The channel knowledge map (CKM) is regarded as a digital twin of the wireless channel and provides location-specific channel information for environment-aware wireless communications. The channel gain map (CGM) is a typical form of CKM that stores spatially indexed channel gains over a target area, enabling fast link scheduling, coverage planning, and beam selection. Existing CGM construction methods mainly rely on propagation modeling or data-driven spatial completion, which can be computationally expensive or may inadequately capture site-specific propagation characteristics, thereby limiting prediction accuracy. To address this issue, we propose a three-stage Transformer-based CGM construction framework for predicting channel gains at arbitrary locations. In the first stage, the city map is discretized into grids, and the channel gain values within each grid are normalized to facilitate continuous spatial prediction. In the second stage, a Transformer-based coarse CGM prediction method is proposed to extract environmental features from the city map and generate a coarse CGM. In the third stage, a lightweight refinement network is designed, which leverages a small number of sparse measurements to refine the coarse predictions. Simulation results demonstrate that the proposed method significantly outperforms the state-of-the-art approaches across all performance metrics. In particular, compared with the existing optimal scheme, the proposed method achieves 32.81% and 20.63% reductions in normalized mean square error (NMSE) and root mean square error (RMSE), respectively, while improving the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR) by 2.08% and 3.71%, respectively.

In this paper, a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communication system with hybrid beamforming is considered, where the base station (BS) transmits signals to users via the RIS. Due to the difficulty in acquiring accurate channel state information (CSI) and the high complexity of hybrid beamforming design, a codebook-based method is employed to balance communication performance and computational cost. However, conventional codebook construction typically neglects the influence of surrounding communication environments, resulting in poor adaptability in complex mmWave scenarios. Therefore, we propose a three-stage environment-aware codebook design framework based on channel knowledge map (CKM) to enhance the adaptability of systems under different channel conditions. Specifically, in the first stage, a deep learning (DL)-based method is proposed to construct the CKM, which is a site-specific database that provides accurate CSI for given locations. In the second stage, the CSI of potential users is rapidly acquired based on the CKM, then a deep reinforcement learning (DRL) algorithm is employed to generate environment-aware codebooks by solving a sum achievable rate maximization problem. In the third stage, the end-to-end composite channel is first estimated. Then, the digital beamforming is computed by using the minimum mean square error (MMSE) criterion. Finally, the optimal analog beamforming and RIS phase shifts are selected through codebook scanning. Simulation results demonstrate that the proposed CKM-based environment-aware codebooks significantly outperform other codebook schemes in terms of sum achievable rate.

A novel design approach is presented to extend the bandwidth and output power back-off (OBO) range of symmetric Doherty power amplifier. The traditional single variable regulation techniques are effective for output power back-off range extension only at single frequency points. Firstly, one phase dispersion (PD factor) is introduced to characterize the OBO range versus frequency. Secondly, multi-parameters optimization of PD factor, complex combining load (CCL), and out-phased current combining (OCC) is used to alleviate the conflict of bandwidth and OBO range. Dynamically optimizing frequencydependent CCL and OCC parameters to counteract the dispersion effect of the matching network thus enable the wideband OBO extension and expanding design flexibility of symmetric DPA. For demonstration, a symmetric DPA with 9dB OBO range is proposed with a fractional operation bandwidth of 36% within 1.8-2.6 GHz. Under the continuous-wave excitation, the fabricated DPA features a 9-dB output back-off (OBO) efficiency of 37.9%– 55% and a saturated efficiency of 55.7%–66.9%, respectively. When driven by a 20-MHz long-term evolution (LTE) modulated signal with 9-dB peak-to-average ratio (PAPR), the adjacent channel power ratio (ACPR) of the fabricated DPA is better than -50.1 dBc after digital predistortion.

This paper presents a compact PIN-diode-based single-pole double-throw (SPDT) switch for wideband RF systems. The series-shunt topology is adopted for the proposed switch. Compensating inductors are introduced to parallelly resonate with the off-capacitance of series diodes, creating an isolation peak. A two-pole highpass matching network is realized by introducing an additional inductor and reusing existing DC components, which ensures compact size and wideband capability. Moreover, the matching network enables independent control of ON-state operating band over the OFF-state, allowing optimized insertion loss and isolation at the desired center frequency. To assist analytical design, closed-form equations of the highpass matching network are derived through equivalent circuit analysis. For demonstration, one SPDT switch utilizing commercial PIN diodes and lumped RLC components is designed, fabricated, and measured. The measured results exhibit superior return loss of better than 20dB across 1.02-3.36 GHz attributed to the two-pole response. The insertion loss remains below 0.81 dB, reaching 0.457 dB at 2.6 GHz, while isolation is better than 27 dB, with a maximum of 71.8 dB at 2.25 GHz. Importantly, the proposed design can be scaled to mm-wave bands with better performances than their widely developed lowpass counterpart.

In this paper, a reconfigurable intelligent surface (RIS) is utilized to assist wireless-powered communication network (WPCN) for achieving integrated sensing and communication (ISAC). In this system, the ISAC base station (BS) first performs radar sensing for multiple targets and wireless power transfer (WPT) for multiple energy-constrained devices with assistance of RIS during the downlink (DL), then all devices transmit their data to the ISAC BS in the uplink (UL) by employing non-orthogonal multiple access (NOMA) with the use of harvested energy. To maximize the sum achievable throughput subject to the quality of service (QoS) constraints of communication devices and sensing quality constraint of targets, we jointly optimize the energy beamforming matrix, radar beamforming matrix, phase shift matrix of RIS, and transmission timeslot allocation in the DL and UL durations. To tackle this non-convexity problem, a two-stage solution is proposed. In the first stage, the energy and radar beamforming matrices, and phase shift matrix of RIS in the DL can be derived according to the penalty-based successive convex approximation (SCA) algorithm and Riemannian conjugate gradient (RCG) algorithm, respectively. In the second stage, the transmission timeslot allocation and phase shift matrix of RIS in the UL are obtained via the polar point method and penalty-based RCG algorithm, respectively. The final optimal results of each stage are yielded based on alternating optimization (AO) algorithm. Numerical results verify the significant improvement of deploying RIS for WPT and data transmission, and the superiority of the proposed optimization solution in improving the sensing performance and achievable throughput compared to other schemes.

While single reconfigurable intelligent surface (RIS)- aided systems have demonstrated potential in improving transmission performance, their ability to serve multi-user scenarios is inherently limited. In this paper, we investigate the deployment of multiple RISs to assist downlink (DL) millimeter-wave (mmWave) non-orthogonal multiple access (NOMA) communications between a base station (BS) and multiple users. For maximizing the system achievable throughput, we propose a two-stage resource allocation scheme. In the first stage, a two-step RIS-user pairing scheme is proposed by combining the Gale-Shapley (GS) algorithm and the worst connection swapping (WCS) algorithm. In the second stage, based on the pairing results from the first stage, an alternating optimization (AO) algorithm is utilized to iteratively optimize RIS phase shifts, digital precoder, and power allocation. During each iteration, with fixed digital precoder and arbitrary feasible power allocation, the optimal RIS phase shifts can be obtained by the difference of convex (D. C.) programming and semidefinite relaxation (SDR) techniques. Then, the minimum mean-squared error (MMSE)-based digital precoder is adopted. Based on the derived RIS phase shifts and digital precoder, the optimized power allocation can be derived by reusing the D. C. programming subject to the constraint of decoding rate for successive interference cancellation (SIC). Simulation results show that the proposed pairing scheme can achieve near-optimal system achievable throughput, while significantly reducing the computational complexity. In addition, the proposed two-stage resource allocation scheme also demonstrates advantages in improving the overall achievable throughput of the system.

In this paper, a reconfigurable intelligent surface(RIS)-assisted millimeter-wave (mmWave) non-orthogonal multiple access (NOMA) communication system is considered, where the RIS is virtually partitioned into several sub-RISs to serve different user clusters. We aim to maximize the sum achievable rate of the considered system by jointly optimizing user clustering, RIS partitioning, active beamforming, passive beamforming, and power allocation, subject to the quality-of-service (QoS) requirements of each user and the maximum transmit power constraint of the base station (BS). To tackle the formulated non-convex joint optimization problem, a three-stage algorithm based on machine learning (ML) is proposed. In the first stage, user clustering is performed by combining the K-means algorithm and the Gaussian mixture model (GMM). In the second stage, a deep learning (DL)-based RIS partitioning method is proposed, which utilizes double cascaded deep neural networks (DNNs) to partition the RIS into multiple sub-RISs virtually, each of which serves a user cluster. In the third stage, a deep reinforcement learning (DRL) algorithm based on deep deterministic policy gradient (DDPG) is invoked to obtain the active beamforming, passive beamforming, and power allocation. Simulation results demonstrate that the proposed RIS partitioning method can effectively improve the utilization of RIS and thereby increase the total throughput of the system. Moreover, the sum achievable rate obtained by the proposed ML-based three-stage algorithm is higher than that of the conventional alternating optimization (AO) algorithm.

In this paper, a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) multi-hop communication system is considered, where the base station (BS) transmits signals to remote users via multi-hop paths formed by multiple RISs. We aim to maximize the sum achievable rate by jointly optimizing multi-hop path scheduling, active beamforming, and passive beamforming, while satisfying the maximum transmit power of the BS. To tackle the formulated non-convex joint optimization problem, a machine learning (ML)-based two-stage algorithm is proposed. In the first stage, a multi-hop path scheduling algorithm based on graph neural networks (GNN) is investigated. Specifically, the considered system is first modeled as a graph topology, where the BS, RIS, and users are all served as nodes. Then, the weights between adjacent nodes associated with the path gain and the number of RIS reflection units are defined based on the expressions of RIS-assisted equivalent channels. Finally, a GNN-based multi-hop path scheduling algorithm is proposed. In the second stage, according to the obtained optimal multi-hop path, the deep deterministic policy gradient (DDPG) strategy is adopted to optimize the active beamforming of the BS and the passive beamforming of the selected RISs. Simulation results demonstrate that the proposed GNN-based multi-hop path scheduling algorithm maintains an error within 3% compared to the global optimal solution and outperforms the graph-based methods. Additionally, the proposed two-stage algorithm improves the sum achievable rate by approximately 27.6% compared to the alternating optimization (AO) algorithm.

A design strategy to enhance the efficiency of the dual-mode Doherty power amplifier (DPA) is proposed. The continuous multiple harmonic regulation approach is adopted to realize high efficiency of dual-mode DPA in a wide operating frequency band. Multi-harmonic control and dual-mode impedance matching are performed by a single load modulation network. The DPA’s operating frequency band and harmonic control type can be simultaneously switched through reciprocal gate bias. A DPA using GaN devices with continuousClass$F$for Mode I (1.5–2.4 GHz) andClass$J$/continuous inverseClass$F$for Mode II (1.3–1.4 GHz/2.5–2.7 GHz) is designed and implemented. The fabricated DPA achieves a measured 10.5–14.3-dB gain and 39.2–41.8-dBm saturated power. Due to the proposed strategies, 39.1%–58.2% and 55.6%-71.9% drain efficiencies are obtained respectively at 6-dB output power back-off and saturation output power for the designed DPA. For the down-link long-term evolution (LTE) signal with a channel bandwidth of 20 MHz and a peak-to-average power ratio (PAPR) of 7.5 dB, the average adjacent channel power ratio of the fabricated DPA is better than 40 dBc after digital predistortion at 1.3, 2.0 and 2.6 GHz with average output power back-off (OBO) efficiency of 37.8%, 39.3% and 43.3%.

In this paper, two types of balanced-to-unbalanced quadrature couplers with six hybrid ports and arbitrary power division ratio are proposed. For Type 1, the quadrature coupler can realize two power division functions, including balanced/unbalanced to balanced-unbalanced-hybrid, and for Type 2, the quadrature coupler contains balanced -to-unbalanced and unbalanced-to-balanced power division functions. To reduce the circuit size, double-sided parallel-strip line (DSPSL) swap structure is used to replace the half-wavelength transmission line, and compact circuit size can be easily achieved. To validate the performance of the quadrature couplers, two types of the balanced-to-unbalanced quadrature couplers centered at 1 GHz with power division ratios 1:1 and 3:1 are fabricated and measured, respectively. The common mode suppression higher than 10 dB (bandwidths over than 176%) can be realized, the isolation and in-band matching level can be also improved.

In this paper, a Doherty power amplifier (DPA) using a novel three-port harmonic injection network (HIN) to extend its high-efficiency range is proposed. With the help of a part of the three-port HIN, which is placed between the carrier and peaking branches, the optimal drain fundamental frequency termination for the carrier device at back-off and saturation can be realized simultaneously thus enhancing the efficiency at back-off significantly. Meanwhile, this three-port HIN can also realize second harmonic drain terminations for the carrier and peaking device with the quasi-open and quasi-short circuit at the current plane, respectively. Consequently, the saturated power ratio between peaking and carrier devices is increased, which further extends the back-off range of the asymmetric drain-biased DPA. For demonstration purposes, an extended high efficiency range DPA was designed and fabricated using commercially available GaN HEMT (Cree CGH 40010F) devices. Measured results for this novel idea gave drain efficiencies better than 62.5% at 9-9.5 dB back-off point from 1.60 to 1.95 GHz.