

In this paper, we derive closed form bit error rate (BER) expressions for orthogonal frequency division multiplexing (OFDM) systems with residual carrier frequency offset (CFO). Most of the published work treats CFO as a nonrandom parameter. But in our study we consider it as a random parameter. The BER performance of binary phase shift keying (BPSK) OFDM system is analyzed in the cases of additive white Gaussian noise (AWGN), frequency-flat and frequency-selective Rayleigh fading channels.We further discuss how these expressions can be related to systems with practical estimators. The simulation results are provided to verify the accuracy of these error rate expressions.

This paper addresses the problem of energy detection of unknown deterministic signal of a primary user in a cognitive radio environment. As an extension to the previous works, we focus on equal gain combining technique when the wireless channel is modeled as Nakagami-m. We derive series form exact expressions for probability of detection and false alarm when the number of diversity branches are 1, 2, 3 and L ges 4. Finally, performance variation is shown against the number of diversity branches and the time bandwidth product in decision statistic with the aid of numerical results.

In this paper, we examine a general time-division multiple-access (TDMA) cooperative protocol. The wireless network consists of a source terminal, a destination terminal and n - number of intermediate relay terminals that operate in amplify-and-forward (AF) mode with a fixed-gain. Starting with the moment generating function (MGF) of the end-to-end output signal-to-noise ratio (SNR), the symbol error rate (SER) performance of M-ary phase shift keying (M-PSK) modulation schemes is theoretically investigated over independent and identical (i.i.d) Nakagami-m fading environments with maximal ratio combining (MRC) scheme at the destination. Subsequently, numerical and simulation results are provided to verify the accuracy of the formulation.

In this paper, we derive closed-form symbol error rate (SER) expressions for orthogonal frequency division multiplexing (OFDM) systems with residual carrier frequency offset (CFO). We treat CFO/residual CFO as a random parameter in this study. In particular, we consider channel-independent as well as channel-dependent random residual CFOs. We derive SER expressions for 4-quadrature amplitude modulation (4-QAM) OFDM systems in the cases of additive white Gaussian noise (AWGN) and frequency flat Rayleigh fading channels. The simulation results are provided to verify the accuracy of the new SER expressions.

An exact expression for the joint density of three correlated Rician variables is not available in the open literature. In this letter, we derive new infinite series representations for the trivariate Rician probability density function (pdf) and the joint cumulative distribution function (cdf). Our results are limited to the case where the inverse covariance matrix is tridiagonal. This case seems the most general one that is tractable with Miller¿s approach and cannot be extended to more than three Rician variables. The outage probability of triple branch selective combining (SC) receiver over correlated Rician channels is presented as an application of the density function.

This paper addresses preamble-based low complexity synchronization, channel estimation and equalization for Zero-padded (ZP) MB-OFDM based UWB systems. The proposed synchronization method consists of sync detection, coarse timing estimation, fine timing estimation, and oscillator frequency offset estimation. The distinctive features of MB-OFDM systems and the interplay between the timing and carrier frequency hopping at the receiver are judiciously incorporated in the proposed synchronization method. In order to apply the low complexity one-tap frequency-domain equalizer, the required circular convolution property of the received signal is obtained by means of an overlap-add method after the frequency offset compensation. The proposed low complexity channel estimator for each band is developed by first averaging the over-lapadded received preamble symbols within the same band and then applying time-domain least-squares method followed by the discrete Fourier transform. We develop an MMSE equalizer and its approximate version with low complexity. We also derive the probability density functions of the UWB channel path delays, and using them we present several optimization criteria for our proposed synchronization, channel estimation, and equalization. The effectiveness of our proposed methods and optimization criteria are confirmed by computer simulation results.

In this paper, we derive new infinite series representations for the quadrivariate Nakagami-m distribution and cumulative distribution functions (cdf). we make use of the Miller's approach and the Dougall's identity to derive the joint density function. The classical joint density function of exponentially correlated Nakagami-m variables can be identified as a special case of our joint density function. Our results are based on the most general arbitrary correlation matrix possible. Moreover, the trivariate density function and cdf for an arbitrary correlation matrix is also derived from our main result. Bounds on the error resulting from truncation of the infinite series are also presented. Finally, numerical results are presented to verify the accuracy of our formulation.

This paper presents a preamble-based low complexity synchronization method for MB-OFDM based UWB systems. The proposed synchronization method consists of sync detection, coarse timing estimation, fine timing estimation, and oscillator frequency offset estimation. The distinctive features of MB-OFDM systems and the interplay between the timing and carrier frequency hopping at the receiver are judiciously incorporated in the proposed synchronization method. We also derive the probability density functions (pdfs) of the delays of the UWB channel paths which are used in optimizing our synchronization method. These pdfs are also useful in evaluating or optimizing the energy capture of other UWB systems. The effectiveness of our proposed synchronization method and optimization criteria are confirmed by computer simulation results.

In this paper, we derive a new infinite series representation for the trivariate non-central chi-squared distribution when the underlying correlated Gaussian variables have tridiagonal form of inverse covariance matrix. We make use of the Miller's approach and the Dougall's identity to derive the joint density function. Moreover, the trivariate cumulative distribution function (cdf) and characteristic function (chf) are also derived. Finally, bivariate noncentral chi-squared distribution and some known forms are shown to be special cases of the more general distribution. However, non-central chi-squared distribution for an arbitrary covariance matrix seems intractable with the Miller's approach.

In this paper, we derive closed form bit error rate (BER) expressions for orthogonal frequency division multiplexing (OFDM) systems with residual carrier frequency offset (CFO). Several papers have been written treating CFO as a nonrandom parameter where as we consider it as a random variable in this study. The BER performance of binary phase shift keying (BPSK) OFDM system is analyzed in the cases of additive white Gaussian noise (AWGN), frequency flat and frequency selective channels. We further derive an expression for the BER in flat fading Rayleigh channels without the perfect channel state information (CSI) at the receiver. The simulation results are provided to verify the accuracy of the new BER expressions.

In this paper, we investigate the classical problem of finding the probability density function (pdf) of the sum of Nakagami-mrandom variables. Exact infinite series representations are derived for the sum of three and four identically and independently distributed (i.i.d.) Nakagami-mrandom variables, and subsequently, it is extended to the sum of general number of random variables. A useful pattern emerged as a result of these extensions, and a new Fourier transform pair that involves parabolic cylinder function and Gauss hypergeometric function is obtained. Bounds on the error resulting from truncation of the infinite series of pdfs are also presented. These pdfs are used to analyze the performance of dual, triple, and quadruple branch predetection equal gain combining (EGC) receivers over Nakagami-mfading environment. Furthermore, a hypergeometric relation is derived as a result of those derivations. Subsequently, the analysis is extended to the case of L branch EGC receiver performance as well. Selected simulation plots are provided to compare the results with the approximate results available in the literature and to illustrate the validity of the formulation.

In this paper, using Miller's approach and Dougall's identity, we derive new infinite series representations for the quadrivariate Nakagami-m joint density function, cumulative distribution function (cdf) and characteristic functions (chf). The classical joint density function of exponentially correlated Nakagami-m variables can be identified as a special case of the joint density function obtained here. Our results are based on the most general arbitrary correlation matrix possible. Moreover, the trivariate density function, cdf and chf for an arbitrary correlation matrix are also derived from our main result. Bounds on the series truncation error are also presented. Finally, we develop several representative applications: the outage probability of triple branch selection combining (SC), the moments of the equal gain combining (EGC) output signal to noise ratio (SNR) and the moment generation function of the generalized SC(2,3) output SNR in an arbitrarily correlated Nakagami-m environment. Simulation results are also presented to verify the accuracy of our theoretical results.

In this paper, we investigate the classical problem of finding the probability density function of the sum of Nakagami-m random variables. An exact infinite series formula is derived for the sum of three identically and independently distributed (i.i.d.) Nakagami-m random variables and subsquently it is extended to the sum of four and in general M number of random variables. A detailed discussion on the cumulative density function (cdf) of the sum of three Nakagami-m variables is also given. The newly derived probability density functions are used to analyze the performance of dual and triple predetection equal gain combining (EGC) receiver over Nakagami-m fading environment. Selected numerical plots are provided to compare the results with the approximate results available in the literature and to illustrate the validity of the results.

In this paper, we present a novel distributed video coding algorithm based on turbo trellis coded modulation (TTCM). As in the conventional turbo based Slepian-Wolf encoder, quantised information is applied to the TTCM encoder and parity bits are generated from both constituent encoders. However, TTCM symbols are not generated at the encoder since they are not sent to the decoder. Parity bits produced by the TTCM encoder are stored in a buffer and transmitted to the decoder upon request. TTCM symbols are generated at the decoder and these symbols are passed to the TTCM decoder for demodulation. Experimental results show that the proposed TTCM based codec can improve the PSNR by up to 6 dB at the same bit rate with less memory compared to the turbo coded distributed video codecs.

Mobile Ad hoc networks (MANETs) are susceptible to several attacks due to their general infrastructureless scenario, dynamic topology and other characteristics. General security mechanisms that exist in wired network cannot be applied efficiently in MANET. In the current literature, there are many proposals for intrusion detection system (IDS) in MANET as a second defense. However, all such IDS suffer from performance penalties and high false alarm rates. In this paper we propose a collaborative IDS for MANET using Bayesian method. We use a set of very useful features which guarantee the effectiveness of the IDS and the Bayesian method improves the efficiency in the detection procedure. We expect it to be more versatile than other existing collaborative methods. Considering the protocol-independence of our method, it may be applied in any Ad hoc scenario.

Exact expression for the joint density of the trivariate Rician variables is not available in the open literature. In this paper, we derive new infinite series representations for the joint probability density function (pdf) and the joint cumulative distribution function (cdf) of three correlated Rician random variables. The cdf and chf of bivariate Rician density are also derived as special cases of the trivariate case. The most celebrated Millers approach is used here in deriving the trivariate Rician distribution . Bounds on the error resulting from truncating the infinite series are also presented.

Multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) techniques are applied to multiband ultrawideband (UWB) systems to obtain high rate communications over indoor wireless channels. In this paper, we setup a general scenario to investigate the performance of MIMO-OFDM multiband UWB systems without specific coding schemes. We also derive a lower bound of pairwise-error probability (PEP) of an (KNtNrL) system (K bandhopping, Nttransmit antennas, Nrreceive antennas and L paths combined) in case of lognormal fading channel.

We investigate the pairwise error probability (PEP) bounds for space-time trellis codes (STTC) in Nakagami-m fading channels with the arbitrary fading severity parameter, m. We derive an analytical expression for PEP upper bound in the case of two transmit antennas in terms of the higher transcendental functions, such as the Appell hypergeometric function or the Lauricella function, over uncorrelated and correlated Nakagami-m fading channels. Furthermore, we show that the probability bound for the general multiple transmit antenna scenario can also be expressed as an integral involving nested summations.

In this paper, a new procedure for generating a mesh structure of image representation has been proposed. The proposed algorithm employs a feature map extraction with a specific threshold level to place initial nodes densely in regions that contain high frequency features and nodes are placed coarsely in smooth regions. After that, many insignificant nodes are removed using node elimination scheme. The proposed algorithm is well suited for usage at very low bit-rate image and video coding as processing results have demonstrated that it provides a good subjective and objective image quality at a lower number of required nodes. Moreover, comparison shows that the proposed method provides comparable image quality while requiring fewer mesh nodes than the several existing methods.

This paper investigated a reduced complexity iterative multiuser detector based on sequential sequence estimation technique for joint detection and decoding of asynchronous code division multiple access (CDMA) signals over frequency-selective fading channels. The soft-input soft-output (SISO) multiuser detector is realized by the soft-output sequential algorithm (SOSA), previously introduced for turbo equalization systems. The advantage of this scheme comes from the low complexity of the sequential algorithm, which is only linearly dependent on the number of users. Simulation results show that the iterative sequential multiuser detector can achieve a significant performance gain over the conventional single-user detector as well as the multistage interference cancellation (MIC) receiver, and that it suffers a small amount of degradation compared with the single user performance.

Self intercarrier interference cancellation (ICI), a half rate repetition coding scheme, is a simple and efficient technique to reduce frequency errors in OFDM. However at higher fractional frequency offset values, normalized to subcarrier separation, the self ICI cancellation scheme can no longer provide the same performance. Further due to the simple structure of repetition code, the self ICI cancellation scheme alone cannot provide much immunity to channel interference. In this paper we combine a maximum likelihood estimation technique with that scheme to get better immunity for frequency errors at higher fractional frequency offset values. This paper also discusses the possibility of application of a bandwidth efficient coding scheme for the considered cancellation scheme, to obtain the immunity to channel interference without further reducing bandwidth efficiency.

In this paper we investigate a block-level partitioning scheme suitable for ITU-T H.263 compressed video streams. The compressed video stream is split into two partitions and they are assigned different degrees of error protection for each depending on their importance. The first partition contains the source significant information such as header information and first few variable length codes (VLC) per block depending on the type of the picture. The rest of the VLCs in each block are transferred to the second partition. Convolutional coding is considered to provide unequal error protection (UEP) for the two partitions. The performance of the proposed scheme is evaluated over a correlated frequency selective channel closely matched to wideband code division multiple access (W-CDMA) down link parameters. Simulation results show an improvement in video quality compared to an equal error protection scheme and a reference scheme, without bandwidth expansion.

The performance of the receiver in digital mobile radio systems can be improved if the equalizer delivers the reliability information to the channel decoder. The soft-output Viterbi algorithm (SOVA) is well known for this application. However, for long channel memory, SOVA becomes impractical due to the exponential increase in computational complexity. On the other hand, the sequential estimation algorithms can be applied since the computational complexity is independent of the channel memory. This paper presents a soft-output sequential algorithm (SOSA) for estimation of signal transmitted over frequency selective fading channel. SOSA generates the soft-output information by applying the likelihood post-processing technique after each decoded block of symbols. Simulation results show that SOSA can achieve performance gain over the conventional maximum-likelihood sequence estimation (MLSE) and that its performance approaches SOVA as the signal-to-noise ratio increases. Other possible applications of SOSA are also mentioned.

This paper presents the performance of turbo codes with multilevel modulation over frequency selective Rayleigh fading channel. The method of turbo equalization with 8-PSK modulation proposed by V. Franz and G. Bauch (1999) is modified here for 8-PSK and 16-QAM modulation in order to be applied to turbo codes. The joint turbo equalization and turbo decoding receiver is presented. From simulation results, good BER performance can be achieved with only 3 iterations.

An orthogonal frequency division multiplexing (OFDM) system is sensitive to frequency errors, causing intercarrier interference (ICI) among subcarriers. We analyze the performance degradation of an OFDM system due to frequency offset errors and propose a new ICI cancellation method to reduce the effect of frequency offset errors. The main idea is to map each data symbol a/sub k/ which is to be transmitted onto a pair of non-adjacent subcarriers k and (N-1-k), with weightings +1 and -1, other than to a single subcarrier. The carrier-to-interference ratio (CIR) of the proposed method is better than that of normal OFDM by an amount between 10 dB to 30 dB. This method has another advantage of the frequency diversity effect in the multipath fading channel. This study shows that the proposed method performs better than the previously described self-ICI cancellation method for a small value of frequency offset.

Trellis-coded modulation (TCM) is well-known for achieving coding gain in additive white Gaussian noise (AWGN) channels without sacrificing additional bandwidth. Block-coded modulation (BCM) has been studied as a counterpart to TCM. In this paper, we consider the possibility of applying BCM to a frequency-selective Rayleigh fading channel with soft-output equalizers. A comparison of error performance between TCM and BCM has been made. Our results show that BCM schemes have comparable performance to TCM schemes with less decoding complexity and the relative performance among coding schemes depends on the type of equalizers.

High-speed data communication over channels with limited bandwidth gives rise to intersymbol interference at the receiver usually resulting in a poor receiver error performance. An important parameter that determines the receiver error performance is the minimum Euclidean distance between two received sequences. Channel properties that maximize this distance are investigated. It is shown that maximum distance channels exist for any length of interference.

Turbo trellis-coded modulation (T-TCM) is one of several techniques for combining turbo codes with multilevel modulation in order to save the bandwidth of the transmission systems. T-TCM was shown to perform very well on AWGN channels, but its performance over frequency-selective fading channels is unknown. In this paper, a system model to investigate the performance of T-TCM schemes over frequency-selective fading channels is proposed. Simulation results show that when an appropriate receiver is available, T-TCM also performs well on frequency-selective fading channels. T-TCM performance is much better than any other coded-modulation scheme for this type of channel.

Orthogonal frequency division multiplexing (OFDM) is a robust transmission technique in a Raleigh fading and Rician fading environments. The growing interest in OFDM research motivated us to propose OFDM for satellite communications. In this paper, trellis coded modulation (TCM) and turbo coding are considered with OFDM for a Rician fading channel according to the specifications given by the Iridium system. Bit error rat (BER) performances are analyzed with L and Ka bands. Turbo coded OFDM gives the best performance compared to convolutional coded and trellis coded OFDM systems. Peak to average power ratio (PAPR) can be reduced approximately by 1.6 dB with the help of partial transmit sequence technique.