Search

New near-capacity continuous-variable quantum key distribution (CV-QKD) reconciliation schemes are proposed, where both the authenticated classical channel (ClC) and the quantum channel (QuC) for QKD are protected by separate forward error correction (FEC) coding schemes. More explicitly, all of the syndrome-based QKD reconciliation schemes found in literature rely on syndrome-based codes, such as low-density parity-check (LDPC) codes. Hence at the current state-of-the-art the channel codes that cannot use syndrome decoding such as the family of convolutional codes (CCs) and polar codes cannot be directly applied. Moreover, the ClC used for syndrome transmission in these schemes is generally assumed to be idealistically error-free, where the realistic additive white Gaussian noise (AWGN) and Rayleigh fading of the ClC have not been taken into account. To circumvent this limitation, a new codeword-based - rather than syndrome-based - QKD reconciliation scheme is proposed, where Alice sends an FECprotected codeword to Bob through a ClC, while Bob sends a separate FEC protected codeword to Alice through a QuC. Upon decoding the codeword received from the other side, the final key is obtained by applying a simple modulo-2 operation to the local codeword and the decoded remote codeword. As a result, first of all, the proposed codeword-based QKD reconciliation system ensures protection of both the QuC and of the ClC. Secondly, the proposed system has a similar complexity at both sides, where both Alice and Bob have an FEC encoder and an FEC decoder. Thirdly, the proposed system makes QKD reconciliation compatible with a wide range of FEC schemes, including polar codes, CCs and irregular convolutional codes (IRCCs), where a near-capacity performance can be achieved for both the QuC and for the ClC. Our simulation results demonstrate that thanks to the proposed regime, the performance improvements of the QuC and of the ClC benefit each other, hence leading to an improved secret key rate (SKR) that inches closer to both the Pirandola-Laurenza-Ottaviani-Banchi (PLOB) bound and to the maximum achievable rate bound.

The demodulation reference signal of the 5G Multiple-Input Multiple-Output Orthogonal Frequency-Division Multiplexing (MIMO-OFDM) waveform has been designed for supporting Minimum Mean-Square Error-Interference Rejection Combining (MMSE-IRC) equalization, which has become the state-of-the-art, owing to its enhanced performance in the case of dense frequency reuse, which is typical in 5G. By contrast, in the 4G LTE system, typically turbo equalization techniques were used. The family of Non-Linear receiver techniques tend to be eminently suitable for tough rank-deficient scenarios, when the received signal constellation becomes linearly non-separable. Hence, we propose a novel receiver for interference-constrained MIMO-OFDM systems, relying on a linear MMSE-IRC detector intrinsically amalgamated with an additional NL equalizer. In this way, we may achieve the best of both worlds, retaining the interference rejection capability of the MMSE-IRC detector and the superior performance of the NL equalizer. Our solution circumvents the potential failure of the MMSE-IRC, when the MIMO channels' degree freedom is completely exhausted by the desired users in case the transmitter has a high number of transmission layers for example. Based on this concept, we then design a novel NL equalizer relying on the Smart Ordering and Candidate Adding (SOCA) algorithm. This reduced complexity NL detection algorithm is particularly well suited for practical hardware implementation using parallel processing at a low latency. Briefly, the proposed scheme employs the MMSE-IRC detector for mitigating the interference. It makes the first estimate of the desired user signals and then uses the SOCA detector for further decontaminating the received signals. It also generates the soft information, enabling turbo equalization, wherein iterative detector and decoder iteratively exchange their soft information. We present BLock Error Rate (BLER) results, which show that the proposed scheme can always achieve superior performance to the conventional MMSE-IRC detector at the cost of increasing the complexity. In some cases, our proposed scheme can obtain about 1.5 dB gain, at the cost of 4 times higher complexity. We demonstrate that the complexity of the SOCA detector can be reduced by adjusting its parameterization or at the cost of reducing the self-consistency of the soft information produced by the SOCA detector, which slightly erodes the BLER performance. In order to mitigate this, we propose to use Deep Learning (DL) for enhancing the accuracy of the soft information. Using this technique, we show that the MMSE-IRC-NL-SOCA detector relying on DL attains about 3 dB gain at the cost of only marginally increasing the complexity, compared to the proposed MMSE-IRC-NL-SOCA scheme.

A new affine-precoded superimposed pilot (AP-SIP) scheme is conceived for both wireless channel and radar target parameter estimation in a millimeter wave (mmWave) multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing (OFDM)-based integrated sensing and communication (ISAC) systems. The AP-SIP scheme leads to enhanced estimation accuracy and improved utilization of spectral resources. Initially, the pilot-assisted radar (PAR) and data-assisted radar (DAR) parameter estimation models are separately developed for the estimation of the radar target parameters. Subsequently, these are combined into a joint pilot-data radar (JPDR) model for simultaneously harnessing both the signals to further boost the estimation accuracy. The sparse Bayesian learning (BL)-based joint-BL (J-BL) technique is developed for this system that efficiently exploits the sparsity of the radar scattering environment. Next, a group sparse BL (G-BL) technique is also derived that exploits the group sparsity across subcarriers for the estimation of the wireless beamspace channel vector, which outperforms the competing techniques, including conventional sparse BL. The optimal pilot, transmit precoder (TPC) and receive combiner (RC) are determined at the dual-function radar-communication (DFRC) base station (BS) and also at the user equipment (UE) for maximizing the performance attained. The Bayesian Cramer-Rao bounds (BCRB) are explicitly derived to benchmark the performance of the wireless channel and radar target parameter estimation. Simulation results are provided to demonstrate the improved performance of the proposed schemes considering multiple metrics, such as the normalized mean squared error (NMSE), bit error rate (BER) and achievable spectral efficiency (ASE).

Index Modulated Orthogonal Frequency-Division Multiplexing (IM-OFDM) based Integrated Sensing and Communication (ISAC) is potentially capable of outperforming Orthogonal Frequency-Division Multiplexing (OFDM) ISAC, since Index Modulation (IM) concentrates increased power on the activated subcarriers. This has been confirmed by authoritative publications for the IM-OFDM communication component. However, no evidence is found in the open literature that IM-OFDM sensing is capable of outperforming OFDM sensing, because the blank subcarriers impair the system's sensing functionality. The existing solutions either insert a radar signal into the deactivated subcarriers, thereby using a radar signal for sensing, or employ compressed sensing, which leads to a lower sensing performance than OFDM ISAC. Hence, a novel low complexity algorithm is proposed for ensuring that an IM-OFDM ISAC system outperforms its OFDM ISAC counterpart for both communication and sensing. The algorithm collects observations of the received signal to “fill in” the blank subcarriers in the sensing data created by IM-OFDM, whilst taking advantage of the increased subcarrier power attained by activating fewer subcarriers. This occurs over multiple transmit frames, which inevitably delays the target estimation. As OFDM sensing assumes low target velocities, this delay is shown to have a negligible impact on the sensing performance of IM-OFDM. The simulation results show that IM-OFDM ISAC is indeed capable of outperforming its OFDM ISAC counterpart for both sensing and communication. The impact of block interleaving and of the modulation type on the sensing performance is also discussed.

Asynchronous distributed hybrid beamformers (ADBF) are conceived for minimizing the total transmit power subject to signal-to-interference-plus-noise ratio (SINR) constraints at the users. Our design requires only limited information exchange between the base stations (BSs) of the mmWave multi-cell coordinated (MCC) networks considered. To begin with, a semidefinite relaxation (SDR)-based fully-digital (FD) beamformer is designed for a centralized MCC system. Subsequently, a Bayesian learning (BL) technique is harnessed for decomposing the FD beamformer into its analog and baseband components and construct a hybrid transmit precoder (TPC). However, the centralized TPC design requires global channel state information (CSI), hence it results in a high signaling overhead. An alternating direction based method of multipliers (ADMM) technique is developed for a synchronous distributed beamformer (SDBF) design, which relies only on limited information exchange among the BSs, thus reducing the signaling overheads required by the centralized TPC design procedure. However, the SDBF design is challenging, since it requires the updates from the BSs to be strictly synchronized. As a remedy, an ADBF framework is developed that mitigates the inter-cell interference (ICI) and also control the asynchrony in the system. Furthermore, the above ADBF framework is also extended to the robust ADBF (R-ADBF) algorithm that incorporates the CSI uncertainty into the design procedure for minimizing the the worst-case transmit power. Our simulation results illustrate both the enhanced performance and the improved convergence properties of the ADMM-based ADBF and R-ADBF schemes.

Low-complexity fusion rules relying on hybrid combining are proposed for decision fusion in frequency selective millimeter wave (mmWave) massive multiple-input multiple-output (MIMO) sensor networks (SNs). Both centralized (C-MIMO) and distributed (D-MIMO) antenna architectures are considered, where the error-prone local sensor decisions are transmitted over orthogonal subcarriers to a fusion center (FC) employing a large antenna array. Fusion rules are designed for the FC, followed by closed-form expressions of the false alarm and detection probabilities to comprehensively characterize the performance of distributed detection. Furthermore, efficient transmit signaling vectors are designed for optimizing the detection performance. Both the asymptotic performance analysis and the pertinent power reduction laws are presented for the large antenna regime considering both the C-MIMO and D-MIMO topologies, which potentially lead to a significant transmit power reduction. Low-complexity fusion rules and their analyses are also given for the realistic scenario of incorporating channel state information (CSI) uncertainty, where the sparse Bayesian learning (SBL) framework is utilized for the estimation of the sparse frequency selective mmWave massive MIMO channel. Finally, the performance of the proposed low-complexity detectors is characterized through extensive simulation results for different scenarios.

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.

The downlink (DL) of a reconfigurable intelligent surface (RIS)-aided multi-user (MU) millimeter wave (mmWave) multiple-input multiple-output (MIMO) system relying on a non-diagonal RIS (NDRIS) phase shift matrix is considered. A max-min fairness (MMF) problem is formulated under the total transmit power constraint while employing joint active hybrid beamforming (HBF) both at the base station (BS) as well as at each user equipment (UE), and passive beamforming at the NDRIS. To solve this non-convex problem, a sequential optimization method is conceived, wherein the UE having the poorest channel is identified first, which is termed as the worst-case UE. Then the phase shifter coefficients of the NDRIS are optimized using the alternating direction method of multipliers (ADMM) followed by the hybrid transmit precoder (TPC) and receiver combiner (RC) design using the Karcher mean, the least squares and the regularized zero forcing (RZF) principles. Finally, the optimal power allocation is computed using the path-following algorithm. Simulation results show that the proposed NDRIS-HBF system yields an improved worst-case UE rate in comparison to its conventional diagonal RIS (DRIS)-HBF counterpart, while approaching the half-duplex relay (HDR)-HBF benchmark for large values of the number of reflecting elements (REs). Furthermore, the energy efficiency (EE) of the NDRIS structure is significantly higher than that of the DRIS, HDR systems, while being higher than that achieved by the full-duplex relay (FDR) system at high SNR.

A new multi-antenna transmission scheme, referred to as difference-based spatial modulation (DBSM), is proposed to enhance the reliability of spatial modulation (SM). With the DBSM, a pair of amplitude/phase-shift keying (APSK) symbols are conveyed by each channel-use, but only one of them is radiated from a single transmit antenna. In contrast to conventional SM, DBSM selects the active transmit antenna based on the difference between the two symbols of a channel-use, rather than one of them. The design principle of the APSK constellation in the DBSM is detailed, which guarantees that the mean Euclidean distance between any two DBSM signals mapped by a pair of complementary bit-sequences is the largest possible among all the candidates. In this way, DBSM improves the error performance of conventional SM, even though they have the same achievable data rate given the same system configuration. We demonstrate that channel coding techniques, e.g., convolutional and turbo codes, will enhance the performance gain achieved by DBSM over conventional SM. Both theoretical analysis and simulation results are provided to substantiate the advantages of our proposed DBSM scheme.

Sparse multipath channel impulse response (CIR) estimation schemes are conceived for optical orthogonal frequency division multiplexing (O-OFDM) visible light communication (VLC) systems. We commence by deriving the input-output models for both asymmetrically clipped optical OFDM (ACO-OFDM) and direct current-biased optical OFDM (DCO-OFDM) systems. A multipath CIR model is derived that captures both the diffusive as well as specular reflections of the VLC channel. Next, we introduce both the sparsity-agnostic conventional least square (LS) and the linear minimum mean square error (LMMSE) channel estimation (CE) techniques. This is followed by the orthogonal matching pursuit (OMP)-based sparse recovery technique, which exploits the delay-domain sparsity of the CIR. Furthermore, a novel sparse multipath CIR estimation scheme is proposed using the Bayesian learning (BL) framework, which requires only a limited number of pilot subcarriers, hence resulting in a reduced pilot overhead as compared to other state-of-the-art (SoA) CE techniques. The Bayesian Cramer Rao lower bound (BCRLB) as well as the Oracle-minimum mean squared error (O-MMSE) estimator are also derived for benchmarking the estimation performance of the proposed BL-based framework. Our simulation results demonstrate that the proposed BL method outperforms other existing sparse and conventional CE methods in terms of various metrics, such as the normalized mean-square-error (NMSE), the outage probability (OP), and the bit error-rate (BER) despite its reduced pilot overhead.

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.

We propose a novel quantum turbo short-block code, which subsumes multiple-rate quantum short-block codes (MR-QSBCs) as the outer codes and a quantum unity-rate code (QURC) as the inner code. The proposed design is denoted as MR-QSBC-QURC. More specifically, the proposed design exhibits multiple quantum coding rates despite relying only on a single quantum encoder. Moreover, the flexibility offered by the single-encoder MR-QSBCs enables us to leverage extrinsic information transfer (EXIT)-chart based heuristic optimization for determining the optimal weighting in the fractional encoding of MR-QSBCs. Our simulation results show that the MR-QSBC-QURC scheme conceived performs relatively close to the ultimate limit of the quantum hashing bound. Specifically, when considering the target quantum coding rates of $r_{Q} = \lbrace 0.3, 0.4, 0.5, 0.6, 0.7 \rbrace $ , the MR-QSBC-QURC operates at a distance of $D = \lbrace 0.042, 0.029, 0.030, 0.024, 0.017 \rbrace $ from the quantum hashing bound, respectively, at a quantum bit error ratio (QBER) of 10−3.

The energy efficiency (EE) of the reconfigurable intelligent surface (RIS) aided multiuser (MU) millimeterwave (mmWave) multi-input multi-output (MIMO) downlink is maximized by jointly optimizing the transmit power and number of active radio frequency (RF) chains. The base band (BB) transmit precoder (TPC) of this system is derived first by using the zero-forcing (ZF) technique for a given RF TPC and combiner at the base station (BS) as well as the phase shift matrix at the RIS. Furthermore, the ergodic sum-rate of the system is derived assuming a large number of antennas at the BS and users, followed by formulating the EE maximization problem subject to specific constraints imposed on the transmit power and on the number of RF chains. Then a low complexity sequential method is proposed for solving the above optimization problem, thus determining both the optimal transmit power and the number of active RF chains. Finally, simulation results are presented for demonstrating the improved EE performance of an RIS-aided mmWave MIMO system for a limited number of RF chains and transmit power at the BS.

In a transmit preprocessing aided frequency division duplex (FDD) massive multi-user (MU) multiple-input multiple-output (MIMO) scheme assisted orthogonal frequency-division multiplexing (OFDM) system, it is required to feed back the frequency domain channel transfer function (FDCHTF) of each subcarrier at the user equipment (UE) to the base station (BS). The amount of channel state information (CSI) to be fed back to the BS increases linearly with the number of antennas and subcarriers, which may become excessive. Hence we propose a novel CSI feedback compression algorithm based on compressive sensing (CS) by designing a common dictionary (CD) to reduce the CSI feedback of existing algorithms. Most of the prior work on CSI feedback compression considered single-UE systems. Explicitly, we propose a common dictionary learning (CDL) framework for practical frequency-selective channels and design a CD suitable for both single-UE and multi-UE systems. A set of two methods is proposed. Specifically, the first one is the CDL-K singular value decomposition (KSVD) method, which uses the K-SVD algorithm. The second one is the CDL-orthogonal Procrustes (OP) method, which relies on solving the orthogonal Procrustes problem. The CD conceived for exploiting the spatial correlation of channels across all the subcarriers and UEs compresses the CSI at each UE, and upon reception reconstructs it at the BS. Our simulation results show that the proposed dictionary's estimated channel vectors have lower normalized mean-squared error (NMSE) than the traditional fixed Discrete Fourier Transform (DFT) based dictionary. The CSI feedback is reduced by 50%, and the memory reduction at both the UE and BS starts from 50% and increases with the number of subcarriers.

Unified statistics are valuable in the performance analysis of communication systems. In this context, Fox's H-function has been shown to be eminently suitable for diverse scenarios. Another pivotal requirement is to have a low computational complexity, which is often hard to achieve for generalized models. Given this motivation, in this article we have presented high-power, low-complexity solutions for the outage probability (OP) and the average symbol error probability (SEP). Additionally, diversity techniques are harnessed for mitigating the effect of fading, which are then analysed based on the results derived. The entire methodology is governed by the origin probability density function rather than approximating the performance metrics under high-power. All the presented mathematical expressions are compared and validated through computer simulations (Monte-Carlo) to verify the accuracy of the proposed framework.

A universal decoding scheme is conceived for quantum stabilizer codes (QSCs) by appropriately adapting the ‘guessing random additive noise decoding’ (GRAND) philosophy of classical domain codes. We demonstrate that the generalized quantum decoder conceived is eminently suitable for different QSC decoding paradigms, namely for both stabilizer-measurement-based as well as the inverse-encoder-based decoding. We then harness the resultant decoder for both quantum Bose-Chaudhuri-Hocquenghem (BCH) codes and quantum polar codes and quantify both their quantum block error rate (QBLER), and QBLER per logical qubits as well as their decoding complexity. Furthermore, we provide a parametric study of the associated design trade-offs and offer design guideline for the implementation of GRAND-based QSC decoders.

A hybrid transceiver architecture is conceived for a cognitive radio (CR) aided millimeter wave (mmWave) multiuser (MU) multiple-input multiple-output (MIMO) downlink system relying on multiple radio frequency (RF) chains both at the CR base station (CBS) and the secondary users (SUs). To begin with, a hybrid transceiver design algorithm is proposed for the CBS and SUs, to maximize the sum spectral efficiency (SE) by decoupling the hybrid transceiver into a blind minimum mean squared error (MMSE) receiver combiner (RC) and optimal-capacity two-stage hybrid transmit precoder (TPC) components. These RC-weights and TPC-weights are subsequently found by using the popular simultaneous orthogonal matching pursuit (SOMP) technique. A closed-form solution is derived for the optimal power allocation that maximizes the sum SE under the associated interference and transmit power constraints. To achieve user fairness, we also propose an optimal power allocation scheme for maximizing the geometric mean (GM) of the SU rates. Finally, a low-complexity limited feedback aided hybrid transceiver is designed, which relies on the random vector quantization (RVQ) technique. Our simulation results demonstrate that an improved SE is achieved in comparison to the state-of-the-art techniques.

With the rapid deployment of Next-Generation Networks (NGN), the research community has initiated discussions on an entirely new suite of optical enabling techniques. To pave the way for the development of future wireless networks, this article aims to unify the existing infrared, visible light, and ultraviolet subbands while also exploring the potential of the Petahertz (PHz) band to support extremely bandwidth-thirsty telepresence style applications. Our focus is on the emerging Petahertz Communication (PetaCom) framework, scenario-dependent propagation channels, modulation schemes, system performance, multiple access techniques, and networking. We conclude with a range of PetaCom challenges and open research issues.

Improved low-density spreading (LDS) code designs based on the Gaussian separability criterion are conceived. We show that the bit error rate (BER) hinges not only on the minimum distance criterion, but also on the average Gaussian separability margin. If two code sets have the same minimum distance, the code set having the highest Gaussian separability margin will lead to a lower BER. Based on the latter criterion, we develop an iterative algorithm that converges to the best known solution having the lowest BER. Our improved LDS code set outperforms the existing LDS designs in terms of its BER performance both for binary phase-shift keying (BPSK) and for quadrature amplitude modulation (QAM) transmissions. Furthermore, we use an appallingly low-complexity minimum mean-square estimation (MMSE) and parallel interference cancellation (PIC) (MMSE-PIC) technique, which is shown to have comparable BER performance to the potentially excessive-complexity maximum-likelihood (ML) detector. This MMSE-PIC algorithm has a much lower computational complexity than the message passing algorithm (MPA).Code sets for MPA are designed similar to low-density parity-check (LDPC) codes to avoid cycles and to increase girth of the Tanner graph, code sets that are “optimal” for MMSE-PIC might not be optimal for MPA.

This article presents a framework for converting wireless signals into structured datasets, which can be fed into machine learning algorithms for the detection of active eavesdropping attacks at the physical layer. More specifically, a wireless communication system, which consists of an access point (AP), K legitimate users and an active eavesdropper, is considered. To detect the eavesdropper who breaks into the system during the authentication phase, we first build structured datasets based on different features and then apply sophisticated support vector machine (SVM) classifiers to those structured datasets. To be more specific, we first process the signals received by the AP and then define a pair of statistical features based on the post-processing of the signals. By arranging for the AP to simulate the entire process of transmission and the process of constructing features, we form the so-called artificial training data (ATD). By training SVM classifiers on the ATD, we classify the received signals associated with eavesdropping attacks and nonattacks, thereby detecting the presence of the eavesdropper. Two SVM classifiers are considered, including a classic twin-class SVM (TC-SVM) and a single-class SVM (SC-SVM). While the TC-SVM is preferred in the case of having perfect channel state information (CSI) of all channels, the SC-SVM is preferred in the realistic scenario when we have only the CSI of legitimate users. We also evaluate the accuracy of the trained models depending on the choice of kernel functions, the choice of features and on the eavesdropper's power. Our numerical results show that careful parameter-tuning is required for exceeding an eavesdropper detection probability of 95%.

Unmanned aerial vehicle (UAV)-assisted device-to-device (D2D) communications can be deployed flexibly thanks to UAVs' agility. By exploiting the direct D2D interaction supported by UAVs, both the user experience and network performance can be substantially enhanced at public events. However, the continuous moving of D2D users, limited energy and flying time of UAVs are impediments to their applications in real-time. To tackle this issue, we propose a novel model based on deep reinforcement learning in order to find the optimal solution for the energy-harvesting time scheduling in UAV-assisted D2D communications. To make the system model more realistic, we assume that the UAV flies around a central point, the D2D users move continuously with random walk model and the channel state information encountered during each time slot is randomly time-variant. Our numerical results demonstrate that the proposed schemes outperform the existing solutions. The associated energy efficiency game can be solved in less than one millisecond by an off-the-shelf processor using trained neural networks. Hence our deep reinforcement learning techniques are capable of solving real-time resource allocation problems in UAV-assisted wireless networks.

The fifty-year progress of faster-than-Nyquist (FTN) signaling is surveyed. FTN signaling exploits non-orthogonal dense symbol packing in the time domain for the sake of increasing the data rate attained. After reviewing the system models of both the conventional Nyquist-based and FTN signaling transceivers, we survey the evolution of FTN techniques, including their low-complexity detection and channel estimation. Furthermore, in addition to the classic FTN signaling philosophy, we introduce the recent frequency-domain filtering and precoding aided schemes. When relying on precoding, the information rate of FTN signaling becomes related to the eigenvalues of an FTN-specific intersymbol interference matrix, which provides a unified framework for the associated information-theoretic analysis and simplifies the associated power allocation specifically designed for increasing the information rate attained. We show that the FTN signaling scheme combined with bespoke power allocation employing a realistic raised-cosine shaping filter achieves the Shannon capacity associated with ideal rectangular shaping filters.

Relying on multi-hop communication techniques, aeronautical ad hoc networks (AANETs) seamlessly integrate ground base stations (BSs) and satellites into aircraft communications for enhancing the on-demand connectivity of planes in the air. The goal of the paper is to assess the performance of the classic shortest-path routing algorithm in the context of the real flight data collected in the North-Atlantic Region. Specifically, in this integrated AANET context we investigate the shortest-path routing problem with the objective of minimizing the total delay of the in-flight connection from the ground BS subject to certain minimum-rate constraints for all selected links in support of low-latency and high-speed services. Inspired by the best-first search and priority queue concepts, we model the problem formulated by a weighted digraph and find the optimal route based on the shortest-path algorithm. Our simulation results demonstrate that aircraft-aided multi-hop communications are capable of reducing the total delay of satellite communications, when relying on real historical flight data.

Integrated ground-air-space (IGAS) networks intrinsically amalgamate terrestrial and non-terrestrial communication techniques in support of universal connectivity across the globe. Multi-hop routing over the IGAS networks has the potential to provide long-distance highly directional connections in the sky. For meeting the latency and reliability requirements of in-flight connectivity, we formulate a multi-objective multi-hop routing problem in aeronautical ad hoc networks (AANETs) for concurrently optimizing multiple end-to-end performance metrics in terms of the total delay and the throughput. In contrast to single-objective optimization problems that may have a unique optimal solution, the problem formulated is a multi-objective combinatorial optimization problem (MOCOP), which generally has a set of trade-off solutions, called the Pareto optimal set. Due to the discrete structure of the MOCOP formulated, finding the Pareto optimal set becomes excessively complex for large-scale networks. Therefore, we employ a multi-objective evolutionary algorithm (MOEA), namely the classic NSGA-II for generating an approximation of the Pareto optimal set. Explicitly, with the intrinsic parallelism of MOEAs, the MOEA employed starts with a set of candidate solutions for creating and reproducing new solutions via genetic operators. Finally, we evaluate the MOCOP formulated for different networks generated both from simulated data as well as from real historical flight data. Our simulation results demonstrate that the utilized MOEA has the potential of finding the Pareto optimal solutions for small-scale networks, while also finding a set of high-performance nondominated solutions for large-scale networks.

In the past few years, a large body of literature has been created on downlink Non-Orthogonal Multiple Access (NOMA), employing superposition coding and Successive Interference Cancellation (SIC), in multi-antenna wireless networks. Furthermore, the benefits of NOMA over Orthogonal Multiple Access (OMA) have been highlighted. In this paper, we take a critical and fresh look at the downlink Next Generation Multiple Access (NGMA) literature. Instead of contrasting NOMA with OMA, we contrast NOMA with two other multiple access baselines. The first is conventional Multi-User Linear Precoding (MU–LP), as used in Space-Division Multiple Access (SDMA) and multi-user Multiple-Input Multiple-Output (MIMO) in 4G and 5G. The second, called Rate-Splitting Multiple Access (RSMA), is based on multi-antenna Rate-Splitting (RS). It is also a non-orthogonal transmission strategy relying on SIC developed in the past few years in parallel and independently from NOMA. We show that there is some confusion about the benefits of NOMA, and we dispel the associated misconceptions. First, we highlight why NOMA is inefficient in multi-antenna settings based on basic multiplexing gain analysis. We stress that the issue lies in how the NOMA literature, originally developed for single-antenna setups, has been hastily applied to multi-antenna setups, resulting in a misuse of spatial dimensions and therefore loss in multiplexing gains and rate. Second, we show that NOMA incurs a severe multiplexing gain loss despite an increased receiver complexity due to an inefficient use of SIC receivers. Third, we emphasize that much of the merits of NOMA are due to the constant comparison to OMA instead of comparing it to MU–LP and RS baselines. We then expose the pivotal design constraint that multi-antenna NOMA requires one user to fully decode the messages of the other users. This design constraint is responsible for the multiplexing gain erosion, rate and spectral efficiency loss, ineffectiveness to serve a large number of users, and inefficient use of SIC receivers in multi-antenna settings. Our analysis and simulation results confirm that NOMA should not be applied blindly to multi-antenna settings, highlight the scenarios where MU–LP outperforms NOMA and vice versa, and demonstrate the inefficiency, performance loss, and complexity disadvantages of NOMA compared to RSMA. The first takeaway message is that, while NOMA is suited for single-antenna settings (as originally intended), it is not efficient in most multi-antenna deployments. The second takeaway message is that another non-orthogonal transmission framework, based on RSMA, exists which fully exploits the multiplexing gain and the benefits of SIC to boost the rate and the number of users to serve in multi-antenna settings and outperforms both NOMA and MU–LP. Indeed, RSMA achieves higher multiplexing gains and rates, serves a larger number of users, is more robust to user deployments, network loads and inaccurate channel state information and has a lower receiver complexity than NOMA. Consequently, RSMA is a promising technology for NGMA and future networks such as 6G and beyond.

Fiber-wireless-Fiber links require alignment between the transmitter and receiver to a high degree of precision (typically ~ 0.01° for a link of a few meters), and channel coding can be used for mitigating the link margin reduction caused by the limited precision of the beam-steering and tracking system. This paper reports results from an experimental study of the misalignment tolerance attained by channel coding. Explicitly, the received power penalties imposed by misalignment are characterized, and then forward error correction techniques are adopted for mitigating the performance degradation inflicted, which is quantified experimentally. Our results characterize trade-offs between coding rate, decoding complexity and the degree of misalignment. Overall, an improvement of the tolerance to misalignment up to ~50% was attained for coded links compared with the uncoded counterpart.

Multi-access edge computing (MEC) is capable of meeting the challenging requirements of next-generation networks, e.g., 6G, as a benefit of providing computing and caching capabilities in the close proximity of the users. However, the traditional MEC architecture relies on specialized hardware and its bespoke software functions are closely integrated with the hardware, hence it is too rigid for supporting the rapidly evolving scenarios in the face of the demanding requirements of 6G. As a remedy, we conceive the compelling concept of open-source cellular networking and intrinsically amalgamate it with MEC, which is defined by open-source software running on general-purpose hardware platforms. Specifically, an open-source MEC (OS-MEC) scheme is presented relying on a pair of core principles: the decoupling of the MEC functions and resources from each other with the aid of network function virtualization (NFV); as well as the reconfiguration of the disaggregated MEC functions and resources into customized edge instances. This philosophy allows operators to adaptively customize their users’ networks. Then, we develop improved networking functions for OS-MEC decoupling and discuss both its key components as well as the process of OS-MEC reconfiguration. The typical use cases of the proposed OS-MEC scheme are characterized with the aid of a small-scale test network. Finally, we discuss some of the potential open-source-related technical challenges when facing 6G.

Experiments conducted on open-access cloud-based IBM Quantum devices are presented for characterizing their fault tolerance using [4, 2, 2]-encoded gate sequences. Up to 100 logical gates are activated in the Ibmq_Bogota and Ibmq_Santiago devices and we found that a [4, 2, 2] code’s logical gate set may be deemed fault-tolerant for gate sequences larger than 10 gates. However, certain circuits did not satisfy the fault tolerance criterion. In some cases the encoded-gate sequences show a high error rate that is lower bounded at ≈0.1, whereby the error inherent in these circuits cannot be mitigated by classical post-selection. A comparison of the experimental results to a simple error model reveal that the dominant gate errors cannot be readily represented by the popular Pauli error model. Finally, it is most accurate to assess the fault tolerance criterion when the circuits tested are restricted to those that give rise to an output state with a low dimension.

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.

Visible light communication (VLC) is capable of satisfying the escalating data rate demand in indoor scenarios. The lighting infrastructure may also be exploited in the downlink of a wireless local area network (WLAN), where light-emitting diodes are used for transmitting information. The backhaul of VLC access points (APs) constitutes a potential bottleneck in WLANs, especially in large rooms where the number of APs is high. Thus, an alternative reflection-based cooperative wireless system concept is proposed, where no wired connections are required for connecting the APs. Then, its data rate analysis is developed based on the signal-to-noise ratio attained. The results show that the performance of the cooperative wireless system advocated closely approximates the performance of the traditional wired backhaul. Finally, we demonstrate by using Jain's index of fairness that the proposed scheme provides fair coverage quality for all users.

With the escalating demand for high speed, high reliability, low latency, low cost and ubiquitous connectivity, the telecommunications industry is entering a new era where the ultimate optimality of the current wireline-wireless access network has to be achieved. Regarding the current wireline network paradigm, dominated by the copper-based digital subscriber lines (DSL) technology, multi-Gigabit data rate is the ambitious design objective at the customer end for the forthcoming ITU-T G.mgfast standard. In order to prepare for the new challenges in the era of total network convergence, both the wireline and the wireless community must be able to think beyond their respective conventions and learn from each other if necessary. Overall, the current DSL-based wireline network architecture is prone to the mutual interference resulting in far-end crosstalk (FEXT). The newly expanded 424/848 MHz spectrum of the ambitious G.mgfast project introduces far higher FEXT than that over the current 212/30 MHz G.fast/VDSL2 band. Additionally, the coexistence of multiple standards will also cause `alien' FEXT. In these cases, using the plain zero forcing precoding (ZFP) will no longer attain a sufficiently high performance. However, as shown in the field of wireless communications, using lattice reduction as a signal space remapping technique significantly improves the performance of traditional multi-user detectors (MUD) and of the respective multi-user precoders (MUP). These promising techniques have largely remained unexploited in commercial wireless communications, due to their complexity in the face of the rapidly fluctuating wireless channels. In this survey, we present an overview of the state-of-the-art in wireline access network and an outlook for recent technological advances in the holistic `wireline + wireless' access network in the context of network convergence, focusing on the dominant challenge of FEXT mitigation in future DSL networks. Against this background, we investigate both the family of linear precoding and of the Tomlinson-Harashima precoding (THP) schemes conceived for classic DSL. Furthermore, we present a tutorial on the family of lattice reduction aided MUPs, as well as quantifying their expected performances in realistic DSL scenarios. As a by-product, we also demonstrate the duality between MUP and MUD, in the hope that the fifty years' history of MUD could be used to accelerate the development of efficient near-optimal MUPs for future DSL. Under the recommended operating conditions of the 212 MHz profile of G.fast, our benchmark comparisons indicate that the lattice reduction aided techniques are very powerful compared to conventional schemes. In particular, their good performances do not rely on optimized spectrum balancing, and they are also shown to be relatively robust against channel state information (CSI) estimation error, under the assumption of perfectly time-invariant DSL channels.

Unmanned aerial vehicles (UAVs) with the potential of providing reliable high-rate connectivity, are becoming a promising component of future wireless networks. A UAV collects data from a set of randomly distributed sensors, where both the locations of these sensors and their data volume to be transmitted are unknown to the UAV. In order to assist the UAV in finding the optimal motion trajectory in the face of the uncertainty without the above knowledge whilst aiming for maximizing the cumulative collected data, we formulate a reinforcement learning problem by modelling the motion-trajectory as a Markov decision process with the UAV acting as the learning agent. Then, we propose a pair of novel trajectory optimization algorithms based on stochastic modelling and reinforcement learning, which allows the UAV to optimize its flight trajectory without the need for system identification. More specifically, by dividing the considered region into small tiles, we conceive state-action-reward-state-action (Sarsa) and Q-learning based UAV-trajectory optimization algorithms (i.e., SUTOA and QUTOA) aiming to maximize the cumulative data collected during the finite flight-time. Our simulation results demonstrate that both of the proposed approaches are capable of finding an optimal trajectory under the flight-time constraint. The preference for QUTOA vs. SUTOA depends on the relative position of the start and the end points of the UAVs.

Quantum secure direct communication (QSDC) is an important branch of quantum communication that is capable of directly transmitting secret messages over a quantum channel. It may be viewed as a concrete realization of Wyner's wiretap channel theory, which ensures the reliable and secure communication of information in the presence of noise and eavesdropping. Hence it is a fully-fledged quantum-communications protocol, which does not require a separate secret key negotiation phase. By contrast, its quantum key distribution (QKD) counterpart represents a secret key-negotiation protocol, which has to be followed up by a separate classical communication session. The essential difference between these two modes of quantum communication lies in the employment of a block-based data transmission technique, proposed by Long and Liu in 2000. However, the original block-based data transmission requires quantum memory, which is not widely available at the time of writing. Recently, this difficulty has been overcome by using classical coding theory, which has been successfully applied to the single-qubit DL04 QSDC. Here we will present a single-photon-memory QSDC protocol based on entangled pairs of photons. We commence by comparing QSDC to QKD, followed by an example of the single-photon QSDC and single-photon QKD protocol. Then we continue by modifying the so-called two-step QSDC protocol designed for deterministic QKD by reducing the number of qubits in a block into a single one, in which Alice prepares Einstein-Podolsky-Rosen (EPR) photon pairs and partitions them into two parts: the so-called pioneer qubit and the follow-up qubit. The pioneer photon is transferred first to Bob, while the follow-up photon is used either for performing encoding or for eavesdropping detection. Bob extracts the candidate key by combining the two particles of the EPR pair to perform Bell-basis measurement. Then the protocol is transformed into a single-photon-memory QSDC using coding theory. Our theoretical analysis shows that the resultant protocol is robust to individual attacks. Additionally, a high communication efficiency is achieved.

The recent outbreak of the coronavirus disease 2019 (COVID-19) has rapidly become a pandemic, which calls for prompt action in identifying suspected cases at an early stage through risk prediction. To suppress its further spread, we exploit the social relationships between mobile devices in the Social Internet of Things (SIoT) to help control its propagation by allocating the limited protective resources to the influential so-called high-degree individuals to stem the tide of precipitated spreading. By exploiting the so-called differential contact intensity and the infectious rate in susceptible-exposed-infected-removed (SEIR) epidemic model, the resultant optimization problem can be transformed into the minimum weight vertex cover (MWVC) problem of graph theory. To solve this problem in a high-dynamic random network topology, we propose an adaptive scheme by relying on the graph embedding technique during the state representation and reinforcement learning in the training phase. By relying on a pair of real-life datasets, the results demonstrate that our scheme can beneficially reduce the epidemiological reproduction rate of the infection. This technique has the potential of assisting in the early identification of COVID-19 cases.

Quantum processors require Quantum Error Correction Codes (QECC's) for improving the fidelity of quantum logic gates. Fault tolerant QECC's are capable of providing error rate improvements in quantum processors as long as the components are operating below a certain gate error probability. In this contribution, we quantify the depolarization probability bound, below which transversal QECC's would give a better error probability than an uncoded gate. Both a low-complexity repetition code and Steane's 7-bit QECC are characterized.

One of the key enablers of future wireless communications is constituted by massive multiple-input multiple-output (MIMO) systems, which can improve the spectral efficiency by orders of magnitude. In existing massive MIMO systems, however, conventional phased arrays are used for beamforming. This method results in excessive power consumption and high hardware costs. Recently, reconfigurable intelligent surface (RIS) has been considered as one of the revolutionary technologies to enable energy-efficient and smart wireless communications, which is a two-dimensional structure with a large number of passive elements. In this paper, we develop a new type of high-gain yet low-cost RIS that bears 256 elements. The proposed RIS combines the functions of phase shift and radiation together on an electromagnetic surface, where positive intrinsic-negative (PIN) diodes are used to realize 2-bit phase shifting for beamforming. This radical design forms the basis for the world's first wireless communication prototype using RIS having 256 two-bit elements. The prototype consists of modular hardware and flexible software that encompass the following: the hosts for parameter setting and data exchange, the universal software radio peripherals (USRPs) for baseband and radio frequency (RF) signal processing, as well as the RIS for signal transmission and reception. Our performance evaluation confirms the feasibility and efficiency of RISs in wireless communications. We show that, at 2.3 GHz, the proposed RIS can achieve a 21.7 dBi antenna gain. At the millimeter wave (mmWave) frequency, that is, 28.5 GHz, it attains a 19.1 dBi antenna gain. Furthermore, it has been shown that the RIS-based wireless communication prototype developed is capable of significantly reducing the power consumption.

Deep neural network (DNN)-aided spatial modulation (SM) is conceived. In particular, a pair of DNN structures are designed for replacing the conventional model-based channel estimators and detectors. As our first prototype, the conventional DNN estimates the channel relying on the pilot symbols and then carries out data detection in a data-driven manner. By contrast, our new DeepSM scheme is proposed for operation in more realistic time-varying channels, which updates the channel state information (CSI) at each time-slot (TS) before detecting the data. Hence, our novel DeepSM scheme is capable of performing well even in highly dynamic communication environments. Finally, our simulations show that the proposed DeepSM outperforms the conventional model-based channel estimation and data detection for transmission over time-varying channels.

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.

An encoderless quantum code is capable of connecting quantum information by replacing the encoder circuit with a fault-tolerant single-qubit gate arrangement. As a further benefit, in contrast to state preparation techniques, our encoderless scheme requires no prior knowledge of the input information, therefore totally unknown states can be encoded fault-tolerantly. Our encoderless quantum code delivers a frame error rate that is three orders of magnitude lower than that of the corresponding scheme relying on a non-fault-tolerant encoder, when the gate error probability is as high as 10-3.

The Internet of Things (IoT) is a transformative technology, which is revolutionizing our everyday life by connecting everyone and everything together. The massive number of devices are preferably connected wirelessly because of the easy installment and flexible deployment. However, the broadcast nature of the wireless medium makes the information accessible to everyone including malicious users, which should hence be protected by encryption. Unfortunately, the secure and efficient provision of cryptographic keys for low-cost IoT devices is challenging; weak keys have resulted in severe security breaches, as evidenced by numerous notorious cyberattacks. This paper provides a comprehensive survey of lightweight security solutions conceived for IoT, relying on key generation from wireless channels. We first introduce the key generation fundamentals and protocols. We then examine how to apply this emerging technique to secure IoT and demonstrate that key generation relying on the randomness of wireless channels is eminently suitable for IoT. This paper reviews the extensive research efforts in the areas of theoretical modelling, simulation based validation and experimental exploration. We finally discuss the hurdles and challenges that key generation is facing and suggest future work to make key generation a reliable and secure solution to safeguard the IoT.

Quantum error mitigation (QEM) is a promising technique of protecting hybrid quantum-classical computation from decoherence, but it suffers from sampling overhead which erodes the computational speed. In this treatise, we provide a comprehensive analysis of the sampling overhead imposed by QEM. In particular, we show that Pauli errors incur the lowest sampling overhead among a large class of realistic quantum channels having the same average fidelity. Furthermore, we show that depolarizing errors incur the lowest sampling overhead among all kinds of Pauli errors. Additionally, we conceive a scheme amalgamating QEM with quantum channel coding, and analyse its sampling overhead reduction compared to pure QEM. Especially, we observe that there exist a critical number of gates contained in quantum circuits, beyond which their amalgamation is preferable to pure QEM.

Sixty years of coherent versus non-coherent tradeoff as well as the twenty years of coherent versus non-coherent tradeoff in Multiple-Input Multiple-Output (MIMO) systems are surveyed. Furthermore, the advantages of adaptivity are discussed. More explicitly, in order to support the diverse communication requirements of different applications in a unified platform, the 5G New Radio (NR) offers unprecendented adaptivity, abeit at the cost of a substantial amount of signalling overhead that consumes both power and the valuable spectral resources. Striking a beneficial coherent versus non-coherent tradeoff is capable of reducing the pilot overheads of channel estimation, whilst relying on low-complexity detectors, especially in high-mobility scenarios. Furthermore, since energy-efficiency is of salient importance both in the operational and future networks, following the powerful Index Modulation (IM) pholosophy, we conceive a holistic adaptive pholosophy striking the most appropriate coherent/non-coherent, single-/multiple-antenna and diversity/multiplexing tradeoffs, where the number of RF chains, the Peak-to-Average Power Ratio (PAPR) of signal transmission and the maximum amount of interference tolerated by signal detection are all taken into account. We demonstrate that this intelligent tripple-fold adaptivity offers significant benefits in next-generation applications of mmWave and Terahertz solutions, in space-air-ground integrated networks, in full-duplex techniques and in other sophisticated channel coding assisted system designs, where powerful machine learning algorithms are expected to make autonomous decisions concerning the best mode of operation with minimal human intervention.

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.

A novel total variation (TV) framework is conceived for joint detection and dynamic state estimation (JDSE) for wireless transmission from the measurement devices to the control center in a smart grid. The proposed scheme employs a TV regularization based decoder in conjunction with a Kalman filter-based dynamic power system state estimator to minimize the detection error for transmission of the measurements over a fading wireless channel. A novel application of the Viterbi algorithm is proposed for TV detection of the received measurement vectors. Furthermore, the proposed JDSE scheme is also extended to a system in which each measurement is quantized to a single bit. This reduced-bandwidth-based TV-JDSE leads to a significant bandwidth efficiency improvement in the smart grid and to improved state estimation. Our simulation results provided for the standard IEEE-14 bus test system under different operating conditions demonstrate improved performance in comparison to conventional techniques and they are capable of approaching the ideal Clairvoyant Kalman filter benchmark.

Impulsive noise (IN) constitutes a limiting factor in power-line communication systems, especially in those relying on orthogonal frequency division multiplexing (OFDM). If a single time-domain sample is contaminated by an impulse, all subcarriers become contaminated after the spreading of the discrete Fourier transform-based demodulation. In order to alleviate this problem, hybrid automatic repeat-and-request (HARQ) is often invoked. Moreover, the powerful low-density parity-check (LDPC) codes have been increasingly employed in a variety of current and next-generation communication standards. Against this background, in this paper, we explicitly characterize the performance of LDPC-coded HARQ-assisted OFDM systems in the face of IN, where the performance metrics of the outage probability (OP) and the average number of retransmissions, as well as the effective throughput, are analyzed. First of all, we conceive a new algorithm for evaluating the OP in a realistic finite-length LDPC regime, by adapting both the so-called density evolution technique and the waterfall signal-to-noise ratio analysis method. Following this, both the average number of retransmission attempts and the effective throughput are investigated based on our analysis of the OP. The accuracy of the proposed analysis is confirmed by the simulation results, which also effectively quantify the impact of IN on HARQ-assisted OFDM systems in a finite-length LDPC regime.

An iterative initial sequence acquisition technique is proposed for the pseudo-noise signal derived from a pair of m-sequences used for generating the concatenated sequence, which relies on the soft-in-soft-out detection to improve the acquisition performance. This recursive soft sequential estimation technique has a linearly increasing complexity with the number of chips in the concatenated sequence. Receiving as few as S consecutive chips of a (2S - 1)-chip sequence is sufficient for the local concatenated-sequence generator of the receiver to synchronize. Hence, this initial synchronization technique is eminently suitable for long m-sequences and concatenated sequences. Another key result is the comparison of m-sequences and concatenated sequences regarding the acquisition time. It is also observed that low-order primitive polynomials (PPs) achieve better performances than higher-order polynomials for both the m-sequences and concatenated sequences. When considering PPs having a higher number of taps, the exploitation of concatenated sequences is capable of achieving in excess of 3-dB signal-to-noise ratio gains over m-sequences.

With the explosive proliferation of mobile devices, the scarcity of communication resources has emerged as a critical issue in the next generation (NG) communication systems. To overcome this, non-orthogonal multiple access (NOMA) has been shown to provide a beneficial spectral efficiency gain, while supporting massive connectivity. Sparse code multiple access (SCMA) is one of the NOMA schemes, but its application is hampered by its high-complexity multi-user detection. Fortunately, quantum search techniques were shown to substantially reduce the complexity of multi-user detectors (MUD). However, the quantum search-aided multiuser detection of SCMA is an open problem at the time of writing. Hence, we conceive a pair of quantum search-aided MUDs for SCMA, namely, a quantum-assisted message passing algorithm (Q-MPA) -based MUD and a quantum-assisted sphere decoder-based MPA (QSD-MPA) MUD.

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.