Search

Virtual reality (VR) technology has recently achieved notable success and been widely expected to interplay with more mobile multimedia services. To further enhance real-time immersive experience for VR applications, exploiting cooperative offloading among capable terminal devices should be emerged as an effective means. However, faced with diverse and surging mobile VR user requests, terminal-assisted offloading needs to support comprehensive cached content, ultra-low latency delivery, and continuous energy provisioning, to guarantee stringent quality of service requirements, which poses a critical challenge for resource-constrained terminals. Hence, this paper proposes a Cooperative Offloading framework for Terminal Clusters (named CO-TC), in which VR terminal clusters form several cooperation groups for sharing cached field of view (FoV) tiles and available computing resources to cooperatively perform FoV rendering and content delivery. To maximize energy efficiency in CO-TC, an optimization problem is formulated to jointly decide the task offloading and computing resource utilization. An intelligent offloading scheme is designed based on multi-agent reinforcement learning (MARL) specially using agent relation feature graph embeddings. Moreover, we theoretically prove the permutation invariance and convergence of the proposed algorithm and derive the optimal observation range of the agent to balance the performance gain and interaction overhead in the distributed MARL frame. Finally, simulation results show that the proposed offloading scheme outperforms other baselines in terms of VR service performance, including latency, energy consumption, and energy efficiency.

Semantic communication (SemCom) has recently been considered a promising solution to guarantee high resource utilization and transmission reliability for future wireless networks. Nevertheless, the unique demand for background knowledge matching makes it challenging to achieve efficient wireless resource management for multiple users in SemCom-enabled networks (SC-Nets). To this end, this article investigates SemCom from a networking perspective, where two fundamental problems of user association (UA) and bandwidth allocation (BA) are systematically addressed in the SC-Net. First, considering varying knowledge matching states between mobile users and associated base stations, we identify two general SC-Net scenarios, namely perfect knowledge matching-based SC-Net and imperfect knowledge matching-based SC-Net. Afterward, for each SC-Net scenario, we describe its distinctive semantic channel model from the semantic information theory perspective, whereby a concept of bit-rate-to-message-rate transformation is developed along with a new semantics-level metric, namely system throughput in message (STM), to measure the overall network performance. In this way, we then formulate a joint STM-maximization problem of UA and BA for each SC-Net scenario, followed by a corresponding optimal solution proposed. Numerical results in both scenarios demonstrate significant superiority and reliability of our solutions in the STM performance compared with two benchmarks.

In recent years, gesture recognition system based on radio frequency (RF) sensing has a wide application prospect and attraction in noncontact electronic interaction with its advantages of privacy security, lighting independence, and wide sensing range. The traditional RF sensing system depends on the environment and the subject, and the multichannel sensing equipment is expensive, which brings great challenges to the practical application. To address the above issues, a single-channel, low-cost, and domain-independent gesture recognition system is proposed. Specifically, the time-modulation technology is adopted to expand the number of antennas of the sensing device. The time-modulation array (TMA) is converted into a traditional array through harmonic recovery technology. The 2D-fast Fourier transform (FFT), moving target indication filter, and data normalization are used to extract domain-independent angle-Doppler maps (ADMs) gesture features. In order to ensure recognition accuracy, we propose a lightweight neural network with an attention mechanism, which only needs one training and can be applied to different data domains. The experimental results show that the accuracy of in-domain recognition of the proposed system is 98.9%, and the accuracy of cross-domain (i.e., new environments, new users, and new positions) recognition is 85.6%–97.4% without model retraining.

Semantic communication (SemCom), as an emerging paradigm focusing on meaning delivery, has recently been considered a promising solution for the inevitable crisis of scarce communication resources. This trend stimulates us to explore the potential of applying SemCom to wireless vehicular networks, which normally consume a tremendous amount of resources to meet stringent reliability and latency requirements. Unfortunately, the unique background knowledge matching mechanism in SemCom makes it challenging to simultaneously realize efficient service provisioning for multiple users in vehicle-to-vehicle networks. To this end, this paper identifies and jointly addresses two fundamental problems of knowledge base construction (KBC) and vehicle service pairing (VSP) inherently existing in SemCom-enabled vehicular networks in alignment with the next-generation ultra-reliable and low-latency communication (xURLLC) requirements. Concretely, we first derive the knowledge matching based queuing latency specific for semantic data packets, and then formulate a latency-minimization problem subject to several KBC and VSP related reliability constraints. Afterward, a SemCom-empowered Service Supplying Solution ( $\text{S}^{{4}}$ ) is proposed along with the theoretical analysis of its optimality guarantee and computational complexity. Numerical results demonstrate the superiority of $\text{S}^{{4}}$ in terms of average queuing latency, semantic data packet throughput, user knowledge matching degree and knowledge preference satisfaction compared with two benchmarks.

For multiple-input multiple-output (MIMO) frequency diverse array (FDA) communications, this paper proposes a frequency offsets index modulation (FOIM) scheme, which conveys extra information by selecting transmitting frequency offsets from a frequency offsets pool. To improve system spectral efficiency, we firstly design an orthogonal baseband waveform for FDA with the exact expression, then the corresponding receiver structure is proposed. Further, considering that the traditional maximum likelihood (ML) detection algorithm suffers from high complexity, an output combined maximum likelihood (OCML) approach is presented. Moreover, the closed-form expressions for the upper bound on bit error rates (BERs) of both ML and OCML methods are derived, as well as the counterpart of the system capacity. The simulation results show that the capacity of the proposed FOIM scheme outperforms the MIMO scheme, and meanwhile, our method achieves a higher communication rate when compared with the quadrature spatial modulation (QSM) scheme. Additionally, the proposed OCML algorithm can lead the BER performance of FOIM to be superior to that of the aforementioned approaches with significantly lower computational complexity.

The 57–71-GHz millimeter-wave (mmWave) industrial, scientific, and medical (ISM) band holds significant potential for enhancing the performance of next-generation industrial wireless applications. This article first presents the design and analysis of a compact and high-performance eight-element series-fed frequency beam-scanning array designed to cover the entire 21.87% fractional bandwidth (FBW) of the 57–71-GHz ISM band. Using this as a subarray, a hybrid parallel–series feed topology is designed to construct a 64-element ( $8\times8$ ) planar array with high-gain directional beams. The planar array provides a peak measured gain of 20.12 dBi at 64 GHz and maintains a flat gain of over 19.23 dBi throughout the band, with a 1-dB gain bandwidth of 13 GHz. Its narrow directional beams provide an average half-power beamwidth (HPBW) of 9.7° and 11.78° in the elevation and azimuth planes, respectively, facilitating point-to-point mmWave connectivity and high-resolution beam scanning. The inherent phase variation of the series-fed topology is employed to produce a beam-scanning range of 40° within the 57–71-GHz band, with a scan loss of less than 1 dB. The proposed array is a low-cost and reproducible solution for seamless integration with V-band mmWave equipment, as elucidated through practical demonstration frameworks using the mmWave power sensor and EK1HMC6350 evaluation board. The proposed array is well-suited for emerging industrial wireless sensing and imaging applications, and mmWave frequency scanning radars. Its versatility extends to various 60-GHz protocols, such as IEEE 802.11ay, IEEE 802.11ad, IEEE 802.15.3c/d, and WirelessHD, and other customized industrial protocols such as WirelessHP.

Driven by the development of blockchain infrastructures and the promotion of Web 3, more than 4000 Decentralized Autonomous Organizations (DAOs) have been developed as online organizations jointly owned and managed by their members who work for the same interests. Voting mechanisms as the democratic administration of DAOs without the involvement of central authority, are crucial to both the development of the DAO community and the protection of individual interests. This paper is one of the first analyses of the critical role of voting mechanisms in DAOs’ operation. In the absence of systematic studies of voting mechanisms in DAOs, we propose five tiers of decentralization in DAO voting which marks the critical difference between DAO voting and conventional voting. We also define four dimensions to comprehensively evaluate the performance of DAO voting mechanisms, which identify the demands and characteristics of DAO voting and put forward clear design guidelines for voting mechanisms in DAOs. Finally, we take seven typical voting mechanisms as examples and analyze their performance in our proposed evaluation schemes.

Reconfigurable intelligent surface (RIS) has been identified as a promising disruptive innovation to realize a faster, safer and more efficient communication system. In this paper, we study the broad beamwidth design of RIS. A problem is formulated to achieve broadbeam with maximum and equal power gain within a pre-defined angular region given constraints of the unit modulus weights of RIS. Since the formulated problem is non-convex, where the optimal solution cannot be analytically obtained, we propose the difference-of-convex-based semi-definite programming (DC-SDP) algorithm. In addition, as important guidance of signal coverage for arbitrary angular regions, we mathematically derive the relationship between the angular range of the spatial sector and the maximum average received power. The upper bounds of the average received power with different RIS configurations are also obtained, where uniform rectangular array (URA) and uniform linear array (ULA) are considered. Simulation results demonstrate the effectiveness of our derivations and verify that our proposed DC-SDP algorithm is applicable in practical applications and outperforms other baseline methods. Overall, this work can be viewed as a foundation for the practical implementation of RIS on coverage enhancement and can also be seen as an initial step towards achieving channel estimation.

Future wireless communication networks are expected to extensively utilise unmanned aerial vehicles (UAVs) and reconfigurable intelligent surfaces (RISs) to improve performance, extend coverage, and enhance energy efficiency. In this letter, the fundamental limits of the system performance of a multi-hop RIS-assisted UAV communication system are analysed and characterised. We derive accurate closed-form approximations for the statistical distributions of the RIS-assisted channels which are then exploited to derive an analytical expression for the signal-to-noise ratio (SNR), outage probability, and bit error rate (BER). Moreover, we derive outage probability expressions in the high SNR regime. Our analysis demonstrates that strategically placing RIS with an adequate number of elements can effectively enhance the performance of UAV communications in different fading scenarios. RIS dynamically adapts to changing channels and user positions, enhancing performance as the UAV navigates diverse geographical areas, when ideal Line of Sight (LoS) conditions may not be consistently attainable due to the UAV’s altitude and dynamic mobility.

In this paper, we establish a task-oriented cross-system design framework to minimize the required packet rate for timely and accurate modeling of a real-world robotic arm in the Metaverse, where sensing, communication, prediction, control, and rendering are considered. To optimize a scheduling policy and prediction horizons, we design a Constraint Proximal Policy Optimization (C-PPO) algorithm by integrating domain knowledge from relevant systems into the advanced reinforcement learning algorithm, Proximal Policy Optimization (PPO). Specifically, the Jacobian matrix for analyzing the motion of the robotic arm is included in the state of the C-PPO algorithm, and the Conditional Value-at-Risk (CVaR) of the state-value function characterizing the long-term modeling error is adopted in the constraint. Besides, the policy is represented by a two-branch neural network determining the scheduling policy and the prediction horizons, respectively. To evaluate our algorithm, we build a prototype including a real-world robotic arm and its digital model in the Metaverse. The experimental results indicate that domain knowledge helps to reduce the convergence time and the required packet rate by up to 50%, and the cross-system design framework outperforms a baseline framework in terms of the required packet rate and the tail distribution of the modeling error.

Symbiotic communication (SC), exploiting the analogy of biological ecosystem to establish communication device ecosystems, can enable cooperative service/resource exchanges across heterogeneous devices thus realizing the complementarity among different communication resources. However, considering unstable wireless links, high network dynamics, and complex electromagnetic interference in such an ecosystem, it is difficult to perform service/resource exchanges without securing a trusted environment. Moreover, multi-dimensional service/resource exchange demanded by massive symbiotic devices (SDs) in the ecosystems exposes additional challenges for exchange decision-making. To deal with the above difficulties, in this article, we propose a blockchain-empowered intelligent coevolution for symbiotic devices (BIO-SD). Specifically, to guarantee the trustworthiness of service/resource exchange and resist malicious attacks, a direct acyclic graph (DAG)-based blockchain architecture is applied to the BIO-SD scheme. Furthermore, a modified multi-agent deep deterministic policy gradient (MADDPG) approach is adopted to make service/resource exchange decisions under this trusted environment. The simulation results show that the proposed BIO-SD scheme outperforms some conventional solutions in terms of transmission rate and transmission latency under both non-attack and malicious attack scenarios.

The detection of head movement plays a crucial role in human–computer interaction systems. These systems depend on control signals to operate a range of assistive and augmented technologies, including wheelchairs for Quadriplegics, as well as virtual/augmented reality and assistive driving. Driver drowsiness detection and alert systems aided by head movement detection can prevent major accidents and save lives. Wearable devices, such as MagTrack consist of magnetic tags and magnetic eyeglasses clips and are intrusive. Vision-based systems suffer from ambient lighting, line of sight, and privacy issues. Contactless sensing has become an essential part of next-generation sensing and detection technologies. Wi-Fi and radar provide contactless sensing, however, in assistive driving they need to be inside enclosures or dashboards, which for all practical purposes in this article have been considered as through walls. In this study, we propose a contactless system to detect human head movement with and without walls. We used ultra-wideband (UWB) radar and Wi-Fi signals, leveraging machine and deep learning (DL) techniques. Our study analyzes the six common head gestures: right, left, up, and down movements. Time-frequency multiresolution analysis based on wavelet scalograms is used to obtain features from channel state information values, along with spectrograms from radar signals for head movement detection. Feature fusion of both radar and Wi-Fi signals is performed with state-of-the-art DL models. A high classification accuracy of 83.33% and 91.8% is achieved overall with the fusion of VGG16 and InceptionV3 model features trained on radar and Wi-Fi time–frequency maps with and without the walls, respectively.

While the fifth-generation (5G) is bringing an innovative fabric of breakthrough technologies, enabling smart factories, cities, and Internet-of-Things (IoT), the unprecedented strain on communication networks put by these applications, in terms of highly cognitive, agile architectures and the support of massive connectivity, energy efficiency, and extreme ultra-low latency, is pushing 5G to their limits. As such, the focus of academic and industrial efforts has shifted toward beyond 5G (B5G) and the conceptualization of sixth-generation (6G) systems. This article discusses four main digital and societal use cases (UCs) that will drive the need to reconcile a new breed of network requirements. Based on this, we provide our vision of the fundamental architectural ingredients that will enable the promise of 6G networks of bringing the unification of experiences across the digital, physical, and human worlds. We outline key disruptive technological paradigms that will support 6G materialize a bouquet of unique expectations and redefine how we live and protect our planet. Finally, we adopt the recently envisaged ecosystem of the Internet-of-Musical Things (IoMusT) to depict how the discussed UCs and technological paradigms may be exploited to realize this ecosystem.

In this article, we propose a new method to inpaint videos with removed regions. Our method was developed based on combining both short-term propagation-based inpainting (STPI) and long-term propagation-based inpainting (LTPI) modules. The STPI module is designed to in-fill an image from a single frame with local reference information, while the LTPI module uses multiple STPI modules to inpaint the whole video, giving high temporal consistency and low complexity. With both of the proposed modules, the correlated spatiotemporal information of frames can be propagated throughout the video, offering reliable short-long-term source information for inpainting. The experimental results demonstrate that our proposed method provides better results when compared with the state of the art.

Vision-based gait analysis and human identification systems have been widely proposed in the literature. However, these systems cannot be readily applied in many real-time applications due to involved challenges such as video quality, occlusion, and serious privacy concerns. To overcome such issues, we propose the LoGait system that leverages ubiquitous LoRa signals recognize gait in different indoor environments. Our work is based on the intuition that the walking pattern of different users can be distinguished by distinct stride size and frequency. The wireless LoRa signal which is interfered by human walking will capture the gait information of subjects. In combination with the long-distance transmission ability of LoRa signal, the system enables a larger sensing range of gait recognition compared to the WiFi-based gait recognition system. The proposed LoGait system utilizes the phase difference between two LoRa receiver channels, along with a set of filtering techniques, to extract distinctive features and generate a human gait profile. This profile is then matched against a database using a dynamic time warping (DTW)-based recognition algorithm, enabling accurate identification based on unique gait patterns. It has been validated in three different scenarios for gait recognition namely line of sight (LOS), non-LOS (NLOS), and long distance, with an accuracy of 85.13%, 79.14%, and 84.14%, respectively.

The distributed consensus intends to improve the reliability of critical decision making in wireless connected autonomous systems. The performance of distributed consensus heavily depends on the reliability of wireless links, which should be stochastic with limited communication resources. Therefore, advanced communication resource allocation schemes are needed to achieve high reliability and low latency for the distributed consensus. This article first derives optimized resource allocation schemes for the distributed consensus. The optimal number of nodes for the best reliability performance of the distributed consensus is also investigated to solve the inadequate overall communication resources issue. The revealed derivation and simulation results can provide guidelines to deploy the appropriate paradigm of communication resource allocation in autonomous wireless systems.

The Internet of Things (IoT) is delivering solutions with improved efficiency and security, and providing better productivity in manufacturing, retail, and other sectors. Maritime Transportation Systems (MTSs) is currently adopting the IoT to move toward a digitalized, data-driven world with increased efficiency and lower costs, and creating new revenue opportunities. Integration of the IoT also enables real-time tracking of shipments, improved efficiency in cargo handling, pre-emptive maintenance, route optimization, reduced fuel consumption, and improved safety in maritime transportation systems. With IoT technology expanding and evolving rapidly, more applications are predicted to assist and improve all aspects of MTSs, making them hassle-free and safe.

Radio frequency (RF) signals are widely used in respiratory detection due to their non-contact, remote sensing advantages. In order to satisfy multiperson monitoring, the existing RF sensing system greatly increases the complexity of the feeder network, which will affect the stability of the system and have high maintenance costs. In this study, we propose a single-channel, low-complexity multiperson respiratory monitoring system. It uses a time-modulated array (TMA) to detect the position and respiration rate of multiple targets. The TMA is reconstructed into a conventional multichannel array by the harmonic recovery method. Beamforming is used to scan the scene horizontally. At the same time, the beamformed signal is converted to the frequency domain to construct the angle of arrival-frequency map (AFM). By forming a beam pointing to the target, the time domain respiration signal of each target can be acquired. The experimental results show that the maximum respiratory rate (RR) estimation errors of the proposed system in single-person, two-person, and three-person scenarios are 0.77, 0.96, and 1.32 bmp, respectively. In addition, the Bland-Altman analysis shows that our system has good consistency with the reference sensor.

Space-air-ground integrated network (SAGIN) has emerged as a paradigm shifting architecture that offers large-scale, flexible wireless coverage and seamless, high-rate connectivity to complement terrestrial communications. Nonetheless, unlocking the potentials of SAGIN is subject to addressing non-trivial challenges associated with their inherent time-variability, self-organization, and heterogeneity. Meanwhile, the concept of reconfigurable intelligent surfaces (RISs) is recognized as a disruptive technology that supports an unprecedented control of electromagnetic waves propagation and potentially offers significant enhancements in spectral efficiency, coverage expansion, and security, all achieved in a low-cost and energy-efficient manner. We anticipate that the integration of RISs into SAGIN will not only play a fundamental role in improving the quality of inter- and intra-layer communications, but will also provide complex interactions among the three network segments, and, hence, opens the horizons for a new breed of applications across all industries. In this article, we explore the underlying opportunities and challenges of integrating RIS-enabled communications into SAGIN, and present a forward-looking overview of the cross-layer operational elements supported by RISs. Finally, we outline major enabling technologies and present a look ahead toward addressing open research issues.

Prediction has recently been considered as a promising approach to meet low-latency and high-reliability requirements in long-distance haptic communications. However, most of the existing methods did not take features of tasks and the relationship between prediction and communication into account. In this paper, we propose a task-oriented prediction and communication co-design framework, where the reliability of the system depends on prediction errors and packet losses in communications. The goal is to minimize the required radio resources subject to the low-latency and high-reliability requirements of various tasks. Specifically, we consider the just noticeable difference (JND) as a performance metric for the haptic communication system. We collect experiment data from a real-world teleoperation testbed and use time-series generative adversarial networks (TimeGAN) to generate a large amount of synthetic data. This allows us to obtain the relationship between the JND threshold, prediction horizon, and the overall reliability including communication reliability and prediction reliability. We take 5G New Radio as an example to demonstrate the proposed framework and optimize bandwidth allocation and data rates of devices. Our numerical and experimental results show that the proposed framework can reduce wireless resource consumption up to 77.80% compared with a task-agnostic benchmark.

This article presents a multibit reconfigurable intelligent surface (RIS) with a high phase resolution, capable of beam-steering in the azimuthal plane at sub-6 GHz. Field trials in realistic indoor deployments have been carried out, with coverage enhancement performance ascertained for three common wireless communication scenarios. Namely, serving users in an open lobby with mixed line-of-sight (LoS) and nonline-of-sight (NLoS) conditions, communication via a junction between long corridors, and a multifloor scenario with propagation via windows. This work explores the potential for RIS deployment to mitigate NLoS effects in indoor wireless communications. In a single transmitter (Tx), single receiver (Rx) NLoS link, received power improvement of as much as 40 dB is shown to be achievable by suitable placement of an RIS, with an instantaneous bandwidth of at least 100 MHz possible over a 3–4.5 GHz range. In addition, the effects of phase resolution on the optimal power reception for the multibit RIS have been experimentally verified, with a 2.65 dB improvement compared with a 1 bit case.

This article presents a study on contactless localization for activity recognition based on radio frequency (RF) sensing. The focus of this study is on the quantitative analysis of the collected data, which is in the form of channel state information (CSI). The proposed method utilizes a software-defined radio (SDR) system in combination with an ensemble learning technique to localize and identify daily living activities such as leaning, sitting, standing, and walking. Specifically, an SDR device, a universal software radio peripheral (USRP) model X300/X310, is utilized to collect data on the aforementioned activities. The data is collected from an empty space and a participant performing daily living activities in different territories. The acquired data is labeled based on the region, zone, and performed activity. The CSI data is subsequently preprocessed and fed into an ensemble-based machine-learning algorithm for classification. Furthermore, the stability analysis of the proposed method is performed to evaluate its reliability and robustness. The performance of the algorithm is evaluated using various metrics, including a confusion matrix, accuracy, cross-validation score, and training time (Shah et al., 2017 and Taylor et al., 2020). The results demonstrate that the proposed ensemble-based approach achieves a high accuracy of up to 90% in activity recognition, highlighting the effectiveness of the proposed method in contactless localization for activity recognition.

This article presents a bio-inspired circularly polarized ultrahigh-frequency (UHF) radio frequency identification (RFID) tag antenna for metallic and low-permittivity substances. This tag design is based on a leaf-shaped radiator, two shorting stubs, and slots etched on F4B substrate. Initially, the tag antenna is designed using a characteristics mode analysis (CMA) by analyzing the first six characteristic modes (CM) and characteristic angles. The width of orthogonal slots is varied to get the resonance of CM modes in the required US RFID band. Moreover, the edges are blended to get orthogonal current distribution, which is necessary for circular polarization. In addition, the proposed tag design is optimized further using CST Microwave studio and an RFID chip is exploited as a capacitive coupling element (CCE) to run CM modes with the orthogonal current pattern. This tag can also be tunable to European RFID (EU) band (866–868 MHz) by changing the length of shorter diagonal slot. The tag design offers a read range of 7–4.5 m on $100\times100$ mm2 metals plate and low-permittivity substrates (for the 902–928-MHz band). In the EU band, the corresponding read ranges are 5.7 and 3.5 m above metal and low-permittivity objects, respectively. This circularly polarized tag antenna is advantageous in terms of cost, circular polarization feature, and ease of fabrication due to the absence of vias, shorting pins, and matching circuits. Therefore, this tag design is suitable for labeling various low-permittivity objects, industrial conveyer belt applications, baggage handling systems, and Internet of Things (IoT) applications.

Virtual reality (VR) over wireless is expected to be one of the killer applications in next-generation communication networks. Nevertheless, the huge data volume along with stringent requirements on latency and reliability under limited bandwidth resources makes untethered wireless VR delivery increasingly challenging. Such bottlenecks, therefore, motivate this work to seek the potential of using semantic communication, a new paradigm that promises to significantly ease the resource pressure, for efficient VR delivery. To this end, we propose a novel framework, namely Wireless Semantic delivery for VR (WiserVR), for delivering consecutive 360° video frames to VR users. Specifically, multiple deep learning-based modules are well-devised for the transceiver in WiserVR to realize high-performance feature extraction and semantic recovery. Among them, we dedicatedly develop a concept of semantic location graph and leverage the joint-semantic-channel-coding method with knowledge sharing to not only substantially reduce communication latency, but also to guaran-tee adequate transmission reliability and resilience under various channel states. Moreover, imple-mentation of WiserVR is presented, followed by corresponding initial simulations for performance evaluation compared with benchmarks. Finally, we discuss several open issues and offer feasible solutions to unlock the full potential of WiserVR.

WiFi sensing has received recent and significant interest from academia, industry, healthcare professionals, and other caregivers (including family members) as a potential mechanism to monitor our aging population at a distance without deploying devices on users’ bodies. In particular, these methods have the potential to detect critical events such as falls, sleep disturbances, wandering behavior, respiratory disorders, and abnormal cardiac activity experienced by vulnerable people. The interest in such WiFi-based sensing systems arises from practical advantages including its ease of operation indoors as well as ready compliance from monitored individuals. Unlike other sensing methods, such as wearables, camera-based imaging, and acoustic-based solutions, WiFi technology is easy to implement and unobtrusive. This paper reviews the current state-of-the-art research on collecting and analyzing channel state information extracted using ubiquitous WiFi signals, describing a range of healthcare applications and identifying a series of open research challenges, including untapped areas of research and related trends. This work aims to provide an overarching view in understanding the technology and discusses its use-cases from a perspective that considers hardware, advanced signal processing, and data acquisition.

Harvesters for implantable medical applications need to generate enough energy to power their loads, but their efficiency is reduced when implanted under the tissue. Conventional photovoltaic (PV) cell harvesters made with CMOS technology stack cells in series, which raises output voltage but lowers power conversion efficiency. In addition, it is difficult to assess harvester performance prior to fabrication. To address these challenges, we developed a novel parallel PV cell configuration that fully utilizes all triple-well diodes and responds efficiently to near-infrared light. Using an optimized structure, the PV cells were fabricated through standard TSMC 65-nm CMOS technology, achieving an efficiency of 18.6%, open circuit voltage of 0.45 V, and short circuit current of 1.9 mA cm $^{-{2}}$ . These results confirm the ability of the device to generate sufficient energy even when implanted beneath the tissue. Multiphysics finite element modeling (FEM) was used to optimize the stacking structure of the CMOS PV cell, and experimental results showed a successfully delivered power density of 1.2 mW cm $^{-{2}}$ (single cell 1.04 mm2) when placed 2 mm below porcine skin. Different array configurations of six PV cells were also experimentally studied using external wire switching, demonstrating the flexibility of the PV array in delivering different output energy for various implantable devices.

This article presents an ink-jet printed ultrahigh-frequency (UHF) radio frequency identification (RFID) label for tagging fruits. The proposed tag antenna is based on a hybrid configuration, which consists of a loop, dipole-like strips (DLSs), and eye-shaped rectangular nested slots. The loop fulfills impedance matching proposes, while the eye-shaped slots assist to maintain high imaginary impedance to mitigate capacitive effects of high permittivity surfaces of fruits. Moreover, the dipole strips help to reduce the loss resistance caused by the high permittivity of the surface of the fruit. Therefore, the overall tag configuration provides a good impedance match with the Alien H3 RFID chip in the whole U.S. UHF RFID band. Furthermore, the fabricated prototype of the proposed tag antenna achieved a read range of 3 m after mounting on apple. Moreover, an automatic weighing and billing experiment is performed by placing the tagged apples inside a refrigerator. Overall, the performance of the proposed tag antenna is tested after mounting on apple fruits of different shapes and sizes, which indicates the robustness of this tag. Moreover, the tag antenna is also tested for sensing spoilage of apple fruits. The read range of the proposed tag antenna reduces up to 42% on spoiled apples. Therefore, this tag can be suitable for automatic sorting, weighting, and billing of fruits in smart cities, vending machines, and departmental stores.

Coverage analysis of aerial communication networks based on high altitude platforms (HAPs) and low altitude platforms (LAPs) is of great significance to understand the service provisioning capability of aerial base stations. This letter uses stochastic geometry to analyze network coverage of an integrated HAP and LAP (IHL) system with respect to backhaul constraints, where LAPs aim to provide services for ground user equipments in the malfunction area and a HAP is to provide backhaul connectivity for LAPs. Based on stochastic geometry theory, the analytical framework of the IHL system coverage is derived along with the analysis on the impact of some key parameters, such as aerial platform altitudes and LAP densities. The derived analytical framework can also provide insights for the backhaul design of LAP aerial base stations, which is also revealed in the numerical analyses part.

Gaussian process (GP) is a very popular machine learning method for online surrogate-model-assisted antenna design optimization. Despite many successes, two improvements are important for the GP-based antenna global optimization methods, including: 1) the convergence speed (i.e., the number of necessary electromagnetic (EM) simulations to obtain a high-performance design) and 2) the GP model training cost when there are several tens of design variables and/or specifications. In both aspects, the state-of-the-art GP-based methods show practical but not desirable performance. Therefore, a new method, called the self-adaptive Bayesian neural network surrogate-model-assisted differential evolution (DE) for antenna optimization (SB-SADEA), is presented in this article. The key innovations include: 1) the introduction of the Bayesian neural network (BNN)-based antenna surrogate modeling method into this research area, replacing GP modeling, and 2) a bespoke self-adaptive lower confidence bound (LCB) method for antenna design landscape making use of the BNN-based antenna surrogate model. The performance of SB-SADEA is demonstrated by two challenging design cases, showing considerable improvement in terms of both convergence speed and machine learning cost compared with the state-of-the-art GP-based antenna global optimization methods.

We present a review of how reinforcement learning (RL) is helping to tackle some of the most challenging problems in the Internet-of-underwater-things (IoUTs). Scientists estimate that 50-80 percent of atmospheric oxygen comes from the ocean, implying that life on earth depends heavily on clean and healthy oceans. This huge significance of the ocean in supporting life on earth is motivating the use of artificial intelligence (AI) and machine learning (ML) tools to create a sustainable marine ecosystem. We briefly review the RL paradigm, its categorisations and RL algorithms developed to solve important problems in IoUTs. New literature keeps emerging that show innovative applications of RL in underwater communications and networking that far outperform conventional solutions and other ML-based methods. Due to its online learning nature, RL is particularly useful for decision making in dynamic environments such as underwater where the communication channel is stochastic and rapidly varying. We explore the applications of RL in IoUTs, showing different classes of IoUTs problems and high-lighting RL algorithms that are tailored to solving them. Despite the significant progress that has made in the RL field, there are still many challenges and open research problems in the use of RL in IoUTs. We conclude the article with an outline of some of these challenges and suggest some ways forward.

Intelligent transportation systems are pivotal in enhancing road safety by enabling intra-vehicle communication. Due to the nature of the wireless communication link, several potential risks of attacks exist, including impersonation, modification, and replay. To ensure the security of vehicular ad hoc networks (VANETs) against malicious activities, secure data exchange between inter-communicating terminals, specifically vehicle-to-everything (V2X) communication, becomes a critical technological challenge that requires attention. Existing authentication methods for VANET applications mainly rely on crypto-based techniques. The emergence of physical (PHY)-layer authentication has gained prominence, leveraging the inherent characteristics of wireless channels and hardware imperfections to distinguish between wireless devices. The PHY-layer-based authentication is not a standalone alternative to cryptographic methods, but it shows potential as a supplementary approach for re-authentication in VANETs, referred to as “cross-layer authentication”. This comprehensive survey thoroughly evaluates the state-of-the-art of crypto-based, PHY-layer-based, and cross-layer-based authentication methods in VANETs. Furthermore, this survey delves into integrating different sixth-generation (6G) and beyond technologies, such as reconfigurable intelligent surfaces (RIS) and federated learning, for enhancing PHY-layer authentication performance in the presence of active attackers. Furthermore, in-depth insights into the advantages of cross-layer authentication methods are presented, along with exploring various state-of-the-art VANET security techniques. A detailed technical discussion is provided on these advanced approaches, and it is concluded that they can significantly enhance the security of intelligent transportation systems, ensuring safer and more efficient vehicular communications.

Narrowband Internet of Things (NB-IoT) is a cellular IoT communication technology standardized by 3rd Generation Partnership Project (3GPP) for supporting massive machine type communication and its deployment can be realized by a simple firmware upgrade on existing long term evolution (LTE) networks. The NB-IoT requirements in terms of energy efficiency, achievable rates, latency, extended coverage, make the resource allocation, in a limited bandwidth, even a more challenging problem w.r.t. to legacy LTE. The allocation, done with subcarrier (SC) granularity in NB-IoT, should maintain adequate performance for the devices while keeping the power consumption as low as possible. Nevertheless, the optimal solution of the resource allocation problem is typically unfeasible since nonconvex, NP-hard and combinatorial because of the use of binary variables. In this article, after the formulation of the optimization problem, we study the resource allocation approach for NB-IoT networks aiming to analyze the tradeoff between rate and latency. The proposed suboptimal algorithm allocates radio resource (i.e., SCs) and transmission power to the NB-IoT devices for the uplink transmission and the performance is compared in terms of latency, rate, and power. By comparing the proposed allocation to a conventional round robin (RR) and to a brute-force approach, we can observe the advantages of the formulated allocation problem and the limited loss of the suboptimal solution. The proposed algorithm outperforms the RR by a factor 2 in terms of spectral efficiency and, moreover, the study includes techniques that reduce the dropped packets from $\text{29}\%$ to $\text{1.6}\%$.

Atrial Fibrillation (AF) the most commonly occurring type of cardiac arrhythmia is one of the main causes of morbidity and mortality worldwide. The timely diagnosis of AF is an equally important and challenging task because of its asymptomatic and episodic nature. In this paper, state-of-the-art ECG data-based machine learning models and signal processing techniques applied for auto diagnosis of AF are reviewed. Moreover, key biomarkers of AF on ECG and the common methods and equipment used for the collection of ECG data are discussed. Besides that, the modern wearable and implantable ECG sensing technologies used for gathering AF data are presented briefly. In the end, key challenges associated with the development of auto diagnosis solutions of AF are also highlighted. This is the first review paper of its kind that comprehensively presents a discussion on all these aspects related to AF auto-diagnosis in one place. It is observed that there is a dire need for low energy and low cost but accurate auto diagnosis solutions for the proactive management of AF.

There are still many challenges in effectively harvesting and generating power for implantable medical devices. Most of today's research focuses on finding ways to harvest energy from the human body to avoid the use of batteries, which require surgical replacement. For example, current energy harvesters rely on piezoelectricity, thermoelectricity, and solar electricity to drive the implantable device. However, the majority of these energy harvesting techniques suffer from a variety of limitations such as low power output, large size, or poor efficiency. Due to their high efficiency, we focus our attention on solar photovoltaic cells. We demonstrate that the tissue absorption losses severely influence their performance. We predict the performance of these cells using simulation through the verified experimental data. Our results show that our model can obtain 17.20% efficiency and 0.675 V open-circuit voltage in one sun condition. In addition, our device can also harvest up to 15 mW/cm2 in dermis and 11.84 mW/cm2 in hypodermis by using 100 mW/cm2 light source at 800 nm and 850 nm, respectively. We propose implanting our device in hypodermis to obtain a stable power output.

In this article, using the stochastic geometry, we develop a tractable uplink modeling framework for the outage probability of the multitier millimeter wave (mmWave) cellular networks. Each tier’s mmWave base stations (BSs) are randomly located and they have particular spatial density, antenna gain, receiver sensitivity, blockage parameter, and pathloss exponents. Our model takes account of the maximum power limitation and the per-user power control. More specifically, each user, which could be in line-of-sight (LOS) or non-LOS to its serving mmWave BS, controls its transmit power such that the received signal power at its serving BS is equal to a predefined threshold. Hence, a truncated channel inversion power control scheme is implemented for the uplink of mmWave cellular networks. We derive closed-form expressions for the signal-to-interference-plus-noise-ratio (SINR) outage probability for the uplink of the multitier mmWave cellular networks, which we later degrade to the single-tier network. Furthermore, we analyze the case with a dense network by utilizing the simplified model, where the LOS region is approximated as a fixed LOS disk. The results show that imposing a maximum power constraint on the user significantly affects the SINR outage probability in the uplink of mmWave cellular networks.

This paper presents a low-cost, inkjet printed radio frequency identification (RFID) tag antenna for remote healthcare applications. The electrically small tag consists of nested-slot configuration and parallel strips. The tag antenna is exploited as a sensor by modifying its equivalent circuit to mitigate the effects of water, blood sample phantom, and the human body. As a result, the proposed RFID tag antenna with compact dimensions of 40 × 14 mm2, features a conjugate match with Impinj R6 RFID chip ranging from 890 to 937 MHz. Moreover, this tag has a read range of 3 m, 2.5 m, and 1.5 m on the water bottle, intravenous (IV) solution and blood bag, respectively. However, the read range of RFID tag on an empty water bottle or IV solution bag is 0.5 m. By comparing the read range of tag on empty and solution filled IV bags, the proposed tag was used as a water proximity sensor. Experimental testing of the tag is performed for sensing the level of the IV solution. Also, this tag is tested after mounting on the liquid mixture (a mixture of salt and sugar is used as a phantom to mimic the blood) filled plastic bags, which leads to a low-cost solution for blood storage management. Experimental results show a good agreement of the proposed tag toward its use in healthcare applications, which leads to better healthcare facilitation regarding cost, time, and care.

High mobility in ITS, especially V2V communication networks, allows increasing coverage and quick assistance to users and neighboring networks, but also degrades the performance of the entire system due to fluctuation in the wireless channel. How to obtain better QoS during multimedia transmission in V2V over future generation networks (i.e., edge computing platforms) is very challenging due to the high mobility of vehicles and heterogeneity of future IoT-based edge computing networks. In this context, this article contributes in three distinct ways: to develop a QoS-aware, green, sustainable, reliable, and available (QGSRA) algorithm to support multimedia transmission in V2V over future IoT-driven edge computing networks; to implement a novel QoS optimization strategy in V2V during multimedia transmission over IoT-based edge computing platforms; to propose QoS metrics such as greenness (i.e., energy efficiency), sustainability (i.e., less battery charge consumption), reliability (i.e., less packet loss ratio), and availability (i.e., more coverage) to analyze the performance of V2V networks. Finally, the proposed QGSRA algorithm has been validated through extensive real-time datasets of vehicles to demonstrate how it outperforms conventional techniques, making it a potential candidate for multimedia transmission in V2V over self-adaptive edge computing platforms.

The emerging 5G communication network is gaining tremendous attention from mobile network operators, regulators, and academia due to the provisions of network densification, ultra-low latency and improved spectral and energy efficiencies. However, post-disaster EMS, which nowadays predominantly depends on the wireless communication infrastructure, is significantly lagging behind in terms of innovation, standards, and investments. Since the 5G vision is the revolution of the telecommunication industry, provisions of efficiently handling EMS is expected to be distributed, autonomous, and resilient to the network vulnerabilities due to both manmade and natural disasters. In this article, the 4G LTE approaches for typical post-disaster communication and their shortcomings will be discussed. We elaborate three typical post-disaster network scenarios when the network is congested, partly functional or completely isolated. The possible solution framework, for instance, Device-to-Device communication, drone-assisted communication, mobile ad hoc networks and Internet-of-Things, for post-disaster scenarios will be discussed. Given that spectrum allocation is critical for EMS, we assess the possible schemes for radio resource allocation specific for EMS in addition to the social responsibility of users in such critical situations.

This paper proposes a novel load-balancing scheme for an operator-deployed cellular-wireless local area network (WLAN) heterogeneous network (HetNet), where the user association is controlled by employing a cell-breathing technique for the WLAN network. This scheme eliminates the complex coordination and additional signaling overheads between the users and the network by allowing the users to simply associate with the available WLAN networks similar to the traditional WLAN-first association, without making complex association decisions. Thus, this scheme can be easily implemented in an existing operator-deployed cellular-WLAN HetNet. The performance of the proposed scheme is evaluated in terms of load distribution between cellular and WLAN networks, user fairness, and system throughput, which demonstrates the superiority of the proposed scheme in load distribution and user fairness, while optimizing the system throughput. In addition, a cellular-WLAN interworking architecture and signaling procedures are proposed for implementing the proposed load-balancing schemes in an operator-deployed cellular-WLAN HetNet.

The proliferation of mobile Internet and connected devices, offering a variety of services at different levels of performance, represents a major challenge for the fifth generation of wireless networks and beyond. This requires a paradigm shift toward the development of key enabling techniques for the next generation wireless networks. In this respect, VLC has recently emerged as a new communication paradigm to provide ubiquitous connectivity by complementing radio frequency communications. One of the main challenges of VLC systems, however, is the low modulation bandwidth of the light-emitting diodes, which is in the megahertz range. In this article, a promising technology, referred to as O-NOMA, is presented, which is envisioned to address the key challenges in the next generation of wireless networks. We provide a detailed overview and analysis of the state-of-the-art integration of O-NOMA in VLC networks. Furthermore, we provide insights on the potential opportunities and challenges as well as some open research problems that are envisioned to pave the way for the future design and implementation of O-NOMA in VLC systems.

Ultra densification in HetNets and the advent of mmWave technology for 5G networks have led researchers to redesign the existing resource management techniques. A salient feature of this activity is to accentuate the importance of CI resource allocation schemes offering less complexity and overhead. This article overviews the existing literature on resource management in mmWave-based Het- Nets with a special emphasis on CI techniques and further proposes frameworks that ensure quality of service requirements for all network entities. More specifically, HetNets with mmWave-based small cells pose different challenges compared to an all-microwave- based system. Similarly, various modes of small cell access policies and operations of base stations in dual mode, that is, operating both mmWave and microwave links simultaneously, offer unique challenges to resource allocation. Furthermore, the use of multi-slope path loss models becomes inevitable for analysis due to irregular cell patterns and blocking characteristics of mmWave communications. This article amalgamates the unique challenges posed because of the aforementioned recent developments and proposes various CI-based techniques, including game theory and optimization routines, to perform efficient resource management.

The backhaul is a cornerstone in the development of future cellular networks and not the least challenging. The difficulty of designing the future backhaul is in the expected range of performance metrics that is increasing in breadth and in rising standards. Novel ideas for solving the heterogeneous backhaul problem are frequently proposed. Tractable analytical models that capture the performance of such backhauls are crucial in order to verify and validate these ideas. We present the first modular and scalable framework for analytically modelling the performance of hybrid multi-hop backhauls, which employs technology-specific models of different performance aspects of each hop.

In order to improve the manageability and adaptability of future 5G wireless networks, the software orchestration mechanism, named software defined networking (SDN) with control and user plane (C/U-plane) decoupling, has become one of the most promising key techniques. Based on these features, the hybrid satellite terrestrial network is expected to support flexible and customized resource scheduling for both massive machine-type- communication (MTC) and high-quality multimedia requests while achieving broader global coverage, larger capacity and lower power consumption. In this paper, an end-to-end hybrid satellite terrestrial network is proposed and the performance metrics, e. g., coverage probability, spectral and energy efficiency (SE and EE), are analysed in both sparse networks and ultra-dense networks. The fundamental relationship between SE and EE is investigated, considering the overhead costs, fronthaul of the gateway (GW), density of small cells (SCs) and multiple quality-ofservice (QoS) requirements. Numerical results show that compared with current LTE networks, the hybrid system with C/U split can achieve approximately 40% and 80% EE improvement in sparse and ultra-dense networks respectively, and greatly enhance the coverage. Various resource management schemes, bandwidth allocation methods, and on-off approaches are compared, and the applications of the satellite in future 5G networks with software defined features are proposed.

Vehicle-to-everything (V2X) communication is expected to offer users available and ultra-reliable transmission, particularly for critical applications related to safety and autonomy. In this context, establishing a secure and resilient authentication process with low latency and high functionality may not be achieved using conventional cryptographic methodologies due to their significant computation costs. Recent research has focused on employing the physical (PHY) characteristics of wireless channels to develop efficient discrimination techniques to overcome the shortcomings of crypto-based authentication. This paper presents a cross-layer authentication scheme for multicarrier communication, leveraging the spatially/temporally correlated wireless channel features to facilitate key verification without exposing its secrecy. By mapping the time-stamped hashed key and masking it with channel phase responses, we create a PHY-layer signature, allowing for verifying the sender's identity while employing the correlated channel responses between subcarriers to verify messages' integrity. Furthermore, we developed a Diffie-Hellman secret key extraction algorithm that employs the computationally intractable problems of the Chebyshev chaotic mapping for channel probing. Thus, terminals can extract high entropy shared keys that can be used to create dynamic PHY-layer signatures, supporting forward and backward secrecy. We evaluated the scheme's security strength against active/passive attacks. Besides theoretical analysis, we designed a 3-Dimensional (3D) scattering Doppler emulator to investigate the scheme's performance at different speeds of a moving vehicle and signal-to-noise ratios (SNRs) for a realistic vehicular channel. Theoretical and hardware implementation analyses proved the capability of the proposed scheme to support high detection probability at SNR ≥ 0 dB and speed ≤ 45 m/s.

An IoT-enabled vehicle recognition system is developed and tested in this work, besides the tag antenna design and prototype for the vehicle windshield. Firstly, a flexible, surface-tolerant, low-profile, and low-cost passive UHF-RFID windshield tag inlay is designed. The tag is prototyped as wet-inlay with silver conductive-ink inkjet printed antenna on Polyethylene-Terephthalate layer, bonded with Higgs-EC IC. The dimension of the antenna is 0.25×0.05λ0. The overall inlay structure is around 0.27×0.06λ0. The antenna is designed at ETSI band, with a return loss of -14 dB at 900 MHz and a read range of 13.3 m and 8 m over lower and upper bands, respectively. The antenna demonstrates an omnidirectional radiation pattern and a maximum gain of 1.92 dBi. The developed prototype is tested in both indoor and outdoor environments. Secondly, the data is sent over the IoT cloud where decision-making is made for authorization. The reader data is transmitted over MQTT broker, via the Scotland 5G network to the MongoDB database using Python module Pymongo. The algorithm for vehicle recognition is written and verified by testing the dummy registered tags for access authorization, with a 100% recognition accuracy. We achieved higher read ranges, surface tolerance, and low profile features, comparatively. The overall system shows optimally great results.

Minimization of drive test (MDT) has recently been standardized by 3GPP as a key self organizing network (SON) feature. MDT allows coverage to be estimated at the base station (BS) using user equipment (UE) measurement reports with the objective to eliminate the need for drive tests. However, most MDT-based coverage estimation methods recently proposed in literature assume that UE position is known at the BS with 100% accuracy, an assumption that does not hold in reality. In this letter, we develop an analytical model that allows the quantification of error in MDT-based autonomous coverage estimation (ACE) as a function of error in UE as well as BS positioning. Our model also allows characterization of error in ACE as function of standard deviation of shadowing.

The ultra-dense network (UDN) has been considered as a promising candidate for future 5G networks to meet the explosive data demand. To realize UDN, a reliable, gigahertz bandwidth, and cost-effective backhaul connecting ultradense small-cell BSs and macrocell BS are prerequisite. Millimeter-wave can provide the potential gigabit-per-second traffic for wireless backhaul. Moreover, mmWave can easily be integrated with massive MIMO for improved link reliability. In this article, we discuss the feasibility of mmWave massive-MIMO-based wireless backhaul for 5G UDN, and the benefits and challenges are also addressed. In particular, we propose a digitally controlled phase shifter network (DPSN)-based hybrid precoding/combining scheme for mmWave massive MIMO, whereby the low-rank property of the mmWave massive MIMO channel matrix is leveraged to reduce the required cost and complexity of a transceiver with a negligible performance loss. One key feature of the proposed scheme is that the macrocell BS can simultaneously support multiple small-cell BSs with multiple streams for each small-cell BS, which is essentially different from conventional hybrid precoding/combining schemes, typically limited to single-user MIMO with multiple streams or multi-user MIMO with single stream for each user. Based on the proposed scheme, we further explore the fundamental issues of developing mmWave massive MIMO for wireless backhaul, and the associated challenges, insight, and prospects to enable mmWave massive-MIMO-based wireless backhaul for 5G UDN are discussed.

Self-organizing network (SON) functions have been introduced in the LTE and LTEAdvanced standards by the Third Generation Partnership Project as an excellent solution that promises enormous improvements in network performance. However, the most challenging issue in implementing SON functions in reality is the identification of the best possible interactions among simultaneously operating and even conflicting SON functions in order to guarantee robust, stable, and desired network operation. In this direction, the first step is the comprehensive modeling of various types of conflicts among SON functions, not only to acquire a detailed view of the problem, but also to pave the way for designing appropriate Self-Coordination mechanisms among SON functions. In this article we present a comprehensive classification of SON function conflicts, which leads the way for designing suitable conflict resolution solutions among SON functions and implementing SON in reality. Identifying conflicting and interfering relations among autonomous network management functionalities is a tremendously complex task. We demonstrate how analysis of fundamental trade-offs among performance metrics can us to the identification of potential conflicts. Moreover, we present analytical models of these conflicts using reference signal received power plots in multi-cell environments, which help to dig into the complex relations among SON functions. We identify potential chain reactions among SON function conflicts that can affect the concurrent operation of multiple SON functions in reality. Finally, we propose a selfcoordination framework for conflict resolution among multiple SON functions in LTE/LTEAdvanced networks, while highlighting a number of future research challenges for conflict-free operation of SON.

It is well-established that transmitting at full power is the most spectral-efficient power allocation strategy for point-to-point (P2P) multi-input multi-output (MIMO) systems, however, can this strategy be energy efficient as well? In this letter, we address the most energy-efficient power allocation policy for symmetric P2P MIMO systems by accurately approximating in closed-form their optimal transmit power when a realistic MIMO power consumption model is considered. In most cases, being energy efficient implies a reduction in transmit and overall consumed powers at the expense of a lower spectral efficiency.

The recent widespread use of mobile Internet together with the advent of many smart applications has led to the explosive growth of mobile data traffic in the last few years. This remarkable growing momentum of mobile traffic will most likely continue due to the emerging needs of connecting people, machines, and applications through mobile infrastructure. As a result, the current and projected dramatic growth of mobile data traffic necessitates the development of fifth-generation (5G) mobile communications technology. 5G cellular communications will provide us with the promise of a mobile broadband experience far beyond our current 4G systems. 5G has a broad vision and envisages design targets that include 10–100× peak date rate, 1000× network capacity, 10× energy efficiency, and 10–30× lower latency. In achieving these expectations, operators and carriers are planning to leverage metro cells and small cells to improve user experience and improve overall network performance. Hence, there is considerable market interest in the development of innovative backhaul and fronthaul solutions for ultra-dense heterogeneous networks that are an evolution of the existing backhaul and fronthaul technologies.