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Persistent security challenges in Industry 4.0 due to the limited resources of IoT devices necessitate innovative solutions. Addressing this, this study introduces the ASCON algorithm for lightweight authenticated encryption with associated data, enhancing confidentiality, integrity, and authenticity within IoT limitations. By integrating Digital Twins, the framework emphasizes the need for robust security in Industry 4.0, with ASCON ensuring secure data transmission and bolstering system resilience against cyber threats. Practical validation using the MQTT protocol confirms ASCON’s efficacy over AES-GCM, highlighting its potential for enhanced security in Industry 4.0. Future research should focus on optimizing ASCON for microprocessors and developing secure remote access tailored to resource-constrained devices, ensuring adaptability in the digital era.

This study builds on our previous systematic literature review (SLR) that assessed the applications and performance of zk-SNARK, zk-STARK, and Bulletproof non-interactive zero-knowledge proof (NIZKP) protocols. To address the identified research gaps, we designed and implemented a benchmark comparing these three protocols using a dynamic minimized multiplicative complexity (MiMC) hash application. We evaluated performance across four general-purpose programming libraries and two programming languages. Our results show that zk-SNARK produced the smallest proofs, while zk-STARK generated the largest. In terms of proof generation and verification times, zk-STARK was the fastest, and Bulletproof was the slowest. Interestingly, zk-SNARK proofs verified marginally faster than zk-STARK, contrary to other findings. These insights enhance our understanding of the functionality, security, and performance of NIZKP protocols, providing valuable guidance for selecting the most suitable protocol for specific applications.

Insurance is a pivotal element in modern society, but insurers face a persistent challenge from fraudulent behaviour performed by policyholders. This behaviour could be detrimental to both insurance companies and their honest customers, but the intricate nature of insurance fraud severely complicates its efficient, automated detection. This study surveys fifty recent publications on automobile insurance fraud detection, published between January 2019 and March 2023, and presents both the most commonly used data sets and methods for resampling and detection, as well as interesting, novel approaches. The study adopts the highly-cited Systematic Literature Review (SLR) methodology for software engineering research proposed by Kitchenham and Charters and collected studies from four online databases. The findings indicate limited public availability of automobile insurance fraud data sets. In terms of detection methods, the prevailing approach involves supervised machine learning methods that utilise structured, intrinsic features of claims or policies and that lack consideration of an example-dependent cost of misclassification. However, alternative techniques are also explored, including the use of graph-based methods, unstructured textual data, and cost-sensitive classifiers. The most common resampling approach was found to be oversampling. This SLR has identified commonly used methods in recent automobile insurance fraud detection research, and interesting directions for future research. It adds value over a related review by also including studies published from 2021 onward, and by detailing the used methodology. Limitations of this SLR include its restriction to a small number of considered publication years and limited validation of choices made during the process.

The advent of Internet of Things (IoT) devices has revolutionized our daily routines, fostering interconnectedness and convenience. However, this interconnected network also presents significant security challenges concerning authentication and data integrity. Traditional security measures, such as Public Key Infrastructure (PKI), encounter limitations when applied to resource-constrained IoT devices. This paper proposes a novel decentralized PKI system tailored specifically for IoT environments to address these challenges. Our approach introduces a unique “zone” architecture overseen by zone masters, facilitating efficient certificate management within IoT clusters while reducing the risk of single points of failure. Furthermore, we prioritize the use of lightweight cryptographic techniques, including Elliptic Curve Cryptography (ECC), to optimize performance without compromising security. Through comprehensive evaluation and benchmarking, we demonstrate the effectiveness of our proposed solution in bolstering the security and efficiency of IoT ecosystems. This contribution underlines the critical need for innovative security solutions in IoT deployments and presents a scalable framework to meet the evolving demands of IoT environments.

Highly constrained devices that are interconnected and interact to complete a task are being used in a diverse range of new fields. The Internet of Things (IoT), cyber-physical systems, distributed control systems, vehicular systems, wireless sensor networks, tele-medicine, and the smart grid are a few examples of these fields. In any of these contexts, security and privacy might be essential aspects. Research on secure communication in Internet of Things (IoT) networks is a highly contested topic. One method for ensuring secure data transmission is cryptography. Because IoT devices have limited resources, such as power, memory, and batteries, IoT networks have boosted the term “lightweight cryptography”. Algorithms for lightweight cryptography are designed to efficiently protect data while using minimal resources. In this research, we evaluated and benchmarked lightweight symmetric ciphers for resource-constrained devices. The evaluation is performed using two widely used platform: Arduino and Raspberry Pi. In the first part, we implemented 39 block ciphers on an ATMEGA328p microcontroller and analyzed them in the terms of speed, cost, and energy efficiency during encryption and decryption for different block and key sizes. In the second part, the 2nd-round NIST candidates (80 stream and block cipher algorithms) were added to the first-part ciphers in a comprehensive analysis for equivalent block and key sizes in the terms of latency and energy efficiency.

The Internet of Things (IoT) is the ability to provide everyday devices with a way of identification and another way for communication with each other. The spectrum of IoT application domains is very large including smart homes, smart cities, wearables, e-health, etc. Consequently, tens and even hundreds of billions of devices will be connected. Such devices will have smart capabilities to collect, analyze and even make decisions without any human interaction. Security is a supreme requirement in such circumstances, and in particular authentication is of high interest given the damage that could happen from a malicious unauthenticated device in an IoT system. This paper gives a near complete and up-to-date view of the IoT authentication field. It provides a summary of a large range of authentication protocols proposed in the literature. Using a multi-criteria classification previously introduced in our work, it compares and evaluates the proposed authentication protocols, showing their strengths and weaknesses, which constitutes a fundamental first step for researchers and developers addressing this domain.

In recent years, research on reconfigurable intelligent surface (RIS) has received extensive attention due to its capability to manipulate the propagation of incident electromagnetic waves in a programmable manner to smartly configure the channel environment, thereby optimizing the overall performance of the system. Several RIS architectures have been proposed, including simultaneous transmitting and reflecting RIS (STAR-RIS) and beyond diagonal RIS (BD RIS) architectures. Compared to the conventional RIS architecture, these structures offer broader service ranges and enhanced performance, albeit accompanied by more complex circuit design and higher computational overhead. In this paper, we design a multi-sector RIS joint service model based on the BD RIS architecture and we compare the corresponding system rates, circuit complexity, and gains provided by different architectures. Additionally, we derive the theoretical receive power for the proposed model based on non-diagonal and diagonal phase shift optimization methods, demonstrating that the total rate of the non-diagonal group connected architecture approaches the theoretical values of a fully connected architecture. Theoretical analysis based on the gains between different models under various user-RIS positions confirm that the multi-sector RIS joint service model can achieve $30\%-100\%$ gains as the users' positions change, while also saving on the overall hardware costs of the RIS system design. Furthermore, we explore the optimal trade-off between the performance and circuit complexity among different architectures. Simulation results show that performance versus complexity trade-off of the different considered architectures.

In this paper, we investigate the impact of user pairing on the power consumption of 2-user non-orthogonal multiple access (NOMA) systems in the downlink. We formulate the joint power allocation and user pairing problem as a mixed-integer programming problem with the objective of minimizing the total transmit power consumption. While the pairwise power allocation strategy is straightforward, for a system with $2K$ users and K NOMA pairs, there exist ${}\frac {(2K)!}{2^{K} \times K!}$ possible pairing strategies, resulting in a combinatorial search space that grows drastically with the number of users in the system. Hence, we propose an analytical approach to obtain the globally optimum user pairing strategy. Notably, our procedure has a linear time complexity of $\mathcal {O}(2K)$ , which is a significant improvement over the suboptimal and computationally expensive methods in the existing literature. We demonstrate through extensive simulations that the proposed optimal pairing strategy can attain considerable performance gains in terms of power savings compared to benchmark schemes. In particular, in a typical deployment environment, 63% of the total power budget is saved at a mean received signal-to-noise ratio (SNR) of 15.7 dB among the users. Finally, we evaluate the energy efficiency (EE) of NOMA transmission compared to the EE achieved through orthogonal multiple access (OMA) transmission. We demonstrate that the EE gain of NOMA transmission compared to OMA is improved more than sixfold at convergence by adopting the power minimization approach studied in this work, rather than adopting the sum rate maximization approach found in the literature.

Integrated access and backhaul (IAB) networks operating in full-duplex (FD) mode at millimeter wave frequencies have been actively investigated in the context of future-generation communications networks. However, conventional analog cancellation techniques cannot adequately mitigate the self-interference resulting from the FD operation and the multi-user interference. Hence, in this paper, we consider a multi-cell, multi-user IAB network and jointly design the beamforming and combining matrices to maximize the network’s weighted sum rate. Given the non-convex nature of the problem, we reformulate it using weighted minimum-mean-square-error (WMMSE) and extended fractional programming (FP) techniques followed by a block coordinate descent (BCD) approach. Extensive simulation results validate the superior performance of our proposed algorithms. Specifically, the WMMSE and FP methods can achieve 50 bits/sec/Hz higher than the benchmark scheme for a network employing three cells with two uplink and two downlink users per cell.

Abstract This paper proposes a novel machine learning (ML) assisted low‐latency low density parity check (LDPC) coded adaptive modulation (AM) system, where short block‐length LDPC codes are used. Conventional adaptive modulation and coding (AMC) system includes fixed look‐up table method, which is also called inner loop link adaptation (ILLA) and outer loop link adaptation (OLLA). For ILLA, the adaptive capability is achieved by switching the modulation and coding modes based on a look‐up table using signal‐to‐noise ratio (SNR) thresholds at the target bit error rate (BER), while OLLA builds upon the ILLA method by dynamically adjusting the SNR thresholds to further optimize the system performance. Although both improve the system overall throughput by switching between different transmission modes, there is still a gap to optimal performance as the BER is comparatively far away from the target BER. Machine learning (ML) is a promising solution in solving various classification problems. In this work, the supervised learning based k‐nearest neighbours (KNN) algorithm is invoked for choosing the optimum transmission mode based on the training data and the instantaneous SNR. This work focuses on the low‐latency communications scenarios, where short block‐length LDPC codes are utilized. On the other hand, given the short block‐length constraint, we propose to artificially generate the training data to train our ML assisted AMC scheme. The simulation results show that the proposed ML‐LDPC‐AMC scheme can achieve a higher throughput than the ILLA system while maintaining the target BER. Compared with OLLA, the proposed scheme can maintain the target BER while the OLLA fails to maintain the target BER when the block length is short. In addition, when considering the channel estimation errors, the performance of the proposed ML‐LDPC‐AMC maintains the target BER, while the ILLA system's BER performance can be higher than the target BER.

Index Modulation (IM) is a flexible transmission scheme capable of striking a flexible performance, throughput, diversity and complexity trade-off. The concept of Multi-dimensional IM (MIM) has been developed to combine the benefits of IM in multiple dimensions, such as space and frequency. Furthermore, Compressed Sensing (CS) can be beneficially combined with IM in order to increase its throughput. However, having accurate Channel State Information (CSI) is essential for reliable MIM, which requires high pilot overhead. Hence, Joint Channel Estimation and Detection (JCED) is harnessed to reduce the pilot overhead and improve the detection performance at a modestly increased estimation complexity. We then circumvent this by proposing Deep Learning (DL) based JCED for CS aided MIM (CS-MIM) of significantly reducing the complexity, despite reducing the pilot overhead needed for Channel Estimation (CE). Furthermore, we conceive training-aided Soft-Decision (SD) detection. We first analyze the complexity of the conventional joint CE and SD detection followed by proposing our reduced-complexity learning-aided joint CE and SD detection. Our simulation results confirm a Deep Neural Network (DNN) is capable of near-capacity JCED of CS-MIM at a reduced pilot overhead and reduced complexity both for Hard-Decision (HD) and SD detection.

Index Modulation (IM) is capable of striking an attractive performance, throughput and complexity trade-off. The concept of Multi-dimensional IM (MIM) combines the benefits of IM in multiple dimensions, including the space and frequency dimensions. On the other hand, IM has also been combined with compressed sensing (CS) for attaining an improved throughput. In this paper, we propose Joint MIM (JMIM) that can utilize the time-, space- and frequency-dimensions in order to increase the IM mapping design flexibility. Explicitly, this is the first paper developing a jointly designed MIM architecture combined with CS. Three different JMIM mapping methods are proposed for a space- and frequency-domain aided JMIM system, which can attain different throughput and diversity gains. Then, we extend the proposed JMIM design to three dimensions by combining it with the time domain. Additionally, to circumvent the high detection complexity of the proposed CS-aided JMIM design, we propose Deep Learning (DL) based detection. Both Hard-Decision (HD) as well as Soft-Decision (SD) detection are conceived. Additionally, we investigate the adaptive design of the proposed CS-aided JMIM system, where a learning-based adaptive modulation configuration method is applied. Our simulation results demonstrate that the proposed CS-aided JMIM (CS-JMIM) is capable of outperforming its CS-aided separate-domain MIM counterpart. Furthermore, the learning-aided adaptive scheme is capable of increasing the throughput while maintaining the required error probability target.

A novel Soft-Input Soft-Output (SISO) polar decoding algorithm is proposed, which is capable of iterating between an inner and outer decoder in a three-stage serial concatenated iterative receiver. The proposed polar decoding algorithm leverages a hybrid of Soft Cancellation (SCAN) and g-function aided-SCAN (G-SCAN) decoding. The SCAN decoder enables iterative soft-information exchange with the outer decoders and the G-SCAN decoder facilitates iterative soft-information exchange with the inner decoders while exploiting the error correction capability of the classic Successive Cancellation List (SCL) decoder. Furthermore, we present the Three-Dimensional (3D) Extrinsic Information Transfer (EXIT) chart analysis of polar codes for the first time, in order to characterise the iterative exchange of extrinsic information between these three concatenated stages. This offers an insight into the interactions of these three decoders and characterises their iterative convergence. In this three-stage serial concatenated scheme the first stage is a Joint Source Channel Coding (JSCC) decoder, the second stage is a 5th Generation (5G) 3rd Generation Partnership Project (3GPP) New Radio (NR) polar decoder based on our novel hybrid SISO polar algorithm, and the third stage is a 2 × 2 Multiple Input Multiple Output (MIMO) detector. We characterized the Symbol Error Rate (SER) vs. complexity of the proposed scheme, and compare it to various soft- and hard-decision benchmarkers, as well as to the relevant JSCC and Separate Source Channel Coding (SSCC) schemes. In comparison to a three-stage serial concatenated JSCC benchmarker, the proposed SISO scheme offers 11${\%}$ complexity reduction over to the state-of-the-art SISO SCAN polar decoder at a similar SER performance. Additionally, the proposed SISO scheme achieves a 0.75 dB SNR gain over the SCAN polar decoder of a two-stage serial concatenated SSCC benchmarker.

Joint Source-Channel Coding (JSCC) is a powerful technique that allows for the efficient transmission of information by simultaneously considering the characteristics of both the source and the channel. The recently proposed Exponential Golomb Error Correction (ExpGEC) and Rice Error Correction (REC) codes provide generalized JSCC schemes for the near capacity coding of symbols drawn from large or infinite alphabets. Yet these require impractical decoding structures, with large buffers and inflexible system design, this was mitigated by the introduction of the Reordered Elias Gamma Error Correction (REGEC) which itself had limited flexibility with regards to source distribution. In this paper, we propose a novel Reordered Exponential Golomb Error Correction (RExpGEC) coding scheme, which is a JSCC technique designed for flexible and practical near-capacity performance. The proposed RExpGEC encoder and decoder are presented and its performance is analysed using Extrinsic Information Transfer (EXIT) charts. The flexibility of the RExpGEC is shown via the novel trellis encoder and decoder design. Finally, the Symbol Error Rate (SER) performance of RExpGEC code is compared when integrated into the novel RExpGEC-URC-QPSK scheme against other comparable JSCC and Separate Source Channel Coding (SSCC) benchmarkers. Specifically the RExpGEC-URC-QPSK scheme is compared against the REGEC-URC-QPSK scheme, and a serial concatenation of the Exponential Golomb and Convolution Code, which becomes the novel Exp-CC-URC-QPSK scheme. Our simulation results demonstrate the performance gains and flexibility of the proposed RExpGEC-URC-QPSK scheme against the benchmarkers in providing reliable and efficient communications. Specifically, the RExpGEC-URC-QPSK scheme outperforms the SSCC in a uncorrelated Rayleigh fading channel by 2 to 3.6 dB (dependent on source distribution). Furthermore, the RExpGEC-URC-QPSK scheme consistently operates within 2.5 dB of channel capacity when measuring $E_{b}/N_{0}$ , whilst providing flexibility in SNR performance when compared to the REGEC-URC-QPSK scheme. These performance gains come at the cost of complexity, whereby the RExpGEC-URC-QPSK scheme is 3.6 times more complex than Exp-CC-URC-QPSK scheme under certain conditions. This paper highlights the unique capabilities of RExpGEC as a high performance, practical and flexible JSCC technique.

The sparsity of millimeter wave (mmWave) channels in angular and temporal domains has been exploited for channel estimation, while the associated channel parameters can be utilized for localization. However, line-of-sight (LoS) blockage makes localization highly challenging, which may lead to big positioning inaccuracy. One promising solution is to employ reconfigurable intelligent surfaces (RIS) to generate virtual line-of-sight (VLoS) paths. Hence, it is essential to investigate the wireless positioning in RIS-aided mmWave systems. In this paper, an adaptive joint LoS and VLoS localization scheme is proposed, where the VLoS is constructed by a beamforming protocol operated between RIS and mobile station (MS). More specifically, to sense the location and orientation of a MS, a novel interlaced scanning beam sweeping algorithm is proposed to acquire the optimal beams. In this algorithm, LoS and VLoS paths are separately estimated and the optimal beams are selected according to the received signal strength so as to mitigate the LoS blockage problem. Then, based on the selected beams and received signal strength, angle of arrival (AoA), angle of departure (AoD), angle of reflection (AoR) and time of arrival (ToA) are estimated. Finally, with the aid of these estimated parameters, the location and orientation of the MS are estimated. We derive the Cramer-Rao lower bounds (CRLBs) for both location estimation and orientation estimation, and compare them with the corresponding results obtained from simulations. We compare the performance of our proposed scheme with that attained by three legacy schemes, when various aspects are considered. The performance results show the superiority of our proposed beam training algorithm, which is capable of achieving a localization error within 15 cm and an orientation error within 0.003 rads. Furthermore, the training overhead is 100 times less than that of the conventional exhaustive search algorithm, while obtaining a 13 dBm power gain when compared with the hierarchical codebook based search algorithm.

The concept of the “connected car” offers the potential for safer, more enjoyable and more efficient driving and eventually autonomous driving. However, in urban Vehicular Networks (VNs), the high mobility of vehicles along roads poses major challenges to the routing protocols needed for a reliable and flexible vehicular communications system. Thus, urban VNs rely on static Road-Side-Units (RSUs) to forward data and to extend coverage across the network. In this paper, we first propose a new Q-learning-based routing algorithm, namely Infrastructure-aided Traffic-Aware Routing (I-TAR), which leverages the static wired RSU infrastructure for packet forwarding. Then, we focus on the multi-source, multi-destination problem and the effect this imposes on node availability, as nodes also participate in other communications paths. This motivates our new hybrid approach, namely Hybrid Infrastructure-aided Traffic Aware Routing (HI-TAR) that aims to select the best Vehicle-to-Vehicle/Infrastructure (V2V/I) route. Our findings demonstrate that I-TAR can achieve up to 19% higher average packet-delivery-ratio (APDR) compared to the state-of-the-art. Under a more realistic scenario, where node availability is considered, a decline of up to 51% in APDR performance is observed, whereas the proposed HI-TAR in turn can increase the APDR performance by up to 50% compared to both I-TAR and the state-of-the-art. Finally, when multiple source-destination vehicle pairs are considered, all the schemes that model and consider node availability, i.e. limited-availability, achieve from 72.2% to 82.3% lower APDR, when compared to those that do not, i.e. assuming full-availability. However, HI-TAR still provides 34.6% better APDR performance than I-TAR, and ~40% more than the state-of-the-art.

High speed trains (HST) have gradually become an essential means of transportation, where given our digital world, it is expected that passengers will be connected all the time. More specifically, the on-board passengers require fast mobile connections, which cannot be provided by the currently implemented cellular networks. Hence, in this article, we propose an analogue radio over fiber (A-RoF) aided multi-service network architecture for high-speed trains, in order to enhance the quality of service as well as reduce the cost of the radio access network (RAN). The proposed design can simultaneously support sub-6GHz as well as millimetre wave (mmWave) communications using the same architecture. Explicitly, we design a photonics aided beamforming technique in order to eliminate the bulky high-speed electronic phase-shifters and the hostile broadband mmWave mixers while providing a low-cost RAN solution. Finally, a beamforming range of 180° is demonstrated with a high resolution using our proposed system.

In order to reduce the hardware complexity and cost of mmWave transceivers, hybrid beamforming techniques have been developed, which rely on the channel state information (CSI) available to the receiver and/or transmitter. In mmWave channel estimation, the compressed sensing (CS)-based algorithms like orthogonal matching pursuit (OMP) have been widely studied to take the advantages of the sparse characteristics of mmWave channels. Specifically, the OMP-assisted adaptive codebook channel estimation has the merit of reduced implementation complexity, but it performs undesirably in low signal to noise ratio (SNR) scenarios. To circumvent this problem, in this paper, we develop an improved adaptive codebook channel estimation algorithm for orthogonal frequency division multiplexing (OFDM) mmWave systems, which enhances the estimation performance by exploiting the multi-carrier signals for joint decision making. Our studies show that the proposed channel estimation is capable of significantly improving the estimation accuracy at low SNR, while enjoying a low complexity for implementation.

Benefiting from the high resolution in beamspace, millimeter wave (mmwave) communication has been regarded as a high-accuracy localization solution, where the location information is embedded in the channel via angle and time delay, for example. In this paper, to locate a user equipment (UE) and scatterers, we present the localization model in mmwave communications as a compressed sensing assisted channel estimation problem, which is solved using a proposed two-stage channel estimation based localization scheme. During the first stage, a sparse Bayesian learning (SBL) algorithm is operated to attain a coarse estimation. Then during the second stage, a multi-stage grid refinement assisted fine estimation is achieved by a distributed compressed sensing simultaneous orthogonal matching pursuit (DCS-SOMP) algorithm. Moreover, in our approach, the few-bit analog to digital converters (ADCs) are utilized by the receiver of UE so as to attain a good trade-off among performance, complexity and energy-efficiency. Finally, the performance of channel estimation and positioning is comprehensively investigated and compared. It can be shown that our proposed two-stage approach is capable of achieving centimeter-level accuracy with the required number of quantization bits of ADCs less than four.

In this article, we propose an orthogonal frequency-division multiplexing system supported by the compressed sensing assisted index modulation, termed as (OFDM-CSIM), applied to millimeter-wave (mmWave) communications. In the OFDM-CSIM mmWave system, information is conveyed not only by the classic constellation symbols but also by the on/off status of subcarriers, where the size of constellation symbols and the number of active subcarriers can be beneficially configured for maximizing the system's throughput. We conceive a machine learning (ML) assisted adaptive OFDM-CSIM mmWave system, which simultaneously benefits from the OFDM with index modulation (IM), compressed sensing (CS) and the hybrid beamforming techniques. Specifically, a ML-assisted link adaptation scheme is designed based on the k -nearest neighbors (k -NN) algorithm with the objective to maximize the system's throughput. Our studies show that the proposed ML-assisted link adaptation is capable of providing higher throughput than the conventional threshold-based link adaptation when different antenna structures are considered. Furthermore, the achievable data rates of four types of antenna arrays, including uniform linear array (ULA), uniform rectangular planar array (URPA), uniform circle planar array (UCPA) and uniform cylindrical array (UCYA), are investigated and compared over mmWave channels. The simulation results show that the UCYA achieves the highest data rate among these antenna arrays.

In this paper, we propose a deep learning assisted soft-demodulator for multi-set space-time shift keying (MS-STSK) millimeter wave (mmWave) systems, where we train a neural network (NN) to provide the soft values of the MS-STSK symbol without relying on explicit channel state information (CSI). Thus, in contrast to the conventional MS-STSK soft-demodulator which relies on the knowledge of CSI at the receiver, the learning-assisted design circumvents the channel estimation while also improving the data rate by dispensing with pilot overhead. Furthermore, our proposed learning-aided soft-demodulation substantially reduces the number of cost-function evaluations at the output of the MS-STSK demodulator. We demonstrate by simulations that despite avoiding CSI-estimation and the pilot overhead, our learningassisted design performs closely to the channel-estimation aided design assuming perfect CSI for BER -4, whilst imposing a low complexity. Furthermore, we show by simulations that upon using realistic imperfect CSI at the receiver employing conventional soft-demodulation, the learning-aided softdemodulator outperforms the conventional scheme. Additionally, we present quantitative discussions on the receiver complexity in terms of the number of computations required to produce the soft values.

In this paper, we propose deep learning assisted detection for index modulation millimeter wave (mmWave) systems, where we train a neural network (NN) to jointly detect the transmitted data and index information without relying on explicit channel state information (CSI). As a design example, we first employ multi-set space-time shift keying (MS-STSK) combined with beamforming for transmission over the mmWave channel, where the information is conveyed implicitly using the index of the antennas, the dispersion matrix and the M-ary constellation. Then, we analyze our design when MS-STSK transmission is considered in conjunction with beam index modulation (BIM), where the information is also conveyed by the beam index in addition to the MS-STSK information. In contrast to the MS-STSK's conventional maximum likelihood (ML) detector, our learning-assisted detection dispenses with the channel estimation stage. We demonstrate by simulations that the learning assisted detection outperforms the ML-aided detection in the face of channel impairments with low complexity. Furthermore, we show by simulations that ML-aided detection produces an error floor, when the MS-STSK transmission is coupled with BIM, when realistic channel estimation errors are considered. Additionally, we present qualitative discussions on the receiver complexity in terms of its search space as well as the number of computations required.

Full-duplex (FD) communication has been shown to provide an increased achievable rate, while millimeter wave (mmWave) communications benefit from a large available bandwidth that further improves the achievable rate. On the other hand, the concept of multi-set space-time shift keying (MS-STSK) has been proposed to provide a flexible design trade-off between throughput and performance. Hence, in this work, we consider the design of an FD-aided MS-STSK transceiver for millimeter wave communications. However, a major challenge is that channel reciprocity is not valid in mmWave communications due to shorter channel coherence time. Thus, the uplink (UL) pilots cannot be utilized to estimate the downlink (DL) channel. To overcome this challenge, we propose a beamforming technique based on channel statistics without assuming channel reciprocity. For this purpose, a closed-form expression for the outage probability of the system is derived by employing the characterization of the ratio of the Indefinite Quadratic Form (IQF). The derived analytical expression is then utilized to design optimum beamforming weights using the Sequential Quadratic Programming (SQP)-based heuristic method. Moreover, an Iterative Statistical Method (ISM) of joint transmit and receive beamforming algorithm is also developed by utilizing Principle Eigenvector (PE) and Generalized Rayleigh Quotient (G-RQ) optimization techniques. Finally, we verify our simulation results with the theoretical analysis.

Given the increasing demand for high data-rate, high-performance wireless communications services, the demand on the radio access networks (RAN) has been increasing significantly, where optical fiber has been widely used both for the backhaul and fronthaul. Additionally, advances in signal processing such as multiple-input multiple-output (MIMO) techniques, have improved the performance as well as transmission rate of communications networks. Beamforming has been used as an efficient MIMO technique for providing a signal to noise ratio (SNR) gain as well as reducing the multi-user interference. However, beamforming requires the employment of phase-shifters, which suffers from reduced phase resolutions, degraded noise figures as well as beam-squinting in addition to the implementation challenges. Hence, in this paper we employ an analogue radio over fiber (A-RoF) aided architecture for supporting the requirements of the current and future mobile networks, where we design a photonics aided beamforming technique in order to eliminate the bulky electronic phase-shifters and the beam-squinting effect, while also providing a low-cost RAN solution. Additionally, this photonics aided beamforming is combined with a reconfigurable multi-user MIMO technique, where users can communicate with one or multiple remote radio heads (RRHs), while employing stand-alone beamforming, beamforming combined with diversity or with multiplexing depending on the available resources and the user channel information as well as the quality of service requirements.

The cloud/centralized radio access network (C-RAN) architecture is recognized as a strong candidate for the next generation wireless standards, potentially reducing the total cost of ownership. Spatial modulation (SM) is a cost-effective multiple-input-multiple-output (MIMO) solution, where only a single-radio frequency chain is required for transmission. In this context, we propose analogue radio over fiber (A-RoF) aided MIMO techniques for learning assisted adaptive C-RAN system, where SM combined with space-time block coding is optically processed relying on the optical frequency indices in the central unit of the C-RAN, which also is capable of tuning the connected remote radio heads (RRHs). Furthermore, to improve the spectral efficiency, we invoke our proposed flexible C-RAN architecture for implementing learning assisted transceiver adaptation, where the number of RRHs connected to a single user and its modulation techniques employed are controlled using the K-nearest neighbor algorithm. The simulation results show that the bit error ratio performance of our A-RoF system is only marginally degraded compared to that operating without the A-RoF link, while benefiting from our energy- and cost-efficient C-RAN design. Moreover, we show that the learning assisted adaptation is capable of outperforming the classic threshold-based adaptation in terms of the system's achievable rate.

The scalable video coding extension of H.265/high efficiency video coding is capable of supporting diverse video resolutions. Due to the fact that the enhancement layer (EL) is encoded and decoded with the aid of the base layer (BL), the decodability of ELs is conditioned on that of the BL. Hence, the EL frames occasionally have to be discarded as a result of corrupted BL frames. Therefore, potent unequal error protection schemes have been conceived for providing a stronger protection to the BL by invoking a lower coding rate based forward error correction (FEC) or a higher transmit power for a specific multiple-input multiple-output sub-channel. This stronger protection is essential for the BL for the sake of both avoiding the waste of resources caused by the dropped undecodable EL bits and for improving the reconstructed video quality. In this treatise, we propose an adaptive system for transmitting the layered video bit stream over the wireless channel. The proposed system is capable of selecting an appropriate interlayer (IL) operation-aided FEC (IL-FEC) scheme in order to maintain the robustness of the system by comparing the near-instantaneous signal-to-noise ratio (SNR) to pre-recorded reconfiguration thresholds. Our simulation results show that the proposed adaptive system is capable of providing better video quality over a large proportion of the channel SNR range than its corresponding fixed-mode counterparts.

Hybrid beamforming (HBF) relying on a large antenna array is conceived for millimeter wave (mmWave) systems, where the beamforming (BF) gain compensates for the propagation loss experienced. The BF gain required for a successful transmission depends on the user's distance from the base station (BS). For the geographically separated users of a multi-user mmWave system, the BF gain requirements of different users tend to be different. On the other hand, the BF gain is directly related to the number of antenna elements (AEs) of the array. Therefore, in this paper, we propose an HBF design for the downlink of multi-user mmWave systems, where the number of AEs employed at the BS for attaining BF gains per user is dependent on the user's distance. We then propose grouping of the RF chains at the BS, where each group of RF chains serves a specific group of users depending on the nature of the channel. Furthermore, to support the escalating data rate demands, the exploitation of link-adaptation techniques constitutes a promising solution, since the rate can be maximized for each link while maintaining a specific target bit error rate (BER). However, given the time-varying nature of the wireless channel and the non-linearities of the amplifiers, especially at mmWave frequencies, the performance of conventional link adaptation relying on pre-defined threshold values degrades significantly. Therefore, we additionally propose a two-stage link adaptation scheme. Specifically, in the first stage, we switch on or off both the digital precoder and the combiner depending on the nature of the channel, while in the second stage a machine-learning assisted link-adaptation is proposed, where the receiver predicts whether to request spatial multiplexing- or diversity-aided transmission from the BS for every new channel realization. We demonstrate by the simulation that having both a digital precoder and a combiner in a single dominant path scenario is redundant. Furthermore, our simulations show that the learning assisted adaptation provides significantly higher data rates than that of the conventional link-adaptation, where the reconfiguration decision is simply based on pre-defined threshold values.

Millimeter Wave (mmWave) technology coupled with full duplex (FD) communication has the potential of increasing the spectral efficiency. However, the self-interference (SI) encountered in the FD mode and the ubiquitous multi-user interference (MI) contaminates the signal. Furthermore, the system performance may also be limited by channel aging that arises because of the time-varying nature of the channel. Therefore, in this paper, we conceive FD hybrid beamforming (HBF) for K-user multiple-input multiple-output (MIMO)-aided orthogonal frequency division multiplexing (OFDM) using learning-aided channel prediction. We first derive a joint precoder and combiner design for full duplex K-user MIMO-OFDM interference channels, where we aim for minimizing both the residual SI and the MI, followed by an iterative hybrid decomposition technique developed for OFDM systems. Then, we propose a learning-aided channel prediction technique for systems suffering from channel aging relying on a radial basis neural network, where we show by simulation that upon using sufficient training, learning-assisted channel prediction can faithfully estimate the current channel. Furthermore, we demonstrate by simulations that our proposed joint hybrid precoder and combiner design outperforms the popular Eigen beamforming (EBF) technique by about 5 dB for a 128 × 32-element MIMO aided OFDM system having 32 sub-carriers.

In pursuit of optimal index modulation -aided multiple-input multiple-output (MIMO) systems, where information is implicitly conveyed by relying on the on/off mechanism of the system's components in addition to the classical amplitude, phase, or frequency components, we present in a tutorial style our novel multi-functional (MF) architecture of layered multi-set (LMS) modulation. This generalized framework subsumes various MIMO techniques exhibiting different multiplexing and diversity functionalities. Our LMS design relies on three constituents, namely the space-time (ST) unit, the layered unit and the spatial switching unit. More specifically, the ST unit relies on the generalized space-time shift keying (GSTSK) scheme, where P - rather than one - out of Q ST dispersion matrices are selected for dispersing an equivalent number of phase-shift keying/quadrature amplitude modulation symbols across the antennas and timeslots. In the layered unit, multiple GSTSK codewords are stacked within the layers of codewords spread over time and space. The spatial switching unit activates Nct out of Nt transmit antennas. Owing to its hierarchical MF architecture, our LMS system strikes a flexible design trade-off between the achievable throughput as well as the attainable diversity gain and it can potentially subsume various conventional MIMO schemes, such as Bell Lab's Layered Space-Time, space-time block codes, layered steered space-time codes, spatial modulation (SM), space-shift keying, linear dispersion codes, generalized SM, STSK, GSTSK, quadrature SM and multi-set STSK . Additionally, we derive the LMS system's discrete-input continuous-output memoryless channel capacity, which encompasses the capacity limit of all the LMS subsidiaries. We also propose a two-stage serially concatenated soft-decision (SD) based LMS detector by relying on an inner and an outer decoder that iteratively exchange their extrinsic information in order to achieve a near-capacity performance. Last but not least, we utilize the extrinsic information transfer charts for analyzing the convergence behavior of our SD-aided coded LMS scheme.

An adaptive array design is proposed for hybrid beamforming in millimeter-wave (mmWave) communications in the context of cloud radio access networks (C-RAN). More explicitly, the adaptive design focuses on the physical layer aspect of C-RAN. The adaptation is performed at two levels, depending on whether the channel is of line-of-sight (LOS) or non-line-of-sight (NLOS) nature. First, the antenna array architecture can be adapted between a fully connected and a sub-array-connected architecture. Then, the employment of a digital precoder in the baseband is decided based on both the channel conditions and the architecture employed. We show that the proposed adaptive design performs better than the fully connected and sub-array-connected constituent designs, when the channel varies between LOS and NLOS scenarios. Then, we extend our proposed adaptive design to mmWave communications in the context of C-RAN, where we propose an adaptive virtual cell formation algorithm, where a user can be connected to one or two remote radio heads, depending on the channel conditions.

Spatial modulation (SM) and multi-set space-time shift-keying (MS-STSK) are capable of striking a flexible design trade-off between the multiplexing and diversity gains attained. These techniques can also be combined with beamfoming for the sake of supporting reliable millimeter-wave (mmWave) communications. In this treatise, we propose an analogue radio-over-fiber (RoF) downlink network relying on SM and MS-STSK combined with beamforming and up-conversion to mmWave carrier frequencies, whilst using all-optical processing. In the proposed analogue RoF (A-RoF) system, most of the digital processing of the baseband SM and MS-STSK is carried out by the central unit and the implicitly carried bits of SM/MS-STSK, such as the antenna index selection and dispersion matrix selection bits, are recovered by the remote radio head of our A-RoF network for creating a multi-functional multiple-input multipleoutput arrangement. As detailed by Hanzo et al., the classic modulated bits are conveyed by the radio signal, without requiring any additional analogue-to-digital conversion and digital-to-analogue conversion before transmission from the antennas. Moreover, A-RoF-aided techniques are invoked for achieving optical processing-aided beamforming and optical up-conversion to an mmWave carrier frequency. Thus, we invoke A-RoF techniques for the generation of our SM/MS-STSK signal with the aid of optically up-converted millimetre-wave beamforming without using any electronic oscillators, mixers, or phase shifters. Furthermore, our A-RoF-aided system's bit error ratio performance is similar to the conventional all-electronic SM/MS-STSK scheme.

Flexible modulation schemes and smart multiple-input multiple-output techniques, as well as low-complexity detectors and preprocessors may become essential for efficiently balancing the bit error ratio performance, throughput, and complexity tradeoff for various application scenarios. Millimeter-Wave systems have a high available bandwidth and the potential to accommodate numerous antennas in a small area, which makes them an attractive candidate for future networks employing spatial modulation and spacetime shift keying (STSK). Non-Orthogonal Multiple Access (NOMA) systems are capable of achieving an increased throughput, by allowing multiple users to share the same resources at the cost of a higher transmission power, or an increased detection (preprocessing) complexity at the receiver (transmitter) of an uplink (downlink) scenario. In this paper, we propose the new concept of joint-alphabet space time shift keying. As an application scenario, we employ it in the context of the uplink of NOMA mm-Wave systems. We demonstrate with the aid of extrinsic information transfer charts that a higher capacity is achievable when compared with STSK, while retaining the attractive flexibility of STSK in terms of its diversity gain and coding rate. Finally, we conceive quantum-assisted detectors for reducing the detection complexity, while attaining a near-optimal performance, when compared with the optimal iterative maximum A posteriori probability detector.

In this treatise, we present the concept of compressed-sensing (CS)-aided space-time shift keying index modulation (STSK-IM), where a virtual domain orthogonal frequency division multiplexing (OFDM) symbol is divided into Na-sized blocks, which carry K STSK codewords over a specific combination of virtual-domain sub-carriers and then converted to the frequency domain with the aid of a CS matrix. We design the system for operation in the milli-meter wave (mmWave) frequency band. Furthermore, we amalgamate our soft-decision-aided scheme both with the concept of coordinate-interleaving as well as a discrete Fourier transform-aided codebook design conceived for analogue beamforming, which is vitally important for mmWave systems. In the proposed system, the number of implicit bits conveyed by the activated sub-carrier frequency index (FI) is determined by the number of the available K to Na subcarrier permutations. Hence, we propose two FI allocation techniques, namely the distinct FI and the shared FI-based schemes, which strike a tradeoff between the attainable sparsity level and the achievable capacity limit. We then introduce a reduced-complexity detection technique in order to mitigate the detection complexity order of the optimum detector from O(Nc · (Q · L)K) to O(N̂c · (Q · L)K), whereN̂c ≤ Nc. We also formulate the discrete-input continuous-output memoryless channel capacity and invoke EXtrinsic Information Transfer charts for characterizing the achievable performance limit of the reduced-complexity aided detector. Finally, we analyze the bit error rate performance of both the uncoded and of our coded CS-aided STSK-IM systems associated with both the optimum and the reduced-complexity detectors.

In this paper, we introduce a novel OFDM-aided multifunctional multiple-input multiple-output scheme based on multi-set space-time shift keying (MS-STSK), where the information transmitted over each subcarrier is divided into two parts: STSK codeword and the implicit antenna combination (AC) index. In MS-STSK, a unique combination of antennas can be activated at each subcarrier to convey extra information over the AC index while additionally transmitting the STSK codeword. Furthermore, inspired by the MS-STSK concept, this scheme is extended also to the frequency domain in the novel context of our multi-space-frequency STSK (MSF-STSK), where the total number of subcarriers is partitioned into blocks to implicitly carry the block's frequency index. The proposed MSF-STSK scheme benefits from the huge bandwidths available at mmWaves for partitioning the total number of OFDM subcarriers into blocks to convey more information over the frequency domain. Both proposed systems use STSK codewords as the basic transmission block, and they can achieve higher data throughput and better BER performance than STSK. Moreover, given that the system is meant to operate at mmWaves, antenna arrays relying on several antenna elements are employed at both the transmitter and receiver for analogue beamforming with the aid of phase shifters and power amplifiers to overcome the effect of high path loss.