ABSTRACT Millimetre wave (mmWave) multiple‐input multiple‐output (MIMO) has emerged as a promising physical layer solution to address traffic demands in beyond 5G wireless communication systems. However, modern signal processing techniques for mmWave hybrid MIMO systems fall short of addressing the performance degradations due to residual transceiver hardware impairments (HIs). This paper thus considers a mmWave hybrid MIMO system with residual HIs. Using Bussgang decomposition, the residual transceiver HIs are modelled as an additive non‐Gaussian noise that severely affects the received pilot and information signals, which makes channel state acquisition challenging. In this context of channel‐estimation over non‐Gaussian noise due to HIs, this work presents a hyperparameter‐free maximum Versoria criterion (MVC)‐based channel estimation technique. In details, the MVC‐based channel‐estimator is rendered hyperparameter‐free by proposing a sampling rule for its spread parameter and a gradient descent‐based optimisation for its shape parameter . Finally, simulations are presented to show the generalisation and scenario‐invariance of the proposed MVC‐based channel‐estimator. The analytical expression for steady‐state misadjustment is also derived and validated by simulations.

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.

With the arrival of sixth-generation communication systems, advanced technologies like reconfigurable intelligent surfaces (RIS), integrated sensing and communication (ISAC), and non-orthogonal multiple access (NOMA) are poised to drive a broad range of Internet of Things (IoT) applications. Integrating ISAC into multi-input single-output (MISO) networks calls for a reassessment of performance metrics such as outage probability and sensing rate. Furthermore, a critical challenge is managing heterogeneous user deployments and dynamically adapting to varying user locations while minimizing interference. To address these challenges, this work proposes an innovative multi-sector RIS framework. By dividing the RIS into independently controlled sectors, the system dynamically selects the sector closest to each user. For downlink transmission, a dual-function base station (BS) utilizes NOMA to serve the user clusters and transmit a sensing signal. The RIS dynamically selects the sector closest to the close-proximity user based on their location. To support this, the proposed approach employs a nearest-sector selection strategy centered around a reference close-proximity user. Closed-form approximations for the outage probability are then derived, assuming a blocked direct link between the BS and the users. This framework also enables ISAC by transmitting sensing signals within the selected sector, facilitating both user communication and target detection. We characterize the sensing by the sensing rate, with results showing that increasing RIS elements in the multi-sector design enhances the sensing rate. Overall, the proposed system demonstrates superior performance over traditional simultaneously transmitting and reflecting (STAR)RIS configurations and space division multiple access (SDMA) systems. In particular, the proposed system achieves a gain of 8dB, 12dB and 2dB over SDMA, conventional RIS and STAR-RIS systems, respectively.

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.

Visible light communication (VLC) is an energy-efficient, green, and low-cost technology for high-speed next-generation communication systems. However, it has been observed that the performance of a VLC-based system is limited due to the light-emitting diode's (LED) nonlinear characteristics, low coverage area, and loss of the VLC signal due to the absence of a direct link between the transmitter and receiver caused by blockages present in the environment. In addition, the secrecy sum rate (SSR) of a VLC system is compromised due to the presence of an eavesdropper. To address the problem of dead zones and low coverage area, an optical simultaneously transmitting and reflecting intelligent surface (OSTAR-RIS) is proposed in the literature. For SSR maximization, the particle swarm optimization (PSO) method is employed and compared with the benchmark exhaustive search method. For implicit channel estimation and direct symbol detection, an LSTM-based algorithm for a nonlinear OSTAR-RIS VLC system is proposed. Simulations indicate superiority of the proposed optimization and implicit channel estimation technique over benchmark schemes.

A reconfigurable intelligent surface (RIS) tames the wireless propagation environment and emerges as a key enabler for beyond fifth-generation communication systems. In this paper, the performance of a wireless-powered Internet-of-Things (IoT) network is studied under the assistance of passive and active RIS. In particular, a nonlinear energy harvesting (EH) model is employed at the IoT devices, which harvest radio frequency energy from a dedicated energy station. The analytical expression of outage probability (OP) is derived in terms of the Meijer-G function over-generalized Nakagami-m fading channels. The asymptotic (high signal-to-noise ratio) OP is also analytically characterized, and the diversity order of the considered network is obtained. Further, sum throughput and energy efficiency expressions are derived for delay-limited and delay-tolerant transmission modes, while the ergodic capacity is derived analytically by employing the Gaussian Chebyshev quadrature approximation. The performance attained under the considered nonlinear EH model is compared to that attained with a traditional linear EH model, which is impractical. Finally, the derived analytical expressions are verified via Monte-Carlo simulations.

In the ever-evolving landscape of next-generation wireless communication systems, the need for high data rates, seamless connectivity, and energy efficiency continues to increase. To meet these demands, the integration of emerging technologies such as cell-free massive multiple-input multiple-output (CFm-MIMO) and reconfigurable intelligent surfaces (RIS) has gained significant attention. This paper presents a comprehensive performance analysis of a downlink active RIS-assisted CFm-MIMO system in the context of user terminal (UT) mobility, emphasizing the critical aspect of constrained fronthaul capacity. The paper employs a rigorous analytical framework to determine the outage performance of the proposed system considering imperfections in the channel state information (CSI). The findings presented in this paper demonstrate the impact of the number of RIS elements ($N$), quantization parameters, UT mobility, scattering models, and imperfect CSI on the outage probability. It is revealed that by increasing the$N$from 8 to 32 will significantly decreases the OP by 99.75%. Moreover, it is interesting to observe that the choice and optimization of quantization parameters remain consistent regardless of RIS presence.

Backscatter communication and non-orthogonal multiple access (NOMA) represent emerging paradigms in wireless communication, offering promising solutions to boost spectral and energy efficiency for energy-constrained devices in 6G networks. This work introduces a tailored symbiotic radio system designed for Internet of Things (IoT) devices, proposing an innovative framework that integrates backscatter-based IoT devices into power-domain NOMA networks. In this system, the primary base station employs NOMA principles to serve both far and near users simultaneously, while IoT devices equipped with backscattering technology transmit their data over the primary signal. The IoT transmitter not only serves the IoT receiver but also enhances the performance of the far user. The paper thoroughly examines outage probabilities for both NOMA and IoT networks. It delves explicitly into the outage probabilities of IoT receivers, considering both perfect and imperfect direct link interference cancellation. The results demonstrate significant performance improvements over traditional orthogonal multiple-access methods. Furthermore, Monte Carlo simulations are employed to validate the accuracy of the theoretical analysis.

The multi-input multi-output (MIMO) millimeter wave (mmWave) is a promising technology for beyond 5G communication systems, which provides high data rates, ultra-low latency, massive connectivity, and high spectral efficiency. However, a typical mmWave communication link is severely impaired by path loss, nonlinear device impairments, and non-Gaussian noise. Furthermore, MIMO mmWave channel exhibits sparseness due to blockage and scattering. The conventional orthogonal matching pursuit (OMP), zero-attracting least mean square (ZA-LMS), and their variants deliver suboptimal performance for nonlinear mmWave systems. To improve the estimation accuracy, we propose a random Fourier features (RFF) based sparsity-aware kernel maximum correntropy (SA-KMC) algorithm, which is robust for MIMO mmWave sparse channel estimation in the presence of nonlinear hardware impairments and non-Gaussian distortions. Simulations indicate that the proposed RFF-SA-MCC algorithm outperforms the existing state-of-art approaches.

Terahertz (THz) wireless communication is a promising technique to meet the rising demand for high-bandwidth services. Propagation of the $\mathbf{T H z}$ waves through the atmosphere is influenced by factors like attenuation, water vapour, weather, turbulence, rain and beam misalignment. Furthermore, performance of a THz link is severely degraded by non-linear hardware impairments introduced by power amplifier (PA). Conventional estimators such as zero-forcing (ZF) and minimum mean squared error (MMSE) fall short of handling beam misalignment, rain attenuation, and non-linearity. To circumvent this limitation, we propose a deep neural network (DNN) based receiver for THz communication systems in the presence of PA non-linearity. Simulation results for bit error rate (BER) performance considering non-linear distortion due to power amplifier indicate that the proposed deep learning (DL) based receiver performs better in terms of BER and is more robust compared to ZF, MMSE and orthogonal matching pursuit (OMP) based receiver.

With the imminent arrival of 6G communication, the relevance of advanced technologies, such as multi-input-multioutput (MIMO), nonorthogonal multiple access (NOMA), reconfigurable intelligent surfaces (RISs), and integrated sensing and communication (ISAC), has become prominent for plethora of Internet of Things (IoT) applications. However, integrating ISAC into a MIMO heterogeneous network (HetNets) necessitates reevaluating network performance in terms of outage probability and ergodic rates. This article introduces a novel analytical framework for evaluating downlink transmissions in MIMO HetNets. The proposed framework considers independent homogeneous Poisson point processes (PPP) for spatial arrangement of the NOMA-enabled base stations (BSs) and users. BS in the tth tier exploits superimposed NOMA signal for target sensing. Active RISs are considered to be distributed with homogeneous PPP and are used to mitigate blockage for user equipments when the direct link from the BSs does not exist. The approximated and asymptotic outage probability expressions are derived for two distinct scenarios: one involving direct transmission from the BS to the typical blocked user and the other entailing transmission via active RIS. Moreover, a practical case of imperfect successive interference cancelation (i-SIC) is considered. The analysis emphasizes the benefits of the proposed active RIS-NOMA compared to conventional orthogonal multiple access HetNets, and valuable insights are drawn by varying the number of RIS elements. Additionally, an increase in the RIS elements significantly improves the proposed active RIS-NOMA outage performance. The approximated expressions of ergodic rates, system throughput and beampattern for the sensing performance are also derived.

Wireless-powered relaying based on backscatter communication has emerged as a viable solution to extend the transmission range of energy-constrained nodes with improved power efficiency. Non-orthogonal multiple access (NOMA), on the other hand, is regarded as a key technology for the next-generation wireless communication system since it offers massive connectivity for ultra-dense networks with improved spectrum efficiency and user fairness. Incorporating backscatter communication into the NOMA system enables more efficient utilization of resources and results in improved performance. We propose backscatter-based coordinated direct and relay transmission with NOMA in this work. The key idea is to backscatter the received downlink signal at the relay to enhance performance of the NOMA user. The proposed system consists of a base station, a strong user, a weak user, and a hybrid backscatter relay. In downlink, the base station communicates directly with the strong user, and with the weak user with the help of a hybrid backscatter relay. The closed-form outage probability (OP) expression are derived for the NOMA users. The asymptotic expression for the OP is also presented. Through Monte-Carlo simulations, the derived analytical expressions are validated.

In this paper, we investigate the performance of a full-duplex (FD) Internet-of-Things (IoT) network with hardware impairments (HIs) over generalized α − μ fading channels, where IoT devices transfer their information to the access point via a simultaneous wireless information and power transfer (SWIPT) enabled relay node. The α − μ fading channel explores the non-linearity of the propagation medium and includes Nakagami-m, Rayleigh, exponential, one-sided Gaussian, and Weibull fading channels as special cases. For SWIPT enabling, we consider a power splitting (PS) receiver at the relay node. Further, we consider a practical scenario where the system suffers from residual self-interference (RSI) due to imperfect self-interference cancellation. We derive the closed-form expressions of outage probability (OP), asymptotic OP, system throughput, and energy efficiency over α − μ fading channels. The diversity order of the considered network at high SNR is obtained. An optimal value of the PS factor is obtained which depicts optimum outage performance. We highlight the impact of HIs, RSI, PS factor, and other system parameters on the performance of the considered network. Finally, Monte-Carlo simulations validate the derived closed-form expressions.

In this paper, the problem of distributed content caching in a small-cell Base Stations (sBSs) wireless network that maximizes the cache hit performance is considered. Most of the existing works consider static demands, however, here, data at each sBS is considered to be correlated across time and sBSs. Federated learning (FL) based caching strategy is proposed which is assumed to be a weighted combination of past caching strategies of the sBS as well as the neighbouring sBSs. A high probability generalization guarantees on the performance of the proposed federated caching strategy is derived. The theoretical guarantee provides following insights on obtaining the caching strategy: (i) run regret minimization at each sBS to obtain a sequence of caching strategies across time, and (ii) maximize an estimate of the bound to obtain a set of weights for the caching strategy which depends on the discrepancy. Theoretical guarantee on the performance of the least recently frequently used (LRFU) caching strategy is derived. Further, FL based heuristic caching algorithm is also proposed. Finally, it is shown through simulations using Movie Lens dataset that the proposed algorithm significantly outperforms the recent online learning algorithms.

Non-orthogonal multiple access (NOMA) is envisioned as the solution for increasing capacity and serving exponentially increasing number of devices (users) in beyond 5G networks. Breaking orthogonality, with NOMA users having different channel gain (preferably a cell edge user (CEU) and a cell center user (CCU)) are selected and served on the same resource block. In urban areas, impact of blockages may limit the system performance. Impact of blockages is more prominent for the CEU because the CCU's close proximity to the base stations (BS) lowers probability of CCU being blocked. In order to overcome performance degradation due to blockages, the research interest have move towards intelligent reflecting surfaces (IRS). One of the prime utilization of IRS is to improve connectivity of the blocked users. Since IRS comprises of low-cost passive elements which reflect a signal incident on them using a certain phase shift, hence, are capable of effectively directing the signal from the BS towards the blocked CEU. Intuitively, the location of IRS from the BS, and from the user should play a vital role in directing the intended signal to the desired user. In the current literature, such phenomenon based on the location of IRS is not considered, and mostly fixed/dedicated IRS are considered for the users, which is not a practical approach, since, the users are mobile. The novel numerical results from the study of proposed NOMA-IRS network suggest strong relation between the distance of IRS from the BS and users on the performance of CEU using NOMA.

The caching of popular contents in a cellular network is known to reduce the data load in the backhaul link, and have been an active area of research. This paper considers the problem of efficient distributed content coded caching in a small-cell Base Station (sBS) wireless network to improve the cache hit performance. The demands at each sBS across time and sBSs is assumed to be correlated, and is unknown. A new weighted (across time and sBS) caching strategy is proposed. A high probability lower bound on the cache hit is derived, which is obtained using the proposed strategy as a function of the cache hit of the optimal caching strategy. The bound is shown to depend on (i) the weighted average of cache hits, (ii) regret, and (iii) the discrepancy across time and sBSs (a measure of correlation of demands across time and sBSs). This provides the following insight on obtaining the caching strategy: (i) find a sequence of caching strategies by running regret minimization across time at each sBS, and (ii) maximize an estimate of the bound to obtain a set of weights. The insight is shown to result in an iterative distributed algorithm to obtain caching strategies at each sBS. The performance of the proposed caching strategy is shown to outperform Least Recently Frequently Used (LRFU) algorithm by a large margin.

Spectrally-efficient full-duplex (FD) non-orthogonal multiple access (NOMA) communications is a promising technology for future wireless communications that superimposes users in the power domain and allows them to simultaneously transmit and receive information from the base station (BS). However, self-interference cancellation in FD is still a major challenge, and thus the concept of virtual full duplex relaying (VFD) has evolved. In this work, without loss of generality, we consider a decode-and-forward (DF) relaying based NOMA transmission with a BS, two near users, and a far user constituting VFD over Nakagami-m fading channels. Framework for the exact and asymptotic outage probability (OP) and ergodic rate (ER) expressions is developed for the considered system model. Further, the impact of fading parameters, along with pathloss, is analyzed for the first time. Accuracy of the derived expressions is verified through rigorous simulations.

Content caching is a key technology driving beyond 5G mobile edge computing and hence, an efficient mechanism is needed to satisfy ultra-reliable low-latency communication. One such mechanism to reduce latency is to use recommendation influenced caching algorithm. To enable a more efficient wireless caching, in this paper, joint optimization of both caching and recommendation is formulated and the influence of the recommendation on the popularity is modelled through a probability transition matrix. To maximize the cache hits, an algorithm is presented to find the optimal joint caching and recommendation actions. Two estimation algorithms namely Point estimation and Bayesian estimation methods are presented. Further, theoretical guarantees are provided on the performance of the algorithm. Finally, simulation results are provided to demonstrate that the proposed algorithm significantly outperforms the existing algorithms in terms of average cache hit rate.

In this paper, we investigate the impact of nonlinear distortion on the overall system outage probability of simultaneous wireless information and power transfer enabled two-way amplify-and-forward relaying network by employing three different nonlinear power amplifier (NLPA) models such as traveling wave tube amplifier, soft envelope limiter, and solid-state power amplifier at the relay node. We consider a time-switching based protocol at the energy-constrained relay node to harvest energy and information transmission. We derive the closed-form expression of the system outage probability by utilizing the selection combining technique at the source nodes over Nakagami-m fading channels. System throughput and energy efficiency of the network are also investigated. The impact of NLPA, threshold data-rate, fading severity, and time-switching factor are highlighted on the network's performance. Finally, the derived analytical results are validated by the Monte Carlo simulations.

Unmanned-aerial-vehicles (UAVs) based communications are envisioned to play an important role in 5G and beyond 5G (B5G) systems. UAV-to-ground communications in urban cities are often characterized by highly dynamic propagation environments that can be described by composite fading channels. Most of the UAV-to-ground systems are based on first order (FO) performance evaluation, however the models based on FO statistics are insufficient for characterization of time variant fading channels. We provide comprehensive mathematical framework for the second order (SO) statistics over double-scattered, double-shadowed (DS-DS) fading channels, modeled as the product of double Nakagami-m (DN) and double inverse Gamma (DIG) random processes (RPs). In particular, we obtained exact mathematical expressions for average fade duration (AFD) and level crossing rate (LCR) of the proposed UAV-to-ground channel model. Moreover, the exact, integral form SO statistical expressions are approximated by Laplace Integration (LI) and exponential LI in order to provide closed form, easily computing mathematical expressions. Numerical results show that approximate and exact results are fitting well, especially for higher output threshold values. The impact of DS-DS fading severities on the SO statistics are well investigated. Furthermore, the proposed method is extended to analyze SO performances for the selection scenario of UAV with the highest signal level from among N-UAVs links.

In this work, we study the effect of a non-linear power amplifier (NLPA) on the performance of simultaneous wireless information and power transfer (SWIPT) enabled two-way relay network with transceiver hardware impairments (HIs). We employ a hybrid receiver at the relay to process information and harvest energy through the radio-frequency signal. we derive the closed-form expressions of outage probability and asymptotic outage probability by using a selection combining technique at the destination node over Nakagami-m fading channels. The diversity order of the system is determined by using the asymptotic outage probability. We also investigate the throughput and energy efficiency of the system. Further, the impact of NLPA, transceiver HIs, fading severity, hybrid receiver, threshold data-rate, and overall system ceiling are highlighted on the network's performance. Finally, the derived closed-form expressions are verified through Monte Carlo simulations.

Simultaneous wireless information and power transfer (SWIPT) plays a vital role in power-constrained cooperative communication by harvesting energy at the relay node from the ambient radio frequency signals while transmitting information to the destination. In this work, we consider SWIPT based overlay device-to-device (D2D) multi-input and multi-output (MIMO) relay communications with outdated channel state information (CSI) overgeneralized Nakagami-m fading channels. Further, transmit antenna selection is employed in choosing the antenna which maximizes the received signal-to-noise ratio at the receiver to reduce the system complexity. Framework for outage probability and asymptotic outage probability for the SWIPT based D2D MIMO relay system with outdated CSI is developed. Impact of energy harvesting and outdated CSI over throughput is analyzed for the considered system model. Simulations are carried out to verify the correctness of the obtained analytical expressions.

Caching popular contents in advance is an important technique to achieve low latency and reduced backhaul congestion in future wireless communication systems. In this article, a multi-cell massive multi-input-multi-output system is considered, where locations of base stations are distributed as a Poisson point process. Assuming probabilistic caching, average success probability (ASP) of the system is derived for a known content popularity (CP) profile, which in practice is time-varying and unknown in advance. Further, modeling CP variations across time as a Markov process, reinforcement Q-learning is employed to learn the optimal content placement strategy to optimize the long-term-discounted ASP and average cache refresh rate. In the Q-learning, the number of Q-updates are large and proportional to the number of states and actions. To reduce the space complexity and update requirements towards scalable Q-learning, two novel (linear and non-linear) function approximations-based Q-learning approaches are proposed, where only a constant (4 and 3 respectively) number of variables need updation, irrespective of the number of states and actions. Convergence of these approximation-based approaches are analyzed. Simulations verify that these approaches converge and successfully learn the similar best content placement, which shows the successful applicability and scalability of the proposed approximated Q-learning schemes.

Nonlinear device characteristics present a severe performance-bottleneck for several upcoming next-generation wireless communication systems and prevent them from delivering high data-rates to the end-users. In this context, reproducing kernel Hilbert space (RKHS) based signal processing methods have gained widespread deployment and have been found to outperform classical polynomial-filtering-based solutions significantly. Furthermore, recent RKHS based techniques that rely on explicit feature-maps called random Fourier features (RFF) have emerged. These techniques alleviate the dependence on learning a dictionary and avoid the computations and errors incurred in dictionary-based learning. However, the performance of existing RKHS based solutions depends on choosing a suitable kernel-width. For the widely-used Gaussian kernel, we propose a methodology of assigning kernel-bandwidths that capitalizes on a stochastic sampling of kernel-widths using an ensemble drawn from a pre-designed probability density function. The technique is found to deliver a comparable convergence/error-rate performance to the scenario when the kernel-width is chosen by brute-force trial and error for tuning it for best performance. The desirable properties of the proposed kernel-sampling technique are supported by analytical proofs and are further highlighted by computer-simulations presented in the form of case studies in the context of next-generation communication systems.

In this work, we investigate the performance of a power domain based downlink decode and forward coordinated-non-orthogonal multiple access (NOMA) system with two users and one relay node. The system is considered to operate in half-duplex mode over a Rayleigh faded channel, where the weak user is heavily shadowed with no direct link. Framework for average symbol rate error (ASER) and asymptotic ASER of generalized M-ary QAM for both weak and strong user is developed. Further, the impact of various power levels over different QAM schemes for the users is analyzed. Furthermore, the impact of symmetric and asymmetric constellations for the users is also considered and their impact over the system is detailed and useful insights drawn. The derived analytical expressions are verified through Monte-Carlo simulations, and compared with non-coordinated NOMA.

With the recent outbreak of COVID-19 and other pandemics, improving the public safety communication is essential for efficient communication in the 5G and beyond wireless communication networks. The key requirements shall be lower delays, improved coordination and efficient resource utilization, to achieve higher efficiency in the network performance in an emergency/pandemic situation. Since deployment costs and scarce resource availability are major constraints in the network functioning, looking forward to a new network solution, a heterogenous network (HetNet) architecture has been proposed in this paper, for an efficient broadcast network set up during emergency situations. This paper proposes a two-tier heterogenous network (HetNet) architecture, with the macro base station (MBS) tier being Tier 1 and the small cell tier (SCT) being Tier 2. Here the SCT is mostly involved in setting up of a public warning communication system. The HetNets also intend to promote device-to-device (D2D) communication links, in case of absence of connectivity to the user via the MBT or the SCT. Use of small cells and D2D links shall improve the overall system performance. Certain research challenges however persist, and are stated in the paper.

Visible light communication (VLC) is eco-friendly and low-cost supplement to the existing radio frequency (RF) based communication system. However, the performance of VLC based system is limited by dispersive characteristics of the VLC channel which leads to inter-symbol-interference (ISI) and inter-carrier-interference (ICI). To alleviate the ISI and ICI, orthogonal time frequency space (OTFS) modulation has been proposed in the literature for both RF and millimeter wave communication systems. However, the performance analysis of OTFS over mobile multipath VLC channels is still an open area to investigate. This paper investigates the performance of OTFS over mobile multipath channels for the VLC system. Simulations performed over the realistic mobile multipath VLC channel indicate that OTFS with message passing detector exhibit better bit error rate over the conventional orthogonal frequency division multiplexing scheme with minimum mean square error based detector.

Terahertz (THz) band communication is considered as a promising technology for 6G wireless technology due to availability of large bandwidth at THz bands. Most receivers for symbol detection need channel state information (CSI), however estimating CSI is complex for THz bands. Hence, in this paper, we design and analyze low complexity THz receiver without considering the CSI for symbol detection. The rate adaptation for THz communication systems is also analysed for the distance attenuation. Since, at THz the performance is sensitive to distance. We consider modified transmitted-reference (MTR) pulsed and optimal lth-order energy detector (OED) based THz communication system. Simulations are carried out to show effectiveness of MTR and OED for pulse-based THz band wireless systems by considering the effect of distance, data rate, and absorption by molecular gases.

In this study, the authors present an analytical approach to evaluate the performance of a two‐way multi‐relay system with direct link using a three‐phase analogue network coding and opportunistic relay selection scheme. Herein, the imperfect channel state information is considered over independent and non‐identically distributed Nakagami‐ m fading channels with integer‐valued fading parameter. For analysis, initially, the closed‐form expressions of cumulative distribution function and moment generating function (MGF) for the considered system are derived. Further, by using the MGF‐based approach, generalised expressions of average symbol error rate (ASER) for higher‐order quadrature amplitude modulation (QAM) schemes such as hexagonal QAM, rectangular QAM, and cross QAM are derived. They then analyse the asymptotic behaviour of ASER expression to evaluate the system's diversity order. Furthermore, a comparative analysis of the ASER performance for various QAM constellations is illustrated and impact of the number of relays, fading parameter, relay location, and channel estimation error are highlighted on the system's performance. Finally, all the analytical results are validated through Monte‐Carlo simulations.

Caching popular contents in advance is an important technique to achieve low latency and reduce the backhaul costs in future wireless communications. Considering a network with base stations distributed as a Poisson point process, optimal content placement caching probabilities are obtained to maximize the average success probability (ASP) for a known content popularity (CP) profile, which in practice is time-varying and unknown in advance. In this paper, we first propose two online prediction (OP) methods for forecasting CP viz., popularity prediction model (PPM) and Grassmannian prediction model (GPM), where the unconstrained coefficients for linear prediction are obtained by solving constrained non-negative least squares. To reduce the higher computational complexity per online round, two online learning (OL) approaches viz., weighted-follow-the-leader and weighted-follow-the-regularized-leader are proposed, inspired by the OP models. In OP, ASP difference (i.e, the gap between the ASP achieved by prediction and that by known content popularity) is bounded, while in OL, sub-linear MSE regret and linear ASP regret bounds are obtained. With MovieLens dataset, simulations verify that OP methods are better for MSE and ASP difference minimization, while the OL approaches perform well for the minimization of the MSE and ASP regrets.

Massive multiple input multiple output (MIMO) is a key enabling technology for 5G and beyond communication systems. However, devising matrix inversion-less symbol detection techniques for uplink massive MIMO systems is a major challenge for the practical feasibility of 5G and beyond communication systems. In this article, we propose an iterative symbol detection algorithm for near-optimal symbol detection in uplink massive MIMO systems with a large number of users. In the proposed algorithm, first, a low complexity initial solution is utilized to compute the residual error. Next, the estimated actual error is refined through an iterative line search method. Simulation results justify the viability of the proposed detection algorithm than several existing massive MIMO detection algorithms, in terms of bit error rate performance with comparable computational complexity.

Color-shift keying (CSK) has emerged as an efficient modulation scheme for visible light communication (VLC) to enhance the data rate, which transmits data imperceptibly by variation of the color emitted by red, green and blue (RGB) light emitting diodes (LEDs). However, performance of the CSK-VLC systems is limited by the crosstalk between the adjacent color channels, which arises due to mismatch between the spectra of the RGB-LEDs and the filters at the receiver. Additionally, the RGB-LEDs' nonlinearity also degrades the overall symbol error rate (SER). In this paper, we propose a novel multivariate kernel least mean square based adaptive nonlinear equalizer for joint mitigation of crosstalk and RGB-LEDs' nonlinearity for CSK modulation scheme based VLC systems. Simulation results indicate that the proposed algorithm exhibits better SER performance as compared to the existing techniques. Analytical expression for SER is also derived for the proposed algorithm and validated by simulations.

Diffusion based molecular communication (DBMC) acts as an indispensable element of nano-networking and Internet of things based applications. In DBMC, bio-molecules serve as information carriers for nanoscale systems. However, performance of the DBMC is limited by the following factors; nonlinear channel impulse response (CIR) of the DBMC which makes the mapping between the received symbols and transmitted symbols nonlinear, inter-symbol-interference (ISI) due to long tail of CIR of the DBMC which degrades the overall bit error rate (BER) performance, and stochastic movement of the molecules which introduces noise at the receiver. Most of the prior works on receiver design for molecular communication (MC) are based on linear detection algorithms, which are sub-optimal for the nonlinear DBMC system. Thus, to improve the performance of MC system, for the first time, we propose adaptive nonlinear receiver for MC based on the kernel least mean square algorithm, in the reproducing kernel Hilbert space. Numerical results show that the proposed nonlinear receiver improves BER performance as compared to the existing linear detection algorithms-based receiver.

In this work, a two-way multi-relay system using three phase analog network coding (3P-ANC) with opportunistic relay selection scheme is considered, and performance is analyzed over independent and non-identically distributed (i.n.i.d.) Nakagami-m fading channels with imperfect channel state information (CSI) and non-linear power amplifier (NLPA) at the relays. For this, closed-form expression of outage probability is derived and diversity order of the system is obtained with the help of asymptotic outage probability. Further, the impact of number of relays, fading parameter, channel estimation error and non-linear distortion are highlighted on the system's performance. Finally, theoretical expressions are validated through simulations.

In impulse radio ultra-wideband (IR-UWB) literature, simple transmitted reference (TR) autocorrelation receiver has been analyzed for data symbol detection. However, TR receiver is neither energy efficient nor data transmission rate efficient due to transmission of two pulses per data symbol. In this paper, we propose an iterative TR (ITR) UWB receiver for the joint time of arrival (ToA) and data symbol estimation. The proposed ITR receiver uses only single reference pulse for a burst of data symbols. The ITR receiver estimates a new reference pulse from the burst of data symbols' pulses to further enhance the signal-to-noise ratio (SNR) of UWB system. Hence, the proposed ITR receiver has high energy efficiency and data rate transmission efficiency with improved SNR as compared to a TR receiver. Further, the proposed ITR receiver is implemented at sub-Nyquist rate to overcome the high sampling rate analog-to- digital converter. The ITR receiver's ToA and data symbol estimation performance is analyzed for the binary phase shift keying modulated UWB system in standard IEEE 802.15.4a channels in the presence of additive white Gaussian noise.

Visible light communications (VLC) has lately emerged as a viable supplement to existing radio frequency (RF) based communication systems, and is an integral component of future communication systems. The possibility of deploying massive arrays of light emitting diodes (LED) and photo-detectors (PD), which translates to a massive array of multiple input multiple output (MIMO) system for VLC, have been recently proposed. However, massive-MIMO in VLC has the following drawbacks which limit its practical deployment: a) high condition number of the channel matrix, which negatively affects the robustness of traditional channel-inversion based detection, and b) high interference in a multi-user setting due to correlated columns of the overall MIMO channel matrix. In this paper, the multi-user (MU) massive-MIMO in VLC is considered, and a precoding technique is proposed that induces diverse effective channel matrices for different users, and hence facilitates MU detection using ordered successive interference cancellation. Further, it is shown that the proposed precoding technique maximizes an upper bound of the overall sum-rate of the MU massive-MIMO system. Lastly, simulations have been performed for MU massive LED and PD arrays demonstrating that the proposed precoding technique outperforms existing precoding techniques, and hence is a viable solution for massive-MIMO in VLC.

Molecular communication is used to form a network of nano devices where bio-molecules are used as information carriers. Information can be encoded as number, type and release time of molecules. In molecule shift keying (MoSK), an encoding scheme, transmitter sends variety of molecule for each bit, which results in increased complexity at the transmitter and the receiver, especially for higher modulation scheme. In this work, we consider depleted molecule shift keying (D-MoSK), an efficient modulation scheme, where different molecules are transmitted for each bit, only when bit value is '1'. This reduces the overall requirement to log_2M for M-ary symbols. In this work, performance analysis of the flow assisted diffusion channel molecular communication is investigated. The effect of flow velocity, interference of time slotted transmission and diffusion coefficient on average symbol error rate and achievable mutual information rate of the channel is derived. Delay introduced by the medium in time slotted transmission is also investigated. Numerical results show lower average symbol error rate and higher achievable information rate for the considered system as compared to no drift MoSK scheme.

Recently, IEEE 802.11ah standard has been proposed to extend the range of wireless local area network operating in the sub-1-GHz frequency band. This standard along with other protocols can provide communication services to the Internet of Things applications. However, in future, this band is also expected to be crowded like 2.45 GHz ISM band and cause interference to other devices operating in the same band. For a communication channel affected by additive white Gaussian noise, the least square (LS)-based estimator and Euclidean distance-based Viterbi decoder give optimal performance. However, the receiver's performance with LS estimator followed by the Viterbi decoder degrades for high interference affected communication channels. In this paper, a new orthogonal frequency division multiplexing-based receiver structure operating in high interference environment is proposed. The proposed receiver is based on nonparametric maximum likelihood channel estimation followed by Viterbi decoder. The Viterbi decoder's branch metric is updated based on the distribution of residual error. The proposed receiver structure is tested on IEEE 802.11ah-based receiver in two different type of additive interference: 1) IEEE 802.15.4 device and 2) impulsive noise. Both simulations and real-world experimental results on standard compliant platform show that the proposed algorithm performs better in terms of bit error rate than other receivers in all the considered interference models. Additionally, we also derive analytical expression for the probability of symbol error.

The natural gradient (NG) based non-parametric maximum likelihood (NPML) adaptive algorithm gives better sparse channel estimation in terms of convergence rate as compared to stochastic gradient (SG) based NPML in the presence of additive non-Gaussian noise. The step-size of NG- NPML is proportional to the magnitude of respective active tap weights and hence achieves initial faster convergence. However, the mean square error (MSE) and bit error rate performance of both NG-NPML and SG-NPML is same. In this paper, we propose an algorithm to improve the MSE floor by introducing the l1norm penalty in the cost function. This l1norm penalty term introduces a zero-attractor (ZA) term in the NG- NPML weight update recursion which shrinks the coefficients of inactive taps and hence reduces the steady state MSE floor. We have also derived the stability condition for the proposed ZA-NG- NPML in terms of mean weight error. Improved performance of the proposed algorithm is validated by simulation for standardized channel model.

Ultra-wide band (UWB) ranging requires precise estimation of time-of-arrival (ToA) of first path (FP) signal. In UWB communication, estimation of ToA is challenging in the presence of impulsive interference and multipath. Generally in literature, ToA is estimated using threshold crossing techniques. Although threshold based ToA methods are simple, optimal threshold value is hard to determine in practice. In this paper, we propose joint estimation of ToA and data symbols by exploiting the cluster-sparsity of the received UWB signal in the presence of impulsive interference. The proposed receiver structure enhances the signal-to- noise ratio at the receiver output by mitigating the impulsive interference and barring inter-clusters noise accumulation. The proposed method is also free from any threshold, training, and optimization process. Robustness of the proposed algorithm is validated for standardized IEEE 802.15.4a channel models in the presence of both additive white Gaussian noise and impulsive interference.

This paper presents closed-form expression of average symbol error rate (ASER) for general order rectangular quadrature amplitude modulation (RQAM) with selection combining (SC) receiver in orthogonal frequency division multiplexing (OFDM)-based nonlinear amplify-and-forward (AF) relay network over independent and non-identically distributed (i.n.i.d.) frequency selective Nakagami-m fading channel for integer-valued fading parameter (m). The asymptotic ASER behavior is also investigated for the considered system model. Further, the impact of nonlinear distortion (NLD) of relay's amplifier is highlighted on the ASER performance for different constellation size and different signal-to-noise ratio (SNR) levels. The numerical results are compared with computer simulations, and accuracy of the derivations is verified.

Ultra-wide band (UWB) communication is a viable solution for Wireless Sensor Network (WSN) and Internet of Things (IoT) due to low cost and low power requirement. However, UWB transceiver design is more complex due to large bandwidth and precise synchronization requirement. In this paper, we propose a simple peak detection based non-coherent UWB receiver, suitable for low data rate WSN and IoT based applications. The proposed receiver divides each data symbol frame duration into smaller multiple time windows. In each time window, peak of received signal is detected independently using threshold comparison. The transmitted signal is detected in a frame by employing decisions on all multiple time windows. From simulation, it is observed that the proposed receiver outperforms existing non-coherent receivers. The performance analysis of the proposed receiver is carried out by using time hopping pulse position modulation (TH-PPM) UWB signal in additive white Gaussian noise (AWGN), multipath communication using the existing IEEE 802.15.4a standard.

In this paper, a 3-hop orthogonal frequency division multiple access (OFDMA) based variable- gain amplify and forward (AF) relaying network with selection combining receiver is considered, which is based on a scattered infrastructure known as shared user equipment (UE)-side distributed antenna system (SUDAS). The considered system model uses millimeter wave (mmWave) wireless links along with ultra high frequency (UHF) links, which can provide high data rate transmission (targeted for 5G systems). Wireless links are considered to be independent but non-identically distributed (i.n.i.d.), since Rician and Rayleigh distributions are preferred for mmWave and UHF links, respectively. In this work, closed-form expressions for the lower and upper bound of the outage probability are derived for the system under consideration. Derived analytical results are verified through Monte-Carlo simulations and impact of the Rician K-factor(s) of the considered links is also highlighted.

The explosion of cellular users and high demand for data has evolved the single-tier cellular network to multi-tier network. This paper analyses the gain of non-orthogonal multiple access (NOMA), an enabling technique for 5G, in a heterogeneous cellular network (HCN) consisting of a macro base station (MBS) tier and femto base station (FBS) tier. Offloading users to the FBS tier is performed to relieve the MBS tier of over congestion. In order to support offloaded user, the FBS tier employs NOMA. The results are plotted for different channel conditions while offloading the user to FBS tier. Benefits of NOMA (for e.g., higher ergodic rate, user fairness, performance enhancement at cell edge user) are observed for different channel conditions for the offloaded user. It is also observed that offloading to FBS tier with NOMA gives better performance as compared to offloading without NOMA.

In this paper, we propose a Markov chain Monte Carlo (MCMC) based layered Gibbs sampling (GS) algorithm for low-complexity symbol vector detection in multiple-input multiple-output (MIMO) systems with large (tens to hundreds) number of antennas i.e. large-MIMO systems. The underlying idea in the proposed work is to use the MCMC based GS technique in a layered fashion where, in each layer the GS is used to make decision about a symbol probabilistically by sampling from a distribution. The proposed algorithm successfully alleviates the stalling problem encountered at high signal to noise ratios in the conventional GS based detection methods. Since, layered detection is prone to error propagation, therefore, two different sorting techniques namely, signal to noise ratio based sorting and log-likelihood ratio based sorting of the detection sequence are used for mitigating the error propagation. Simulation results validate superiority of the proposed algorithm over the existing GS based detection methods, and also, the bit error rate performance of the proposed algorithm approach near-optimal performance with low-complexity.

In this study, performance analysis of variable‐gain amplify‐and‐forward relaying orthogonal frequency division multiplexing‐based systems over independently but not necessarily identically distributed Nakagami‐ m fading channels using non‐linear power amplifier (PA) at the relay and maximal ratio combining scheme at destination. Specifically, novel closed‐form expressions of outage probability and asymptotic outage probability are investigated for the considered system model. Further, an average symbol error rate (ASER) expression is derived for general‐order rectangular quadrature amplitude modulation (RQAM) scheme using cumulative distribution function‐based approach, which is also the novel contribution of the work. Since the transfer function of PA used at relay is nonlinear, this introduces non‐linear distortion (NLD) in the considered system model. Thus, the impact of NLD and fading parameters on outage probability, diversity gain of the system and ASER performances are analysed. Further, the impact of NLD on constellation order for RQAM scheme is also discussed. The theoretical results are compared with computer simulations to verify the accuracy of the derivations. The derived expressions are generalised over variety of fading environments for integer‐valued fading parameter.

In this study, the authors propose an improved multiple feedback successive interference cancellation (IMF‐SIC) algorithm and an ordered IMF‐SIC (OIMF‐ SIC) algorithm for near‐optimal multiple‐input multiple‐output (MIMO) detection. In particular, the multiple feedback (MF) strategy in successive interference cancellation (SIC) detector is based on the concept of shadow region, where, if a decision falls in the shadow region, then multiple neighbouring constellation points are used in the decision feedback loop followed by the SIC technique, and the best candidate symbol is selected by using maximum likelihood cost. However, while deciding the best symbol, the shadow condition is not checked in the subsequent layers which may result in an unreliable decision. Thus, to improve the accuracy of a decision, the authors propose an improved MF strategy where the shadow region condition is checked recursively. Further, the authors also propose an OIMF‐SIC algorithm where the log likelihood ratio based dynamic ordering is utilised for ordering the detection sequence. Simulation results validate superiority of the proposed algorithms over the other SIC based detection techniques. In addition, to validate robustness of the proposed algorithms, BER performance is computed and compared under channel state information mismatch.

Visible light communication (VLC) is one of the main technologies driving the future 5G communication systems due to its ability to support high data rates with low power consumption, thereby facilitating high speed green communications. To further increase the capacity of VLC systems, a technique called non-orthogonal multiple access (NOMA) has been suggested to cater to increasing demand for bandwidth, whereby users' signals are superimposed prior to transmission and detected at each user equipment using successive interference cancellation. Some recent results on NOMA exist which greatly enhance the achievable capacity as compared with the orthogonal multiple access techniques. However, one of the performance-limiting factors affecting VLC systems is the nonlinear characteristics of a light emitting diode (LED). This paper considers the nonlinear LED characteristics in the design of pre-distorter for cognitive radio inspired NOMA in VLC, and proposes singular value decomposition-based Chebyshev precoding to improve performance of nonlinear multiple-input multiple-output NOMA-VLC. A novel and generalized power allocation strategy is also derived in this paper, which is valid even in scenarios when users experience similar channels. Additionally, in this paper, analytical upper bounds for the bit error rate of the proposed detector are derived for square M-quadrature amplitude modulation.

This paper presents analysis of outage probability of an amplify-and-forward (AF) orthogonal frequency division multiplexing (OFDM) based relay network with maximal ratio combining (MRC) technique at the receiver. In this paper, a closed-form expression for a tight lower-bound of outage probability is derived under the frequency selective Nakagami-m fading channels, where m is an integer. A non-linear power amplifier (PA) is considered at the relay node which introduces the non-linear distortion. This analysis describes the effect of non-linear PA parameters over outage probability at various signal-to-noise ratio (SNR) levels. The numerical values of outage probability expression is compared with computer simulations to verify the accuracy of the derivation.

A maximum-likelihood channel estimator for the orthogonal frequency division multiplexing communication environments, in the presence of interference is discussed here. In a training-based scenario, the channel is estimated based on pilots that precede the transmission of the information. To reduce the number of estimation parameters, the channel is estimated iteratively in time-domain. Since interference from other users provide no useful information, parameters of the interference are neither estimated nor the effect of the interference neglected, instead interference along with Gaussian noise is perceived as non-Gaussian noise process. The algorithm assumes no a priori knowledge about the interfering channel and signal at the receiver, further no assumption on the statistical properties of the interferer is assumed, which makes this algorithm robust. The estimated channel information along with the estimated distribution are then utilised to equalise the subsequent data blocks. The strength of the algorithm is in its robustness to both synchronous and asynchronous interference, which is confirmed by the simulation results for both flat and multipath fading channels in presence of synchronous and asynchronous interference.