In the 6G era, communication systems must accommodate extremely high data rates, steering attention toward the sub-terahertz (sub-THz) band, which can offer substantially wider bandwidth than lower-frequency bands. However, key challenges in sub-THz communications include high path loss, limited RF efficiency, and especially phase noise from local oscillator (LO) imperfections. Although DFT-s-OFDM effectively mitigate the high peak-to-average power ratio (PAPR) of CP-OFDM, research on phase noise compensation for this waveform is limited. Specifically, to the best of our knowledge, while joint channel and phase noise estimation has been investigated for CP-OFDM, it remains unexplored in the context of DFT-s-OFDM. This paper proposes the first iterative joint estimation framework for DFT-s-OFDM that exploits both frequency-domain and time-domain reference signals. The proposed method alternately refines channel and phase noise estimates, creating a virtuous cycle where accurate channel estimation improves phase noise tracking and vice versa. We propose least squares (LS) and linear minimum mean squared error (LMMSE) methods to reduce computational complexity without sacrificing performance. Simulation results demonstrate that the proposed LS and LMMSE methods achieve normalized mean squared error (NMSE) improvements of 12.44 dB and 12.17 dB in channel estimation, and 15.15 dB and 14.24 dB in phase noise estimation, respectively. Consequently, these gains translate to bit-error-rate (BER) reductions from 1.03 × 10−2to 4.13 × 10−3for the LS scheme and 4.48 × 10−3for the LMMSE scheme at an signal-to-noise ratio (SNR) of 40 dB. Additionally, a 140 GHz sub-THz prototype system validates these gains under realistic channel and phase noise conditions, demonstrating the practical viability of the proposed algorithms.

To realize next-generation wireless systems, the sub-terahertz (sub-THz) band has been identified as a promising candidate due to its wide bandwidth. As the operating frequency enters the sub-THz range, several challenges arise, including high path loss and aggravated phase noise (PN). This underscores the critical importance of PN estimation in DFT-s-OFDM waveforms, which offer a lower peak-to-average power ratio (PAPR) than CP-OFDM. To the best of our knowledge, existing works focus solely on PN estimation without considering channel estimation. However, separately estimating PN and the channel can degrade overall system reliability, since PN distorts channel estimation, which in turn impairs subsequent PN estimation. To address this problem, we propose a novel joint PN and channel estimation algorithm in DFT-s-OFDM, which has not been previously investigated. Simulation results demonstrate the effectiveness of the proposed method by comparing it against conventional non-joint approaches.

Wireless resource allocation is a fundamental challenge in modern wireless networks, requiring efficient allocation of limited radio resources while satisfying strict quality of service (QoS) constraints. Traditional optimization techniques, including heuristic-based approaches, struggle with the scalability and computational complexity of large-scale wireless resource allocation problems. In this work, we formulate the wireless resource allocation problem as a combinatorial optimization (CO) problem and leverage a deep reinforcement learning (DRL) framework to efficiently solve it. Unlike conventional DRL methods that optimize decision-making over time domain, our algorithmic-step DRL iteratively allocates radio resources to maximize future rewards. Through extensive simulations, we compare our approach against heuristic baselines, demonstrating better computational efficiency and performance quality. Our results highlight the potential of DRL as a scalable and effective alternative for wireless resource allocation in next-generation networks.

The rapid increase in the number of mobile devices and the diversity of service requirements have escalated complexity in wireless networks. This has fueled the need for seamless wireless access technologies that can support high data rates across heterogeneous devices. To address these challenges, massive multiple-input multiple-output (MIMO) systems have been considered in the 5G and upcoming 6G cellular systems. However, the independent channel estimation process for each antenna element for all base stations (BSs) is impractical because it requires a substantial pilot overhead. In response to this limitation, we propose a transformer-based site-specific channel estimation algorithm to infer the channel of a BS based on the UE position or the channels of other BSs, without pilot transmission. The algorithm effectively captures the nonlinear characteristics of channels using a multi-head attention (MHA) module. We evaluated the performance using a channel dataset collected from a 3D ray tracing environment replicating a large-scale real-world city, which includes multiple BSs and UEs. The proposed algorithm successfully depicts the channel characteristics of the communication environment.

This article presents a novel reconfigurable intelligent surface (RIS)-integrated base station (BS) by deploying an RIS very close to the base station antennas (BAs), within its radiative near-field range. We propose a practical algorithm to maximize the performance of uplink communications with the proposed RIS-integrated BS while maintaining reasonable complexity. Furthermore, we have implemented a testbed to experiment with the proposed RIS-integrated BS-based uplink multiple-input-multiple-output (MIMO) system. The experimental data validates the performance of the proposed RIS-integrated BS-based uplink MIMO system.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for next-generation wireless communications due to its ability to manipulate electromagnetic (EM) waves. This work introduces a novel RIS direct modulation scheme based on the backscatter communication concept. In this work, the RIS acts as a sole information modulator. Unlike conventional RIS systems that relay incoming data-carrying waves, this work enables RIS to encode its own information into unmodulated waves without requiring any hardware modifications. We propose an adaptive algorithm that maximizes modulation order while meeting the quality of service (QoS) requirements by selecting appropriate constellation points to minimize error probability. The proposed scheme was validated through simulations and experiments using a passive 5.8 GHz RIS prototype. The validations demonstrate consistent performance in both distributed and co-located transmitter-receiver scenarios. These results highlight its feasibility as an alternative approach for RIS-assisted communications and contribute to further exploration of RIS-enabled symbiotic radio (SR) and over-the-air (OTA) modulation systems.

Conventional integrated sensing and communication (ISAC) systems are typically unable to exploit both spatial and spectral diversity simultaneously, which limits overall system performance. To fully harness the benefits of jointly increasing spatial and spectral degrees of freedom (DoF), this paper proposes a novel multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) ISAC architecture that leverages both frequency and antenna switching. In the proposed architecture, each RF chain dynamically selects its transmit/receive antennas and frequency band in each interval, enabling improved radar resolution and offering the potential for increased communication diversity. To support sensing functionality under this dynamic configuration, we introduce a 4D radar imaging algorithm based on orthogonal matching pursuit (OMP). The algorithm achieves super-resolution in range and velocity via oversampling while simultaneously estimating azimuth and elevation angles. Notably, the formulation inherently compensates for phase variations arising from data acquisition across diverse time intervals and frequency bands, enabling accurate target detection. Simulation results demonstrate that the proposed method improves range resolution from 0.41 m to 0.08 m and velocity resolution from 5.0 m/s to 0.125 m/s, while achieving an average precision (AP) of 0.66 compared to 0.02 for the baseline under challenging conditions. On the communication side, we show that the channel gain varies by up to 7 dB across different antenna–frequency band pairs, and this diversity leads to notable improvements in communication performance. Moreover, the implemented testbed confirms the feasibility of reliable multi-band OFDM communication under dynamic switching, highlighting the practical viability of the proposed ISAC framework for next-generation wireless systems.

Reconfigurable intelligent surface (RIS) has gained significant momentum as a cost-effective and energy-efficient technology to enable the next generation of mobile communications. In this article, we propose an RIS-integrated base station (BS) by deploying an RIS sufficiently close to the base station antennas (BAs), within its radiative near-field range. In the proposed RIS-integrated BS system, we utilize RIS as a passive array to reconfigure incoming signals from user equipments (UEs) without experiencing substantial path loss. The near-field channel model between the RIS and BAs is analyzed and applied to the RIS beam control model. Furthermore, we develop a practical algorithm to maximize the performance of the proposed RIS-integrated BS-based uplink multiple-input-multiple-output (MIMO) system with the aim of maintaining reasonable complexity. This goal is achieved by combining the two proposed algorithms, beam search and path-antenna pairing algorithms. The beam search algorithm identifies the radio paths of all UEs to the RIS by sweeping the beams of the RIS. Then, the path-antenna pairing algorithm allocates the strongest radio path between each UE and the RIS to one of the BAs by controlling an RIS beam to direct the signal from that path to the BA. Finally, we have built a real-time testbed to experiment with the proposed RIS-integrated BS-based uplink MIMO system. Numerical results, including experimental data, validate the effectiveness of the proposed RIS-integrated BS-based uplink MIMO system.

In this article, we propose a new fully distributed cell-free multiple-input and multiple-output (MIMO) architecture that enables independent deployment of access points (APs) without any infrastructure such as a central processing unit (CPU). In the proposed architecture, all baseband processing is distributively executed by homogeneous APs without any CPU, thanks to the fully distributed functional split structure. Since the proposed architecture only consists of APs, we expect that a 5G/6G-based private network with a cell-free MIMO capability can easily be built for Industrial Internet of Things (IIoT) applications without the help of mobile network operators (MNOs). We also propose a novel precoding algorithm for orthogonal frequency division multiplexing (OFDM)-based cell-free MIMO systems. In the proposed precoding algorithm, the APs in a user-centric cluster cooperatively maximize the capacity of the target user equipment (UE) while minimizing the interference to nearby UEs. The proposed precoding algorithm solves the OFDM power allocation problem to allocate power on a per-subcarrier basis under the per- AP power constraints, which has not been addressed in previous works. We have built a full-fledged and real-time cell-free MIMO testbed that implements the proposed cell-free MIMO architecture and precoding algorithm. The validity of the proposed architecture and precoding algorithm is verified by experiments and simulations.

In this article, we propose a model-based reinforcement learning (RL) algorithm for wireless channel access. The model-based RL is a relatively new RL paradigm that integrates the concept of the world model into the agent. The world model is built based on the neural network and is capable of predicting the future trajectories of actions, rewards, and observations. In this article, we focus on developing a sophisticated world model based on the partially observable Markov decision process (POMDP). The proposed world model can describe the environment in which only the partial observation emitted from the hidden state is available. For establishing the wireless channel access problem, we introduce two separate environments, one of which describes the channel occupancy dynamics and the other governs data traffic arrival patterns. Both environments are modeled by the proposed partially observable MDP (POMDP)-based world model. For designing an agent capable of making a decision on the next action, we propose a planning algorithm, which makes use of the future trajectories generated from the trained world model differently from the existing model-free RL algorithms. We have conducted extensive simulations to verify the performance of the proposed method in various wireless channel access scenarios.

To enable high-tech lifestyles in the near future, trillions of connected low-power internet of things (IoT) devices should perpetually operate to meet the high-demand requirements of the users. Simultaneous wireless information and power transfer (SWIPT) is an indispensable technology for guaranteeing the endurable operation of massive IoT devices. Reconfigurable intelligent surface (RIS) is currently emerging as a cost-effective and energy-efficient solution for controlling wireless communication environments to enhance the quality of service. In this paper, we propose an efficient beam-sharing algorithm (BSA) designed for SWIPT systems that incorporate RIS to realize the coexistence of wireless power and information. The considered RIS-assisted SWIPT system consists of one RIS, one data transmitter (DTx), one power transmitter (PTx), one data user (DU), and one power user (PU). Since the required power for the power transfer is radically higher than that for the information transmission, the high power signal leaked from the PTx can cause fatal damage to data transmission and sensitive electronic components (e.g., LNA) integrated with DU. Hence, we primarily aim to maximize the desired power transfer from PTx to PU while minimizing the leakage power (i.e., interference) delivered to the DU. Additionally, the algorithm maximizes the quality of the information signal transmitted from DTx to DU. We then develop a simulator to verify the effectiveness of the proposed algorithm. We have investigated the performance of the proposed BSA algorithm with various quantization phase shifts (i.e., 1-bit, 2-bit, 3-bit, and continuous phase). A suppression ranging between 15 dB and 38 dB is witnessed in all simulation scenarios, while the DTx-DU power and PTx-PU power are simultaneously maximized in the simulated scenario. For further confirmation, we have built a real-life RIS-assisted SWIPT testbed and validated the proposed BSA algorithm. Experimental results indicate that the proposed BSA algorithm successfully delivers the maximum power/signal from PTx/DTx to PU/DU while limiting the interference signal sent by PTx to the DU to ensure robust and reliable data transmission.

Wireless information and energy transfer (WIET) represents an emerging paradigm that employs controllable transmission of radio frequency signals for the dual purpose of data communication and wireless charging. As such, WIET is widely regarded as an enabler of envisioned sixth-generation (6G) use cases that rely on energy-sustainable Internet-of-Things (IoT) networks, such as smart cities and smart grids. Meeting the quality-of-service demands of WIET, in terms of both data transfer and power delivery, requires effective codesign of the information and energy signals. In this article, we present the main principles and design aspects of WIET, focusing on its integration in 6G networks. First, we discuss how conventional communication notions, such as resource allocation and waveform design, need to be revisited in the context of WIET. Next, we consider various candidate 6G technologies that can boost WIET efficiency, namely, holographic multiple-input multiple-output, near-field beamforming, terahertz communication, intelligent reflecting surfaces (IRSs), and reconfigurable (fluid) antenna arrays. We introduce respective WIET design methods, analyze the promising performance gains of these WIET systems, and discuss challenges, open issues, and future research directions. Finally, a near-field energy beamforming scheme and a power-based IRS beamforming algorithm are experimentally validated using a wireless energy transfer testbed. The vision of WIET in communication systems has been gaining momentum in recent years, with constant progress with respect to theoretical and also practical aspects. The comprehensive overview of the state of the art of WIET presented in this article highlights the potential of WIET systems and their overall benefits in 6G networks.

Simultaneous wireless information and power transfer (SWIPT) is a key technology for enabling future high-tech lifestyles by guaranteeing the perpetual operation of trillions of low-power IoT devices. Currently, reconfigurable intelligent surface (RIS) is a promising technology for achieving cost- effective and energy-efficient wireless technologies. In this paper, we propose an efficient beam-sharing algorithm for RIS-aided SWIPT systems. The proposed algorithm maximizes the power transferred from the power transmitter (Ptx) to the power user (PU) while minimizing that to the data user (DU) but maximizing the signal from the data transmitter (Dtx) to DU. Simulation results demonstrate that the proposed beam-sharing algorithm effectively delivers power from Ptx to PU while limiting the power sent by Ptx to DU but maximizing the received signal from Dtx.

This article proposes a sparsity-aware channel estimation scheme for reconfigurable intelligent surface (RIS)-assisted wireless communications. We present an angular domain-channel sparsity model in a closed-form mathematical expression. A comprehensive formulation of the RIS channel estimation problem based on the sparsity analysis and RIS reflection model is also presented in this manuscript. This work aims to achieve a practical RIS beamforming algorithm without requiring excessive training overhead or any sensor deployment. We achieve the goal by proposing two sparsity-aware RIS channel estimation schemes based on the compressive sensing (CS) algorithms, such as the Dantzig selector (DS) and orthogonal matching pursuit (OMP). Differently from the existing works on the CS algorithms for RIS, we consider a fully passive RIS without any active sensor. We validate the theory and algorithm through both simulations and experiments. We have experimented orthogonal frequency-division multiplexing (OFDM) communications on our 5.8-GHz 1-bit RIS testbed with QPSK, 16QAM, 64QAM, and 256QAM modulation schemes. By experiments, it is shown that the proposed scheme is able to adaptively form a beam toward the receiver and improves the quality of the wireless communication to a notable level. Thanks to the properties of the proposed scheme, the wireless channel can be estimated without excessive training time and complexity.

Multiple-input–multiple-output (MIMO) and long range (LoRa) have been synergistically combined to better support a variety of Internet of Things (IoT) applications. This letter investigates the problem of demodulation for a MIMO-LoRa system. The existing demodulation strategy for MIMO-LoRa is based on the maximum-likelihood criterion, which, however, suffers from its high computational complexity. To cope with this issue, in this letter, we develop efficient coherent and noncoherent demodulation algorithms for the MIMO-LoRa system by leveraging useful properties of modulated chirps through multiple antennas. We also analyze the computational complexities of the proposed algorithms and demonstrate their validity through numerical simulations.

In this paper, we propose a novel transformer-based classification algorithm for the brain computer interface (BCI) using a motor imagery (MI) electroencephalogram (EEG) signal. To design the MI classification algorithm, we apply an up-to-date deep learning model, the transformer, that has revolutionized the natural language processing (NLP) and successfully widened its application to many other domains such as the computer vision. Within a long MI trial spanning a few seconds, the classification algorithm should give more attention to the time periods during which the intended motor task is imagined by the subject without any artifact. To achieve this goal, we propose a hierarchical transformer architecture that consists of a high-level transformer (HLT) and a low-level transformer (LLT). We break down a long MI trial into a number of short-term intervals. The LLT extracts a feature from each short-term interval, and the HLT pays more attention to the features from more relevant short-term intervals by using the self-attention mechanism of the transformer. We have done extensive tests of the proposed scheme on four open MI datasets, and shown that the proposed hierarchical transformer excels in both the subject-dependent and subject-independent tests.

Recently, reconfigurable intelligent surface (RIS) has drawn massive attention among researchers and entrepreneurs as a potential technology for next-generation wireless technologies. This paper proposes a beam splitting method to simultaneously deliver power and information to different users (i.e., power user/information user). We use orthogonal training patterns generated by the Hadamard matrix to estimate the end-to-end channel information of each user. Then, a pattern addition (PA) method is applied to split the beam for each user. We implement a real-life testbed of the RIS-aided simultaneous wireless information and power transfer (SWIPT) system to verify the proposed algorithm. By experiment, we show that the proposed method can effectively distribute power and information to the corresponding users.

In this work, we propose a drone-based sensor information gathering system that utilizes an aerial drone to wirelessly transfer an electromagnetic (EM) wave signal toward a battery-less forward-scatter tag device with multiple antennas. The proposed tag device is designed to harvest the EM signal from the drone as a source of power, and at the same time to reuse the EM signal for the data transmission. We propose a beam-rotation forward-scattering algorithm for the data transmission, which creates the main beam rotating along the azimuth plane. In the proposed algorithm, data are transmitted from the tag device to the tag reader by mapping each bit of information to varying beam rotation speeds. A unique hardware design of the forward-scatter tag device is proposed in this article, and it has been fabricated and implemented for wireless power transfer (WPT) and data transmission experiments. By experiment, we have verified that power is successfully harvested by the proposed tag device, with the average power conversion efficiency rated at 57%. The proposed tag device is able to transmit the sensor data to the tag readers without the knowledge of the location of the tag readers. We have tested the proposed beam-rotation forward-scattering communication at up to 500 m communication distance and shown that the sensor data can successfully be delivered.

Recently, reconfigurable intelligent surface (RIS) with passive beamforming capability is emerging as a potential technology for wireless power transfer (WPT) applications thanks to its cost-effective and energy-efficient features. This work studies multifocus techniques for RIS-aided WPT systems to simultaneously and adaptively charge multiple Internet of Things (IoT) devices in the Fresnel zone. In particular, we propose three multifocus methods, which are pattern addition (PA), random unit cell interleaving (RUI), and RIS tile division (RTD), for enabling multifocus RIS-aided WPT applications. We elaborate the algorithms for computing the RIS reflection phases for all methods. The proposed methods can balance the power levels of beams focused on multiple receivers by controlling weight factors. Furthermore, we have implemented a real-life RIS-aided WPT testbed to verify these proposed methods. The system consists of a phased antenna array transmitter, two receivers, and a 1-bit RIS with 512 unit cells. The WPT experiments have been performed in several test scenarios to show the effectiveness of the proposed techniques. The experiment results demonstrate that the proposed schemes effectively generate multiple focusing beams with adjustable power levels toward the desired receivers.

Reconfigurable intelligent surface (RIS) is a promising technology for radio-frequency wireless power transfer (WPT) as it is capable of beamforming and beam focusing without using active and power-hungry components. In this article, we propose a multitile RIS beam scanning (MTBS) algorithm for powering up Internet of Things (IoT) devices. Considering the hardware limitations of the IoT devices, the proposed algorithm requires only power information to enable the beam focusing capability of the RIS. Specifically, we first divide the RIS into smaller RIS tiles. Then, all RIS tiles and the phased array transmitter are iteratively scanned and optimized to maximize the receive power. We elaborately analyze the proposed algorithm and build a simulator to verify it. Furthermore, we have built a real-life testbed of RIS-aided WPT systems to validate the algorithm. The experimental results show that the proposed MTBS algorithm can properly control the transmission phase of the transmitter and the reflection phase of the RIS to focus the power at the receiver. Consequently, after executing the algorithm, about 20-dB improvement of the receive power is achieved compared to the case that all unit cells of the RIS are in OFF state. By experiments, we confirm that the RIS with the MTBS algorithm can greatly enhance the power transfer efficiency.

As wireless has disrupted communications, wireless will also disrupt the delivery of energy. Future wireless networks will be equipped with (radiative) wireless power transfer (WPT) capability and exploit radio waves to carry both energy and information through unified wireless information and power transfer (WIPT). Such networks will make the best use of the RF spectrum and radiation, as well as the network infrastructure for the dual purpose of communicating and energizing. Consequently, those networks will enable trillions of future low-power devices to sense, compute, connect, and energize anywhere, anytime, and on the move. In this article, we review the foundations of such a future system. We first give an overview of the fundamental theoretical building blocks of WPT and WIPT. Then, we discuss some state-of-the-art experimental setups and prototypes of both WPT and WIPT, and contrast theoretical and experimental results. We draw special attention to how the integration of RF, signal, and system designs in WPT and WIPT leads to new theoretical and experimental design challenges for both microwave and communication engineers and highlight some promising solutions. Topics and experimental testbeds discussed include closed-loop WPT and WIPT architectures with beamforming, waveform, channel acquisition, and single-antenna/multiantenna energy harvester, centralized and distributed WPT, reconfigurable metasurfaces and intelligent surfaces for WPT, transmitter and receiver architecture for WIPT, modulation, and rate–energy tradeoff. Moreover, we highlight important theoretical and experimental research directions to be addressed for WPT and WIPT to become a foundational technology of future wireless networks.

In this article, we propose a novel simultaneous wireless information and power transfer (SWIPT) scheme for the Internet of Things (IoT). Different from the conventional power splitting (PS) and time switching (TS) schemes, the proposed scheme sends the wireless power via the unmodulated high-power continuous wave (CW) and transmits information by using a small modulated signal in order to reduce the interference and to enhance the power amplifier efficiency. We design a receiver circuit for processing such SWIPT signals, which is designed with the aim of minimizing the circuit complexity and power consumption for information decoding. This goal is achieved by first rectifying the received signal and then splitting the power and information signals. We analyze the proposed receiver circuit and derive the closed-form expression for the energy harvesting efficiency and the frequency response of the communication signal. We have implemented the proposed receiver circuit and built the real-time testbed for experimenting with simultaneous transmission of information and power. By experiments, we have verified the correctness of the receiver circuit analysis and shown the validity of the proposed SWIPT scheme.

We propose backscatter-aided cooperative transmission for wireless-powered heterogeneous networks (WPHetNets). In WPHetNets, where various kinds of nodes such as high-power base station (e.g., TV tower and macro base station) and small-power access point (e.g., WiFi access point) coexist, we aim to increase transmission range and support fair communication through internet-of-things (IoT) device cooperation. For this, we first propose long-range bistatic backscatter (BB)-aided cooperative transmission with two-device cooperation where ambient backscatter (AB) enables short-range information exchange in sequential mode between devices nearby under energy neutral operation, termed `Cooperation mode'. To ensure fairness between devices, we formulate common-throughput maximization problem where an algorithm for time allocation is presented. Compared with `Non-cooperation mode' (i.e., no information exchange via AB) and active RF based cooperation schemes, the proposed scheme is shown to increase both the coverage and fairness between devices for battery-less IoT networks. We then generalize the architecture of backscatter-aided cooperative transmission with multiple-device cooperation where the information exchange is performed in sequential mode and parallel (broadcasting) mode. In the sequential mode, a graph-matching based suboptimal pairing algorithm is proposed whose validity is corroborated through comparison with a heuristic search based optimal pairing, and then we compare the two modes for multiple-device cooperation.

We propose a battery-less location tracking system that enables 3-D positioning of an Internet of Things (IoT) device powered by the radio frequency (RF) wireless power transfer (WPT). In the proposed system, a power beacon is equipped with a phased antenna array that has the dual purposes of high-efficiency WPT and phase-based accurate positioning. In order to enhance the efficiency of the RF WPT, we propose a beam focusing algorithm that dynamically controls the respective phases of antenna elements to place the focal point of the electro-magnetic (EM) wave onto the target IoT device. We also propose a phase-based positioning algorithm that requires only one multi-antenna anchor point for determining the distance as well as the direction from the anchor point. We analyze the Cramer-Rao lower bound (CRLB) of the phase-based positioning with a single multi-antenna anchor point, and show that the distance from the anchor point can be estimated as long as the IoT device lies within the radiative near-field region. We propose a joint location tracking and WPT algorithm that performs 3-D positioning and beam-focused WPT in a unified way. We have built a real-life testbed with a large-scale antenna array with 64 antenna elements for testing the proposed algorithm. The experimental results show the effectiveness of the proposed algorithm in a real environment.

The wireless power transfer via an electro-magnetic (EM) wave enables far-field power transfer for supplying power to Internet of Things (IoT) devices. However, the power attenuation of the EM wave leads to low end-to-end power transfer efficiency. In this paper, we provide an analytic and experimental study on the distributed wireless power transfer system as a means to overcome the low power transfer efficiency. In the distributed wireless power transfer system, a number of multiantenna power beacons, which are distributed over space, send out wireless power to charge IoT devices. Since each power beacon has a separate local oscillator and controller, it is very challenging to achieve frequency and phase synchronization among power beacons, which is the prerequisite for optimal distributed beamforming. In this paper, we study the performance of the distributed wireless power transfer system with or without the frequency and phase synchronization. Based on the experiment and simulation results, we show that the distributed wireless charging is advantageous in terms of the coverage probability as long as the optimal distributed beamforming is available in the distributed wireless power transfer system.

In this paper, we propose an ambient backscatter (AB)-enabled decode-and-forward (DF) cognitive relay network with wireless energy-harvesting capabilities. In our proposed scheme, a source node communicates with its destination node via a radio frequency-powered DF relay. It is assumed that the relay node is equipped with two different interfaces and can concurrently harvest/decode and backscatter the received source signals. A power-splitting-based approach is adopted for the information processing and the energy harvesting at the relay. The analytical expressions for the outage probability at all of the receiving nodes are derived. It has been shown that the analytical results match the simulation results. It has also been shown that using the AB for secondary communications can significantly improve the overall network performance in terms of the achievable throughput and the energy efficiency.

Traffic patterns associated with different primary users (PUs) might provide different spectral access and energy harvesting opportunities to secondary users (SUs) in wireless powered cognitive radio networks (WP-CRNs). Since the traffic applications have their own distinctive patterns, spectral access and energy harvesting opportunities are also expected to be distinctive. In this paper, we propose a novel approach to identify the PU traffic patterns and estimate the energy harvested from each traffic pattern so that SU can maximize its capacity accordingly. More specifically, we propose a theoretical framework based on a variational inference algorithm to cluster various traffic patterns and design a threshold-based SU transmission strategy by taking into account the spectral access and energy harvesting opportunities for each traffic pattern, so as to optimize SU transmission. Through simulations, we demonstrate the effectiveness of the proposed scheme in terms of throughput gains and show the transmission thresholds under various traffic applications (patterns). Further, we illustrate the effects of different collision costs on throughput for different traffic applications using real wireless traces.

In this paper, we investigate a multi-node multi-antenna wireless-powered sensor network (WPSN) comprised of one power beacon and multiple sensor nodes. We have implemented a real-life multi-node multi-antenna WPSN testbed that operates in real time. We propose a beam-splitting beamforming technique that enables a power beacon to split microwave energy beams toward multiple nodes for simultaneous charging. We experimentally demonstrate that the beam-splitting beamforming technique achieves the Pareto optimality. For perpetual operation of the sensor nodes, we adapt an energy neutral control algorithm that keeps a sensor node alive by balancing the harvested and consumed power. The joint beam-splitting and energy neutral control algorithm is designed by means of the Lyapunov optimization technique. In our experiments, the proposed algorithm has successfully kept all sensor nodes alive by optimally splitting energy beams toward multiple sensor nodes.

Wireless energy harvesting has emerged as an efficient solution to prolong the lifetime of wireless networks composed of energy‐constrained nodes. In this paper, we consider a multipoint‐to‐multipoint relay network, where multiple source nodes communicate with their respective destination nodes via intermediate energy‐constrained decode‐and‐forward (DF) relay. The performance of two different transmission modes, namely, delay tolerant and delay nontolerant, is studied. Based on power‐splitting relaying protocol (PSR), optimal energy harvesting and distribution schemes for both transmission modes are provided. In addition, for more realistic and practical analysis, we consider a nonlinear energy conversion model for energy harvesting at the relay node. Our numerical results provide useful insights into different system parameters of a nonlinear energy harvesting‐based multipair DF relay network.

In this paper, we study a multi-antenna wireless-powered sensor network (WPSN), in which a power beacon wirelessly transfers electric energy to a sensor node via an electromagnetic wave. We have implemented a real-life multi-antenna WPSN testbed and conducted extensive experiments on the testbed. The key technology for the high-efficiency WPSN is an adaptive energy beamforming scheme that dynamically steers a microwave beam towards a sensor node. We propose a receive power-based channel estimation and energy beamforming algorithm. In addition, an adaptive duty cycle control algorithm is proposed to prevent energy storage of a sensor node from being depleted. The proposed duty cycle control algorithm is designed based on a proportional-integral-derivative controller. These algorithms are all implemented in the multi-antenna WPSN testbed. By experiments, we validate the feasibility of the multi-antenna WPSN, and show the performance of the proposed algorithms.

Wireless energy harvesting is an efficient way to prolong the lifetime of energy constrained networks. In this paper, a two-way amplify-and-forward (AF) based relay network is considered, where two communicating nodes concurrently transmit there information signals to a relay node using time switching based relaying protocol (TSR). The relay node is energy constrained and therefore it first harvests the energy from the received radio frequency (RF) signals. After successful energy harvesting the relay utilizes the harvested energy to amplify and forward the received information signal to its destination. We derive the analytical expression for outage probability and achievable throughput at the receiving nodes. Our numerical results verify our analytical derivation and shows the effect of different system parameters on achievable throughput at receiving nodes.

We propose a code-based discovery protocol for cellular device-to-device (D2D) communications. To realize proximity based services such as mobile social networks and mobile marketing using D2D communications, each device should first discover nearby devices, which have mobile applications of interest, by using a discovery protocol. The proposed discovery protocol makes use of a short discovery code that contains compressed information of mobile applications in a device. A discovery code is generated by using either a hash function or a Bloom filter. When a device receives a discovery code broadcast by another device, the device can approximately find out the mobile applications in the other device. The proposed protocol is capable of quickly discovering massive number of devices while consuming a relatively small amount of radio resources. We analyze the performance of the proposed protocol under the random direction mobility model and a real mobility trace. By simulations, we show that the analytical results well match the simulation results and that the proposed protocol greatly outperforms a simple non-filtering protocol.

In this paper, we propose a stochastic optimal control algorithm for the wireless powered communication networks (WPCNs), in which the access point (AP) supplies energy to wireless nodes by means of the RF energy transfer technology. The energy beamforming is used to enhance the RF energy transfer efficiency by concentrating the radiated power on target nodes. Each wireless node is equipped with an energy queue and a data queue. We propose an algorithm that minimizes the expected energy transmission power from the AP while stabilizing the data queues of all nodes. The proposed algorithm is an online algorithm that adaptively decides the beamforming vector, the data scheduling, and the data transmission power, only based on the current state of the energy and the data queues. The proposed algorithm dynamically steers the energy beam to nodes that currently have low energy in the energy queue. We apply the Lyapunov optimization technique to design such an algorithm. We mathematically prove that the proposed algorithm achieves the optimal performance.

In this paper, we propose a device-to-device (D2D) discovery scheme as a key enabler for a proximity-based service in the Long-Term Evolution Advanced (LTE-A) system. The proximity-based service includes a variety of services exploiting the location information of user equipment (UE), for example, the mobile social network and the mobile marketing. To realize the proximity-based service in the LTE-A system, it is necessary to design a D2D discovery scheme by which UE can discover another UE in its proximity. We design a D2D discovery scheme based on the random access procedure in the LTE-A system. The proposed random-access-based D2D discovery scheme is advantageous in that 1) the proposed scheme can be readily applied to the current LTE-A system without significant modification; 2) the proposed scheme discovers pairs of UE in a centralized manner, which enables the access or core network to centrally control the formation of D2D communication networks; and 3) the proposed scheme adaptively allocates resource blocks for the D2D discovery to prevent underutilization of radio resources. We analyze the performance of the proposed D2D discovery scheme. A closed-form formula for the performance is derived by means of the stochastic geometry-based approach. We show that the analysis results accurately match the simulation results.

To address random access channel (RACH) congestion and high signaling overhead problems of machine-to-machine (M2M) communication in cellular networks, we propose a new design of a random access procedure that is exclusively engineered for the M2M communication. Our design has two prominent features. One is a fast signaling process that allows M2M user equipment to transmit data right after preamble transmission on a physical RACH to reduce the signaling overhead. The other is a self-optimization feature that allows the cellular system to produce optimal M2M throughput by adaptively changing resource block (RB) composition and an access barring parameter according to the amount of available RBs and the M2M traffic load. We derive a closed-form analytic formula for the M2M traffic throughput and propose a joint adaptive resource allocation and access barring scheme based on the analytic results. By simulation, we show that the proposed scheme exhibits a near-optimal performance in terms of the capacity.

In this paper, we propose a fully-distributed random access protocol for the device-to-device (D2D) communication in a cellular network. The D2D communication can provide a significant capacity gain by enabling a cellular network to offload data traffic to direct communication links between devices (i.e., D2D link). However, a D2D link can generate serious interference to other D2D links as well as cellular devices without any proper interference control mechanism. Compared to centralized resource allocation and power control schemes for the D2D communication, a distributed scheme is advantageous in that it has smaller control overhead and is more responsive to traffic demands. To protect a D2D receiver, the proposed scheme employs a collision avoidance mechanism that creates an exclusion region around the D2D receiver, where interferers are prohibited from transmitting a signal. We analyze the proposed scheme by assuming that the locations of devices follow a Poisson point process. By simulation, we show that the analysis results accurately match the simulation results and that the proposed scheme outperforms a distributed D2D scheme without collision avoidance by a very wide margin.

In this paper, we propose a fully-distributed random access protocol for the device-to-device (D2D) communications in a cellular network. Recently, numerous resource allocation schemes have been proposed to tackle the problem of excessive interference caused by D2D links (i.e., a direct communication link between devices). However, most of the existing resource allocation schemes are centralized, which incurs substantial communication and computational overhead. To overcome the difficulty, this paper proposes a fully-distributed D2D communication protocol for collision avoidance. To protect a D2D receiver, the proposed protocol creates an exclusion region around the D2D receiver, where interferers are prohibited from transmitting a signal. We analyze the proposed protocol by assuming that the locations of devices follow a Poisson point process. By simulation, we show that the analysis results accurately match the simulation results and that the proposed protocol outperforms a slotted ALOHA scheme by a very wide margin.

In this paper, we propose a code-based discovery protocol for device-to-device (D2D) communications. To realize proximity-based services in D2D communications, such as mobile social networks and mobile marketing, each device should first discover nearby devices, which have mobile applications of interest, by using a discovery protocol. The proposed discovery protocol makes use of a hash function-based discovery code that contains compressed information of mobile applications in a device. When a device receives a discovery code broadcasted by the other device, the device can approximately find out the mobile applications in the other device. The proposed protocol is capable of quickly discovering massive number of devices while consuming a relatively small amount of radio resources. By simulation, we show that the proposed protocol greatly outperforms a simple name-based protocol.

In cellular networks, the device-to-device (D2D) communication increases the network capacity by spatial reuse of radio resources and prolongs the battery life of devices by reducing the transmission power. In this paper, we propose a two-stage semi-distributed resource management framework for the D2D communication. At the first stage of the framework, the base station (BS) allocates resource blocks (RBs) to BS-to-user device (B2D) links and D2D links, in a centralized manner. At the second stage, the BS schedules the transmission using the RBs allocated to B2D links, while the primary user device of each D2D link carries out link adaptation on the RBs allocated to the D2D link, in a distributed fashion. The proposed framework has the advantages of both centralized and distributed design approaches, i.e., high network capacity and low control/computational overhead, respectively. We formulate the problems of RB allocation to maximize the radio resources efficiency, taking account of two different policies on the spatial reuse of RBs. To solve these problems, we suggest a greedy algorithm and a column generation-based algorithm. By simulation, it is shown that the proposed scheme achieves the near-optimal performance while reducing the control/computational overhead.

The strength of carrier-sense multiple access with collision avoidance (CSMA/CA) can be combined with that of time-division multiple access (TDMA) to enhance the channel access performance in wireless networks such as the IEEE 802.15.4-based wireless personal area networks. In particular, the performance of legacy CSMA/CA-based medium access control scheme in congested networks can be enhanced through a hybrid CSMA/CA-TDMA scheme while preserving the scalability property. In this paper, we present distributed and centralized channel access models that follow the transmission strategies based on Markov decision process (MDP) to access both contention period and contention-free period in an intelligent way. The models consider the buffer status as an indication of congestion provided that the offered traffic does not exceed the channel capacity. We extend the models to consider the hidden node collision problem encountered due to the signal attenuation caused by channel fading. The simulation results show that the MDP-based distributed channel access scheme outperforms the legacy slotted CSMA/CA scheme. The centralized model outperforms the distributed model but requires the global information of the network.

Recently, device-to-device (D2D) communication in cellular networks has gained much attention for enabling a base station (BS) to offload traffic to direct D2D links and for facilitating new network services. In this paper, we propose a semi-distributed resource allocation scheme for the D2D communication where the BS allocates radio resources for cellular user links and D2D links in a centralized manner while the modulation and coding scheme (MCS) level and the transmission power of D2D links are distributively decided by the device of each D2D link. We first formulate a resource allocation problem of which the objective is to maximize the spatial reuse of radio resources by allowing the simultaneous transmission of D2D links on the same resources. To solve the problem, we present a suboptimal greedy algorithm. By simulation, we show that the cellular networks with the proposed scheme achieve a higher network throughput by maximizing the spatial reuse of radio resources.

The authors propose an adaptive transmission scheme for cooperative communication networks. The cooperative network with the proposed scheme chooses the transmission rate and decides to involve the relay in transmission, adapting to the channel state estimated from limited feedback information (e.g. positive/negative acknowledgement (ACK/NACK) feedback). Considering that the limited feedback information provides only partial knowledge about the actual channel states, the authors design a decision‐making algorithm on cooperative transmission by using a partially observable Markov decision process framework. The simulation results show that, when the feedback overhead is taken into account, the proposed scheme outperforms even the scheme with which the states of all relevant channels are estimated and fed back.

We propose novel cooperative spectrum sensing (CSS) algorithms for cognitive radio (CR) networks based on machine learning techniques which are used for pattern classification. In this regard, unsupervised (e.g., K-means clustering and Gaussian mixture model (GMM)) and supervised (e.g., support vector machine (SVM) and weighted K-nearest-neighbor (KNN)) learning-based classification techniques are implemented for CSS. For a radio channel, the vector of the energy levels estimated at CR devices is treated as a feature vector and fed into a classifier to decide whether the channel is available or not. The classifier categorizes each feature vector into either of the two classes, namely, the "channel available class" and the "channel unavailable class". Prior to the online classification, the classifier needs to go through a training phase. For classification, the K-means clustering algorithm partitions the training feature vectors into K clusters, where each cluster corresponds to a combined state of primary users (PUs) and then the classifier determines the class the test energy vector belongs to. The GMM obtains a mixture of Gaussian density functions that well describes the training feature vectors. In the case of the SVM, the support vectors (i.e., a subset of training vectors which fully specify the decision function) are obtained by maximizing the margin between the separating hyperplane and the training feature vectors. Furthermore, the weighted KNN classification technique is proposed for CSS for which the weight of each feature vector is calculated by evaluating the area under the receiver operating characteristic (ROC) curve of that feature vector. The performance of each classification technique is quantified in terms of the average training time, the sample classification delay, and the ROC curve. Our comparative results clearly reveal that the proposed algorithms outperform the existing state-of-the-art CSS techniques.

We consider novel cooperative spectrum sensing (CSS) algorithms based on the pattern classification techniques for cognitive radio (CR) networks. In this regard, support vector machine (SVM) and weighted K-nearest-neighbor (KNN) classification techniques are implemented for CSS. The received signal strength at the CR users are treated as features and fed into the classifier to detect the availability of the primary user (PU). Each instance of PU activity (i.e., availability and unavailability) is categorized into positive and negative classes (respectively). In the case of SVM, for minimization of classification errors the support vectors are obtained by maximizing the margin between the separating hyperplane and data. Towards this end, we investigate the effect of different kernels through quantifying in terms of detection probability by representing the receiver operating characteristic (ROC) curves. Furthermore, weighted KNN classification technique is proposed for CSS and the corresponding weights are calculated by evaluating the area under ROC curve of each feature. Our comparative results clearly reveal that the proposed SVM and weighted KNN algorithms outperform the existing state-of-the-art pattern classification-based CSS techniques.

We consider the scheduling and power control problem for spectrum sharing between secondary users and primary users in a spatial reuse time-division multiple access (STDMA) network. The objective is to minimize the transmission length of secondary users in a frame subject to the interference constraints for primary users and the traffic demand of secondary users. The uncertainty of the channel gains is taken into account. Since the power allocation can be improper with respect to the link gain realization, transmissions in the secondary links may fail, and hence, require more time slots. Therefore, traffic demand uncertainty resulting from channel gain variation is also considered. We propose an efficient algorithm based on column generation for robust optimal scheduling and power control for secondary users in presence of channel gain and traffic demand uncertainty. Numerical results show that the proposed algorithm has high computation speed with very low penalty cost when compared to the optimal algorithm. By adjusting the degree of conservatism, we can balance the tradeoff between the robustness and the transmission length of secondary users in a frame.

IEEE 802.15.4 is a popular technology for short-range wireless networking due to the features such as low duty cycle operation, low power consumption, and both contention-based and contention-free transmissions. This standard can be enhanced to provide an optimal medium access mechanism in presence of congestion in the network. We present a Markov decision process (MDP)-based medium access control (MAC) model for IEEE 802.15.4 for the optimal use of contention and contention-free period to minimize energy consumption in repeated transmissions and carrier sensing without degrading latency in packet transmission. The simulation results show that the MDP strategy works more efficiently in presence of congestion when compared to a non- optimal (i.e., traditional) slotted CSMA/CA scheme.

We present a cognitive radio (CR) mechanism for opportunistic access to the frequency bands licensed to a data-centric primary user (PU) network. Secondary users (SUs) aim to exploit the short-lived spectrum holes (or opportunities) created between packet bursts in the PU network. The PU traffic pattern changes over both time and frequency according to upper layer events in the PU network, and fast variation in PU activity may cause high sensing error probability and low spectrum utilization in dynamic spectrum access. The proposed mechanism learns a PU traffic pattern in real-time and uses the acquired information to access the frequency channel in an efficient way while limiting the probability of collision with the PUs below a target limit. To design the channel learning algorithm, we model the CR system as a hidden Markov model (HMM) and present a gradient method to find the underlying PU traffic pattern. We also analyze the identifiability of the proposed HMM to provide a condition for the convergence of the proposed learning algorithm. Simulation results show that the proposed algorithm greatly outperforms the traditional listen-before-talk algorithm which does not possess any learning functionality.

We propose a novel cooperative spectrum sensing algorithm for a cognitive radio (CR) network to detect a primary user (PU) network that exhibits some degree of randomness in topology (e.g., due to mobility). We model the PU network as a random geometric network that can better describe small-scale mobile PUs. Based on this model, we formulate the random PU network detection problem in which the CR network detects the presence of a PU receiver within a given detection area. To address this problem, we propose a location-aware cooperative sensing algorithm that linearly combines multiple sensing results from secondary users (SUs) according to their geographical locations. In particular, we invoke the Fisher linear discriminant analysis to determine the linear coefficients for combining the sensing results. The simulation results show that the proposed sensing algorithm yields comparable performance to the optimal maximum likelihood (ML) detector and outperforms the existing ones, such as equal coefficient combining, OR-rule-based and AND-rule-based cooperative sensing algorithms, by a very wide margin.

We propose a novel subchannel and transmission power allocation scheme for multi-cell orthogonal frequency-division multiple access (OFDMA) networks with cognitive radio (CR) functionality. The multi-cell CR-OFDMA network not only has to control the interference to the primary users (PUs) but also has to coordinate inter-cell interference in itself. The proposed scheme allocates the subchannels to the cells in a way to maximize the system capacity, while at the same time limiting the transmission power on the subchannels on which the PUs are active. We formulate this joint subchannel and transmission power allocation problem as an optimization problem. To efficiently solve the problem, we divide it into multiple subproblems by using the dual decomposition method, and present the algorithms to solve these subproblems. The resulting scheme efficiently allocates the subchannels and the transmission power in a distributed way. The simulation results show that the proposed scheme provides significant improvement over the traditional fixed subchannel allocation scheme in terms of system throughput.

This paper proposes a load-aware routing scheme for wireless mesh networks (WMNs). In a WMN, the traffic load tends to be unevenly distributed over the network. In this situation, the load-aware routing scheme can balance the load, and consequently, enhance the overall network capacity. We design a routing scheme which maximizes the utility, i.e., the degree of user satisfaction, by using the dual decomposition method. The structure of this method makes it possible to implement the proposed routing scheme in a fully distributed way. With the proposed scheme, a WMN is divided into multiple clusters for load control. A cluster head estimates traffic load in its cluster. As the estimated load gets higher, the cluster head increases the routing metrics of the routes passing through the cluster. Based on the routing metrics, user traffic takes a detour to avoid overloaded areas, and as a result, the WMN achieves global load balancing. We present the numerical results showing that the proposed scheme effectively balances the traffic load and outperforms the routing algorithm using the expected transmission time (ETT) as a routing metric.

The cyclostationary feature detector is a viable candidate for a primary user (PU) detection method of the cognitive radio (CR) system. However, it requires very long detection time, which leads to inefficient spectrum utilization. To reduce the detection time, we propose to apply the sequential detection framework to the cyclostationary feature detector. Unfortunately, a straightforward application cannot achieve a sufficient gain, which is expected with the sequential detection. To solve this problem, we design a novel detector, taking account of the cyclic phase of the cyclostationary signal. The simulation results show that the proposed detector reduces the average detection time almost in half. The proposed detector can well be applied to the CR systems that operate in the frequency bands, where the PUs have long interarrival and sojourn time. For example, the CR systems equipped with the proposed detector can efficiently exploit the white space in the VHF/UHF TV bands.

Adaptive modulation and coding (AMC) scheme is widely used in wireless and mobile communications systems supporting high-speed Internet services. A system using AMC can be modeled as that with time-varying capacity. In this paper, we propose a generalized framework of packet scheduler for wireless systems with time-varying capacity. First, we develop the fundamental scheduling algorithm (FSA) that maximizes the system throughput under given constraints by using the dual optimization technique. Even though FSA can be used as a stand-alone scheduler supporting only nonreal-time traffic, it mainly plays a role in this paper as a basic building block of the advanced scheduling algorithm (ASA). We design ASA so that it supports real-time and nonreal-time traffic simultaneously. Since ASA operates on the basis of connections, we also suggest a connection admission control algorithm that matches well with the proposed scheduler. Finally, we show an application example of the proposed scheduling algorithm to the time-division multiple access system using AMC