This study presents a novel mechanical metallic reflector array to guide wireless signals to the point of interest, thereby enhancing received signal quality. Comprised of numerous individual units, this device, which acts as a linear Fresnel reflector (LFR), facilitates the reflection of incoming signals to a desired location. Leveraging geometric principles, we present a systematic approach for redirecting beams from an Access Point (AP) toward User Equipment (UE) positions. This methodology is geared towards optimizing beam allocation, thereby maximizing the number of beams directed towards the UE. Ray tracing simulations conducted for two 3D wireless communication scenarios demonstrate significant increases in path gains and received signal strengths (RSS) by at least 50 dB with strategically positioned devices.

The advent of Large Language Models (LLMs) has revolutionized language understanding and human-like text generation, drawing interest from many other fields with this question in mind: What else are the LLMs capable of? Despite their widespread adoption, ongoing research continues to explore new ways to integrate LLMs into diverse systems.This paper explores new techniques to harness the power of LLMs for 6G (6th Generation) wireless communication technologies, a domain where automation and intelligent systems are pivotal. The inherent adaptability of LLMs to domain-specific tasks positions them as prime candidates for enhancing wireless systems in the 6G landscape.We introduce a novel Reinforcement Learning (RL) based framework that leverages LLMs for network deployment in wireless communications. Our approach involves training an RL agent, utilizing LLMs as its core, in an urban setting to maximize coverage. The agent’s objective is to navigate the complexities of urban environments and identify the network parameters for optimal area coverage. Additionally, we integrate LLMs with Convolutional Neural Networks (CNNs) to capitalize on their strengths while mitigating their limitations. The Deep Deterministic Policy Gradient (DDPG) algorithm is employed for training purposes. The results suggest that LLM-assisted models can outperform CNN-based models in some cases while performing at least as well in others.

Human respiratory rate and its pattern convey essential information about the physical and psychological states of the subject. Abnormal breathing can indicate fatal health issues leading to further diagnosis and treatment. Wireless light-wave sensing (LWS) using incoherent infrared light shows promise in safe, discreet, efficient, and noninvasive human breathing monitoring without raising privacy concerns. The respiration monitoring system needs to be trained on different types of breathing patterns to identify breathing anomalies. The system must also validate the collected data as a breathing waveform, discarding any faulty data caused by external interruption, user movement, or system malfunction. To address these needs, this study simulated normal and different types of abnormal respiration using a robot that mimics human breathing patterns. Then, time-series respiration data were collected using infrared light-wave sensing technology. Three machine learning algorithms, decision tree, random forest and XGBoost, were applied to detect breathing anomalies and faulty data. Model performances were evaluated through cross-validation, assessing classification accuracy, precision, and recall scores. The random forest model achieved the highest classification accuracy of 96.75% with data collected at a 0.5 m distance. In general, ensemble models like random forest and XGBoost performed better than a single model in classifying the data collected at multiple distances from the LWS setup.

The performance of user-centric ultra-dense networks (UCUDNs) hinges on the Service zone (Szone) radius, which is an elastic parameter that balances the area spectral efficiency (ASE) and energy efficiency (EE) of the network. Accurately determining the Szone radius requires the precise location of the user equipment (UE) and data base stations (DBSs). Even a slight error in reported positions of DBSs or UE will lead to an incorrect determination of Szone radius and UE- D BS pairing, leading to degradation of the UE-DBS communication link. To compensate for the positioning error impact and improve the ASE and EE of the UCUDN, this work proposes a data-driven optimization and error compensation (DD-OEC) framework. The framework comprises an additional machine learning model that assesses the impact of residual errors and regulates the erroneous data-driven optimization to output Szone radius, transmit power, and DBS density values which improve network ASE and EE. The performance of the framework is compared to a baseline scheme, which does not employ the residual, and results demonstrate that the DD-OEC framework outperforms the baseline, achieving up to a 23% improvement in performance.

With highly heterogeneous application requirements, 6G and beyond cellular networks are expected to be demand-driven, elastic, user-centric, and capable of supporting multiple services. A redesign of the one-size-fits-all cellular architecture is needed to support heterogeneous application needs. This paper addresses this need by proposing an intelligent, demand-driven, elastic user-centric cloud radio access network (UCRAN) architecture capable of providing services to a diverse set of use cases ranging from augmented/virtual reality to high-speed rails to industrial robots to E-health applications, and more. The proposed framework leverages deep reinforcement learning to adjust the size of a user-centered virtual cell based on each application’s heterogeneous throughput and latency requirements. Finally, numerical results are presented to validate the convergence and network adaptability of the proposed approach against the brute-force method.

This paper proposes a novel time-frequency warped waveform for short symbols, massive machine-type communication (mMTC), and internet of things (IoT) applications. The waveform is composed of asymmetric raised cosine (RC) pulses to increase the signal containment in time and frequency domains. The waveform has low power tails in the time domain, hence better performance in the presence of delay spread and time offsets. The time-axis warping unitary transform is applied to control the waveform occupancy in time-frequency space and to compensate for the usage of high roll-off factor pulses at the symbol edges. The paper explains a step-by-step analysis for determining the roll-off factors profile and the warping functions. Gains are presented over the conventional Zero-tail Discrete Fourier Transform-spread-Orthogonal Frequency Division Multiplexing (ZT-DFT-s-OFDM), and Cyclic prefix (CP) DFT-s-OFDM schemes in the simulations section.

The user-centric ultra-dense networks (UUDNs) confront the challenge of performance degradation because of the erroneous user equipment (UE), and data base station (DBS) positions estimated at the central controller (CC). This paper adopts the database aided approach to quantify the error impact on system-level key performance indicators (KPIs) under various configuration and optimization parameters (COPs). Although the performance fall is consistent with the increase in error radius of both UEs and DBS positions, its impact can be alleviated by extrapolating on the erroneous database and adopting to new COP values. To realize this, time-series (TS) forecasting is utilized to determine the extent of compensation and COP variations. Results compared for two TS based schemes show that a significant portion, more than 50%, of the decreased performance can be recovered by the suggested adoption in the COP values.

In this article, we demonstrate the ability to recognize hand gestures in a noncontact wireless fashion using only incoherent light signals reflected from a human subject. Fundamentally distinguished from radar, lidar, and camera-based sensing systems, this sensing modality uses only a low-cost light source (e.g., LED) and a sensor (e.g., photodetector). The lightwave-based gesture recognition system identifies different gestures from the variations in light intensity reflected from the subject's hand within a short (20-35 cm) range. As users perform different gestures, scattered light forms unique, statistically repeatable, time-domain signatures. These signatures can be learned by repeated sampling to obtain the training model against which unknown gesture signals are tested and categorized. These time-domain variations of the lightwave signals reflected from hand are denoised, standardized, and then classified by using machine learning classification tools such as $K$-nearest neighbors and support vector machine. Performance evaluations have been conducted with eight gestures, five subjects, different distances and lighting conditions, and visible and infrared light sources. The results demonstrate the best hand gesture recognition performance of infrared sensing at 20 cm with an average of 96% accuracy. The developed gesture recognition system is low-cost, effective, and noncontact technology for numerous human-computer interaction applications.

Complexity is an inherent property in wireless heterogeneous networks (HetNets). In this paper, we investigate the application of the agent-based modeling (ABM) tool for optimization of complex and dynamic HetNets. The proposed framework contains a diversity of game-theoretic, machine learning, and rule-based algorithms within the same model. We present and analyze a HetNet ABM model that runs parallel reinforcement learning (RL) algorithms for spectrum deployment, interference management, resource allocation, and load balancing at both micro and macrocell levels. In our proposed model, two RL-based algorithms work jointly to manage the co-tier and cross-tier interferences. The macrocell runs the first algorithm to control the transmission power of the small cells. The second RL algorithm is run by small cells to assign the users to the sub-bands with less interference levels. Simultaneously, the user association is decided by the users depending on the available resources at the cells and user preferences. The model is then evaluated under various network load conditions to deduce relationships between the cell loads, aggregate bit rate, latency, and user association. Moreover, the system is assessed in a dynamic network scenario with moving users and is confirmed to possess the ability to attain convergence with sufficient performance levels.

Wavelength-domain multiplexing enables near parallel transmission for visible light communications (VLC). In this paper, we investigate an optimal bias allocation problem for the tri-color VLC channel when there is no color cross-talk (CoC), and propose a joint precoding and bias allocation algorithm when CoC exists. The optimization problems are formulated considering both lighting and communication requirements. The color constraint is depicted using the MacAdam ellipses on the chromaticity diagram, as an effective relaxation from a fixed point restriction. Also, for practical concerns, the addressed system is affected by signal-dependent noise (SDN). The bias allocation problem under SDN and related constraints is shown to be non-convex, thus a convex-concave procedure is utilized in this paper for convexification. The joint design problem is also non-convex, and an iterative optimization procedure is adopted, where the decoder is of Wiener filter form. Simulation results are given to show the impacts of SDN, CoC, and steps of MacAdam ellipses on system performance.

Typical techniques for tracking vital signs require body contact and most of these techniques are intrusive in nature. Body-contact methods might irritate the patient's skin and he/she might feel uncomfortable while sensors are touching his/her body. In this study, we present a new wireless (non-contact) method for monitoring human vital signs (breathing and heartbeat). We have demonstrated for the first time1that vitals signs can be measured wirelessly through visible light signal reflected from a human subject, also referred to as visible light sensing (VLS). In this method, the breathing and heartbeat rates are measured without any body-contact device, using only a simple photodetector and a light source (e.g., LED). The light signal reflected from human subject is modulated by the physical motions during breathing and heartbeats. Signal processing tools such as filtering and Fourier transform are used to convert these small variations in the received light signal power to vitals data.We implemented the VLS-based non-contact vital signs monitoring system by using an off-the-shelf light source, a photodetector and a signal acquisition and processing unit. We observed more than 94% of accuracy as compared to a contact-based FDA (The Food and Drug Administration) approved devices. Additional evaluations are planned to assess the performance of the developed vitals monitoring system, e.g., different subjects, environments, etc. Non-contact vitals monitoring system can be used in various areas and scenarios such as medical facilities, residential homes, security and human-computer-interaction (HCI) applications.

In this work, the joint optimization of sensing threshold and power allocation strategy in orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) networks is studied. The aim is to maximize the total capacity of secondary users (SUs) while limiting the interference caused to the primary users (PUs). Taking into account the sensing errors, the total capacity of SUs is expressed as a function of the sensing threshold and SUs' transmit power. The resulting problem represents a nonconvex mixed integer non-linear programming (MINLP) optimization. Due to the high computational complexity of the MINLP problem, an equivalent simplified problem is formulated without resorting to integer variables. A computationally efficient suboptimal algorithm is proposed to address the joint optimization problem. Using the suboptimal solution as the initial point, an alternating optimization method is then employed to enhance the solution accuracy. Computer simulation results confirm the effectiveness of the proposed algorithm.

Most of the research in cognitive radio field is primarily focused on finding and improving secondary user (SU) performance parameters such as bit error rate, outage probability and capacity etc. Less attention is being paid towards the other side of the network that is the primary network which is under interference from SU. Also, it is the primary user (PU) that decides upon the interference temperature constraint for power adaptation to maintain a certain level of quality of service while providing access to SUs. However, given the random nature of wireless communication, interference temperature can be regulated dynamically to overcome the bottlenecks in entire network performance. In order to do so, we need to analyze the primary network carefully. This study tries to fill this gap by analytically finding the closed form theoretical expressions for signal to interference and noise ratio (SINR), mean SINR, instantaneous capacity, mean capacity and outage probability of PU, while taking peak transmit power adaptation at SU into picture. Furthermore, the expressions generated are validated with the simulation results and it is found that our theoretical derivations are in perfect accord with the simulation outcomes.

In this paper, a new occupancy estimation technique based on visible light sensing (VLS) is presented. A visible light source (e.g., LED) is utilized as the transmitter and a photo-detector (PD) used as a receiver, forming a visible light sensing system. Depending on the number of people in the room crossing the line-of-sight LOS (between the light source and PD), the received power at the receiver change. Consequently, probability density function (PDF) and cumulative distribution function (CDF) of the received power at the receiver change. First, a theoretical analysis of the received power is developed to incorporate the impact of room occupancy on the PDF and CDF of the received power. Second, these received power PDF and CDF expressions are compared with simulation results. Both results are in perfect agreement that verifies the theoretical analysis. In addition, room occupancy estimation is performed by using two methods, namely Kullback-Leibler(KL)-divergence and Euclidean distance. In these methods, the stored PDF of the received power in the database is compared with the measured received power PDF, which reveals the estimated room occupancy. It was shown how a slight variation in room occupancy can dramatically alter the PDF of the received power.

In this paper, the joint spectrum sensing and resource allocation problem is investigated in a multi-band-multiuser cognitive radio (CR) network. Assuming imperfect spectrum sensing information, our goal is to jointly optimize the sensing threshold and power allocation strategy such that the average total throughput of secondary users (SUs) is maximized. In Addition, the power of SUs is constrained to keep the interference introduced to primary users under certain limit, which gives rise to a nonconvex mixed integer non-linear programming (MINLP) optimization problem. Our contribution in this paper is threefold. First, it is illustrated that the dimension of the nonconvex MINLP problem can be significantly reduced, which helps to re-formulate the optimization problem without resorting to integer variables. Second, it is demonstrated that the simplified formulation admits the canonical form of a monotonic optimization, and an E-optimal solution can be achieved using the polyblock outer approximation algorithm. Third, a practical low-complexity spectrum sensing and resource allocation algorithm is developed to reduce the computational cost. Finally, the effectiveness of proposed algorithms is verified by simulations.

In this paper, we study the downlink resource allocation (RA) problem in orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) networks. Our goal is to maximize the aggregated capacity of secondary users (SUs). In addition, the power of SUs is controlled to keep the interference introduced to primary users (PUs) under certain limits, which gives rise to a non-convex mixed integer non-linear programming (MINLP) optimization problem. In this paper, it is illustrated that the non-convex MINLP formulation admits a special structure and the optimal solution can be always achieved using standard convex optimization techniques under a general and practical assumption. In particular, the subgradient method is adopted to address the problem in the dual domain. The effectiveness of the proposed algorithms is verified by simulations.

In cognitive radio (CR) systems, one of the main implementation issues is spectrum sensing because of the uncertainties in propagation channel, hidden primary user (PU) problem, sensing duration and security issues. This paper considers an orthogonal frequency-division multiplexing (OFDM)-based CR spectrum sharing system that assumes random access of primary network subcarriers by secondary users (SUs) and absence of the PU's spectrum utilization information, i.e., no spectrum sensing is employed to acquire information about the PU's activity or availability of free subcarriers. In the absence of information about the PU's activity, the SUs randomly access (utilize) the subcarriers of the primary network and collide with the PU's subcarriers with a certain probability. In addition, inter-cell collisions among the subcarriers of SUs (belonging to different cells) can occur due to the inherent nature of random access scheme. This paper conducts a stochastic analysis of the number of subcarrier collisions between the SUs' and PU's subcarriers assuming fixed and random number of subcarriers requirements for each user. The performance of the random scheme in terms of capacity and capacity (rate) loss caused by the subcarrier collisions is investigated by assuming an interference power constraint at PUs to protect their operation.

ABSTRACT Cognitive radio (CR) with spectrum‐sharing feature is a promising technique to address the spectrum under‐utilization problem in dynamically changing environments. In this paper, the achievable capacity gain of spectrum‐sharing systems over dynamic fading environments is studied. To perform a general analysis, a theoretical fading model called hyper‐fading model that is suitable to the dynamic nature of CR channel is proposed. Closed‐form expressions of probability density function (PDF) and cumulative density function (CDF) of the signal‐to‐noise ratio (SNR) for secondary users (SUs) in spectrum‐sharing systems are derived. In addition, the capacity gains achievable with spectrum‐sharing systems in high and low power regions are obtained. The effects of different fading figures, average fading powers, interference temperatures, peak powers of secondary transmitters, and numbers of SUs on the achievable capacity are investigated. The analytical and simulation results show that the fading figure of the channel between SUs and primary base‐station (PBS), which describes the diversity of the channel, does not contribute significantly to the system performance gain. Copyright © 2011 John Wiley & Sons, Ltd.

Cognitive radio with spectrum sharing feature is a promising technique to address the spectrum under-utilization problem in dynamically changing environments. In this paper, achievable capacity gain of spectrum sharing systems over dynamic fading environments is studied. For the analysis, a theoretical fading model called hyper fading model that is suitable to the dynamic nature of cognitive radio channel is proposed. Closed-form expression of probability density function (PDF) and cumulative density function (CDF) of the signal-to-noise ratio (SNR) for secondary users in spectrum sharing systems are derived. In addition, the capacity gains achievable with spectrum sharing systems in high and low power regions are obtained. Numerical simulations are performed to study the effects of different fading figures, average powers, interference temperature, and number of secondary users on the achievable capacity.