Movable antenna (MA) has been recognized as a promising technology to enhance communication network performance by adjusting the antenna position within a confined region. In this paper, we consider an energy-efficient MA-enabled multiple-input multiple-output (MIMO) network with the hybrid analog-digital transceiver, where energy consumption induced by the MA movement is additionally considered to accurately evaluate the system energy efficiency (EE) performance. Under both fully-connected and partially-connected transceiver structures, we aim to maximize the system EE by jointly optimizing the hybrid beamformers and antenna positions, subject to the unit-modulus constraints and the minimum MA distance constraints. To tackle these two highly non-convex problems effectively, we propose an efficient two-layer successive convex approximation (SCA) based iterative algorithm, where we aim to iteratively update the achievable EE in the outer layer and alternately optimize the hybrid beamformers and antenna positions in the inner layer. Furthermore, considering the asymptotically low-SNR and high-SNR regimes, we respectively develop two low-complexity algorithms by leveraging the structural properties of their corresponding optimal fully-digital beamformers. Simulation results validate the superior EE performance and low-complexity advantage of our proposed algorithms over the existing benchmark schemes.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for greatly enhancing communication performance of future wireless networks. To overcome the multiplicative fading effect of passive RIS and high energy consumption of active RIS, we propose a novel dynamic subarray-based hybrid active-passive RIS (HRIS) architecture by dividing all reflecting elements into multiple sub-RISs, each of which can flexibly switch between active and passive modes. Therefore, the proposed subarray-based HRIS is anticipated to achieve optimal system performance with minimal cost and energy consumption. In this paper, we aim to maximize the energy efficiency (EE) for the subarray-based HRIS assisted multi-user multiple-input single-output (MISO) system, where the transmit beamforming vectors at the base station (BS), the mode switching matrix, and the reflection matrices of active and passive sub-RISs are jointly optimized subject to individual user rate constraints. To tackle this intractable problem, we firstly explore the feasible region of the minimum rate threshold among all users, and then develop an efficient two-layer successive convex approximation (SCA) based iterative algorithm. Considering a simplified single-user scenario, we also derive some interesting insights into the optimal active-passive sub-RISs allocation for maximizing EE. It is revealed that for a small BS transmit power, deploying more active sub-RISs in the subarray-based HRIS is preferred to attain the maximum EE. Conversely, under a high BS transmit power and a small HRIS reflection power, more sub-RISs should be switched to the passive mode. Numerical simulation results verify the superior EE performance of the proposed dynamic subarray-based HRIS over the traditional active and passive RISs.

This article presents a Gm-controlled inverter (GC-INV) based amplifier with multiple common-mode feedback (CMFB) loops for multi-channel bio-signal recording. The GC-INV forms a DC-coupled input to ensure a high input impedance. The multi-CMFB, including twin local (TL), regional system (RS), and averaged system (AS) CMFB loops, is introduced through the paralleled GC terminals to provide multiple feedback paths. The TL-CMFB with capacitor-reused topology not only reduces the die area and increases the differential-mode gain, but also reduces the common-mode (CM) gain. The RS-CMFB mitigates the common-mode interference (CMI) due to the mismatch of the CM feedback paths. The AS-CMFB further mitigates the accumulated CMI from CM sampling paths. These CMFB loops avoid the design trade-off between the intrinsic CMRR and the efficiency of area and power. The proposed GC-INV based amplifier with multi-CMFB is fabricated in a 0.18-$\mu $m CMOS technology. It achieves an intrinsic CMRR of 97 dB, TCMRR of 78 dB, and the single-channel INV consumes a chip area of 0.008 mm2.

In this article, we investigate the analog–digital hybrid transceiver optimization for multiple-input–multiple-output (MIMO) Internet of Things (IoT) systems, aiming at maximizing the sum rate of multiple IoT devices in downlink communications. We first derive the downlink–uplink duality for the MIMO communications with analog–digital hybrid structures. Based on this, the intractable MIMO downlink sum-rate maximization is equivalently transferred into an easier-to-handle virtual uplink counterpart. In order to solve the nonconvex virtual uplink problem effectively, we resort to decouple the involved digital and analog matrix variables. On the one hand, we propose two kinds of algorithms for the analog matrices optimizations, namely, the joint design and the separate design. Specifically, the joint design optimizes the analog precoder and equalizer matrices in an alternating manner. In each iteration, an element-wise optimization algorithm is utilized to optimize the analog matrix variables under constant modulus constraints. For the separate design, the analog precoder and equalizer matrices are optimized separately via the elaborately designed space alignments with lower computational complexities. On the other hand, the digital precoders can be computed with fixed analog matrix variables, in which a modified iterative water-filling algorithm is proposed. Finally, numerical results demonstrate the superior performance advantages of the proposed algorithms over several benchmark algorithms.

Experiencing high-fidelity volumetric video as seamlessly as 2D videos is a long-held dream. However, current dynamic 3DGS methods, despite their high rendering quality, face challenges in streaming on mobile devices due to computational and bandwidth constraints. In this paper, we introduce V 3 (Viewing Volumetric Videos), a novel approach that enables high-quality mobile rendering through the streaming of dynamic Gaussians. Our key innovation is to view dynamic 3DGS as 2D videos, facilitating the use of hardware video codecs. Additionally, we propose a two-stage training strategy to reduce storage requirements with rapid training speed. The first stage employs hash encoding and shallow MLP to learn motion, then reduces the number of Gaussians through pruning to meet the streaming requirements, while the second stage fine tunes other Gaussian attributes using residual entropy loss and temporal loss to improve temporal continuity. This strategy, which disentangles motion and appearance, maintains high rendering quality with compact storage requirements. Meanwhile, we designed a multi-platform player to decode and render 2D Gaussian videos. Extensive experiments demonstrate the effectiveness of V 3 , outperforming other methods by enabling high-quality rendering and streaming on common devices, which is unseen before. As the first to stream dynamic Gaussians on mobile devices, our companion player offers users an unprecedented volumetric video experience, including smooth scrolling and instant sharing. Our project page with source code is available at https://authoritywang.github.io/v3/.

This brief proposes a parallel multiresidual (PMR) integrator to enhance the noise-shaping (NS) effect for successive approximation register (SAR) analog-to-digital converter (ADC). The PMR employs passive integrators in parallel to simultaneously integrate the average result of the multiple sequential residual voltages. The proposed PMR technique provides an alternative scheme to enhance the NS rather than increasing the order of the integrator to suppress the instability and power. A prototype 7-bit second-order NS-SAR ADC is designed and simulated in a 130-nm CMOS process. PMR increases the effective number of bits (ENOBs) to 10.6 bit, which enhances the NS effect of 3.6 bit. It achieves a peak signal-to-noise and distortion ratio (SNDR) of 65.84 dB over a bandwidth of 1.3 kHz at the oversampling ratio (OSR) of 16.

Quantifying the accuracy of burned area (BA) estimations is crucial in wildfire monitoring and loss assessment based on remote sensing technology. In this letter, a novel approach to quantitatively evaluate the accuracy of BA estimations, called the vector distance algorithm (VDA), was proposed based on boundary sampling and$t$tests. To validate the effectiveness of this approach, Sentinel-2 images were used to estimate the BA, and a field survey and a GaoFen-6 (GF-6) image were then utilized to evaluate the accuracy based on the VDA. The results were as follows: 1) the proposed algorithm could provide not only the percent accuracy of the evaluation but also the confidence interval of the BA; 2) the accuracy validation of the BA extracted by the normalized burn ratio (NBR) index and normalized difference vegetation index (NDVI) was verified by the VDA; 3) based on the field survey, the VDA confirmed that the NBR index had high accuracy, while the NDVI index had a large error, which is consistent with the results of using ground truth observations as a reference; and 4) the analysis based on the GF-6 image showed similar results. This study indicated that the VDA is effective and has the potential for widespread use in evaluating the accuracy of a BA.

In many core problems of signal processing and wireless communications, Karush-Kuhn-Tucker (KKT) conditions based optimization plays a fundamental role. Hence we investigate the KKT conditions in the context of optimizing positive semidefinite matrix variables under nonconvex rank constraints. More explicitly, based on the properties of KKT conditions, we optimize a reconfigurable intelligent surface (RIS) aided multi-user multi-input multi-output (MU-MIMO) network. Specifically, we consider the capacity maximization and sum mean square error (MSE) minimization problems of both the RIS-aided MU-MIMO uplink (UL) and downlink (DL) under multiple weighted power constraints and rank constraints. As for the RIS-aided MU-MIMO UL, the optimal structures of the signal covariance matrices are derived based on the KKT conditions. Furthermore, an efficient procedure is designed for solving the capacity maximization and sum mean square error (MSE) minimization problems. Then the UL-DL dualities are exploited for solving the capacity maximization and MSE minimization problems of the RIS-aided MU-MIMO DL based on the results of the UL optimization. Hence in the proposed framework, the phase shifting matrix of the RIS is jointly optimized with the signal covariance matrices for both the UL and DL. Our simulation results demonstrate the performance advantages of the proposed framework.