High-altitude platform stations (HAPSs), which directly deliver communication services to smartphones on the ground, are attracting much attention as novel mobile communication platforms for ultrawide coverage areas. A multi-cell configuration is needed to enhance communication capacity in wide areas. Assuming the use of a phased-array antenna, we proposed an optimization method for controlling antenna parameters, such as beamwidth and beam direction, to improve spectral efficiency. HAPSs are also promising disaster-resilient networks, as they can recover coverage immediately when coverage holes appear due to the breaking down of terrestrial base stations (BSs). Users can recover connections immediately during disasters through HAPSs that use the same frequency band as terrestrial networks. However, in this scenario, HAPSs become a source of interference in terrestrial networks, so network operators should avoid affecting existing terrestrial networks through HAPSs. However, cell optimization in this coexistence scenario between HAPSs and terrestrial networks has not been investigated thoroughly. In this paper, we propose a design scheme that optimizes antenna parameters for each cell to maximize HAPS coverage in this coexistence scenario using a genetic algorithm (GA). The proposed method can effectively protect terrestrial networks through optimization by setting the initial direction of each beam to avoid terrestrial BSs. Using this scheme, the GA can start optimization with candidate combinations that satisfy the interference constraints of terrestrial networks, which can expedite optimization for better coverage. Simulation results show that the proposed scheme can achieve better coverage than the conventional scheme, which only controls beam direction. Furthermore, they show that the initial beam direction setting effectively reduces the number of performed combinations required for GA to converge.
Physical layer security (PLS) offers a unique approach to protecting information confidentiality against eavesdropping by malicious users. This paper studies a joint design of adaptive M−ary pulse amplitude modulation (PAM) and precoding for performance improvement of PLS in visible light communications (VLC). It is known that higher-order modulation results in a better secrecy capacity at the expense of a higher bit-error rate (BER). On the other hand, a proper precoding design can also enhance secrecy performance. The proposed design, therefore, aims at the optimal PAM modulation order and precoder to maximize a utility function that takes into account the secrecy capacity and BERs of the legitimate user (Bob)’s and the eavesdropper (Eve)’s channel. Due to the lack of a closed-form expression for the utility function, a Q-learning-based design is proposed and evaluated. Compared to the non-adaptive approach under all different settings of Bob’s and Eve’s positions, simulation results verify that the proposed joint adaptive design achieves a good balance between the secrecy capacity and BER of Bob’s channel while maintaining a sufficiently high BER of Eve’s channel.
Reconfigurable intelligent surface (RIS) technology and media based modulation (MBM) are promising physical layer techniques in wireless communication. While RIS uses electronically tunable surfaces in the far field of the transmit antenna(s) for the purpose of beamforming towards the receiver of interest, MBM uses such surfaces in the near field for the purpose of modulation. Both RIS and MBM offer improved communication performance at low radio-frequency (RF) hardware complexity. In this paper, we investigate MBM aided by RIS and show interesting performance results. We develop the system model for an RIS-aided MBM (RIS-MBM) system and analyse its bit error performance through analysis and simulation. For this system, we propose two schemes for optimizing the phases at the RIS, one maximizing the Frobenius norm of the channel and the other maximizing a bound on the achievable rate. We also compare the performance of RIS-aided MBM with that of RISaided generalized spatial modulation (RIS-GSM). Our results show that i) the performance of MBM improves with the use of RIS, ii) RIS phase optimization using rate maximization is better compared to that using Frobenius norm maximization, and iii) for a given rate, RIS-aided MBM achieves better performance compared to RIS-aided GSM.
Full-duplex communications are potential candidate for increasing the spectral efficiency for 5G mobile systems and beyond, which can transmit and receive simultaneously on the same carrier frequency. The key challenge is the self-interference (SI) caused by the coupling between the transmitter (TX) and its own receiver (RX). This paper proposes a new type of SI canceller based on the channel estimation between TX and its own RX through the use of a specific demodulation reference signal (DMRS) which is special type of physical layer signal. In this paper, we first show the configuration of the proposed SI canceller using the DMRS. Then, we demonstrate the BER performance of OFDM-based QPSK and QAM signals under static and Rician fading channels for SI, using link-level simulations. The simulation results indicate that the proposed SI canceller can sufficiently improve the BERs for OFDM-based QPSK and QAM signals.
Modern cars are equipped with sensors that can detect other moving vehicles and obstacles on the road. However, their range is usually limited to line-of-sight and their accuracy is also limited. To provide information beyond the sensor range, each vehicle broadcasts Basic Safety Messages (BSMs) with its position and speed. For road awareness, it would be best if multiple vehicles could confirm the position (redundancy), using their on-board sensors for verification (diversity), and excluding position and speed errors (plausibility). This paper presents a decentralized solution that uses multiple vantage points to provide more trust in moving vehicle position data. It extends broadcast messages with sensor verification and plausibility filtering. It processes a stream of data from nearby vehicles and for short time periods, to achieve the safety benefits without the privacy risks of long-term data retention. The proposal was evaluated with detailed simulations with different levels of traffic and misbehavior. It provides good detection results with only a limited increase in network and computing resources.
Extremely large-scale multiple-input multiple-output (XL-MIMO) is a promising technology for future 6G communications. To realize effective precoding, channel estimation schemes are essential to acquire precise channel state information (CSI), while most existing schemes work relying on the spatial stationary assumption. In XL-MIMO systems, however, the spatial non-stationary effect naturally exists. Such effect can hardly be recognized by existing channel estimation schemes, leading to a severe accuracy loss of channel estimation. To address this problem, in this paper, we study the spatial non-stationary channel estimation for XL-MIMO systems. Specifically, we propose a group time block code (GTBC) based signal extraction scheme. The key idea is to artificially create and exploit the time-domain relevance of non-stationary effect, which allows XL-MIMO to recognize such effect in the space domain. In this way, the spatial non-stationary channel is converted to a series of spatial stationary channels. To effectively estimate these channels, a GTBC-based polar-domain simultaneous orthogonal matching pursuit (GP-SOMP) algorithm is proposed as a solution. Simulation results reveal that the proposed GP-SOMP algorithm can recognize the spatial non-stationary effect in XL-MIMO systems and realize a much more accurate channel estimation than existing schemes.
We investigate the feasibility of using machine learning methods for predicting the Quality of Experience (QoE) of end users in the context of video streaming over satellite networks. To achieve this, we analyzed QoE and traffic data from 2,400 YouTube video sessions over emulated geosynchronous (GSO) satellite links. The objective is to determine whether existing learning methods, originally developed for wired or mobile networks, can be adapted to accurately predict key QoE factors over SATCOM. We particularly investigate a specific existing framework, which achieves outstanding performance in predicting resolution and initial delay. However, we point out some discrepancies in their hypothesis, leading to optimistic forecasting results. We then refine their methodology to ensure a complete independence between training and test datasets, leading to a fairer QoE video streaming forecast over satellite networks.
In recent years, there has been a growing demand for robotic environment perception and autonomous driving due to the increasing popularity of visual and geometry-based localization and mapping techniques, such as simultaneous localization and mapping (SLAM). To address this trend, this paper proposes the EMS-SLAM framework, which utilizes cooperative adaptive wireless communication between servers and multi-robot agents to enhance environment perception and self-localization efficiency and accuracy. EMS-SLAM can reduce mapping time and CPU and memory utilization of individual robots while maintaining high accuracy OctoMap based on multi-map fusion and optimization. EMS-SLAM’s effectiveness and real-time performance have been validated and tested on publicly available datasets and real robots for real-world operations. The experimental results demonstrate that EMS-SLAM can reduce the CPU utilization of a single robot by approximately 10% and improve the efficiency of large-scale SLAM. The constructed OctoMap achieves centimeter-level accuracy. EMS-SLAM provides reliable, agile, and energy-efficient assistance for large-scale environment perception of robots.
The last decade witnessed proliferating in the adaptation of smart devices, sensors, autonomous vehicles, drones, and robots etc for daily human use under the umbrella of Internet of Things (IoT) paradigm. The next wave of IoT devices will demand for increased connectivity with minimum delay and higher availability to ensure quality of service along with energy efficiency. The current IoT deployments are carried out using 4G/5G/wireless and optical transport networks. Remote areas, such as animal farms, agricultural land, forests, seas, and so on are facing challenges with the IoT deployments due to poor or no network connectivity. Satellite communications aim to resolve these issues but due to huge amount of costs it is an ambitious task. Moreover, the existing satellites won’t be able to serve the huge amount of IoT deployments in near future. In this context, Internet of Space Things (IoST) emerged as a game changer that allows deployment of small sized Satellites in the lower orbit around the earth which would enable the IoT deployments to directly communicate through space with enhanced quality of service and would eventually resolve the current challenges faced in the remote areas. This paper presents an SDN-based simulation framework for orchestrating large scale satellites using a multi-level of SDN controller and a centeralized orchestrator.
The smart home integrates home-related facilities, which can greatly facilitate people’s life. To support applications such as breath detection and health monitoring for the smart home, it is significant to understand the effect of the human body on the characteristics of the wireless channel. In this paper, the channel characteristics for human exhalation and inhalation at 6.5 GHz are measured and analyzed. Based on the measurement results in the living room scenario, the ray tracing (RT) simulator is calibrated and used to analyze the continuous process of exhalation and inhalation. According to the measurement and simulation results, the effect of human exhalation and inhalation processes on the power and phase of the received signal is studied. The analysis of this paper could be useful in guiding the deployment of smart home devices for human body monitoring. The calibrated electromagnetic (EM) parameters of typical indoor materials will support RT-based channel simulation and modeling in indoor scenarios.
In this paper we propose a lane keep assist and warning system using automotive radars in different real time scenarios. Owing to radar’s immunity to weather and lighting conditions, it is possible to detect objects with high accuracy. We use Bayesian filtering technique to accurately detect and track vehicles moving towards the ego vehicle. A warning signal is generated if there is a vehicle in the proximity zone. In this work we explain the architecture of lane keep assist and warning (LKAW) systems using radar and also compare it with other lane keep assist systems which use camera as their primary sensors.
Applications envisioned to enable connected and autonomous vehicles entail the exchange of data through reliable and low-latency direct communications between vehicles. The current specifications for the cellular-vehicle-to-everything (C-V2X) technology can still barely match such requirements. To tackle this issue, we investigate here the joint application to the C-V2X sidelink with autonomous resource allocation of two techniques that are being considered for the evolution of cellular systems, i.e., in-band full-duplex (FD) and non-orthogonal multiple access (NOMA). In particular, we consider FD capabilities to detect ongoing collisions while transmitting and thus resolve persistent resource allocations, which are causing repeated errors at the neighboring receivers. Regarding NOMA, we consider the use of successive-interference cancellation (SIC) for the decoding of multiple signals when different vehicles are transmitting simultaneously using the same resources, thus converting packet losses into improved spectral efficiency. The investigation is based on the latest fifth generation (5G) New Radio (NR)-V2X specifications and focuses on broadcast transmissions, which are expected to cover the majority of the exchanged traffic in V2X contexts. As shown through simulations in large-scale scenarios, the two techniques, both relying on signal processing at the receiver to cancel the (self or successive) interference, provide complementary advantages under different conditions. FD is in fact very effective to improve the delivery performance at short distances when periodic data traffic is assumed, while SIC-based NOMA improves the reception probability at medium distances both under periodic and aperiodic data traffic conditions.
In this paper, we propose a rate-splitting design and characterize the sum-degrees-of-freedom (DoF) for the K - user multiple-input-single-output (MISO) broadcast channel with mixed channel state information at the transmitter (CSIT) and order-(K - 1) messages, where mixed CSIT refers to the delayed and imperfect-current CSIT, and order-(K - 1) message refers to the message desired by K - 1 users simultaneously. In particular, for the sum-DoF lower bound, we propose a rate-splitting scheme embedding with retrospective interference alignment. In addition, we propose a matching sum-DoF upper bound via genie signalings and extremal inequality. Opposed to existing works for K = 2, our results show that the sum-DoF is saturated with CSIT quality when CSIT quality thresholds are satisfied for K > 2.
In this paper, we analyze performance of an intelligent reflecting surface (IRS)-assisted simultaneous wireless information and power transfer (SWIPT) system with the optimal phase shift. Specifically, we consider a transmitter sends power and information signals with the assistance of an IRS and spatially correlated fading channels. In practice, the channel between the transmitter and the IRS and between IRS and the receiver are spatially correlated, which constitutes a challenge for accurate performance analysis. In the system, we derive an optimal phase shift, in which the main lobe of the reflected signal at the IRS is directed to the receiver. Then, we develop a closed-form expression to evaluate the average harvested energy and information outage probability. We validate that the proposed model via Monte Carlo simulation.
Massive multiple-input multiple-output (MIMO) precoders are typically designed by minimizing the transmit power subject to a quality-of-service (QoS) constraint. However, current sustainability goals incentivize more energy-efficient solutions and thus it is of paramount importance to minimize the consumed power directly. Minimizing the consumed power of the power amplifier (PA), one of the most consuming components, gives rise to a convex, non-differentiable optimization problem, which has been solved in the past using conventional convex solvers. Additionally, this problem can be solved using a proximal gradient descent (PGD) algorithm, which suffers from slow convergence. In this work, in order to overcome the slow convergence, a deep unfolded version of the algorithm is proposed, which can achieve close-to-optimal solutions in only 20 iterations as compared to the 3500 plus iterations needed by the PGD algorithm. Results indicate that the deep unfolding algorithm is three orders of magnitude faster than a conventional convex solver and four orders of magnitude faster than the PGD.
Opportunistic navigation using cellular signals is appealing for scenarios where other navigation technologies face challenges. In this paper, long-term evolution (LTE) downlink signals from two neighboring commercial base stations (BS) are received by a massive antenna array mounted on a passenger vehicle. Multipath component (MPC) delays and angle-of-arrival (AOA) extracted from the received signals are used to jointly estimate the positions of the vehicle, transmitters, and virtual transmitters (VT) with an extended fast simultaneous localization and mapping (FastSLAM) algorithm. The results show that the algorithm can accurately estimate the positions of the vehicle and the transmitters (and virtual transmitters). The vehicle's horizontal position error of SLAM fused with proprioception is less than 6 meters after a traversed distance of 530 meters, whereas un-aided proprioception results in a horizontal error of 15 meters.
While conventional communication systems sufficiently meet the demands of human-to-human (H2H) information exchange, they cannot support the seamless mass-scale inclusion of non-human communication entities for next generation technological applications. In this context, intelligent reflecting surfaces (IRS) appear as a promising eco-friendly disruptive technology for the extremely dense practical realizations of wireless infrastructures required for futuristic cyber-physical systems. To better exploit IRS to enable ultra-massive connectivity, this work employs element-sharing between multiple users in a practical reflection model enabled IRS-aided wireless system. The element-sharing paradigm of allotment provides spectral efficiency gains while keeping the scale of IRS panels in feasible physical deployment and cost constraints. We investigate the dedicated and shared element-sharing schemes for IRSs under different operating conditions. Simulation results show that element-sharing outperforms dedicated element-allotment in systems with a higher number of served users and a limited number of reflecting elements while also being more robust to channel estimation and phase optimization errors.
This work proposes a low-complexity estimation approach for an orthogonal time frequency space (OTFS)-based integrated sensing and communication (ISAC) system. In particular, we first define four low-dimensional matrices used to compute the channel matrix through simple algebraic manipulations. Secondly, we establish an analytical criterion, independent of system parameters, to identify the most informative elements within these derived matrices, leveraging the properties of the Dirichlet kernel. This allows the distilling of such matrices, keeping only those entries that are essential for detection, resulting in an efficient, low-complexity implementation of the sensing receiver. Numerical results, which refer to a vehicular scenario, demonstrate that the proposed approximation technique effectively preserves the sensing performance, evaluated in terms of root mean square error (RMSE) of the range and velocity estimation, while concurrently reducing the computational effort enormously.
This paper investigates the impact of imperfect channel state information (ipCSI) on the performance of RIS-assisted NOMA vehicular networks while considering the effect of imperfect successive interference cancellation (ipSIC). Moreover, we present novel closed-form pairwise error probability (PEP) expressions with arbitrary L users. The PEP is used to evaluate the union bound on the bit error rate of NOMA users. Finally, the analysis is supported by numerical and Monte Carlo simulation results. We show that the impact of ipCSI with ipSIC on each user’s error rate performance is great at the high P s . We also confirm that the error rate performance of the system decreases with the increase of RUs.
This paper studies the distribution of Signal-to-Interference-plus-Noise Ratio (SINR) for the downlink of cellular networks taking into account the joint impact of path-loss, shadowing and fast-fading. We show that the calculation of the probability density function (PDF) of the SINR is not tractable. We propose two approaches for deriving the analytical expressions of the statistics of the SINR using two different approximations and we compare the results to Monte Carlo simulations. In the first approach, the interfering signals are approximated to their means, the PDF of SINR approximates well the one obtained by simulation only when the variances of the shadowing signals are small. In the second approach, only the fast-fading is averaged for the interfering signals. We give a closed form expression of the distribution of the SINR. We show, by comparing to simulations, that this approximation is more accurate than the first one for any value of the shadowing variance. We also deduce a closed form for the average data rate expression.
