Integrated data and energy transfer (IDET) is considered as a key enabler of 6G, as it can provide both wireless energy transfer (WET) and wireless data transfer (WDT) services towards low power devices. Thanks to the extra degree of freedom provided by fluid antenna (FA), incorporating FA into IDET systems presents a promising approach to enhance energy efficiency performance. This paper investigates a FA assisted IDET system, where the transmitter is equipped with multiple FAs and transmits wireless signals to the data receiver (DR) and the energy receiver (ER), both of which are equipped with a single traditional antenna. The switching delay and energy consumption induced by port selection are taken into account in IDET system for the first time. We aim to obtain the optimal beamforming vector and the port selection strategy at the transmitter, in order to maximize the short-term and long-term WET efficiency, respectively. The instant sub-optimal solution is obtained by alternatively optimizing the beamforming vector and port selection in each transmission frame, while a novel constrained soft actor critic (C-SAC) algorithm is proposed to find the feasible policy of port selection from the long-term perspective. Simulation results demonstrate that our scheme is able to achieve greater gain in terms of both the short-term and long-term WET efficiency compared to other benchmarks, while not degrading WDT performance.

Deep learning (DL) has emerged as a powerful tool for addressing the intricate challenges inherent in communication and sensing systems, significantly enhancing the intelligence of future sixth-generation (6G) networks. While substantial research has demonstrated the potential of DL-based techniques, challenges remain in ensuring robustness, generalization, and interpretability under highly dynamic and unpredictable environments. To address these limitations, this paper introduces a family of mathematically grounded and modularized models, termed bedrock models, designed for seamless integration into communication and sensing systems. Unlike traditional black-box neural architectures, each bedrock module inherits its structure from classical physical-layer operators, enabling a key rollback capability: when the environment becomes adverse or uncertain, the trainable model can deterministically revert to a closed-form classical solution by simply resetting its parameters. This ensures that the system does not perform worse than the well-understood baseline, while retaining the flexibility of AI enhancements under favorable conditions. In communication systems, bedrock models achieve notable performance gains and exhibit strong transferability, supporting direct parameter reuse across tasks and modulation schemes. In sensing applications, the integration of bedrock models significantly improves performance, reducing delay and Doppler estimation errors by an order of magnitude. Additionally, a transmitter-side pre-equalization strategy is proposed, enabling real-time waveform adaptation using sensing information, while preserving the structure of a pretrained communication model. This approach allows the system to mitigate doubly dispersive channels and maintain near-optimal performance. Extensive simulations validate the effectiveness, robustness, and deployment readiness of the proposed bedrock models across diverse scenarios in both communication and sensing domains.

Due to the innovative application of metamaterials, holographic multiple-input multiple-output (H-MIMO) is expected to achieve a higher spatial diversity gain with lower hardware complexity. Together with the aid of a circular antenna arrangement in H-MIMO, integrated data and energy multicast (IDEM) can fully exploit the near-field channel to realize wider range of energy focusing and higher achievable rate. In this paper, we focus on beamforming design and investigate the IDEM systems that maximize the minimum rate of data users (DUs) while meeting the energy harvesting requirements for energy users (EUs). Specifically, we first derive the closed-form near-field resolution function in 3D space and show the asymptotic spatial orthogonality of near-field channel for circular antenna arrays. Then, we design an asymptotically optimal fully-digital beamformer based on the spatial orthogonality. After that, we apply the alternating optimization to develop H-MIMO beamforming scheme, where the digital beamformer is given in closed form while the analog beamformers of three different control modes are obtained numerically, respectively. Scaling schemes are also investigated to further improve the IDEM performance. Numerical results verify the correctness of the resolution function and asymptotic orthogonality and demonstrate that the proposed beamforming schemes outperform benchmark schemes, with very low complexity.

Sensing-assisted communication schemes have recently garnered significant research attention. In this work, we design a dual-function reconfigurable intelligent surface (RIS), integrating both active and passive elements, referred to as the reconfigurable intelligent sensing surface (RISS), to enhance communication. By leveraging sensing results from the active elements, we propose communication enhancement and robust interference suppression schemes for both near-field and far-field models, implemented through the passive elements. These schemes remove the need for base station (BS) feedback for RISS control, simplifying the communication process by replacing traditional channel state information (CSI) feedback with real-time sensing from the active elements. The proposed schemes are theoretically analyzed and then validated using software-defined radio (SDR). Experimental results demonstrate the effectiveness of the sensing algorithms in real-world scenarios, such as direction of arrival (DOA) estimation and radio frequency (RF) identification recognition. Moreover, the RISS-assisted communication system shows strong performance in communication enhancement and interference suppression, particularly in near-field models.

Joint waveform and beamforming design is demonstrated as an effective approach to improve the wireless energy transfer (WET) performance. Meanwhile, the emerging technique of reconfigurable holographic surface (RHS) without the half-wavelength limitation is able to achieve a higher spatial gain. Hence, the RHS-assisted WET system is designed to enhance WET performance by optimizing the waveform and beamforming. However, the ideal linear power amplifier is always conceived, while the nonlinearity of the power amplifier is ignored. In this paper, a more practical nonlinear power amplifier (NPA)-based RHS-assisted WET system is proposed, where the energy harvesting (EH) model by considering the channel aging effect is derived to adapt to the time-varying channel. In order to improve the WET performance, a predistortion technique is adopted to tackle the nonlinear input-output characteristic of NPAs, while the waveform and beamforming are jointly optimized. Simulation results demonstrate that the RHS outperforms the phased array benchmark having the same size on the WET performance, when the NPAs work in the unsaturated region. Furthermore, the impact of the NPAs on the WET performance by considering channel aging effect is demonstrated, while the waveform design is invalid when the NPAs work near the saturation region.

Modern network systems have grown considerably in scale and complexity. Deep learning technology has thus become indispensable for unlocking the full potential. Therefore, the demand for high-precision fine-grained data has grown significantly. In this paper, we introduce the critical yet unexplored problem of super-resolution for network traffic data, aiming to reconstruct fine-grained data (i.e., data sampled at high frequencies) from coarse-grained data (i.e., data sampled at low frequencies). Inspired by image super-resolution techniques, we first transform network traffic data into images to expose their inherent periodic patterns. Moreover, we successfully migrate vision backbone network to the temporal super-resolution task. Based on this foundational network, we design a novel cross-attention super-resolution generative adversarial network (SRGAN) model that integrates our cross-attention mechanism to jointly capture local-global temporal correlations. Experimental results on real-world network traffic datasets demonstrate that our model effectively performs super-resolution on traffic network data, outperforming several state-of-the-art models.

Simultaneous wireless information and power transfer (SWIPT) is a promising technology for the upcoming sixth-generation (6G) communication networks, enabling internet of things (IoT) devices and sensors to extend their operational lifetimes. In this paper, we propose a SWIPT scheme by projecting the interference signals from both intra-wireless information transfer (WIT) and inter-wireless energy transfer (WET) into the null space, simplifying the system into a point-to-point WIT and WET problem. Upon further analysis, we confirm that dedicated energy beamforming is unnecessary. In addition, we develop a low-complexity algorithm to solve the problem efficiently, further reducing computational overhead. Numerical results validate our analysis, showing that the computational complexity is reduced by 97.5% and 99.96% for the cases of KI= KE= 2, M = 4 and KI= KE= 16, M = 64, respectively.

Thanks to the transmit antenna array design, the integrated data and energy transfer (IDET) performance can be readily improved by exploiting spatial degrees of freedom (DoFs). Recently, the 6-dimensional movable antennas (6DMA), which includes three spatial translation dimensions and three spatial rotation dimensions, has been proposed to further improve the spatial DoFs in higher dimensional spaces. In this paper, a 6DMA-assisted IDET system is investigated, where the 6DMA is utilized to spatially align the UPA surface with the IDET receivers for pursuing the maximal spatial gain. By adopting the two time-scale optimization approach, the long-term 6DMA spatial orientation is optimized with the statistical channel state information (CSI) and the short-term optimal IDET beamforming is achieved with the instantaneous CSI. Simulation results demonstrate that our proposed scheme can improve the IDET performance significantly compared to the traditional fixed-position antenna (FPA) and 2-dimensional movable antenna (2DMA) benchmarks.

In the mobile network, digital twin (DT)-aided artificial intelligence (AI)-empowered link control is vital to enhance the performance of wireless communication. This paper proposes a deep reinforcement learning (DRL)-convex optimization enhanced time-frequency domain power allocation scheme to reduce the long-term average bit error rate (BER) in multi-user orthogonal frequency division multiplexing (OFDM) systems. To alleviate performance loss caused by trial-and-error during the training period of DRL algorithms, we design a novel practical DT-aided “prediction-then-decision” autonomous wireless link control framework considering the periodic interaction mechanism between the DT and its physical counterpart. A Transformer-based channel generator Mucomformer is implemented in the DT layer to generate large amounts of multi-user virtual channel state information (CSI) in future transmission frames. In addition, the DRL agent is trained over the DT channel in advance and executed in the real-world OFDM system to generate the optimal transmission strategy by considering the interaction mechanism between the DT and the physical counterpart. The simulation results demonstrate that the proposed Mucomformer has lower average prediction error of 2.51 dB compared to the Transformer baseline. The DRL and convex-based power allocation scheme further outperforms the classic strategy. Moreover, the practical DT-aided autonomous link control framework effectively mitigates the performance impairment, achieves an average BER performance gain 45.65% higher than that without DT and achieves faster convergence during the whole training period.

Wireless energy transfer (WET) technology enables Internet of Things (IoT) networks to have longer lifetime without the need of frequent battery replacements. This article proposes a payload-adaptive hybrid MAC (PAH-MAC) protocol by considering both contention and time-slot allocation among sensors for large-scale data collection scenarios within WET-enhanced IoT networks. The PAH-MAC protocol employs different access strategies based on the payload size of data packets. Furthermore, leveraging synchronization mechanisms, the coordinator periodically dispatches energy packets to replenish the battery of all sensors. The stationary performance of PAH-MAC protocol is analyzed by invoking Markov chains. Considering the traffic variations caused by changes in the number of sensors in the network, a slot adaptive adjustment algorithm is proposed to maximize the throughput and energy performance. Therefore, the coordinator adaptively adjusts the duration of the contention period and the energy transmission period within the next superframe according to access conditions observed in current superframe. Another transmission power adjustment algorithm is proposed for sensors to improve their energy efficiency. According to our simulation, our proposed protocol consumes less energy in low-traffic scenarios than the classic carrier-sense multiple access/CA and another baseline protocol. Furthermore, in high-traffic scenarios, our proposed protocol achieves higher throughput, lower latency, and lower energy consumption than its counterpart.

Satellite Internet of things (S-IoT) aims to provide globally covered network services. In this paper, we conceive an uplink grant-free random access scheme for S-IoT network, where ground devices transmit data packets to the low Earth orbit (LEO) satellite, reducing signaling cost and making efficient use of spectrum resources by employing the non-orthogonal multiple access scheme. The impact of high operational speed of the LEO satellite is also taken into account. We further propose an iterative Gaussian approximated message passing-aided sparse Bayesian learning (GAMP-SBL) algorithm to address the joint channel estimation (CE), active user identification (UID) and data detection (DD) problem, where the three steps interacts with each other during the iterative process. Simulation results have demonstrated that our proposed joint receiver design outperforms the existing AMP-based schemes in terms of bit error rate (BER), convergence speed, as well as false alarm rate (FAR).

When applying integrated sensing and communications (ISAC) in future mobile networks, many sensing tasks have low latency requirements, preferably being implemented at terminals. However, terminals often have limited computing capabilities and energy supply. In this paper, we investigate the effectiveness of leveraging the advanced computing capabilities of mobile edge computing (MEC) servers and the cloud server to address the sensing tasks of ISAC terminals. Specifically, we propose a novel three-tier integrated sensing, communication, and computing (ISCC) framework composed of one cloud server, multiple MEC servers, and multiple terminals, where the terminals can optionally offload sensing data to the MEC server or the cloud server. The offload message is sent via the ISAC waveform, whose echo is used for sensing. We jointly optimize the computation offloading and beamforming strategies to minimize the average execution latency while satisfying sensing requirements. In particular, we propose a low-complexity distributed algorithm to solve the problem. Firstly, we use the alternating direction method of multipliers (ADMM) and derive the closed-form solution for offloading decision variables. Subsequently, we convert the beamforming optimization sub-problem into a weighted minimum mean-square error (WMMSE) problem and propose a fractional programming based algorithm. Numerical results demonstrate that the proposed ISCC framework and distributed algorithm significantly reduce the execution latency and the energy consumption of sensing tasks at a lower computational complexity compared to existing schemes.

Fluid antenna multiple access (FAMA) exploits spatial opportunities in wireless channels through port switching to overcome multiuser interference, achieving better performance than traditional fixed MIMO systems. Integrated Data and Energy Transfer (IDET) is capable of providing both wireless data transfer (WDT) and wireless energy transfer (WET) services for low-power devices. This paper investigates an FAMA-assisted IDET system, where a base station (BS) equipped withNfixed antennas provides dedicated IDET services toNuser equipments (UEs). Each UE is equipped with a single fluid antenna, while the power splitting (PS) approach is conceived for coordinating WDT and WET. Under the Rayleigh channel model, we derive both exact expressions and approximate closed forms for the outage probabilities of WDT and WET, where the fluid antenna (FA) at each UE selects the optimal port to maximize either the signal-to-interference-plus-noise ratio (SINR) or the energy harvesting power (EHP). The IDET outage probabilities are defined and subsequently derived and approximated into closed-forms. Further, multiplexing gains of the proposed system are defined and analyzed to evaluate the performance. Further, we analyze the IDET outage probabilities and multiplexing gains of the proposed system. Additionally, to provide a more general analysis, we extend our analytical framework to the Rician channel model. Numerical results validate the theoretical analysis while also illustrating that the trade-off is achieved between WDT and WET performance by exploiting different port selection strategies and numbers of UEs.

The rapid development of low altitude Unmanned Aerial Vehicles (UAVs) as a new mode of transportation has injected a new driving force into the market development, but at the same time, unreported “black flight” UAVs have also created new risks in civil aviation safety, citizen privacy protection and other social security areas. In this regard, the Integrated Sensing And Communication (ISAC) capability of Base Station (BS) can provide an effective means of communication and supervision of low-altitude UAVs. For example, by demarcating the electronic fence area, the ISAC BS can realize automatic detection of illegal invasion of UAVs, effectively guaranteeing low-altitude safety in the context of low-altitude economy. By leveraging the high mobility of UAVs and their strong air-ground Line-of-Sight (LoS) channels, UAV-enabled ISAC is anticipated to provide superior sensing and communication coverage, and enhanced sensing and communication performance compared to terrestrial ISAC. However, existing work mainly focus on single BS sensing with the assistance of communication, which may not fully activate ISAC’s potential and achieve high-precision long-range sensing. Given the above considerations, this paper provides a cellular-connected UAV system, where the BS and connected UAV are employed to perform cooperative detection tasks for precise detection. To unleash the potential of ISAC in cellular-connected UAV systems, on the one hand, we propose an Extended Kalman Filtering (EKF) based data fusion algorithm to provide precise environment information and achieve beyond LoS sensing. On the other hand, according to the fusion results, we optimize the communication rate performance by jointly designing the transmit beamforming and trajectory subject to the power and practical fight constraints to combat the effect of mobility, while ensuring the sensing requirements, which can achieve a positive feedback loop. Extensive simulation results demonstrate that the proposed data fusion algorithm improves the estimation accuracy by 67% and the joint design of beamforming and trajectory algorithm improves the communication data rate by more than 31%.

Fluid antenna multiple access (FAMA) is capable of exploiting the high spatial diversity of wireless channels to mitigate multi-user interference via flexible port switching, which achieves a better performance than traditional multi-input-multi-output (MIMO) systems. Moreover, integrated data and energy transfer (IDET) is able to provide both the wireless data transfer (WDT) and wireless energy transfer (WET) services towards low-power devices. In this paper, a FAMA assisted IDET system is studied, where$N$access points (APs) provide dedicated IDET services towards$N$user equipments (UEs). Each UE is equipped with a single fluid antenna. The performance of WDT and WET, i.e., the WDT outage probability, the WET outage probability, the reliable throughput and the average energy harvesting amount, are analysed theoretically by using time switching (TS) between WDT and WET. Numerical results validate our theoretical analysis, which reveals that the number of UEs and TS ratio should be optimized to achieve a trade-off between the WDT and WET performance. Moreover, FAMA assisted IDET achieves a better performance in terms of both WDT and WET than traditional MIMO with the same antenna size.

In future applications of Internet of Everything (IoE), we need to timely and reliably collect multi-modal sensing data in order to monitor dynamic environment. To this end, we study a timely wireless powered crowdsensing system by enabling cooperative sensing among multiple sensors to increase reliability, by exploiting radio frequency (RF) based wireless power transfer (WPT) to address energy shortage of miniature sensors, and by minimising the average age of information (AoI) to guarantee the timeliness of the sensing data. We jointly optimise the inter-group sensors scheduling and the intra-group sensors scheduling for minimising the weighted AoI among multiple group of sensors. Since the optimisation problem is NP-hard, we propose a joint scheduling algorithm to obtain the optimal scheduling policy. Simulation results demonstrate the superiority of our scheme over the existing state of the art.

This paper proposes a novel non-orthogonal multiple access (NOMA)-assisted orthogonal time-frequency space (OTFS)-integrated sensing and communication (ISAC) network, which uses unmanned aerial vehicles (UAVs) as air base stations to support multiple users. By employing ISAC, the UAV extracts position and velocity information from the user’s echo signals, and non-orthogonal power allocation is conducted to achieve a superior achievable rate. A 3D motion prediction topology is used to guide the NOMA transmission for multiple users, and a robust power allocation solution is proposed under perfect and imperfect channel estimation for max-min fairness (MMF) and maximum sum-rate (SR) problems. Simulation results demonstrate the superiority of the proposed NOMA-assisted OTFS-ISAC system over other systems in terms of achievable rate under both perfect and imperfect channel conditions with the aid of 3D motion prediction topology.

In order to transmit data and transfer energy to the low-power Internet of Things (IoT) devices, integrated data and energy networking (IDEN) system may be harnessed. In this context, we propose a bitwise end-to-end design for polar coded IDEN systems, where the conventional encoding/decoding, modulation/demodulation, and energy harvesting modules are replaced by the neural networks (NNs). In this way, the entire system can be treated as an AutoEncoder (AE) and trained in an end-to-end manner. Hence achieving global optimization. Additionally, we improve the common NN-based belief propagation (BP) decoder by adding an extra hypernetwork, which generates the corresponding NN weights for the main network under different number of iterations, thus the adaptability of the receiver architecture can be further enhanced. Our numerical results demonstrate that our BP-based end-to-end design is superior to conventional BP-based counterparts in terms of both the BER and power transfer, but it is inferior to the successive cancellation list (SCL)-based conventional IDEN system, which may be due to the inherent performance gap between the BP and SCL decoders.

Modulation scheme and power control simultaneously impact the performance of integrated data and energy transfer (IDET). Therefore, some efforts have been invested in deep reinforcement learning (DRL) algorithms to realize adaptive modulation (AM) and adaptive power control (APC), in order to achieve long-term performance improvement. However, the optimal DRL algorithm design for the long-term performance optimization having long-term constraints is still a challenge, while the optimal patterns of IDET-oriented joint AM and APC are not fully understood. This paper aims to maximize the long-term performance of energy harvesting (EH), while satisfying the long-term constraints of spectrum efficiency, bit-error-rate and transmit power, by jointly optimizing the modulation selection and transmit power allocation. Then, a novel DRL algorithm, named constrained parameterized action deep deterministic policy gradient (C-PADDPG), is proposed to find the feasible policy of joint AM and APC for the transformed constraint satisfaction problem. Meanwhile, the optimal policy is searched for via bisection method. Simulation results demonstrate that our solution can achieve significant gain on the long-term EH performance, compared to the traditional genetic algorithm-based solution and other DRL benchmark. Moreover, the communication-efficient and EH-efficient patterns of joint AM and APC generated by the C-PADDPG algorithm are explicitly illustrated and analyzed.

This paper presents a reconfigurable intelligent sensing surface (RISS) that combines passive and active elements to achieve simultaneous reflection and direction of arrival (DOA) estimation tasks. By utilizing DOA information from the RISS instead of conventional channel estimation, the pilot overhead is reduced and the RISS becomes independent of the hybrid access point (HAP), enabling efficient operation. Specifically, the RISS autonomously estimates the DOA of uplink signals from single-antenna users and reflects them using the HAP’s slowly varying DOA information. During downlink transmission, it updates the HAP’s DOA information and designs the reflection phase of energy signals based on the latest user DOA information. The paper includes a comprehensive performance analysis, covering system design, protocol details, receiving performance, and RISS deployment suggestions. We derive a closed-form expression to analyze system performance under DOA errors, and calculate the statistical distribution of user received energy using the moment-matching technique. We provide a recommended transmit power to meet a specified outage probability and energy threshold. Numerical results demonstrate that the proposed system outperforms the conventional counterpart by 2.3 dB and 4.7 dB for Rician factors$\kappa _{h}=\kappa _{G}=1$and$\kappa _{h}=\kappa _{G}=10$, respectively.

To satisfy the heterogeneous service requirements in future internet of things (IoT), this paper investigates the novel framework for intelligent reflecting surface (IRS)-aided wireless powered over-the-air computation (AirComp) and communication networks, where the IoT devices first harvest energy from the downlink signal sent by the base station, and then conduct the information transmissions and AirComp in the uplink. In particular, the IRS is used to improve the efficiency of wireless energy transfer, and alleviate the harmful interference between the communication and AirComp signals. To balance the performance of such an integrated system, we present two joint beamforming and reflection optimization problems via minimizing the computation distortion and maximizing the sum rate, respectively. To solve the non-convex problems, we develop the alternating optimization framework with proved convergence, in which the penalty function-based method and variable substitution technique are exploited to acquire the optimal solutions of beamformers and reflection parameters. Finally, simulation results show that the proposed method realizes significantly higher computation accuracy and communication rate, in comparison with several existing benchmark methods.

A novel Physical Layer Security (PLS) framework is conceived for enhancing the security of wireless communication systems by exploiting multi-domain polarization in Multiple-Input Multiple-Output (MIMO) systems. We design a sophisticated key generation scheme based on multi-domain polarization and the corresponding receivers. An in-depth analysis of the system’s secrecy rate is provided, demonstrating the confidentiality of our approach in the presence of eavesdroppers having strong computational capabilities. More explicitly, our simulation results and theoretical analysis corroborate the advantages of the proposed scheme in terms of its bit error rate (BER), block error rate (BLER), and maximum achievable secrecy rate. Our findings indicate that the innovative PLS framework effectively enhances the security and reliability of wireless communication systems. For example, in a$4\times 4$MIMO setup, the proposed PLS strategy exhibits an improvement of 2dB compared to conventional MIMO, systems at a BLER of$2\cdot 10^{-5}$while the eavesdropper’s BLER reaches 1.

Thanks to the application of metamaterials, holographic multiple-input multiple-output (H-MIMO) is expected to achieve a higher spatial diversity gain by enabling the ability to generate any current distribution on the surface. With the aid of electromagnetic (EM) manipulation capability of H-MIMO, integrated data and energy transfer (IDET) system can fully exploit the EM channel to realize energy focusing and eliminate inter-user interference, which yields the concept of holographic IDET (H-IDET). In this paper, we investigate the beamforming designs for H-IDET systems, where the sum-rate of data users (DUs) are maximized by guaranteeing the energy harvesting requirements of energy users (EUs). In order to solve the non-convex functional programming, a block coordinate descent (BCD) based scheme is proposed, wherein the Fourier transform and the equivalence between the signal-to-interference-plus-noise ratio (SINR) and the mean-square error (MSE) are also conceived, followed by the successive convex approximation (SCA) and an initialization scheme to enhance robustness. Numerical results illustrate that our proposed H-IDET scheme outperforms benchmark schemes, especially the one adopting traditional discrete antennas. Besides, the near-field focusing using EM channel model achieves better performance compared to that using the traditional channel model, especially for WET where the EUs are usually close to the transmitter.

When wideband signals propagate in a rich-scatterer environment, we obtain abundant resolvable multiple transmission paths to form a number of virtual antennas. Therefore, substantial spatial gain can be attained by carefully waveforming in all these resolvable transmission paths without additional antennas. This resultant spatial gain is then exploited for improving the performance of integrated-data-and-energy-transfer (IDET) from a single transmitter to multiple receivers. We aim to maximise the downlink fair-throughput and sum-throughput, while satisfying the energy harvesting requirements by jointly optimising the waveformers at the transmitter and the power splitters at the receivers. A low-complexity fractional-programming (FP) based alternating algorithm is proposed to solve these non-convex optimisation problems. The non-convex wireless energy transfer (WET) constraints are transformed to be convex with a modified quadratic transform (MQT) method. As a result, the stationary points for both the fair-throughput and the sum-throughput maximisation problems are obtained. The numerical results demonstrate the advantage of our proposed algorithm over a minimum-mean-square-error (MMSE) scheme, a zero-forcing (ZF) scheme and a time-reversal (TR) scheme. Simulation results show that the wireless data transfer (WDT) performance of our scheme outperforms the single-input-single-output orthogonal-frequency-division-multiple-access (SISO-OFDMA) when the output direct current (DC) power requirement is high. When we have a practical individual subcarrier power constraint, the WDT performance of our scheme outperforms multiple-input-single-output orthogonal-frequency-division-multiplex-access (MISO-OFDMA).

Integrated data and energy transfer (IDET) is capable of providing both stable wireless energy transfer (WET) and wireless data transfer (WDT) services towards massive low-power devices. In order to enhance both the resource efficiency for WDT and the energy harvesting performance for WET, a novel reflective index modulation (RIM) scheme is proposed in an intelligent reflecting surface (IRS)-assisted IDET system, where various group number of IRS elements are activated for WDT and WET, respectively, while the index of activated IRS elements can also deliver additional data in the reflective domain. The bit error ratio (BER), the achievable data rate of the data receiver (DR) and the average energy harvesting power at the energy receiver (ER) are then derived into the closed form. The phase shifters of the IRS are then optimized, in order to maximize average energy harvesting power at the ER by guaranteeing the BER and the data rate constraints of the DR. Simulation results validate our theoretical analysis, which also demonstrate that RIM scheme is capable of improving the joint WDT and WET performance in the IRS-IDET system.

Age of Information (AoI) has been studied recently for satisfying the timeliness requirements of the emergent applications, i.e. smart cities and smart wearables in future Internet of Everything (IoE). In order to integrate multiple information flows i wireless powered sensor network (WPSN), we propose age of sensing (AoS) for measuring freshness of the fused information, which is defined as the time elapsed from the generation moment of the sensing information used for generating the fresh reliable fused information. Specifically, a wireless power transfer (WPT) aided wireless sensor network (WSN) is studied. Multiple batteryless sensors cooperatively sense common objects and then timely upload the sensing information to a fusion center (FC). The FC fuses the correctly decoded information from different sensors and finally evaluates the reliability of the fused information. The number of time slots for WPT and the deployment of the sensors are then jointly optimised, in order to minimise the average AoS. A Fibonacci aroused integer programming approach is then proposed to solve the nonlinear integer problem having NP-hard complexity. Simulation results validate the accuracy of the theoretical analysis for the AoS, while also demonstrating that our algorithm achieves the superb performance by referring to the optimal exhaustive method.

Unmanned aerial vehicle (UAV) assisted wireless power transfer (WPT) enables the network to provide flexible and controllable energy supplement towards low-powered devices, which is considered as a promising technology in the future Internet of Things (IoT) networks. In this paper, the integrated communication and control (ICAC) for UAV assisted WPT is studied, aiming for mitigating the performance degradation caused by the WPT beam misalignment between the UAV and the low-power device. The optimal blocklength of the control signal is then obtained, in order to maximize the lower bound of the average energy harvesting power of the device by satisfying the control reliability constraint. Simulation results evaluate the performance of the ICAC system, which demonstrates that the WPT performance is a decrease-and-increase-then-decrease function with respect to the blocklength of the control signal. The performance of the ICAC system can be improved by adjusting the blocklength of the control signal.

Thanks to the characteristic that the signals can be reflected to a pre-defined oritention, intelligent reflecting surface (IRS) is becoming a promising technology to improve the efficiency of wireless communications. In this paper, a reflective group number based index modulation (RGNIM) scheme is proposed by exploiting the activating states of the reflecting elements of the IRS, where different group number of activated IRS elements can carry additional information in the reflective domain. The bit error ratio (BER) performance is then theoretically analysed under the imperfect channel estimation by adopting the maximum likelihood (ML) detection approach. Simulation results validate and evaluate the BER performance of the RGNIM assisted system, which also demonstrate that our RGNIM scheme outperforms other IRS aided index modulation schemes.

Simultaneous wireless information and power transfer (SWIPT) has been considered as a promising technique to address energy shortage of communication devices deployed in Internet of Everything (IoE). In this paper, we consider a multi-relay aided cooperative communication network with SWIPT, where relay stations (RSs) receive RF signal from a source node (SN) for energy harvesting and information reception by power splitters. A single activated RS then decodes and forwards information to the destination using the harvested energy. Selective-decode-and-forward (S-DF) protocol is adopted, where the activated RS forwards only when information is correctly decoded. By considering time-varying channels, we maximise the end-to-end (E2E) throughput by jointly designing the transmit beamformers for both SN and RSs, optimising transmit power and power splitters for RSs, as well as the RS selection. The original formulated non-convex optimisation problem with coupled variables is solved by invoking an iterative algorithm. Numerical results demonstrate that our design with SDF protocol outperforms that with the decode-and-forward (DF) protocol. Moreover, the impact of the imperfect channel state information (CSI) on the E2E throughput is also evaluated.

Radio-frequency (RF) signals are capable of simultaneously transferring data and energy from a hybrid access point (HAP) toward battery-powered and batteryless wireless devices (WDs). Battery-powered and batteryless WDs with the capability of RF energy harvesting need a distributed access control protocol with collision avoidance to achieve higher energy efficiency. We study the performance of a data and energy integrated network (DEIN) that adopts an enhanced carrier sensing multiple access with collision avoidance (CSMA/CA) protocol. Each device in this network can switch to RF energy harvesting mode or data reception mode according to HAP’s instruction, and freezes its backoff counter when energy storage is insufficient. By invoking a 3-D Markov chain, we model the operating behaviors of batteryless WDs and an HAP in a DEIN. Apart from backoff operations of devices, the 3-D Markov chain also depicts their dynamic energy changes, including RF energy harvesting and energy consumption. WDs consume energy harvested from the HAP’s downlink transmissions for powering their data upload and random backoff. With the aid of the 3-D Markov chain, the upload throughput of devices can be obtained in semi-closed-form. Moreover, a decoupling method is proposed to approximate throughput performance with low complexity. The accuracy of our theoretical model is validated by simulation results. By characterizing the impact of various parameters on throughput performance, a design guideline for a DEIN with a distributed batteryless access protocol is provided.

Intelligent reflecting surface (IRS) is envisioned as a promising technique to improve the performance of full-duplex wireless-powered communication networks (FD-WPCNs). This article investigates the joint phase beamforming design and resource management for IRS-assisted FD-WPCNs, where multiple wireless devices (WDs) can harvest downlink radio-frequency energy and transmit uplink information to the hybrid access point (HAP) over the same band with the aid of IRS. We first formulate a total transmission time minimization problem subject to the minimum transmit rate and energy causality constraints of WDs. In particular, the random phase error of IRS is integrated into our optimization model. Furthermore, we develop an alternating optimization method to obtain the optimal solution of the formulated nonconvex problem by iteratively solving two subproblems. For the phase beamforming optimization subproblem, we first convert the random phase errors to a deterministic expression, and then utilize the successive convex approximation method to solve the phase beamforming optimization problem. For the transmit power and time-slot allocation subproblem, the optimal transmit power of WDs is derived in closed-form expressions, and the approximation method and variable substitution technique are adopted to obtain the optimal time-slot allocation and transmit power of HAP. Finally, numerical results are provided to evaluate the performance of our proposed method and reveal the benefits introduced by the IRS technique as compared to benchmark methods.

Federated learning (FL) is an emerging technique to support privacy-aware and resource-constrained machine learning, where a base station (BS) will coordinate a set of distributed Internet of Things (IoT) devices to train a shared machine learning model with their local data sets. Nevertheless, due to the frequent interactions between BS and distributed IoT devices for the aggregating/distributing learning model parameters, the performance of FL is fundamentally restricted by the randomness of channel condition. To address this issue, we utilize the intelligent reflecting surface (IRS) to improve the efficiency of learning model aggregation/distribution. In addition, we consider two transmission protocols to enable the model aggregation from IoT devices to BS, i.e., frequency division multiple access (FDMA) and nonorthogonal multiple access (NOMA). For both protocols, we formulate the total training latency minimization problem under the available energy constraints of IoT devices, to jointly optimize the phase shifts of IRS, communication resource scheduling, and transmit power and local computing frequencies of IoT devices. Moreover, we further develop the efficient multidimensional resource management algorithms to solve the formulated training latency minimization problems. Numerical results demonstrate that the proposed IRS-assisted FL systems can achieve significant latency reduction as compared with other benchmark methods, and the NOMA-based model aggregation method exhibits a lower total training latency than the FDMA-based counterpart.

Future 6G is deemed to provide Simultaneous Wireless cOmputing and Power Transfer (SWOPT) services for addressing the shortage of local computation and that of energy at terminal devices in the era of the Internet of Everything (IoE). In this paper, we study a fog radio access network (F-RAN) consisting of enhanced remote radio heads (eRRHs) with multiple antennas and edge computing capabilities. The F-RAN simultaneously supports task offloading of computing users (CUs) and energy harvesting of energy users (EUs). Thanks to the global coordination of a central server, full cooperation among eRRHs in both computing and communication domains can substantially improve the SWOPT performance. Specifically, the energy harvesting performance of EUs is maximised by jointly optimising the bandwidth and the beam resources in the communication domain as well as the computing frequency and the offloading strategy in the computing domain. The original non-convex problem with mixed integer and continuous variables is then solved by sequential convex optimisations in the framework of a greedy algorithm with low complexity. Thorough simulation results demonstrate the advantage of our proposed resource scheduling scheme over the existing state-of-the-art counterparts.

Cell-free networking enables full cooperation among distributed access points (APs). This paper focuses on reducing the long-term energy consumption of a cell-free network in the downlink integrated data and energy transfer (IDET) for achieving energy sustainability. The resultant design includes both the AP classification on a large time-scale and the beamforming of the APs on a small time-scale in order to simultaneously satisfy the IDET requirements of data users and energy users. For dealing with binary integer actions (AP classification) and continuous actions (beamforming) together, we innovatively propose a stable double parameterized deep-Q-network (DP-DQN), which can be enhanced by a digital twin (DT) running in the intelligent core processor (ICP) so as to achieve faster and more stable convergence. Therefore, the cell-free network may avoid suffering from performance fluctuation during the training process. The simulation results demonstrate that our DP-DQN exceeds in convergence compared to other benchmarks while guaranteeing an optimal solution.

In an intelligent reflecting surface (IRS) aided wireless power transfer (WPT) system, a practical architecture of an energy receiver (ER) is proposed, which includes multiple receive antennas, an analog energy combiner, a power splitter and multiple energy harvesters. In order to maximise the output direct-current (DC) power, the transmit beamformer of the transmitter, the passive beamformer of the IRS, the energy combiner, and the power splitter of the ER are jointly optimised. The optimisation problem is equivalently divided into two sub-problems, which independently maximises the input RF power and the output DC power of the energy harvesters, respectively. A successive linear approximation (SLA) based algorithm with a low complexity is proposed to maximise the input RF power to the energy harvesters, which converges to a Karush-Kuhn-Tucker (KKT) point. We also propose an improved greedy randomized adaptive search procedure (I-GRASP) based algorithm having better performance to maximise the input RF power. Furthermore, the optimal power splitter for maximising the output DC power of the energy harvesters is derived in closed-form. The numerical results are provided to verify the performance advantage of the IRS-aided WPT and to demonstrate that conceiving the optimised energy combiner achieves better WPT performance than the deterministic counterpart.

Integrated data and energy transfer (IDET) has been considered as a key technology to tackle the energy shortage in the densely connected wireless networks. In this paper, a novel time index modulation (TIM) assisted IDET system is studied, where data information is delivered not only by the conventional modulation scheme, but also by the time index of the activated symbol durations for either wireless data transfer (WDT) or wireless energy transfer (WET). With the aid of theoretical analysis, the average energy harvesting performance as well as the achievable data rate between the transmitter and the receiver are both derived in the semi-closed form. Further, a population-based optimizer is conceived to obtain the power allocation of WDT and WET signals, in order to maximize the energy harvesting performance by satisfying the constraints of the achievable data rate. Simulation results validate our theoretical analysis, while they also demonstrate that the TIM assisted system can substantially increase the IDET performance.

In order to satisfy timeliness requirements arising from environmental sensing applications, Age of Information (AoI) was proposed to characterize the freshness of the received updates. In this article, we consider a wireless powered relay aided communication network (WPRCN), in which a relay wirelessly powered by a hybrid-access point (H-AP) receives environmental monitoring information update from a sensor and forwards it to the H-AP. The long-term average AoI is studied since the decision correctness depends on the update timely uploaded by the relay. The wireless powered relay adopts either a decode-and-forward (DF) or an amplify-and-forward (AF) protocols, respectively, subject to the energy causality. We also consider the decoding cost of DF protocol owing to the relay’s limited energy storage. Since the expression of the average AoI is nonelementary, we propose a Taylor approximation-based algorithm to obtain its integral. We optimize the transmit power/the equivalent average power consumption of the relay for the sake of minimizing the average AoI in the whole WPRCN with different forwarding protocols. Our simulation results demonstrate the accuracy of the theoretical analysis, while the average AoI of the WPRCN is optimized by our power allocation scheme at the relay. The proposed algorithm is verified to achieve a great approximation effect.

A protocol of batteryless wireless devices massive access network is proposed in this paper. Energy harvesting is a major work for wireless devices(WDs) which has no stable energy source to transmit data, and the access point(AP) performs energy transmission when transmits data through wireless signals to meet the energy requirement for WDs. As a result, limited by energy, the traffic generated by WDs is sporadic. With this feature, the batteryless massive access(BLMA) protocol is designed to achieve massive access of WDs. First, on the basis of SCMA(Sparse Code Multiple Access), sparse codes are used to realize simultaneous transmission for WDs using different codes, and finite codes are allocated to massive WDs which size is greater than the codebook in an orderly manner. Then, different slots are alternately allocated to different WDs to avoid collisions caused by simultaneous transmission. Finally, the limit of slot occupancy is set according to the energy status of the WD to optimize the slots allocation. Simulation shows that the BLMA protocol could increase the device access amount and system throughput by at least six times compared to SCMA.

A typical unmanned aerial vehicle (UAV) aided communication system with multiple user equipments (UEs) is investigated in this paper. The UE's information and energy maximization problem is proposed by optimizing the UAV's trajectory, transmit power and hovering time. The problem is decomposed into four sub-problems: dividing UEs, solving hovering positions of the UAV, designing UAV's trajectory and optimizing problem. Then, a K-clustering algorithm combined with Fermat Problem is considered to solve some sub-problems. In addition, UAV's trajectory is obtained by using dynamic programming (DP) to solve the TSP problem. The numerical results verify that our proposed algorithms effectively reduce the number of hovering positions of the UAV. Besides, the proposed optimization problems also improve the performance of information and energy transmission compared with the energy optimization only and information optimization only schema.

In order to meet timeliness requirements arising from sensing applications, age of information (AoI) was proposed to characterise the freshness of the received updates. In this paper, we study the average AoI for a two-hop network, in which a relay wirelessly powered by a hybrid-access point (H-AP) receives environmental monitoring information update from a sensor and forwards it to the H-AP. We optimise the transmit power of the relay for the sake of minimising the average AoI in the whole wireless powered relay aided communication nework (WPRCN). The wireless powered relay adopts a decode-and-forward strategy by considering the energy causality. Our simulation results demonstrate the impact of the relay's transmit power on the average AoI, while the information freshness of the WPRCN is optimised by our power allocation scheme at the relay.

Relying on the propagation of modulated radio-frequency (RF) signals, we can achieve simultaneous wireless information and power transfer (SWIPT) to support low-power communication devices. In this paper, we proposed a unary coding based SWIPT encoder by considering a practical M-QAM. Markov chains are exploited for characterising coherent binary information source and for modelling the generation process of modulated symbols. Therefore, both mutual information and the average energy harvesting performance at the SWIPT receiver are analysed in semi-closed-form. With the aid of the genetic algorithm, the sub-optimal codeword distribution of the coded information source is obtained by maximising the average energy harvesting performance, while satisfying the requirement of the mutual information. Simulation results demonstrate the advantage of the SWIPT encoder. Moreover, a higher-level unary code and a lower-order M-QAM results in higher WPT performance, when the maximum transmit power of the modulated symbol is fixed.

In millimeter wave (mmWave) based communication system, hybrid beamforming is regarded as a pivotal technique for the sake of reducing the hardware complexity. In this paper, we studied the optimal hybrid beamforming and combiner design for a multi-user mmWave MIMO system in order to simultaneously multicast information to the information users (IUs) and transfer wireless power to the energy users (EUs). By considering practical non-linear RF energy harvester, our transceiver design aims for maximising the downlink multicast throughput, while satisfying the charging requirements of the EUs. This sub-optimal solution is obtained by a low-complexity algorithm. This algorithm firstly designs separately the beamformer and combiners by minimizing mean square error principle. Then given the resultant combiners, the algorithm update the hybrid multicast beamforming at the transmitter until it converges. Our transceiver design also achieves good detection performance for the IUs. The numerical results demonstrate the advantage of our joint transceiver design over the separated counterpart in terms of both the multicast throughput and the energy harvested.

In wireless powered communication networks (W-PCNs), full duplex (FD) is adopted owing to high efficiency. Furthermore, multi-antenna technique is also widely utilised, since it is capable substantially of improving communication throughput and reliability. In this paper, we study the optimal transmit beamforming and receive combining design for a multiantenna and FD aided hybrid access point (H-AP) in a WPCN, which aims for maximising the uplink sum-throughput of bat-teryless user equipments (UEs). Furthermore, self-interference cancellation (SIC) at the H-AP is also considered in the joint transmit beamforming and receive combining design. Since the optimisation problem is non-convex, a block descent method is exploited for finding the near-optimal solution. Our simulation results demonstrate that the sum-throughput obtained by our design outperforms the other counterparts. As the number of antennas increases, the performance of our design with self-interference approaches that without self-interference (or with perfect self-interference cancellation). Furthermore, our design is robust for accommodating increasing number of batteryless UEs.

Due to its long-distance propagation, radio-frequency (RF) signals have been relied upon for remotely charging low-power communication devices, which significantly extends the lifetime of battery-powered devices. Delivering both wireless information provision (WIP) and wireless energy provision (WEP) services in the RF band requires a holistic design of wireless information and energy provision (WIEP) for achieving energy self-sustainability in the next generation of wireless networks, such as 6G. However, most of the existing works studied the integrated WIEP performance by exploiting Gaussian distributed signals with the infinite alphabet, which cannot be realised in a practical communication system. Therefore, we investigate the WIEP performance by considering practical modulation schemes having finite alphabet in a well-known Nakagami-m wireless fading channel. Furthermore, we jointly optimise the transmit power allocation and the transmission switching threshold of a WIEP transmitter and the power splitting ratio of a WIEP receiver in order to maximise the attainable spectrum efficiency for WIP, while satisfying both the WEP requirement and the WIP reliability constraint. Numerical results are provided for characterising the rate-energy-reliability trade-off of different modulation schemes in various wireless fading conditions.

Radio frequency (RF) signals have been relied upon for both wireless information delivery and wireless charging to the massively deployed low-power Internet of Things (IoT) devices. Extensive efforts have been invested in physical layer and medium-access-control layer design for coordinating simultaneous wireless information and power transfer (SWIPT) in RF bands. Different from the existing works, we study the coding controlled SWIPT from the information theoretical perspective with practical transceiver. Due to its practical decoding implementation and its flexibility on the codeword structure, unary code is chosen for joint information and energy encoding. Wireless power transfer (WPT) performance in terms of energy harvested per binary sign and of battery overflow/underflow probability is maximised by optimising the codeword distribution of coded information source, while satisfying required wireless information transfer (WIT) performance in terms of mutual information. Furthermore, a Genetic Algorithm (GA) aided coding design is proposed to reduce the computational complexity. Numerical results characterise the SWIPT performance and validate the optimality of our proposed GA aided unary coding design.

As the number of communication devices rapidly grows, limited radio resources hardly accommodate the every-increasing tele-traffic. As a remedy, spatial modulation (SM) is capable of modulating additional information onto the index of transmit or receive antennas, which results in substantial improvement of spectrum efficiency. Moreover, radio frequency (RF) signal based simultaneous wireless information and power transfer (SWIPT) has attracted tremendous research interest, in order to relieve the energy-thirst of massively deployed low-power communication devices. In this paper, a receive spatial modulation (RSM) aided SWIPT system with finite alphabets is studied, in which three different transmission schemes are proposed, namely the general scheme, the superimposed scheme and the distinct scheme. Furthermore, the performance of these transmission schemes in the RSM aided SWIPT system is theoretically analysed. The energy harvested by the receiver is then maximised by jointly optimising the transmit power of the information signal and the covariance matrix of the energy signal as well as the power splitting ratio, while satisfying the quality of service of the wireless information transfer. At last, simulation results validate our theoretical analysis, while they also demonstrate that the distinct scheme has the best SWIPT performance among these three transmission schemes.

Radio frequency (RF) signal based simultaneous wireless information and power transfer (SWIPT) has emerged as a promising technique for satisfying both the communication and charging requests of the massively deployed IoT devices. Different from the physical layer and the medium-access-control layer design for coordinating the SWIPT in the RF band, we study its coding-level control from the information theoretical perspective. Due to its practical implementation of the decoder and its flexibility on the codeword structure, the unary code is chosen as a potential joint information and energy encoder. By conceiving the classic Z-channel, the mutual information and the energy harvesting performance of the unary coding aided SWIPT transceiver is analysed. Furthermore, the optimal codeword distribution is obtained for maximising the mutual information, while satisfying the minimum energy harvesting requirement. Our theoretical analysis and the optimal coding design are demonstrated by the numerical results.

Radio frequency (RF) signals can be relied upon for conventional wireless information transfer (WIT) and for challenging wireless power transfer (WPT), which triggers the significant research interest in the topic of simultaneous wireless information and power transfer (SWIPT). By further exploiting the advanced non-orthogonal-multiple-access (NOMA) technique, we are capable of improving the spectrum efficiency of the resource-limited SWIPT system. In our SWIPT system, a hybrid access point (H-AP) superimposes the modulated symbols destined to multiple WIT users by exploiting the power-domain NOMA, while WPT users are capable of harvesting the energy carried by the superposition symbols. In order to maximize the amount of energy transferred to the WPT users, we propose a joint design of the energy interleaver and the constellation rotation-based modulator in the symbol-block level by constructively superimposing the symbols destined to the WIT users in the power domain. Furthermore, a transmit power allocation scheme is proposed to guarantee the symbol-error-ratio (SER) of all the WIT users. By considering the sensitivity of practical energy harvesters, simulation results demonstrate that our scheme is capable of substantially increasing the WPT performance without any remarkable degradation of the WIT performance.

We study a distributed coordination function (DCF) aided WLAN system, where the user stations (STAs) are capable of harvesting energy either from the AP's downlink data transmission or from the AP's dedicated downlink energy transfer. The energy harvested by the STAs is then exploited for powering their own uplink data transmission. Based on the classic carrier-sense-multiple-access with collision avoidance (CSMA/CA) protocol, a pair of enhanced versions are proposed for the sake of incorporating the active energy request of the STAs and the dedicated energy transfer of the AP. The first one is the energy-backoff-interval aided CSMA/CA (EBI-CSMA/CA) protocol. The other is the energy-packet aided CSMA/CA protocol (EP-CSMA/CA). According to our simulation results, both of the protocols are capable of substantially increasing the uplink throughput, when compared to their counterpart without the dedicated energy transfer. Furthermore, the EP-CSMA/CA protocol is more energy-efficient than the EBI-CSMA/CA protocol, since it does not require frequent control signalling exchange for the energy requests and responses.

This paper studies the sustainable resource allocation for energy harvesting (EH) powered cloud radio access network (C-RAN), where EH powered remote radio units (RRUs) cooperatively transmit wireless energy and information to the energy-constrained mobile devices. To investigate network resource allocation problem, firstly, a general system utility optimization framework is proposed for the design of coordinated beamforming and power splitting algorithm based on Lyapunov optimization theory. The randomness of channel conditions and energy arrivals are considered in the framework. Secondly, an online algorithm is designed to maximize the system utility subject to energy sustainable constraints at the RRUs, signal-to-interference-plus-noise (SINR) and energy harvesting requirements of mobile devices. Specifically, there is no requirements of prior distribution knowledge about channel condition or energy arrival in the proposed online algorithm. Finally, performance analysis demonstrate that the proposed algorithm can achieve close-to-optimal system utility. In addition, extensive simulation results are provided to validate the theoretical analysis and to evaluate the performance of the proposed algorithm.

This paper investigates energy-efficient resource allocation for cooperative wireless powered cellular networks (WPCNs), where the cellular and device-to-device (D2D) users first harvest energy from the HAP and then the D2D user consumes a portion of power to help the cell-edge cellular user relay the data in exchange for some time from cellular user for D2D communications. Under the proposed cooperation scheme, we formulate an energy efficiency (EE) maximization problem. The energy beamforming vector, time assignment and power allocation are jointly optimized under the transmission rate requirements and available energy constraints of both D2D and cellular users. Based on the fractional programming theory and semi-definite relaxation (SDR) method, we transform the originally non-convex EE maximization problem into a standard convex problem. This allows us to design efficient resource allocation algorithm for achieving optimal solution. Extensive simulation results are provided to show the convergence rate of the proposed iterative algorithm and to demonstrate the EE improved by the proposed system than that of two baseline systems.

Realising both wireless information transfer (WIT) and wireless power transfer (WPT) in the same RF spectral band has imposed great challenges on the system design but the spectral efficiency can be substantially improved, which triggers the significant research interest in the simultaneous wireless information and power transfer (SWIPT). In order to fully exploit the limited spectral resources, a non-orthogonal-multiple-access (NOMA) technique aided SWIPT system is considered for simultaneously realising both of WIT and WPT, which consists of a single access point (AP), a single WPT user and a pair of WIT users. The AP delivers the requested information to the pair of WIT users by adopting the NOMA technique. In order to maximise the amount of energy received by the WPT user, a novel constellation rotation based modulation scheme is proposed for constructively combining the symbols requested by the pair of the WIT users by optimising the constellation rotation angle. By considering the sensitivity of the energy harvester, the simulation results demonstrate that our modulation scheme is capable of efficiently increasing the energy harvested by the WPT user without incurring additional symbol detection errors.

A typical data and energy integrated communication network (DEIN) conceives a conventional base station, which is capable of simultaneously transmitting the data and energy to user equipments (UEs) during the downlink (DL) transmissions by invoking the time-division-multiple-access (TDMA) protocol in the medium access control (MAC) layer. Several UEs operating in this DEIN are capable of harvesting the energy from the DL transmissions by adopting the power splitting (PS) technique and they are also capable of exploiting the harvested energy for powering their uplink (UL) data transmissions by invoking the TDMA protocol in the MAC layer. Both of the UL sum-throughput and the UL fair-throughput of the DEIN is maximized by deciding the duration of each time-slot during the DL/UL transmissions and by determining the optimal PS factor for each UE. Both of these optimization problems are finally solved by the classic method of Lagrange multipliers in close-form. An interesting observation shows that supporting lowthroughput data services during the DL transmissions does not degrade the wireless energy transfer and hence does not reduce the throughput of the UL transmissions.

RF signals can be relied upon for transferring energy to those power-thirsty wireless devices. Thanks to the broadcast nature of wireless channels, dedicated RF signals for delivering information to specific devices can also be received by others for energy harvesting. Coordinating the conventional wireless information transfer and the wireless energy transfer (WET) requires a systematic design in all OSI layers, which yields data and energy integrated communication networks. Based on the classic carrier-sense-multiple-access with collision avoidance protocol, we originally propose a distributed multiple access protocol in the medium access control (MAC) layer for the indoor wireless-local-area-network (WLAN) powered by RF signal-based WET. The operation of our proposed protocol in the WET powered indoor WLAN is modeled by a multi-dimensional Markov chain, which is exploited for deriving the closed-form throughput of the WLAN studied. The simulation results demonstrate the accuracy of our theoretical analysis, which pave the way for the future optimization design in the MAC layer of WET powered indoor WLAN.

Activating direct communications among mobile users (MUs) for sharing a content of common interest (CoCI) becomes an essential paradigm for realising the efficient content distribution in densely populated scenarios. Relying on the cooperative multicast among the MUs, a centralised fair resource allocation algorithm is proposed in this paper for improving the attainable content distribution delay. Apart from the physical wireless transmission, social aspects of MUs are also taken into account, where a MU only multicasts the CoCI to its social contacts. The content distribution process is further modelled by a discrete-time pure-birth-based Markov Chain (DT-PBMC). Relying on the DT-PBMC, we minimise the state-retention probability during each transmission frame in order to reduce the content distribution delay to its smallest possible. The classic branch-and-bound algorithm is invoked for obtaining the optimal resource allocation scheme. The simulation results demonstrate that the proposed algorithm outperforms its existing counterparts, while our novel resource allocation scheme may simultaneously achieve better fairness and reduced content distribution delay.

In order to address the energy shortage in communication networks, RF signals are exploited for transferring energy to miniature devices, which yields wireless powered communication networks (WPCNs). A full-duplex aided hybrid-base-station (H-BS) is conceived in a WPCN for simultaneously transferring energy during downlink transmissions and receiving data during uplink transmissions. UEs may deplete all the energy received from the H-BS for supporting their own uplink transmissions. In this full-duplex WPCN, A joint time allocation and UE scheduling algorithm is proposed for the sake of maximising the sum-uplink-throughput of multiple UEs by further considering UEs' actual data uploading requirements. The numerical results demonstrate that the suboptimal solution is capable of achieving almost the same performance with its optimal counterparts, while our scheme outperforms other existing peers in terms of the sum-uplink-throughput.

Active learning has obtained a great success in supervised remotely sensed hyperspectral image classification, since it can be used to select highly informative training samples. As an intrinsically biased sampling approach, it generally favors the selection of samples following discriminative distributions, i.e., those located in low density areas in feature space. However, the hyperspectral data are often highly mixed, i.e., most samples fluctuate in a local density areas. In this case, the potential of active learning for effective training sample selection is more limited. In order to address this relevant issue, we develop a new Gabor-based active learning approach for hyperspectral image classification, which consists of two main steps. First, we use a Gabor filter for feature extraction, which aims at bringing the data into a discriminative space. Then, we perform active learning to find the most informative training samples in the low density areas prior to the final classification. Our experimental results, conducted using two real hyperspectral datasets, indicate that the proposed Gabor-based approach can greatly improve the potential of active learning for classification purposes.

Based on the common interest of mobile users (MUs) in a social group, the dissemination of content across the social group is studied as a powerful supplement to conventional cellular communication with the goal of improving the delay performance of the content dissemination process. The content popularity is modeled by a Zipf distribution to characterize the MUs' different interests in different contents. The factor of altruism (FA) terminology is introduced for quantifying the willingness of content owners to share their content. We model the dissemination process of a specific packet by a pure-birth-based Markov chain and evaluate the statistical properties of both the network's dissemination delay as well as of the individual user-delay. Compared to the conventional base station (BS)-aided multicast, our scheme is capable of reducing the average dissemination delay by about 56.5%. Moreover, in contrast to the BS-aided multicast, increasing the number of MUs in the target social group is capable of reducing the average individual user-delay by 44.1% relying on our scheme. Furthermore, our scheme is more suitable for disseminating a popular piece of content.

Since mobile communications exhibit strong social characteristics, based on the potential common interests of mobile users, mobile social networks (MSNs) are capable of mitigating the tele-traffic bottleneck. By multicasting the content of common interest from a content owner to content seekers within the owner's transmission range, a distributed MSNs architecture is proposed, which is capable of mitigating the tele-traffic imposed on network operators. In this contribution, the social relationship between a pair of MSN users is defined according to their geographic characteristics. By jointly considering the geographic social relationships and the wireless propagation environment, we derive the closed-form equations for evaluating both the throughput and delay of the social unicast/multicast transmissions. Simulation results are provided, both for supporting our theoretical analysis, as well as for investigating the impact of social relationships on the achievable network performance. Based on the results presented, we conclude that a more socially-minded content owner is particularly efficient in multicasting the content of common interest to content seekers.

A novel hybrid information dissemination scheme is proposed for picocellular systems. At the first stage of our scheme, some of the mobile users (MUs) successfully receive the information of common interest via BS-aided multicast. At the second stage, the information is cooperatively multicast (co-multicast) by the information owners (IOs) in a self-organized ad hoc network until all the MUs receive it. Since limited resources are provided for the second stage of the spontaneous co-multicast, some of the IOs are appointed for relaying the information to the hitherto unserved MUs. Several relay selection protocols are conceived for the sake of improving the performance of our hybrid information dissemination scheme. The simulation results demonstrate that our scheme may reduce the average dissemination delay and the average energy dissipation by 20% and 70%, respectively, when compared to the conventional BS aided single-hop multicast. Furthermore, we demonstrate that relying on the IOs associated with the best links plays a crucial role in facilitating spontaneous information dissemination.

As a promising technique of improving the attainable bandwidth efficiency, cognitive radio (CR) has attracted substantial attention from both the academic and industrial communities. In order to improve the performance of the secondary user (SU), a novel hybrid CR system is introduced, which combines the conventional interweave and underlay paradigms for enhancing the chances of the SU to access the spectrum. Queuing theory is invoked in this paper for analysing the impact of the primary user's (PU) delay tolerance on the performance of the SU. Multiple queues are assumed for the SU engaging in video communication. Besides the Poisson traffic generation, we model a Rayleigh fading channel as a Poisson service process with the aid of the outage probability in the presence of cochannel interferences. Two valuable goals are achieved, namely that of maximizing the average service rate and minimizing the overall average delay of the SU's multiple queues. As our numerical results demonstrate, the overall average delay of the SU becomes 27% and 34% lower than that of the Proportional as well as that of the Round-Robin schemes respectively.