Unmanned aerial vehicles (UAVs) are emerging as a promising platform for enabling integrated sensing and communication (ISAC) in multi-cell systems due to their deployment flexibility. However, this flexibility also introduces significant challenges, particularly co-channel interference at the UAV receiver. In this paper, we propose a novel adaptive co-channel interference mitigation model for UAV-enabled multi-cell systems. Specifically, the proposed model consists of two key components: a cost function and an update algorithm. First, we derive a new cost function that incorporates both magnitude and phase errors–critical metrics for guiding the estimated signal toward the desired signal. Second, the cost function is extended to formulate a parameter update algorithm, whose effectiveness is analyzed using both geometric and entropy-based approaches. Simulation results demonstrate that the proposed method outperforms state-of-the-art techniques, establishing it as a robust solution for interference mitigation in UAV-enabled multi-cell ISAC systems.

In satellite-assisted IoT communications, circularly polarized (CP) phased array antennas enable devices to directly connect with the satellites. This paper proposes a shared-aperture dual-CP antenna where low-band and high-band antennas are coaxially arranged for miniaturization. The low-band antenna is a dual-feed shorted ring patch loaded with small circles, whereas the high-band antenna is a compact single-probe-fed patch loaded with an oval parasitic patch. Both low-band and high-band antennas have broadband characteristics. Cross-band decoupling in the high-band is achieved by exciting the TM21mode of the low-band antenna and loading a parasitic ring. Additionally, the shorted metallic vias of the low-band antenna form a metallic back-cavity for the high-band antenna to further suppress interference from the low-band, and a pair of stubs is loaded on the feed of the high-band antenna to improve the port isolation in the low-band. Four prototypes, each consisting of 2×8 dual-band antennas, are fabricated and measured. Broad overlapped impedance and axial ratio bandwidths from 12.2 to 14 GHz and from 16.1 to 17.8 GHz are achieved for the low-band and high-band operations. A small frequency ratio of 1.29 is realized while the cross-band isolation exceeds 38 dB in the low-band and 26 dB in the high-band. The proposed dual circularly polarized antenna can achieve a scanning range of ±50° in both bands. As a result, the proposed compact dual-CP phased array antenna supports integration with small IoT terminals, while enabling stable connections for mobile devices or satellite terminals.

This paper proposes a direct RF baud clock recovery methodology for low-cost energy-efficient real-time demodulations. A baud clock phase-locked loop (PLL) is proposed to directly extract the baud clock from modulated millimeter-wave signals, which employs a type-I PLL incorporating a baud clock phase detector (BCPD). The BCPD uses a super-regenerative sample-and-hold amplifier (SR-SHA) as a RF sampler and extracts the phase misalignments based on DC components of mixing results between input signals and reconstructed samples. To verify the proposed methodology, a prototype of baud clock recovery circuit with a carrier frequency of 60 GHz was implemented in a 40-nm CMOS process. On-wafer measurement results show that the prototype achieves a baud clock recovery speed of 3.65 Gbaud with an RMS jitter below 0.02 unit interval, demonstrating the ability to provide baud clock recovery for BPSK to 1024-QAM demodulation with a low power consumption of 20.2 mW. By eliminating the needs of oversampling and complex digital signal processing in real-time communications, the proposed methodology provides an energy-efficient and low-cost baud clock recovery solution compared to DSP-based approaches.

This paper proposes a joint beamforming design for optimizing integrated sensing and communication (ISAC) systems under imperfect channel state information (CSI), leveraging a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS). Owing to the deployment of large-scale antenna arrays and high carrier frequencies, the Rayleigh distance can extend to tens or even hundreds of meters. This expansion leads to a fundamental paradigm shift in electromagnetic field characteristics, transitioning from the conventional far-field regime to the emerging near-field regime. As a result, the propagation characteristics experienced by users and the target may differ. Accordingly, we consider a practical scenario in which users and the target are situated in distinct fields. However, this hybrid-field model increases the system’s sensitivity to channel estimation errors (CEE). To this end, we propose a robust design that jointly optimizes beamforming for base station (BS) and STAR-RIS, aiming to maximize the achievable sum-rate of the nodes while satisfying the constraint of sensing requirements. Under a statistical CEE model, we derive the interference covariance matrix and reformulate the maximization problem as an equivalent weighted mean square error (MSE) minimization problem. Subsequently, the transformed problem is decoupled into multiple sub-problems using a block coordinate descent (BCD)-based algorithm. The algorithm capitalizes on semidefinite relaxation and Gaussian randomization to obtain an effective solution. Finally, simulation results validate the effectiveness of the proposed robust design. Compared with the baseline schemes, the proposed algorithm achieves a higher communication sum-rate and illustrates how various parameters affect the performance.

The advent of extremely large antenna arrays and high-frequency signaling is expected to enable next-generation integrated sensing and communication (ISAC) networks to predominantly operate in the near-field region. Due to the dual influence of distance and angle on wave propagation characteristics in the near-field region, accurately modeling these characteristics remains a critical challenge. Motivated by the potential of large language models (LLMs) in angle prediction and distance estimation, an LLM-enhanced multi-objective optimization problem (MOOP) is developed to accurately capture the dependence of the channel on both the angular position and distance. The formulated LLM-enhanced MOOP framework is decomposed into a series of sub-problems, which can balance spectral efficiency for communication and localization accuracy for sensing. To overcome the computational and energy challenges associated with LLMs, a gray wolf optimization (GWO)-based algorithm is integrated as black-box search operator with LLM-specific prompt engineering to solve these sub-problems. Numerical results demonstrate that the proposed LLM-GWO scheme achieves an trade-off between communication and sensing performance, outperforming baseline approaches in terms of both Pareto front quality and convergence.

In this brief, a hybrid look-up table (LUT)-coordinate rotation digital computer (CORDIC) architecture for rectangular-to-polar conversion is proposed. The architecture performs domain folding and a small number of CORDIC iterations to transform the input vector into a reduced angular range, followed by a compact LUT that stores precomputed reference values used to derive the final magnitude and phase. Taylor series approximation and dynamic truncation are employed to minimize LUT storage while maintaining numerical precision. FPGA implementation on a Xilinx XCZU11EG device shows that the design can achieve the lowest latency among competing designs while maintaining competitive performance in power consumption and accuracy, demonstrating its efficiency and suitability for high-performance signal processing systems.

Integrated sensing and communication (ISAC) is seen as a future solution to frequency congestion due to its excellent ability to simultaneously support target sensing and information transmission. However, it also introduces a potential security threat due to the sensing behavior. In this paper, we propose a covert ISAC scheme against collusive wardens. In particular, a dual-function base station continuously sense an aerial target while communicating with a ground receiver. First, we derive a closed-form expression of each warden's detection outage probability to obtain the global detection outage probability. Then, we jointly optimize the communication and sensing beamformings to maximize the covert transmission rate under the worst case that all wardens can collusively adjust their detection thresholds to achieve the best detection. To tackle this non-convex optimization problem, an iteration scheme is proposed. Numerical results demonstrate the validity of the proposed covert ISAC scheme.

Safety incidents caused by children trapped in vehicles are a serious global problem. Existing solutions for child presence detection (CPD) are limited by specialized hardware or detection delays that exceed safety standards. To address this problem, this study proposes an innovative system to detect the presence of a child trapped in a car using channel state information (CSI), analyzing the effect of motion on CSI in subcarrier dimension through modeling and introducing new metrics to quantify environmental changes. The system is implemented using a commercial Wi-Fi chipset and tested in a real vehicle environment using data collected from eight people of different ages. Experimental results show that we achieved 99.19% detection accuracy within a 1-second time window at a low sampling rate of 20 Hz. This result represents a significant advancement in detection latency for CPD systems and lays the groundwork for widespread adoption of CPD systems based on Wi-Fi devices.

Existing monostatic integrated sensing and communication (ISAC) systems face challenges in realizing high-precision sensing due to a singular observation angle. This drives the exploration of cooperative sensing ISAC modes. However, communication and sensing heavily rely on line-of-sight (LoS) links, and the issue of obstacle blocking remains unavoidable even with collaborative efforts among multiple base stations (BSs). Simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), can create virtual links in full space by dynamically adjusting its element coefficients. This effectively mitigates the pathloss caused by blockages. Thus, integrating STAR-RIS into cooperative ISAC networks holds substantial potential, which has not been widely investigated. In this study, a STAR-RIS-aided cooperative sensing framework is first proposed. To maximize the sensing signal-to-clutter-plus-noise ratio (SCNR), we jointly optimize the transmit beamforming at BS, transmission and reflection coefficients at STAR-RIS, and receive beamforming at multi-receivers. Also, the receivers selection is considered. To solve this nonconvex problem, we propose a joint optimization algorithm using alternating optimization (AO), fractional programming (FP), and semidefinite relaxation (SDR) based on the receivers selection mechanism, called the RS-AFS algorithm. Numerical results validate that the RS-AFS algorithm can significantly enhance the sensing SCNR.

The rapid development of the internet of things (IoT) has heightened the demand for timely data delivery in heterogeneous networks, making the age of information (AoI) a key performance metric. In this work, we consider an unmanned aerial vehicle (UAV)-assisted IoT network enhanced by a simultaneous transmission and reflection reconfigurable intelligent surface (STAR-RIS) to improve data freshness. We formulate a joint optimization problem with respect to UAV trajectory, STARRIS phase shifts, and device scheduling to minimize the AoI. To tackle the problem's complexity, we employ a proximal policy optimization (PPO)-based reinforcement learning approach that autonomously discovers effective control policies. Simulation results demonstrate that our proposed solution reduces AoI, demonstrating enhanced adaptability, robustness, and communication performance in dynamic IoT environments.

The paper proposes a hybrid RF and wind energy harvester. It is constructed by structural and functional integration of the two dissimilar energy harvesting techniques, constituting a conformal design. The rectifying efficiency can be boosted by the hybrid power source excitation, thereby increasing DC output power compared to standalone power source. A fan-shaped omnidirectional antenna and a hybrid single shunt-diode rectifier are designed to realize energy receiving and rectifying, respectively. A prototype is implemented and measured. The receiving part can achieve 2.29-dBi peak gain and 0.92-dB non-roundness at 1.85 GHz. It can also work smoothly when the wind speed varies from 0 to 12 m/s. The RF-DC conversion efficiency at -20 dBm and AC-DC output voltage at 12 m/s are measured as 20% and 79 mV, respectively. When both RF and wind power sources are accessible, the hybrid DC output power of 1.0 uW can be obtained with -30-dBm RF power and 10m/s wind speed. Moreover, the output power at hybrid rectifying mode is higher than that of simply superimposed RF and wind energy. Efficiency gain of up to 182% can be achieved. The hybrid energy harvester is a good candidate to power the sensors in battery-free IoTs.

This paper presents a 23.5-34 GHz and 28-40.5 GHz Ka-band reconfigurable dual-band variable gain amplifier (VGA) for 5G applications, realized in TSMC 28-nm CMOS. The VGA employs a two-stage differential amplifier structure, and a reconfigurable inductor is utilized to switch frequency band. A cross-connected gain control structure is used to minimize phase variation by reducing the impedance variation caused by the gain tuning. Additionally, a phase compensation structure based on variable resistance is introduced to further mitigate phase variation. The proposed VGA exhibits switchable 3-dB bandwidth of 23.5-34 GHz and 28-40.5 GHz with peak gain of 10.7 dB and 9.5 dB, respectively. The measured gain average tuning range is 18.1 dB and 16 dB at each band with rms phase variation less than 1.5° and 1°. To the best of the author’s knowledge, this is the lowest rms phase variation with gain tuning range larger than 15 dB in mmWave band. The VGA consumes a DC power of 15.5 mW with core size of 0.22 mm2. The application area is 5G and beyond beamformer system.

This paper presents a broadband low-noise amplifier (LNA) monolithic microwave integrated circuit (MMIC) in 0.15-μm GaN-on-SiC technology. The LNA circuit is designed into a three-stage topology with three coupled-line structures. The first coupled-line structure is designed at the first stage for wideband input impedance matching and noise cancellation, while the second one is employed at the inter-stage to realize thegm-boost for gain enhancement. Then, the last coupled-line structure forms a positive feedback signal paths from the drain to the gate of the output-stage transistor, which compensates the gain degradation at the high frequency band. With these three coupled-line structures, flat gain performance and low noise figure are achieved in a broadband frequency range. For demonstration, the LNA MMIC is fabricated. The measured results show a maximum gain of 22.6 dB at 27.6 GHz and a 3-dB bandwidth of 22.6 GHz from 7.2 to 29.8 GHz. The in-band noise figure is measured as 1.35-2.67 dB, while the output 1dB gain compression point (OP1dB) and output third-order intercept point (OIP3) are 20.9 dBm and 34.8 dBm at 28.5 GHz, respectively. The fabricated LNA has a compact die area of 2.64 mm2including all test pads.

In this paper, we propose a compact wideband on-chip millimeter-wave (mm-wave) reconfigurable wideband filtering switch in 28-nm bulk CMOS technology. A dual-mode LC resonator loaded with transistors is used to achieve wideband filtering responses with a transmission zero at the lower frequency band. The resonant frequency of the resonator and the location of the transmission zero can be conveniently tuned to reconfigure the passband and stopband frequencies by turning on and off the transistor. Moreover, the passband can also be switched on and off, enabling the single-pole single-throw filtering switch circuit function. In this way, the proposed mm-wave reconfigurable filtering switch is applicable to the integrated sensing and communication (ISAC) system, where image rejection in communication operation and a wide bandwidth (or high resolution) in sensing operation are both required. Furthermore, to meet the applications in the ISAC systems with different architectures, extension designs of the proposed reconfigurable filtering switch with the impedance conversion function, high-order responses, balanced-to-unbalanced transition, and differential input/output ports are presented in detailed. For demonstration, the wideband reconfigurable filtering switch has been fabricated. The core circuit has a very compact size of$0.205\times 0.140$mm2. Experimental results show that the passband can be reconfigured between 20-55 GHz and 37-44 GHz, with a rejection >17 dB for sensing operation and >12 dB image-band rejection for communication operation, respectively. High off-state isolation of better than 24.8 dB is also achieved.

Super-regenerative receivers (SRRs) hold great promise as a low-cost solution for wireless communication due to their low power and relative simplicity. However, previous researches have primarily concentrated on super-regenerative amplifiers/oscillators, leading to limited insights into SRRs with inappropriate assumptions or dispensable operations, such as synchronous quench and baseband oversampling. This paper presents an improved general theory of super-regenerative reception in the linear mode that provides more design insights for digital communication. By analyzing the time-domain model of a general super-regenerative circuit, we derived a comprehensive frequency-domain model based on a convolution method, through which a concept of signal-lobe transfer function is introduced. Based on the proposed model, the effects of quench jitter and residual phenomenon are analyzed. Furthermore, an asynchronous quench method is introduced, which eliminates the requirement of synchronization between the modulated symbol and the quench signal, thus reducing the system complexity. To eliminate the baseband oversampling, especially for high-speed communications, main-lobe filtering and sub-sampling techniques are also proposed. To verify this general theory and proposed techniques, two SRRs with main-lobe filtering and sub-sampling were designed with ideal components and simulated using Cadence Virtuoso. The simulation results of two SRRs match with the proposed model very well. Overall, this paper provides a comprehensive analysis of super-regenerative reception for digital communication and offers valuable insights into its potentials and limitations.

As a large variety of applications emerge in vehicle-to-infrastructure (V2I) networks, the explosion of data places greater demands on communications and computing. Sensing technology offers accurate data by collecting real-time environmental information. Meanwhile, mobile edge computing (MEC) can significantly reduce communication latency and enhance computational efficiency. Therefore, integrating the two novel technologies into V2I applications to improve overall performance has become a research hotspot. In this paper, we focus on the optimization problem for determining caching, offloading, and matching strategies to minimize system cost under the joint communications, sensing, and MEC framework of V2I networks. First, we analyze the positive impact of sensing on signaling overhead and age of information (AoI), and derive a linear relationship between delay and AoI. Next, we formulate the optimization function of system cost, which is defined as the weighted sum of AoI and energy consumption and is proved to be NP-hard. To address this problem, we leverage an improved quantum particle swarm optimization (QPSO) algorithm to acquire a suboptimal solution of caching and offloading strategies. This significantly reduces computational complexity compared to the optimal solution obtained via the branch-and-bound (B&B) method. According to the vehicles’ AoI, we design a matching algorithm for the roadside unit (RSU) with vehicles. Building on these, we propose a QPSO-based algorithm under the joint communications, sensing, and MEC framework (QJCSM). Simulation results demonstrate that the QJCSM algorithm outperforms other baseline algorithms in terms of AoI and energy consumption and achieves near-optimal performance with low complexity.

Distributed reconfigurable intelligent surfaces (RISs) provide rich macro-diversity coverage due to different locations of the RISs, which is beneficial to combat coverage holes. However, the system performance relies on the effective coordination of multiple RISs. In particular, distributed RIS-assisted power allocation and the phase shifts of RISs should be jointly designed under nonlinear scheduling constraints. Thus, the resource allocation scheme for distributed RIS-assisted multiuser system is a crucial challenge. To tackle these issues, joint power allocation, phase shifts and communication scheduling design for distributed RIS-assisted systems is investigated in this paper, where all RISs simultaneously and cooperatively serve multiple users. To overcome the formulated nonconvex optimization problem, the original problem is decoupled into three subproblems and solved in an iterative manner. Specifically, we first consider the subproblem of power allocation, which can be solved via maximizing the ergodic achievable rate. By applying the ergodic rate, an approximate closed-form solution is formed for the power allocation. Subsequently, the phase shifts are optimized using the minimization-maximization optimization methods. Finally, a communication scheduling scheme is presented to address the scheduling variables. Numerical simulations are conducted to demonstrate that the considered solution outperforms the existing benchmark and achieves a near-optimal spectral efficiency.

Integrated sensing and communication (ISAC) is seen as a future solution to frequency congestion due to its excellent ability to simultaneously support target sensing and information transmission. To guarantee robust data security and privacy protection, covert communication can be employed in ISAC systems. In this paper, we propose a covert ISAC scheme against collusive wardens. In particular, a dual-function base station transmits the sensing beamforming to continuously sense an aerial target while communicating with a ground receiver with a probability of 0.5 via the communication beamforming. First, we derive a closed-form expression of the detection outage probability of each warden to obtain the global detection outage probability. Under the worst case that the wardens can collusively adjust their detection thresholds to achieve the best detection performance, we jointly optimize the communication and sensing beamformings to maximize the covert transmission rate. To tackle this non-convex problem, unitary-iteration and zero-forcing schemes are proposed to transform it into convex ones via semidefinite relaxation and successive convex approximation, respectively. Numerical results demonstrate the validity of the proposed covert ISAC scheme, which can achieve a better trade-off among communication, sensing and covertness compared to benchmarks.

Reed-Solomon (RS) codes are widely used in storage systems to ensure data reliability. In this paper, we first propose a new construction of RS codes with between three to five parity symbols over a special finite field of size 256. We show that all the operations involved in the encoding/decoding process can be implemented by XOR and cyclic shift. Second, we present a fast encoding/decoding algorithm for our codes by designing a modified Reed-Muller (RM) transform that has both small computational complexity and space complexity. We show that our codes have much lower space complexity and nearly the same computational complexity, compared with the existing RM-based RS codes. Simulation results demonstrate that our codes improve encoding and decoding throughput by 34.06% and 31.66%, respectively, under evaluated parameters, compared with existing RM-based RS codes.

In the Internet of Everything (IoE) scenarios, the extensive deployment of devices may result in more stringent power and communication needs. Within this context, we utilize the reconfigurable intelligent surface (RIS) to support the simultaneous wireless information and power transfer (SWIPT) system, whereby the stable transmission of energy and information services can be guaranteed. Specifically, we construct the system model through electromagnetics (EMs), which is based on the scattering-parameter (S-parameter) analysis, for revealing the crucial factors of the practical hardware. Relying on the model, the energy-efficient (EE) maximization problem constrained to the Quality of Services (QoS) is proposed for the users with the framework of co-located receiver (Rx). However, the problem is more intractable due to the introduced channel model. To resolve it, we propose an effective optimization scheme. First, the Neuman series approximation method is adopted to deconstruct the EM transfer model. Then the reformed problem, which includes the variables (i.e., the power splitting ratio, the active beamformer, and the reflection-coefficient matrix), can be addressed through the strategy of alternative optimization (AO). Further, the inner convex approximation (INCA) scheme and Dinkelbach’s algorithm are applied to tackle each subproblem. In the numerical simulation, we demonstrate that the array configuration can influence not only the hardware properties of RIS but also the EE performance of the whole system. What is more, the proposed scheme performs better for the tightly coupled RIS owing to the awareness of the mutual-coupling (MC) effect.

This paper presents a three-stage wideband LNA with gain switching technique designed for 5G millimeter-wave applications operating at 23.6-46.5 GHz. By deriving an analytical equation of input impedance and noise matching, a two-pole matching network based on ladder transformer is introduced. The coupling between the transformers can be used to control the two poles of S11 and simultaneously match the source impedance to the optimized noise impedance, achieving broadband input matching and low noise figure (NF). A 24 dB gain tuning range with 6 dB per step has been designed to accommodate different input power level for automatic gain control (AGC). The gain control is implemented with current slicing at the 2nd and 3rd stages, which can keep input/output impedance nearly constant during gain switching. The proposed wideband LNA has been fabricated in 28-nm bulk CMOS process with a chip size of only 0.13 mm2. Measured results show a peak gain of 23 dB within a 3-dB bandwidth from 23.6 to 46.5 GHz, with S11 better than −10 dB over the bandwidth. The measured NF is 2.2 – 3.7 dB with an average of 3 dB. The input 1 dB gain compression point (IP1dB) ranges from −27 to −23.8 dBm throughout the gain bandwidth. Moreover, the measured gain can be switched with value of 21.5/14.9/9.1/2.9/−3 dB, and the corresponding NF and IP1dB are 3.7/4.9/7.9/12.8/14.3 dB and −23.8/−19.5/−14.8/ −13.1/−8.8 dBm at 33 GHz, respectively. This design is suitable for wideband 5G millimeter-wave communication.

Reconfigurable intelligent surfaces (RISs) have attracted extensive attention in millimeter wave (mmWave) systems because of the capability of configuring the wireless propagation environment. However, due to the existence of a RIS between the transmitter and receiver, a large number of channel coefficients need to be estimated, resulting in more pilot overhead. In this paper, we propose a joint sparse and low-rank based two-stage channel estimation scheme for RIS-assisted mmWave systems. Specifically, we first establish a low-rank approximation model against the noisy channel, fitting in with the precondition of the compressed sensing theory for perfect signal recovery. To overcome the difficulty of solving the low-rank problem, we propose a trace operator to replace the traditional nuclear norm operator, which can better approximate the rank of a matrix. Furthermore, by utilizing the sparse characteristics of the mmWave channel, sparse recovery is carried out to estimate the RIS-assisted channel in the second stage. Simulation results show that the proposed scheme achieves significant performance gain in terms of estimation accuracy compared to the benchmark schemes.

Reconfigurable intelligent surfaces (RISs) are deemed as a potential technique for the future of the Internet of Things (IoT) due to their capability of smartly reconfiguring the wireless propagation environment using a large number of low-cost passive elements. To benefit from RIS technology, the problem of RIS-assisted channel state information (CSI) acquisition needs to be carefully considered. Existing channel estimation methods usually ignored the different channel characteristics of direct channel and reflected channels. In fact, the reflected channel can be smartly configured by adjusting the phase shifts of the RIS, which is different from the direct channel due to the different path loss exponents between the transmitter and receiver. Therefore, it is necessary to further develop a RIS-assisted channel estimation to determine the direct and reflected channels, respectively. In this paper, we study a RIS-assisted channel estimation that jointly exploits the properties of the direct and the reflected channel to provide more accurate CSI. The direct channel is estimated using weighted$\ell_1$norm minimization, while the reflected channel is modeled based upon the robust$\ell_{1,\tau}$norm minimization to sequentially estimate the channel parameters. Moreover, by combining the gradient descent and the alternating minimization method, a flexible and fast algorithm is developed to provide a feasible solution. Simulation results demonstrate that an RIS-aided MIMO system significantly reduces the active antennas/RF chains compared to other benchmark schemes.

In this paper, we propose a signal design unconstrained ISAC framework, where different transmit signal design has no significant effect on both sensing and communication performance. To begin with, we describe the model of transmit signals with transmission power limitations. Next, the sensing and communication performance are analysed with reasonable metrics respectively. The property of unconstrained signal design is implied in a proved signal intensity distribution theorem, which facilitates the sensing function of our framework. Then, a beamforming optimization problem is formulated to maximize a weighted transmission rate subject to the guaranteed sensing performance. The formulated non-convex problem is approximately solved by alternatively solving two simpler sub-problems. Finally, numerical results are given to validate the effectiveness, practicality and satisfactory performance of our proposed framework.

The simultaneous wireless information and power transfer (SWIPT) technology assisted by the reconfigurable intelligent surface (RIS) can bring flexibility and stability to the end nodes of the internet of things (IoT) during the deployment. In this paper, we propose a RIS-aided SWIPT system based on a hardware transfer model from the electromagnetic perspective. Particularly, an energy efficiency (EE) maximization problem subject to the quality of service (QoS) demands, power resource budget and circuit restrains is introduced. Furthermore, the active beamforming vectors of the BS and the circuit parameters at the RIS are optimized jointly. The problem can be decomposed into two sub-problems and solved iteratively until convergence. In particular, semi-definite relaxation (SDR), successive convex approximation (SCA), Dinkelbach's algorithm are applied to the solutions of the sub-problems. Numerical results reveal the influences of the various QoS requirements on EE performance. Moreover, the actual generated beams of the BS and the RIS are shown to demonstrate the effectiveness of the proposed optimization strategy.

Reconfigurable intelligent surfaces (RISs) have attracted extensive attention in millimeter wave (mmWave) systems because of the capability of configuring the wireless propagation environment. However, due to the existence of a RIS between the transmitter and receiver, a large number of channel coefficients need to be estimated, resulting in more pilot overhead. In this paper, we propose a joint sparse and low-rank based two-stage channel estimation scheme for RIS-assisted mmWave systems. Specifically, we first establish a low-rank approximation model against the noisy channel, fitting in with the precondition of the compressed sensing theory for perfect channel recovery. To overcome the difficulty of solving the low-rank problem, we propose a trace operator to replace the traditional nuclear norm operator, which can better approximate the rank of a matrix. Furthermore, by utilizing the sparse characteristics of the mmWave channel, sparse recovery is carried out to estimate RIS-assisted channels in the second stage. Simulation results show that the proposed scheme achieves significant performance gain in terms of estimation accuracy compared to the benchmark schemes.

In wireless powered communication networks (WPCNs), the wireless energy acquisition is weak due to the fading characteristic of wireless link, which is hard to supply the energy. Reconfigurable intelligent surface (RIS) is a promising performance enhancement technology for WPCNs, which can improve the energy and spectral efficiency, expand the network coverage, and thus improve the throughput. This paper proposes a new framework for energy harvesting and information transmission of non-orthogonal multiple access (NOMA)-based WPCNs aided by the RIS. The total transmission capacity is maximized by joint optimization of energy precoding, information precoding, and the phase shift of RIS, which turns out to be a non-convex problem. So we propose a joint optimization algorithm combing alternating optimization (AO), semidefinite relaxation (SDR), and successive convex approximation (SCA) methods, which is called ASS algorithm for short. Numerical results prove that the throughput of the multi-user system can be significantly improved.

The widespread deployment of the Internet of Things (IoT) is promoting interest in simultaneous wireless information and power transfer (SWIPT), the performance of which can be further improved by employing a reconfigurable intelligent surface (RIS). In this article, we propose a novel RIS-enhanced SWIPT system built on an electromagnetic-compliant framework. The mutual-coupling effects in the whole system are presented explicitly. Moreover, the reconfigurability of RIS is no longer expressed by the reflection-coefficient matrix but by the impedances of the tunable circuit. For comparison, both the no-coupling and the coupling-awareness cases are discussed. In particular, the energy efficiency (EE) is maximized by cooperatively optimizing the impedance parameters of the RIS elements as well as the active beamforming vectors at the base station (BS). For the coupling-awareness case, the considered problem is split into several subproblems and solved alternatively due to its nonconvexity. First, it is transformed into a more solvable form by applying the Neuman series approximation, which can be resolved iteratively. Then, an alternative optimization (AO) framework and semidefinite relaxation (SDR), successive convex approximation (SCA), and Dinkelbach’s algorithm are applied to solve each subproblem decomposed from it. Owning to the similarity between the two cases, the no-coupling one can be viewed as a reduced form of the coupling case and, thus, solved through a similar approach. Numerical results reveal the influence of mutual-coupling effects on the EE, especially in the RIS with closely spaced elements. In addition, physical beam designs are presented to demonstrate how the RIS assists SWIPT through various reflecting states in different conditions.

This paper presents compact 24–35 GHz filtering low noise amplifier (LNA) and filtering switch in 28-nm CMOS technology. A compact mixed magnetic-electric coupling circuit is designed, where a transmission zero is introduced out of the passband due to the cancellation of the magnetic and electric couplings. By analyzing the impedance characteristics, this structure can be designed with the impedance conversion function to replace the widely used transformers in integrated circuit designs. It shows the advantages of easy control of coupling coefficient and out-of-band rejection. Then, an LNA employing the magnetic-electric coupling circuits as impedance matching networks is designed. Image rejection can be achieved without increasing the circuit area. Moreover, by loading transistors to this mixed magnetic-electric coupling circuit, the input impedance can be controlled by the parasitic components of the transistor. Subsequently, a filter passband can be switched on and off, realizing a very compact filtering single-pole single-throw (SPST) switch. The fabricated filtering LNA is measured with a 3-dB bandwidth of 24–35 GHz, a noise figure (NF) of 2.4-3.6 dB, a maximum gain of 22 dB, and suppression of better than 25 dBc below 18 GHz. The filtering switch shows a minimum on-state loss of 2.1 dB at 28.6 GHz with better than 12.9 dB rejection below 16 GHz and off-state isolation of higher than 19 dB.

This paper presents a broadband Doherty power amplifier (DPA) using quarter-wavelength ($\lambda $/4) transmission lines with negative characteristic impedance. In conventional DPA designs, the load modulation network is frequency-dependent, leading to bandwidth limitation at the back-off power region. In this design, by integrating two$\lambda $/4 transmission lines with negative characteristic impedance into the main and auxiliary branches, the impedance at the back-off power can be manipulated to maintain high efficiency at back-off power over a wide frequency range. Thus, the operational bandwidth is extended. In circuit realization, the two negative characteristic impedance$\lambda $/4 transmission lines are replaced by paralleled negative LC components. Then, the negative capacitor is combined into the$\pi $-shaped impedance matching network, while the negative inductor is eliminated by introducing Norton transformation. For verification, a broadband DPA with a fractional bandwidth of 108.6% from 0.8 to 2.7 GHz is implemented. The measured saturated output power is 41.8-44 dBm. The saturated and 6-dB back-off power drain efficiencies are 47.6%-84.4% and 39.5%- 52%, respectively. A 20-MHz LTE modulated signal with peak-to- average power ratio (PAPR) of 7.5 dB is also applied to measure the fabricated DPA. After digital predistortion, the adjacent channel leakage ratio (ACLR) better than −45.35 dBc is achieved, and the measured average efficiency is higher than 40% within the operating band.

Integrating mobile edge computing (MEC) into the Internet of Things (IoT) enables resource-limited mobile terminals to offload part or all of the computation-intensive applications to nearby edge servers. On the other hand, by introducing reconfigurable intelligent surface (RIS), it can enhance the offloading capability of MEC, such that enabling low latency and high throughput. To enhance the task offloading, we investigate the MEC non-orthogonal multiple access (MEC-NOMA) network framework for mobile edge computation offloading with the assistance of a RIS. Different from conventional communication systems, we aim at allowing multiple IoT devices to share the same channel in tasks offloading process. Specifically, the joint consideration of channel assignments, beamwidth allocation, offloading rate and power control is formulated as a multi-objective optimization problem (MOP), which includes minimizing the offloading delay of computing-oriented IoT devices (CP-IDs) and maximizing the transmission rate of communication-oriented IoT devices (CM-IDs). Since the resulting problem is non-convex, we employ$\epsilon $-constraint approach to transform the MOP into the single-objective optimization problems (SOP), and then the RIS-assisted channel assignment algorithm is developed to tackle the fractional objective function. Simulation results corroborate the benefits of our strategy, which can outperforms the other benchmark schemes.

Reconfigurable intelligent surface (RIS) is a promising technique that smartly reshapes wireless propagation environment in the future wireless networks. In this paper, we apply RIS to an unmanned aerial vehicle (UAV)-assisted non-orthogonal multiple access (NOMA) network, in which the transmit signals from multiple UAVs to ground users are strengthened through RIS. Our objective is to minimize the power consumption of the system while meeting the constraints of minimum data rate for users and minimum inter-UAV distance. The formulated optimization problem is non-convex by jointly optimizing the position of UAVs, RIS reflection coefficients, transmit power, active beamforming vectors and decoding order, and thus is quite hard to solve optimally. To tackle this problem, we divide the resultant optimization problem into four independent subproblems, and solve them in an iterative manner. In particular, we first consider the sub-solution of UAVs placement which can be obtained via the successive convex approximation (SCA) and maximum ratio transmission (MRT). By applying the Gaussian randomization procedure, we yield the closed-form expression for the RIS reflection coefficients. Subsequently, the transmit power is optimized using standard convex optimization methods. Finally, a dynamic-order decoding scheme is presented for optimizing the NOMA decoding order in order to guarantee fairness among users. Simulation results verify that our designed joint UAV deployment and resource allocation scheme can effectively reduce the total power consumption compared to the benchmark methods, thus verifying the advantages of combining RIS into the multi-UAV assisted NOMA networks.

Combining simultaneous wireless information and power transfer (SWIPT) and an intelligent reflecting surface (IRS) is a feasible scheme to enhance energy efficiency (EE) performance. In this paper, we investigate a multiuser IRS-aided multiple-input single-output (MISO) system with SWIPT. For the purpose of maximizing the EE of the system, we jointly optimize the base station (BS) transmit beamforming vectors, the IRS reflective beamforming vector, and the power splitting (PS) ratios, while considering the maximum transmit power budget, the IRS reflection constraints, and the quality of service (QoS) requirements containing the minimum data rate and the minimum harvested energy of each user. The formulated EE maximization problem is non-convex and extremely complex. To tackle it, we develop an efficient alternating optimization (AO) algorithm by decoupling the original nonconvex problem into three subproblems, which are solved iteratively by using the Dinkelbach method. In particular, we apply the successive convex approximation (SCA) as well as the semi-definite relaxation (SDR) techniques to solve the non-convex transmit beamforming and reflective beamforming optimization subproblems. Simulation results verify the effectiveness of the AO algorithm as well as the benefit of deploying IRS for enhancing the EE performance compared with the benchmark schemes.

Reconfigurable intelligent surface (RIS) has been developed as a promising approach to enhance the performance of fifth-generation (5G) systems through intelligently reconfiguring the reflection elements. However, RIS-assisted beamforming design highly depends on the channel state information (CSI) and RIS’s location, which could have a significant impact on system performance. In this paper, the robust beamforming design is investigated for a RIS-assisted multiuser millimeter wave system with imperfect CSI, where the weighted sum-rate maximization problem (WSM) is formulated to jointly optimize transmit beamforming of the BS, RIS placement and reflect beamforming of the RIS. The considered WSM maximization problem includes CSI error, phase shifts matrices, transmit beamforming as well as RIS placement variables, which results in a complicated nonconvex problem. To handle this problem, the original problem is divided into a series of subproblems, where the location of RIS, transmit/reflect beamforming and CSI error are optimized iteratively. Then, a multiobjective evolutionary algorithm is introduced to gradient projection-based alternating optimization, which can alleviate the performance loss caused by the effect of imperfect CSI. Simulation results reveal that the proposed scheme can potentially enhance the performance of existing wireless communication, especially considering a desirable trade-off among beamforming gain, user priority and error factor.

Combining simultaneous wireless information and power transfer (SWIPT) and intelligent reflecting surface (IRS) is a feasible scheme to enhance the energy efficiency (EE) performance. In this paper, we investigate a multiuser IRS-aided multiple-input single-output (MISO) system with SWIPT. For the purpose of maximizing the EE of the system, we jointly optimize the base station (BS) transmit beamforming vectors, the IRS reflective beamforming vector and the power splitting (PS) ratios, while considering the maximum transmit power budget, the IRS reflection constraints and the quality of service (QoS) requirements containing the minimum data rate and the minimum harvested energy per user. As the proposed EE maximization problem is non-convex and extremely complex, we propose an efficient alternating optimization (AO) algorithm by decoupling the original problem into three subproblems which are tackled iteratively by using the Dinkelbach method. In particular, we apply the successive convex approximation (SCA) as well as the semi-definite relaxation (SDR) techniques to solve the non-convex transmit beamforming and reflective beamforming optimization subproblems. Numerical results confirm the effectiveness of the AO algorithm as well as the benefit of deploying IRS for enhancing the EE performance compared with the benchmark schemes.

This paper investigates a reconfigurable intelligent surface (RIS)-aided unmanned aerial vehicles (UAVs) system with non-orthogonal-multiple access (NOMA), where the transmit signals from multiple UAVs to ground users are strengthened through a RIS. An innovative framework is designed to minimize the total power consumption of the system, by jointly optimizing the position of UAVs, RIS reflection coefficients, active beamforming vectors and decoding order. To solve this problem, we first consider the sub-solution of the UAV’s location which can be achieved via the successive convex approximation (SCA) and maximum ratio transmission (MRT). By applying the Gaussian randomization procedure, we then yield the closed-form solution for RIS phase coefficients. Subsequently, the transmit power is obtained by the standard convex optimization methods. Finally, a dynamic-order decoding scheme is proposed to optimize the decoding order. Simulation results show that the resource allocation scheme can obviously reduce the total power consumption compared to the benchmark schemes.

This article investigates nonorthogonal multiple access (NOMA)-based cloud radio access networks (C-RANs), where edge caching is adopted to cut down the crowdedness of the fronthaul links. We aim to maximize the energy efficiency (EE) by jointly optimizing the power allocation, analog, and digital precoding, which turns out to be an intractable nonconvex optimization problem. To tackle this problem, we first select cluster heads using the selecting cluster-head (SCH) algorithm, where the analog precoding matrix can be resolved by means of maximizing the array gains. Then, the device grouping algorithm is proposed to group devices according to the equivalent channel correlations, and thus, the NOMA devices in the same beam are capable of sharing the same digital precoding vector. Finally, the joint digital precoding design and power allocation algorithm is proposed to decompose the resultant optimization problem into two subproblems and solve them iteratively by applying the Taylor expansion operation and the minimum mean square error (MMSE) detection. Simulation results validate that the proposed NOMA-based C-RANs with a hybrid precoding (HP) scheme can achieve higher spectral efficiency and EE than the traditional orthogonal multiple access (OMA)-based approach and two-stage HP scheme.

This paper presents a Doherty power amplifier (DPA) with reduced size and wide bandwidth by using a simplified output matching topology. In broadband DPA designs, post-matching networks are usually added after the combing node, which usually occupy large circuit sizes. However, in this design, the post-matching network is not used. Instead, two ideal transformers and specific output matching components are employed at carrier and peaking branches. Then, by rearranging the output matching components in the two branches, Norton transformation can be used and the ideal transformers can be eliminated. In this way, the impedance at the combining node is directly matched to a 50-$\Omega $load without using a post-matching network, resulting in both compact size and wide bandwidth. For verification, a broadband DPA prototype with a very simple structure is implemented. The fabricated circuit shows a compact size. A wide bandwidth from 1.3 to 2.8 GHz (73%) is achieved with a saturated output power of 41.4–44.6 dBm. The drain efficiencies at saturation and 6-dB back-off power level are 60.1–79.1% and 41.8–61%, respectively, which are comparable to those of state-of-the-art designs.

A highly integrated RF power and information receiving system is proposed in this paper. The system consists of an antenna, a rectifier, and an information receiving module. The information receiving module acts as part of the dc load. To realize high integration, a dual-band, dual-circular-polarized shared- aperture antenna is implemented. The antenna consists of a left-circular-polarized element operating at 2.4-GHz band, and a high-gain, right-circular-polarized$2\times 2$array operating at 5.8-GHz band. The lower-frequency antenna is for communication, while the higher-frequency one is for power transmission. All the elements share the same aperture, and the radiator of lower- frequency antenna acts as the reflector of higher-frequency elements. Accordingly, a class-F rectifier that matches the higher- frequency array is designed and verified, and a system-level demonstration is implemented. Owing to the high isolation on frequency splitting and polarization diversity, the baseband signal can be successfully demodulated under a 30-dB input-power-level difference at two frequency bands. The base-band information can be displayed by an ink screen. The received RF power can be effectively rectified and power the data-processing modules.

In this paper, we propose a scheme to design the reconfigurable filtering power divider with arbitrary operating channels based on external quality factor ($Q_{\mathbf {e}}$) control. By using an input feeding line,${n}$resonators, and$m$output feeding lines, the$n^{\mathbf {th}}$-order$m$-way filtering power divider topology can be obtained with a simple configuration. A coupled-line output feeding structure loading with multiple PIN diodes is proposed to adjust the output$Q_{\mathbf {e}}$values. Design theories for obtaining the desired$Q_{\mathbf {e}}$values are provided. Then, the filtering power divider can be fully reconfigured in the states with one to$m$operating channels. Good input matching can be achieved without using an additional reconfigurable impedance matching network in all these states, resulting in a size and loss reduction. For verification, a 2nd-order 4-way reconfigurable filtering power divider is designed, fabricated, and measured. As compared to the reported reconfigurable power dividers, the proposed design shows the merits of fully reconfigurable operating channels, favorable filtering responses, low insertion losses, high isolation, and a simple structure.

The intelligent reflecting surface (IRS)-assisted millimeter wave (mmWave) communication system has emerged as a promising technology for coverage extension and capacity enhancement. Prior works on IRS have mostly assumed perfect channel state information (CSI), which facilitates in deriving the upper-bound performance but is difficult to realize in practice due to passive elements of IRS without signal processing capabilities. In this paper, we propose a compressive channel estimation techniques for IRS-assisted mmWave multi-input and multi-output (MIMO) system. To reduce the training overhead, the inherent sparsity of mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel is converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and sparsity term). Existing sparse recovery algorithms solve the combined contradictory objectives function using a regularization parameter, which leads to a suboptimal solution. To address this concern, a hybrid multiobjective evolutionary paradigm is developed to solve the sparse recovery problem, which can overcome the difficulty in the choice of regularization parameter value. Simulation results show that under a wide range of simulation settings, the proposed method achieves competitive error performance compared to existing channel estimation methods.

In this paper, we propose anew dynamic IRS beamformingframework to boost the sum throughput of an intelligent reflecting surface (IRS) aided wireless powered communication network (WPCN). Specifically, the IRS phase-shift vectors across time and resource allocation are jointly optimized to enhance the efficiencies of both downlink wireless power transfer (DL WPT) and uplink wireless information transmission (UL WIT) between a hybrid access point (HAP) and multiple wirelessly powered devices. To this end, we first study three special cases of the dynamic IRS beamforming, namelyuser-adaptiveIRS beamforming,UL-adaptiveIRS beamforming, andstatic IRS beamforming, by characterizing their optimal performance relationships and proposing corresponding algorithms. Interestingly, it is rigorously proved that the latter two cases achieve the same throughput, thus helping halve the number of IRS phase shifts to be optimized and signalling overhead practically required for UL-adaptive IRS beamforming. Then, we propose a general optimization framework for dynamic IRS beamforming, which is applicable for any given number of IRS phase-shift vectors available. Despite of the non-convexity of the general problem with highly coupled optimization variables, we propose two algorithms to solve it and particularly, the low-complexity algorithm exploits the intrinsic structure of the optimal solution as well as the solutions to the cases with user-adaptive and static IRS beamforming. Simulation results validate our theoretical findings, illustrate the practical significance of IRS with dynamic beamforming for spectral and energy efficient WPCNs, and demonstrate the effectiveness of our proposed designs over various benchmark schemes.

The system capacity can be remarkably enhanced with the help of intelligent reflecting surface (IRS) which has been recognized as a advanced breaking point for the beyond fifth-generation (B5G) communications. However, the accuracy of IRS channel estimation restricts the potential of IRS-assisted multiple input multiple output (MIMO) systems. Especially, for the resource-limited indoor applications which typically contains lots of parameters estimation calculation and is limited by the rare pilots, the practical applications encountered severe obstacles. Previous works takes the advantages of mathematical-based statistical approaches to associate the optimization issue, but the increasing of scatterers number reduces the practicality of statistical approaches in more complex situations. To obtain the accurate estimation of indoor channels with appropriate piloting overhead, an offset learning (OL)-based neural network method is proposed. The proposed estimation method can trace the channel state information (CSI) dynamically with non-prior information, which get rid of the IRS-assisted channel structure as well as indoor statistics. Moreover, a convolution neural network (CNN)-based inversion is investigated. The CNN, which owns powerful information extraction capability, is deployed to estimate the offset, it works as an offset estimation operator. Numerical results show that the proposed OL-based estimator can achieve more accurate indoor CSI with a lower complexity as compared to the benchmark schemes.

In this paper, we propose a compressive channel estimation technique for IRS-assisted mmWave multi-input and multi-output (MIMO) system. To reduce the training overhead, the inherent sparsity in mmWave channels is exploited. By utilizing the properties of Kronecker products, IRS-assisted mmWave channel estimation are converted into a sparse signal recovery problem, which involves two competing cost function terms (measurement error and a sparsity term). Existing sparse recovery algorithms solve the combined contradictory objectives function using a regularization parameter, which leads to a suboptimal solution. To address this concern, a hybrid multi-objective evolutionary paradigm is developed to solve the sparse recovery problem, which can overcome the difficulty in the choice of regularization parameter value. Simulation results show that under a wide range of simulation settings, the proposed algorithm achieves competitive error performance compared to existing channel estimation algorithms.

Beamforming and non-orthogonal multiple access (NOMA) are two key techniques for achieving spectral efficient communication in the fifth generation and beyond wireless networks. In this paper, we jointly apply a hybrid beamforming and NOMA techniques to an unmanned aerial vehicle (UAV)-carried wireless-powered mobile edge computing (MEC) system, within which the UAV is mounted with a wireless power charger and the MEC platform delivers energy and computing services to Internet of Things (IoT) devices. We aim to maximize the sum computation rate at all IoT devices whilst satisfying the constraint of energy harvesting and coverage. The considered optimization problem is non-convex involving joint optimization of the UAV’s 3D placement and hybrid beamforming matrices as well as computation resource allocation in partial offloading pattern, and thus is quite difficult to tackle directly. By applying the polyhedral annexation method and the deep deterministic policy gradient (DDPG) algorithm, we propose an effective algorithm to derive the closed-form solution for the optimal 3D deployment of the UAV, and find the solution for the hybrid beamformer. A resource allocation algorithm for partial offloading pattern is thereby proposed. Simulation results demonstrate that our designed algorithm yields a significant computation performance enhancement as compared to the benchmark schemes.

Beamforming and non-orthogonal multiple access (NOMA) serve as two potential solutions for achieving spectral efficient communication in the fifth generation and beyond wireless networks. In this paper, we jointly apply a hybrid beamforming and NOMA techniques to an unmanned aerial vehicle (UAV)-carried wireless-powered mobile edge computing (MEC) system, within which the UAV is equipped with a wireless power charger and the MEC platform delivers energy and computing services to Internet of Things (IoT) devices. Our aim is to maximize the sum computation rate at all IoT devices whilst satisfying the constraint of energy harvesting and coverage. The resultant optimization problem is non-convex involving joint optimization of the UAV’s 3D placement and hybrid beamforming matrices as well as computation resource allocation in both partial and binary offloading patterns, and thus is quite difficult to tackle directly. By applying the polyhedral annexation method and the deep deterministic policy gradient (DDPG) algorithm, we develop an effective algorithm to derive the closed-form solution for the optimal 3D deployment of the UAV, and find the solution for the hybrid beamformer. Two resource allocation algorithms for partial and binary offloading patterns are thereby proposed. Simulation results verify that our designed algorithms achieve a significant computation performance enhancement as compared to the benchmark schemes.

This paper considers an unmanned aerial vehicle (UAV)-aided millimeter Wave (mmWave) multiple-input-multiple-output (MIMO) non-orthogonal multiple access (NOMA) system, where a UAV serves as a flying base station (BS) to provide wireless access services to a set of Internet of Things (IoT) devices in different clusters. We aim to maximize the downlink sum rate by jointly optimizing the three-dimensional (3D) placement of the UAV, beam pattern and transmit power. To address this problem, we first transform the non-convex problem into a total path loss minimization problem, and hence the optimal 3D placement of the UAV can be achieved via standard convex optimization techniques. Then, the multiobjective evolutionary algorithm based on decomposition (MOEA/D) based algorithm is presented for the shaped-beam pattern synthesis of an antenna array. Finally, by transforming the original problem into an optimal power allocation problem under the fixed 3D placement of the UAV and beam pattern, we derive the closed-form expression of transmit power based on Karush-Kuhn-Tucker (KKT) conditions. In addition, inspired by fraction programming (FP), we propose a FP-based suboptimal algorithm to achieve a near-optimal performance. Numerical results demonstrate that the proposed algorithm achieves a significant performance gain in terms of sum rate for all IoT devices, as compared with orthogonal frequency division multiple access (OFDMA) scheme.

This paper studies an unmanned aerial vehicle (UAV)-assisted wireless powered IoT network, where a rotary-wing UAV adopts fly-hover-communicate protocol to successively visit IoT devices in demand. During the hovering periods, the UAV works on full-duplex mode to simultaneously collect data from the target device and charge other devices within its coverage. Practical propulsion power consumption model and non-linear energy harvesting model are taken into account. We formulate a multi-objective optimization problem to jointly optimize three objectives: maximization of sum data rate, maximization of total harvested energy and minimization of UAV's energy consumption over a particular mission period. These three objectives are in conflict with each other partly and weight parameters are given to describe associated importance. Since IoT devices keep gathering information from the physical surrounding environment and their requirements to upload data change dynamically, online path planning of the UAV is required. In this paper, we apply deep reinforcement learning algorithm to achieve online decision. An extended deep deterministic policy gradient (DDPG) algorithm is proposed to learn control policies of UAV over multiple objectives. While training, the agent learns to produce optimal policies under given weights conditions on the basis of achieving timely data collection according to the requirement priority and avoiding devices' data overflow. The verification results show that the proposed MODDPG (multi-objective DDPG) algorithm achieves joint optimization of three objectives and optimal policies can be adjusted according to weight parameters among optimization objectives.

A filtering antenna is a device with both filtering and radiating capabilities. It can be used to reduce the cross-band mutual coupling between the closely spaced elements operating at different frequency bands. We review the authors’ work on filtering antenna designs and three related dual-band base-station antenna arrays as application examples. The filtering antenna designs include single- and dual-polarized filtering patch antennas, a single-polarized omni-directional filtering dipole antenna, and a dual-polarized filtering dipole antenna for the base station. The filtering antennas in this paper feature an innovative concept of eliminating extra filtering circuits, unlike other available antennas. For each design, the filtering structure is finely integrated with the radiators or feeding lines. As a result, the proposed designs have the advantages of compact size, simple structure, good in-band radiation performance, and low levels of loss, and do not contain complicated filtering circuits. Based on the proposed filtering antennas, single- and dual-polarized dual-band antenna arrays were developed. Separate antenna elements at different frequency bands were used to achieve the dual-band performance. The cross-band mutual couplings between the elements at different bands were reduced substantially using the antenna inherent filtering performance. The dual-band arrays exhibited better performance as compared to typical industrial products. Some of the proposed technologies have been transferred into the industry.

Non-orthogonal multiple access (NOMA) is one of the most significant technologies to meet the demand of high spectral efficiency (SE) in the fifth generation (5G) cellular networks. The utilization of simultaneous wireless information and power transfer (SWIPT) contributes to prolonging the battery life of the mobile users (MUs) and enhancing the system energy efficiency (EE), especially in the NOMA scenario where the inter-user interference can be reused for energy harvesting (EH). In this paper, we study the achievable data rate maximization problem for the downlink multi-carrier NOMA (MC-NOMA) network with power splitting (PS)-based SWIPT, in which power allocation and PS control are jointly optimized with the limitation of available power budget as well as the requirement for EH. The considered non-convex optimization problem is arduous to tackle, resulting from the presence of the coupled variables and the inter-user interference. To cope with the problem, a decoupled approach is developed, in which the power allocation and PS control are separated and the corresponding sub-problems are respectively solved through Lagrangian duality method. Furthermore, an alternative approach based on deep learning is proposed, which is capable of effectively obtaining the approximate optimal solution according to the empirical data. Simulation results confirm the effectiveness of the proposed schemes, and demonstrate the superiority of the combination of PS-based SWIPT with MC-NOMA over SWIPT-aided single-carrier NOMA (SC-NOMA) and SWIPT-aided orthogonal multiple access (OMA).

Combination of unmanned aerial vehicle (UAV) and non-orthogonal multiple access (NOMA) is deemed as an promising solution to achieving massive connectivity in future wireless networks. In this paper, a UAV-aided NOMA scheme is proposed to achieve simultaneous wireless information and power transfer (SWIPT) and guarantee the secure transmission for ground passive receivers (PRs), in which the nonlinear energy harvesting model is applied. Each time frame is divided into two phases. In the first phase, the received power at each PR is maximized to achieve rapid charging. In the second phase, SWIPT is performed via NOMA with the remaining energy at each PR, and artificial jamming is generated at UAV together with the NOMA information to guarantee the security. The throughput of PRs is maximized, with the highest received jamming power cancelled at each PR via successive interference cancellation (SIC). This disrupts the eavesdropping effectively by jamming without affecting the legitimate transmission. Due to the non-convexity of these two optimization problems, we first convert them to convex ones and then propose iterative algorithms to solve them. Simulation results are presented to show the effectiveness of the proposed scheme.

Mobile edge caching (MEC) and device-todevice (D2D) communications are two potential technologies to resolve traffic overload problems in the Internet of Things. Previous works usually investigate them separately with MEC for traffic offloading and D2D for information transmission. In this article, a joint framework consisting of MEC and cache-enabled D2D communications is proposed to minimize the energy cost of systematic traffic transmission, where file popularity and user preference are the critical criteria for small base stations (SBSs) and user devices, respectively. Under this framework, we propose a novel caching strategy, where the Markov decision process is applied to model the requesting behaviors. A novel scheme based on reinforcement learning (RL) is proposed to reveal the popularity of files as well as users' preference. In particular, a Q-learning algorithm and a deep Q-network algorithm are, respectively, applied to user devices and the SBS due to different complexities of status. To save the energy cost of systematic traffic transmission, users acquire partial traffic through D2D communications based on the cached contents and user distribution. Taking the memory limits, D2D available files, and status changing into consideration, the proposed RL algorithm enables user devices and the SBS to prefetch the optimal files while learning, which can reduce the energy cost significantly. Simulation results demonstrate the superior energy saving performance of the proposed RL-based algorithm over other existing methods under various conditions.

Transmission rate and harvested energy are well-known conflictive optimization objectives in simultaneous wireless information and power transfer (SWIPT) systems, and thus their trade-off and joint optimization are important problems to be studied. In this paper, we investigate joint power allocation and splitting control in a SWIPT-enabled non-orthogonal multiple access (NOMA) system with the power splitting (PS) technique, with an aim to optimize the total transmission rate and harvested energy simultaneously whilst satisfying the minimum rate and the harvested energy requirements of each user. These two conflicting objectives make the formulated problem a constrained multi-objective optimization problem. Since the harvested power is usually stored in the battery and used to support the reverse link transmission, we transform the harvested energy into throughput and define a new objective function by summing the weighted values of the transmission rate achieved by information decoding and transformed throughput from energy harvesting, defined as equivalent-sum-rate (ESR). As a result, the original problem is transformed into a single-objective optimization problem. The considered ESR maximization problem which involves joint optimization of power allocation and PS ratio is nonconvex, and hence challenging to solve. In order to tackle it, we decouple the original nonconvex problem into two convex subproblems and solve them iteratively. In addition, both equal PS ratio case and independent PS ratio case are considered to further explore the performance. Numerical results validate the theoretical findings and demonstrate that significant performance gain over the traditional rate maximization scheme can be achieved by the proposed algorithms in a SWIPT-enabled NOMA system.

Non-orthogonal multiple access (NOMA) has been developed as a key multi-access technique for 5G. However, secure transmission remains a challenge in NOMA. Especially, the user with weakest channel is most threatened by eavesdropping, due to its highest transmit power. In this paper, we propose a novel scheme to generate artificial jamming at the NOMA base station (BS), aiming at disrupting the potential eavesdropping without affecting the legitimate transmission. In the scheme, the transmit power of artificial jamming is maximized, with its received power at each receiver higher than that of other users. Thus, the jamming signal can be eliminated via successive interference cancellation before others, and the eavesdropping can be disrupted effectively. Due to the non-convexity of the optimization problems, we first convert it to a convex one and then provide an iterative algorithm to solve it. Simulation results are presented to show the effectiveness of the proposed scheme in guaranteeing the security of NOMA networks.