Visible light communication (VLC) systems often rely on uniformly distributed constellations, which can lead to suboptimal performance and a reduction in spectral efficiency (SE). To address this limitation, we propose a novel spectrally efficient modulation scheme that leverages probabilistic shaping (PS) to enhance the SE of VLC systems. The proposed scheme is based on the color-shift keying (CSK) modulation format for a quadrichromatic LED (QLED)-based system. We derive both the capacity and transmission rate (TR) for the proposed scheme, and adapt the TR based on the optical signal-to-noise ratio (OSNR) by optimizing the distribution of constellation symbols and forward error correction (FEC) coding rate, thus ensuring optimal system performance under varying channel conditions. Furthermore, we introduce an algorithm to compute the optimal capacity-approaching input distribution. To validate the proposed scheme, a QLED CSK system prototype was developed and experimentally tested. We evaluated the performance of the proposed scheme in terms of SE and frame error rate (FER) under different OSNR levels, and compare it against the conventional uniform-based scheme. The results demonstrated that the proposed scheme achieves a 20% improvement in SE over the uniform-based scheme.

Prevailing research on random access schemes often overlooks the complexities of variable slot durations in freshness-critical applications, especially for satellite communication. Our work addresses this by developing an age-optimized irregular repetition slotted ALOHA (IRSA) framework design that adaptively integrates finite-length forward error correction (FEC) to enhance information freshness. We initially model the Age of Information (AoI) dynamics by jointly considering satellite link characteristics, grant-free access collisions, and processing delays. Next, we propose a novel approach for estimating the performance of the packet loss rate (PLR) for finite-length IRSA over the Packet Erasure Channel (PEC), capturing the effects of coding and repetition diversity. To further characterize the temporal behavior of information freshness, a Markovian formulation is then derived to obtain the stationary age distribution, from which the average AoI and the age violation probability are computed in closed form. Building upon the formulations, we perform a joint optimization of frame size and code rate to minimize the average AoI, revealing the inherent trade-off between transmission redundancy and latency. Numerical results confirm the accuracy of the proposed analytical framework and show that the proposed adaptive IRSA scheme achieves about 33% AoI reduction compared with conventional the Slotted ALOHA (SA) and static IRSA baselines, validating its effectiveness for satellite-enabled freshness-critical IoT systems.

With the development of the upcoming sixth-generation (6G) wireless networks, there is a pressing need for innovative technologies capable of satisfying heightened performance indicators. Fluid antenna system (FAS) is proposed recently as a promising technique to achieve higher data rates and more diversity gains by dynamically changing the positions of the antennas to form a more desirable channel. However, worries regarding the possibly harmful effects of electromagnetic (EM) radiation emitted by devices have arisen as a result of the rapid evolution of advanced techniques in wireless communication systems. Specific absorption rate (SAR) is a widely adopted metric to quantify EM radiation worldwide. In this paper, we investigate the SAR-aware multiuser multiple-input multiple-output (MIMO) communications assisted by FAS. In particular, a two-layer iterative algorithm is proposed to minimize the SAR value under signal-to-interference-plus-noise ratio (SINR) and FAS constraints. Moreover, the minimum weighted SINR maximization problem under SAR and FAS constraints is studied by finding its relationship with the SAR minimization problem. Simulation results verify that the proposed SAR-aware FAS design outperforms the adaptive backoff and fixed-position antenna designs.

In this paper, a novel environmental mapping method is proposed to outline the indoor layout utilizing the line-of-sight (LoS) state information of extremely large aperture array (ELAA) channels. It leverages the spatial resolution provided by ELAA and the mobile terminal (MT)'s mobility to infer the presence and location of obstacles in the environment. The LoS state estimation is formulated as a binary hypothesis testing problem, and the optimal decision rule is derived based on the likelihood ratio test. Subsequently, the theoretical error probability of LoS estimation is derived, showing close alignment with simulation results. Then, an environmental mapping method is proposed, which progressively outlines the layout by combining LoS state information from multiple MT locations. It is demonstrated that the proposed method can accurately outline the environment layout, with the mapping accuracy improving as the number of service-antennas and MT locations increases. This paper also investigates the impact of channel estimation error and non-LoS (NLoS) components on the quality of environmental mapping. The proposed method exhibits particularly promising performance in LoS dominated wireless environments characterized by high Rician$K$-factor. Specifically, it achieves an average intersection over union (IoU) exceeding 80% when utilizing 256 service antennas and 18 MT locations.

This paper proposes a multi-stage position and velocity estimation algorithm in the context of near-field (NF) integrated sensing and communications (ISAC). We consider a colocated multiple-input multiple-output (MIMO) orthogonal frequencydivision multiplexing (OFDM) system. Both the wavefront curvature in NF and the wideband feature of OFDM are utilized to jointly estimate the range and the angle of the targets. The proposed algorithm relies on spectral estimation methods and maximum likelihood (ML) refinement steps for target sensing. Also, the estimation performance is compared with the CramérRao lower bound (CRLB). Given the initial estimations of the targets' position and mobility parameters through our proposed algorithm, we further develop a precoding (i.e., beamfocusing) design. The proposed design exploits the finite beam depth and width in NF with minimal beam cross-correlation of different targets across both range and angular domains, allowing better overall resolution. The precoding design also accounts for a tradeoff behavior between communication and sensing performance.

Low Earth orbit (LEO) satellite networks have become pivotal in modern communication systems, offering ubiquitous connectivity for direct-to-satellite (DtS) service in remote and underserved regions. To overcome the limited power of handheld devices, cooperative communication offers an opportunity to enhance connectivity, e.g., coverage probability, by leveraging multiple satellites to mitigate interference and improve signal quality. To this end, this paper proposes a stochastic geometry-based framework to analyze the performance of large-scale cooperative satellite networks. We model the satellites and interfering ground users as Poisson Point Processes (PPPs) and consider a typical user served by its closest subset of visible satellites. The received uplink signals at the serving satellites are combined using the maximum ratio combining (MRC) technique. We derive the coverage probability, accounting for the correlation among serving distances and interference from neighboring devices. Our analytical results, validated via Monte Carlo simulations, demonstrate a 33% increase in coverage probability when the number of cooperating devices doubles at an SINR threshold of -10 dB, offering key design insights for optimizing cooperative LEO satellite networks.

In this paper, we address a physical layer security (PLS) framework for the integrated sensing and semantic communication (ISASC) system, where a multi-antenna dual-function semantic base station serves multiple single-antenna semantic communication users (SCUs) and monitors a malicious sensing target (MST), in the presence of a single-antenna eavesdropper (EVE), with both the MST and EVE aiming to wiretap information from the SCUs' signals. To enhance PLS, we employ joint artificial noise (AN) and dedicated sensing signal (DSS) in addition to wiretap coding. To evaluate the sensing accuracy, we derive the Cramer-Rao bound (CRB) as a function of the communication, sensing, and AN beamforming (BF) vectors. Subsequently, to assess the PLS level of the ISASC system, we determine a closed-form expression for the semantic secrecy rate (SSR). To achieve an optimal trade-off region between these two competing objectives, we formulate a multi-objective optimization problem for the joint design of the BF vectors. We apply semi-definite programming, Gaussian randomization method, and golden-section search techniques to address this problem. Simulation results demonstrate that the proposed scheme outperforms baseline schemes, achieving a superior trade-off between SSR and CRB.

Accurate signal localization is critical for Internet of Things applications, but precise propagation models are often unavailable due to uncontrollable factors. Simplified models, such as planar and spherical wavefront approximations, are widely used but can cause model mismatches that reduce accuracy. To address this, we propose an expected likelihood framework for model mismatch analysis and online (on-the-fly) model selection. The expected likelihood idea is that under the Gaussian assumption of independent samples, the likelihood ratio of the actual covariance matrix of the received signal is described by a distribution that depends only on the number of samples and receive antennas, not the true covariance itself. This scenario independence allows us to avoid the true model need-to-know requirement for model mismatch analysis. In this paper, we formulate an expected likelihood approach for online model and estimation parameters selection, and demonstrate its applicability and efficiency using the analytical electromagnetic model for data generation and considering different simplified models for source localization in both direct and reconfigurable intelligent surface assisted diverse IoT environments.

Reconfigurable intelligent surfaces (RISs) have shown the potential to improve signal-to-interference-plus-noise ratio (SINR) related coverage, especially at high-frequency communications. However, assessing electromagnetic field exposure (EMFE) and establishing EMFE regulations in RIS-assisted large-scale networks remain open issues. This paper proposes a stochastic geometry (SG) based framework to characterize SINR and EMFE in such networks for downlink and uplink scenarios. Particularly, we carefully consider the association rule with the presence of RISs, accurate antenna pattern at base stations (BSs), fading model, and power control mechanism at mobile devices in the system model. Under the proposed framework, we derive the marginal and joint distributions of SINR and EMFE in downlink and uplink, respectively. The first moment of EMFE is also provided. Additionally, we design the compliance distance (CD) between a BS/RIS and a user to comply with the EMFE regulations. To facilitate efficient identification, we further provide approximate closed-form expressions for CDs. From numerical results of the marginal distributions, we find that in the downlink scenario, deploying RISs may not always be beneficial, as the improved SINR comes at the cost of increased EMFE. However, in the uplink scenario, RIS deployment is promising to enhance coverage while still maintaining EMFE compliance. By simultaneously evaluating coverage and compliance metrics through joint distributions, we demonstrate the feasibility of RISs in improving uplink and downlink performance. Insights from this framework can contribute to establishing EMFE guidelines and achieving a balance between coverage and compliance when deploying RISs.

With the development of the upcoming sixth-generation (6G) wireless networks, there is a pressing need for innovative technologies capable of satisfying heightened performance indicators. Fluid antenna system (FAS) is proposed recently as a possible technique to achieve higher data rates and more diversity gains by dynamically changing the positions of the antennas to form a more desirable channel. However, worries regarding the possibly harmful effects of electromagnetic (EM) radiation emitted by devices have arisen due to the rapid evolution of advanced techniques in wireless communication systems. Specific absorption rate (SAR) is a widely adopted metric to quantify EM radiation worldwide. In this paper, we investigate the FAS-assisted multiuser multiple-input multiple-output (MIMO) communications with SAR constraints. In particular, an efficient algorithm is proposed to maximize the minimum weighted signal-to-interference-plus-noise ratio (SINR) under SAR and FAS constraints. Simulation results verify that the proposed SAR-aware FAS design outperforms the adaptive backoff and fixed-position antenna designs.

High-frequency communication systems bring extremely large aperture arrays (ELAA) and large bandwidths, integrating localization and (bi-static) sensing functions without extra infrastructure. Such systems are likely to operate in the near-field (NF), where the performance of localization and sensing is degraded if a simplified far-field channel model is considered. However, when taking advantage of the additional geometry information in the NF, e.g., the encapsulated information in the wavefront, localization and sensing performance can be improved. In this work, we formulate a joint synchronization, localization, and sensing problem in the NF. Considering the array size could be much larger than an obstacle, the effect of partial blockage (i.e., a portion of antennas are blocked) is investigated, and a blockage detection algorithm is proposed. The simulation results show that blockage greatly impacts performance for certain positions, and the proposed blockage detection algorithm can mitigate this impact by identifying the blocked antennas.

Emerging dual-functional radar communication (RadCom) systems promise to revolutionize wireless systems by enabling radar sensing and communication on a shared platform, thereby enhancing spectral efficiency. However, the high transmit power required for efficient radar operation poses risks by potentially exceeding the electromagnetic field (EMF) exposure limits enforced by the regulations. To address this challenge, we propose an EMF-aware signalling design that enhances RadCom system performance while complying with EMF constraints. Our approach considers exposure levels not only experienced by network users but also in sensitive areas such as schools and hospitals, where the exposure must be further reduced. First, we model the exposure metric for the users and the sectors that encounter sensitive areas. Then, we design the waveform by exploiting the trade-off between radar and communication while satisfying the exposure constraints. We reformulate the problem as a convex optimization program and solve it in closed form using Karush-Kuhn-Tucker (KKT) conditions. The numerical results demonstrate the feasibility of developing a robust RadCom system with low electromagnetic (EM) radiations.

Deploying reconfigurable intelligent surfaces (RISs) offers the potential to improve coverage performance in mil-limeter wave (mmWave) communications. However, electric and magnetic field (EMF) exposure related to base station (BS) trans-mission and RIS reflection is still unclear. This paper provides an analytical framework to evaluate the EMF exposure in RIS-assisted mmWave cellular networks. The proposed framework provides insights to establish technical guidelines for ensuring EMF exposure within a safe limit. For example, by considering the compliance distance (CD) set for BSs, we explore the necessity of designing a similar CD for RISs.

This study presents a framework to analyze the performance of uplink localization with reconfigurable intelligent surfaces (RISs) in large-scale cellular networks. First, we propose a novel RIS-aided uplink localization algorithm, where the received signal strength (RSS) is observed at the base station (BS) for various pre-defined phase shift patterns of the RIS, i.e., a codebook of beams. We present a maximum likelihood estimator (MLE) and evaluate its performance by comparing it to the position error bound (PEB), defined as the square root of the Cramér-Rae lower bound (CRLB). Then, to analyze the localization performance on a large scale, we employ stochastic geometry tools, allowing the derivation of a tractable expression for the marginal PEB distribution. The obtained results demon-strate that the proposed algorithm converges to the CRLB for a narrow search grid, in a high SNR regime. Furthermore, higher BS density, number of RIS elements, and RIS element size are shown to enhance localization precision.

Long Range -Frequency Hopping Spread Spectrum (LR-FHSS) framework is a promising technology to enable Direct-to-Satellite (DtS) IoT systems with extensive coverage and high resistance to interference while maintaining costeffectiveness. However, this system currently implements primitive channel coding in Frequency-Hopping Spread-Spectrum (FH-SS) without considering the characteristics of the interference signal. In this work, we propose an innovative coded frequency-hopping (FH) design that incorporates Segment-Level Coding (SLC) in high order Galois field (GF) with erasure detection to enhance immunity against clustered errors commonly encountered in FH-SS, thereby improving the reliability of DtS communication. Additionally, our design inherits the packet structure from LR-FHSS, enabling specific applicability in realworld scenarios. We have also established an analytical model to validate our proposed design in terms of Packet loss rate (PLR) and energy consumption. The mathematical analyses and simulation of the proposed scheme quantify the effectiveness of this enhancement. The numerical results show that the proposed system can accommodate 20 times more users compared to LR-FHSS at a packet loss rate of 0:001, and it costs only approximately 50% of the energy consumption when achieving equivalent performance.

The deployment of 5G networks is sometimes questioned due to the impact of ElectroMagnetic Field (EMF) generated by Radio Base Station (RBS) on users. The goal of this work is to analyze such issue from a novel perspective, by comparing RBS EMF against exposure generated by 5G smartphones in commercial deployments. The measurement of exposure from 5G is hampered by several implementation aspects, such as dual connectivity between 4G and 5G, spectrum fragmentation, and carrier aggregation. To face such issues, we deploy a novel framework, called5G-EA, tailored to the assessment of smartphone and RBS exposure through an innovative measurement algorithm, able to remotely control a programmable spectrum analyzer. Results, obtained in both outdoor and indoor locations, reveal that smartphone exposure (upon generation of uplink traffic) dominates over the RBS one. Moreover, Line-of-Sight locations experience a reduction of around one order of magnitude on the overall exposure compared to Non-Line-of-Sight ones. In addition, 5G exposure always represents a small share (up to 38%) compared to the total one radiated by the smartphone.

The limited modulation bandwidth of the light emitting diodes (LEDs) presents a challenge in the development of practical high-data-rate visible light communication (VLC) systems. In this paper, a novel adaptive coded probabilistic shaping (PS)-based nonorthogonal multiple access (NOMA) scheme is proposed to improve spectral efficiency (SE) of VLC systems in multiuser uplink communication scenarios. The proposed scheme adapts its rate to the optical signal-to-noise ratio (OSNR) by utilizing non-uniformly distributed discrete constellation symbols and low complexity channel encoder. Furthermore, an alternate optimization algorithm is proposed to determine the optimal channel coding rate, constellation spacing, and probability mass function (PMF) of each user. The extensive numerical results show that the proposed PS-based NOMA scheme closely approaches the capacity of NOMA with fine granularity. Presented results demonstrate the effectiveness of our scheme in improving the SE of VLC systems in multiuser scenarios. For instance, our scheme exhibits substantial SE gains over existing schemes, namely, the pairwise coded modulation (PCM), geometric shaping (GS), and uniform-distribution schemes. These findings highlight the potential of our approach to significantly enhance VLC systems.

Recent advances in Big Data Analytics are primarily driven by innovations in Artificial Intelligence and Machine Learning Methods. Due to the richness of data sources at the edge and with the increasing privacy concerns, Distributed privacy-preserving machine learning (ML) methods are increasingly becoming the norm for training ML models on federated big data. In a popular approach known as Federated learning (FL), service providers leverage end-user data to train ML models to improve services such as text auto-completion, virtual keyboards, and item recommendations. FL is expected to grow in importance with the increasing focus on big data, privacy and 5G/6G technologies. However, FL faces significant challenges such as heterogeneity, communication overheads, and privacy preservation. In practice, training models via FL is time-intensive and worse its dependent on client participation who may not always be available to join the training. Our empirical analysis shows that client availability can significantly impact the model quality which motivates the design of an availability-aware selection scheme. We propose A2FL to mitigate the quality degradation caused by the under-representation of the global client population by prioritizing the least available clients. Our results show that, compared to state-of-the-art methods, A2FL can improve the client diversity during the training and hence boost the trained model quality.

LoRa backscatter (LB) communication systems can be considered as a potential candidate for ultra-low-power wide-area networks (LPWANs) because of their low cost and low power consumption. In this article, we comprehensively analyze LB modulation from various aspects, i.e., temporal, spectral, and error performance characteristics. First, we propose a signal model for LB signals that accounts for the limited number of loads in the tag. Then, we investigate the spectral properties of LB signals, obtaining a closed-form expression for the power spectrum. Finally, we derived the symbol error rate (SER) of LB with two decoders, i.e., the maximum likelihood (ML) and fast Fourier transform (FFT) decoders, in both additive white Gaussian noise (AWGN) and double Nakagami-m fading channels. The spectral analysis shows that out-of-band emissions for LB satisfy the European Telecommunications Standards Institute (ETSI) regulation only when considering a relatively large number of loads. For the error performance, unlike conventional LoRa, the FFT decoder is not optimal. Nevertheless, the ML decoder can achieve a performance similar to conventional LoRa with a moderate number of loads.

Over the past few years, the prevalence of wireless devices has become one of the essential sources of electromagnetic (EM) radiation to the public. Facing with the swift development of wireless communications, people are skeptical about the risks of long-term exposure to EM radiation. As EM exposure is required to be restricted at user terminals, it is inefficient to blindly decrease the transmit power, which leads to limited spectral efficiency and energy efficiency (EE). Recently, rate-splitting multiple access (RSMA) has been proposed as an effective way to provide higher wireless transmission performance, which is a promising technology for future wireless communications. To this end, we propose using RSMA to increase the EE of massive MIMO uplink while limiting the EM exposure of users. In particularly, we investigate the optimization of the transmit covariance matrices and decoding order using statistical channel state information (CSI). The problem is formulated as non-convex mixed integer program, which is in general difficult to handle. We first propose a modified water-filling scheme to obtain the transmit covariance matrices with fixed decoding order. Then, a greedy approach is proposed to obtain the decoding permutation. Numerical results verify the effectiveness of the proposed EM exposure-aware EE maximization scheme for uplink RSMA.

Reconfigurable intelligent surfaces (RIS) can be crucial in next-generation communication systems. However, designing the RIS phases according to the instantaneous channel state information (CSI) can be challenging in practice due to the short coherent time of the channel. In this regard, we propose a novel algorithm based on the channel statistics of massive multiple input multiple output systems rather than the instantaneous CSI. The beamforming at the base station (BS), power allocation of the users, and phase shifts at the RIS elements are optimized to maximize the minimum signal-to-interference and noise ratio (SINR), guaranteeing fair operation among various users. In particular, we design the RIS phases by leveraging the asymptotic deterministic equivalent of the minimum SINR that depends only on the channel statistics. This significantly reduces the computational complexity and the amount of controlling data between the BS and RIS for updating the phases. This setup is also useful for electromagnetic fields (EMF)-aware systems with constraints on the maximum user’s exposure to EMF. The numerical results show that the proposed algorithms achieve more than 100 % gain in terms of minimum SINR, compared to a system with random RIS phase shifts, when 40 RIS elements, 20 antennas at the BS and 10 users, are considered.

Installing more base stations (BSs) into the existing cellular infrastructure is an essential way to provide greater network capacity and higher data rates in the 5th-generation cellular networks (5G). However, a non-negligible amount of the population is concerned that such network densification will generate a notable increase in exposure to electric and magnetic fields (EMF) over the territory. In this paper, we analyze the downlink, uplink, and joint downlink&uplink exposure induced by the radiation from BSs and personal user equipment (UE), respectively, in terms of the received power density and exposure index. In our analysis, we consider the EMF restrictions set by the regulatory authorities such as the minimum distance between restricted areas (e.g., schools and hospitals) and BSs, and the maximum permitted exposure. Exploiting tools from stochastic geometry, mathematical expressions for the coverage probability and statistical EMF exposure are derived and validated. Tuning the system parameters such as the BS density and the minimum distance from a BS to restricted areas, we show a trade-off between reducing the population’s exposure to EMF and enhancing the network coverage performance. Then, we formulate optimization problems to maximize the performance of the EMF-aware cellular network while ensuring that the EMF exposure complies with the standard regulation limits with high probability. For instance, the exposure from BSs is two orders of magnitude less than the maximum permissible level when the density of BSs is less than$20 \text {BSs/km}^{2}$.

Radio localization is applied in high-frequency (e.g., mmWave and THz) systems to support communication and to provide location-based services without extra infrastructure. For solving localization problems, a simplified, stationary, narrowband far-field channel model is widely used due to its compact formulation. However, with increased array size in extra-large multiple-input-multiple-output (XL-MIMO) systems and increased bandwidth at upper mmWave bands, the effect of channel spatial non-stationarity (SNS), spherical wave model (SWM), and beam squint effect (BSE) cannot be ignored. In this case, localization performance will be affected when an inaccurate channel model deviating from the true model is adopted. In this work, we employ the misspecified Cramer-Rao lower bound to lower bound the localization error using a simplified mismatched model while the observed data is governed by a more complex true model. The simulation results show that among all the model impairments, the SNS has the least contribution, the SWM dominates when the distance is small compared to the array size, and the BSE has a more significant effect when the distance is much larger than the array size.

Reconfigurable intelligent surfaces (RISs) are expected to play a significant role in the next generation of wireless cellular technology. This paper proposes an uplink localization scheme using a single-snapshot solution for user equipment (UE) that is located in the near-field of the RIS. We propose utilizing the atomic norm minimization method to achieve super-resolution localization accuracy. We formulate an optimization problem to estimate the UE location parameters (i.e., angles and distances) by minimizing the atomic norm. Then, we propose to exploit strong duality to solve the atomic norm problem using the dual problem and semidefinite programming (SDP). The RIS is controlled and designed using estimated parameters to enhance the beamforming capabilities. Finally, we compare the localization performance of the proposed atomic norm minimization with compressed sensing (CS) in terms of the localization error. The numerical results show a superior performance of the proposed atomic norm method over the CS where a sub-cm level of accuracy can be achieved under some of the system configuration conditions using the proposed atomic norm method.

Internet of things (IoT) services have grown to become an integral part of our everyday lives, however, the gap in IoT connectivity between rural and urban areas is growing, leading to what is called the digital divide problem. In this regard, we propose an architecture for IoT data collection in rural areas via mobile fog nodes. We study the effect of gateway mobility in LoRaWAN on the network communication flow and transmission parameters. The limits for reliable communication at different moving speeds are analytically computed, then validated by both numerical simulations and real experiments. The numerical results show that it is beneficial to use spreading factors (SF) lower than 11 for vehicle speeds up to 150 km/hr, with SF7 being the optimum in synchronized transmission.

Spectrum-sharing backscatter communication (SSBC) systems are among the most prominent technologies for ultralow power and spectrum-efficient communications. In this article, we propose an underlay SSBC system, in which the secondary network is a backscatter communication system. We analyze the performance of the secondary network under a transmit power adaption strategy at the secondary transmitter, which guarantees that the interference caused by the secondary network to the primary receiver is below a predetermined threshold. We first derive a novel analytical expression for the cumulative distribution function (CDF) of the instantaneous signal-to-noise ratio of the secondary network. Capitalizing on the obtained CDF, we derive novel accurate approximate expressions for the ergodic capacity, effective capacity, and average bit-error rate. We further validate our theoretical analysis using the extensive Monte Carlo simulations.

Visible light communication (VLC) is a promising technology for 6th-generation (6G) networks because of its attractive feature such as a wide unlicensed spectrum. In this paper, a novel adaptive coded spatial modulation scheme with probabilistic shaping (PS) is proposed to approach the capacity of the spatial modulation (SM) in visible light communication (VLC) channels with intensity modulation and direct detection (IM/DD). In the proposed scheme, spatial and constellation symbols are probabilistically shaped depending on the user’s location inside the room and the optical signal-to-noise ratio (OSNR). Moreover, we optimize the channel coding rate to maximize further the achievable rate of the proposed scheme for a given OSNR. Finally, we propose an algorithm to compute the capacity-achieving distribution of the proposed scheme with unipolar$M$-ary pulse amplitude modulation (PAM) signaling. The proposed scheme outperforms uniform and an orthogonal frequency-division multiplexing (OFDM) based scheme in terms of spectral efficiency (SE) and/or frame error rate (FER). For example, for 8-PAM signaling with$N=8$transmit antennas, the proposed scheme operates within 0.2 dB from the unipolar$M$-PAM SM VLC channel signaling capacity and outperforms the uniform and OFDM based schemes in terms of FER by at least 1.1 dB and 1.3 dB at a normalized data rate of 1.33 bits per channel use per sub-carrier (b/cu/sc), respectively.

Hardware distortions (HWDs) render drastic effects on the performance of communication systems. They are recently proven to bear asymmetric signatures; and hence can be efficiently mitigated using improper Gaussian signaling (IGS), thanks to its additional design degrees of freedom. Discrete asymmetric signaling (AS) can practically realize the IGS by shaping the signals' geometry or probability. In this paper, we adopt the probabilistic shaping (PS) instead of uniform symbols to mitigate the impact of HWDs and derive the optimal maximum a posterior detector. Then, we design the symbols' probabilities to minimize the error rate performance while accommodating the improper nature of HWD. Although the design problem is a non-convex optimization problem, we simplified it using successive convex programming and propose an iterative algorithm. We further present a hybrid shaping (HS) design to gain the combined benefits of both PS and geometric shaping (GS). Finally, extensive numerical results and Monte Carlo (MC) simulations highlight the superiority of the proposed PS over conventional uniform constellation and GS. Both PS and HS achieve substantial improvements over the traditional uniform constellation and GS with up to one order magnitude in error probability and throughput.

In this paper, we propose a practical adaptive coding modulation scheme to approach the capacity of free-space optical (FSO) channels with intensity modulation/direct detection based on probabilistic shaping. The encoder efficiently adapts the transmission rate to the signal-to-noise ratio, accounting for the fading induced by the atmospheric turbulence. The transponder can support an arbitrarily large number of transmission modes using a low complexity channel encoder with a small set of supported rates. Hence, it can provide a solution for FSO backhauling in terrestrial and satellite communication systems to achieve higher spectral efficiency. We propose two algorithms to determine the capacity and capacity-achieving distribution of the scheme with unipolar M-ary pulse amplitude modulation (M-PAM) signaling. Then, the signal constellation is probabilistically shaped according to the optimal distribution, and the shaped signal is channel encoded by an efficient binary forward error correction scheme. Extensive numerical results and simulations are provided to evaluate the performance. The proposed scheme yields a rate close to the tightest lower bound on the capacity of FSO channels. For instance, the coded modulator operates within 0.2 dB from the M-PAM capacity, and it outperforms uniform signaling with more than 1.7 dB, at a transmission rate of 3 bits per channel use.

An important modulation technique for Internet of Things (IoT) is the one proposed by the low power long range (LoRa) alliance. In this paper, we analyze the M-ary LoRa modulation in the time and frequency domains. First, we provide the signal description in the time domain, and show that LoRa is a memoryless continuous phase modulation. The cross-correlation between the transmitted waveforms is determined, proving that LoRa can be considered approximately an orthogonal modulation only for large M. Then, we investigate the spectral characteristics of the signal modulated by random data, obtaining a closed-form expression of the spectrum in terms of Fresnel functions. Quite surprisingly, we found that LoRa has both continuous and discrete spectra, with the discrete spectrum containing exactly a fraction 1/M of the total signal power.

Data originating from devices and sensors in Internet of Things scenarios can often be modeled as sparse signals. In this paper, we provide new source compression schemes for noisy sparse and non-strictly sparse sources, based on channel coding theory. Specifically, nonlinear excision filtering by means of model order selection or thresholding is first used to detect the support of the non-zero elements of sparse vectors in noise. Then, the sparse sources are quantized and compressed using syndrome-based encoders. The theoretical performance of the schemes is provided, accounting for the uncertainty in the support estimation. In particular, we derive the operational distortion-rate and operational distortion-energy of the encoders for noisy Bernoulli-uniform and Bernoulli-Gaussian sparse sources. It is found that the performance of the proposed encoders approaches the information-theoretic bounds for sources with low sparsity order. As a case study, the proposed encoders are used to compress signals gathered from a real wireless sensor network for environmental monitoring.

One of the key issues in the acquisition of sparse data by means of compressed sensing is the design of the measurement matrix. Gaussian matrices have been proven to be information-theoretically optimal in terms of minimizing the required number of measurements for sparse recovery. In this paper, we provide a new approach for the analysis of the restricted isometry constant (RIC) of finite dimensional Gaussian measurement matrices. The proposed method relies on the exact distributions of the extreme eigenvalues for Wishart matrices. First, we derive the probability that the restricted isometry property is satisfied for a given sufficient recovery condition on the RIC, and propose a probabilistic framework to study both the symmetric and asymmetric RICs. Then, we analyze the recovery of compressible signals in noise through the statistical characterization of stability and robustness. The presented framework determines limits on various sparse recovery algorithms for finite size problems. In particular, it provides a tight lower bound on the maximum sparsity order of the acquired data allowing signal recovery with a given target probability. Also, we derive simple approximations for the RICs based on the Tracy-Widom distribution.

Data originating from many devices and sensors can be modeled as sparse signals. Hence, efficient compression techniques of such data are essential to reduce bandwidth and transmission power, especially for energy constrained devices within machine to machine communication scenarios. This paper provides accurate analysis of the operational distortion-rate function (ODR) for syndrome-based source encoders of noisy sparse sources. We derive the probability density function of error due to both quantization and pre- quantization noise for a type of mixed distributed source comprising Bernoulli and an arbitrary continuous distribution, e.g., Bernoulli- uniform sources. Then, we derive the ODR for two encoding schemes based on the syndromes of Reed-Solomon (RS) and Bose, Chaudhuri, and Hocquenghem (BCH) codes. The presented analysis allows designing a quantizer such that a target average distortion is achieved. As confirmed by numerical results, the closed-form expression for ODR perfectly coincides with the simulation. Also, the performance loss compared to an entropy based encoder is tolerable.

This letter provides tight upper bounds on the weak restricted isometry constant for compressed sensing with finite Gaussian measurement matrices. The bounds are used to develop a unified framework for the guaranteed recovery assessment of jointly sparse matrices from multiple measurement vectors. The analysis is based on the exact distribution of the extreme singular values of Gaussian matrices. Several joint sparse reconstruction algorithms are analytically compared in terms of the maximum support cardinality ensuring signal recovery, i.e., mixed norm minimization, MUSIC, and OSMP based algorithms.

Signal compression is essential for energy and bandwidth efficient communication and storage systems. In this paper, we provide two practical approaches for source compression of noisy sparse and non-strictly sparse (compressible) sources. The proposed schemes are based on channel coding theory to construct a source encoder that decreases the number of transmitted bits while preserving the fidelity of the reconstructed signal at the receiver by exploiting its sparsity. In addition, a model order selection scheme is proposed to detect the nonzero elements of sparse vectors embedded in noise, or to find a nonlinear sparse approximation of compressible signals. As illustrated by numerical results, our approach provides a lower distortion-rate function compared to previously known methods. For example, the proposed schemes achieve a lower distortion, about 2 orders of magnitude, compared to compressed sensing, for the same rate.

In the context of compressed sensing, we provide a new approach to the analysis of the symmetric and asymmetric restricted isometry property for Gaussian measurement matrices. The proposed method relies on the exact distribution of the extreme eigenvalues for Wishart matrices, or on its approximation based on the Tracy-Widom law, which in turn can be approximated by means of properly shifted and scaled Gamma distributions. The resulting probability that the measurement submatrix is ill conditioned is compared with the known concentration of measure inequality bound, which has been originally adopted to prove that Gaussian matrices satisfy the restricted isometry property with overwhelming probability. The new analytical approach gives an accurate prediction of such probability, tighter than the concentration of measure bound by many orders of magnitude. Thus, the proposed method leads to an improved estimation of the minimum number of measurements required for perfect signal recovery.

Spectrum Sensing in wideband cognitive radio networks is considered one of the challenging issues facing opportunistic utilization of the frequency spectrum. Collaborative compressive sensing has been proposed as an effective technique to alleviate some of these challenges through efficient sampling that exploits the underlying sparse structure of the measured frequency spectrum. In this paper, we propose to model this problem as a compressive support recovery problem, and apply the adaptive Sequential Compressive Sensing (SCS) approach to recover spectrum holes. We propose several fusion techniques to apply the proposed approach in a collaborative manner. The experimental analysis through simulations shows that the proposed scheme can substantially increase the probability of spectrum hole detection as compared to traditional CS recovery approaches while using a very low sampling rate analog to information converter, and without requiring the knowledge of any statistical information about the environmental noise.

Spectrum sensing in wideband cognitive radio networks is challenged by several factors such as hidden primary users (PUs), overhead on network resources, and the requirement of high sampling rate. Compressive sensing has been proven effective to elevate some of these problems through efficient sampling and exploiting the underlying sparse structure of the measured frequency spectrum. In this paper, we propose an approach for collaborative compressive spectrum sensing. The proposed approach achieves improved sensing performance through utilizing Kronecker sparsifying bases to exploit the two dimensional sparse structure in the measured spectrum at different, spatially separated cognitive radios. Experimental analysis through simulation shows that the proposed scheme can substantially reduce the mean square error (MSE) of the recovered power spectrum density over conventional schemes while maintaining the use of a low-rate ADC. We also show that we can achieve dramatically lower MSE under low compression ratios using a dense measurement matrix but using Nyquist rate ADC.