Recently, dynamic metasurface antennas (DMAs) have emerged as a promising technology for implementing large antenna arrays with reduced hardware cost and power consumption. In this work, we study the joint transmitter and receiver design for DMA-based uplink multi-user MIMO systems. We formulate the achievable sum-rate maximization problem for the considered system. Then we translate the original problem into quadratic form applying the matrix fractional programming and eliminate the structure constraints imposed by the DMA weight matrix for tractability. Accordingly, we propose to solve the equivalent problem in an alternating manner, in which each subproblem can be tackled via well-developed solvers. Numerical results demonstrate the convergence of our proposed technique and that the proposed technique outperforms the baseline.

Uplink broadband communication is very popular for V2X high date rate sensing data uploading. A joint design of UL coherent transmission and coherent receiving (JCTCR) mechanism is proposed to significantly increase the uplink capability of 5G-Advanced networks. In the transmission side, a physical layer cooperation mechanism is introduced, the data is transmitted as a single transport block over two 2Tx UEs’ physical layer. The tight cooperation in physical layer forms a more powerful 4Tx virtual UE (VUE), which significantly improves the uplink edge user perceived throughput by about 143%. The coherent transmission performance sensitivity to the synchronization error is also evaluated and analyzed in the paper. In the receiving side, a broadly applicable UE assisted non-ideal backhaul multi-BS coherent receiving solution is introduced. In order to enable the coherent joint receiving in non-ideal backhaul scenario, a novel mechanism to make use of the UE OTA signaling assistance is proposed to enable the multi-BS channel information acquisition and multi-BS joint receiving, which makes the JCTCR to be widely applicable for general deployment scenarios and no need for very high quality backhaul assumption in traditional way. Evaluation results show, although the pre resource allocation and the channel information acquisition have singling overhead, the CJTJR still can achieve the uplink capacity gain of 42%, only slight 4% decrease comparing with the ideal coherent receiving with ideal backhaul.

In cell-free massive MIMO networks, an efficient distributed detection algorithm is of significant importance. In this paper, we propose a distributed expectation propagation (EP) detector for cell-free massive MIMO. The detector is composed of two modules, a nonlinear module at the central processing unit (CPU) and a linear module at the access point (AP). The turbo principle in iterative decoding is utilized to compute and pass the extrinsic information between modules. An analytical framework is then provided to characterize the asymptotic performance of the proposed EP detector with a large number of antennas. Simulation results will show that the proposed method outperforms the distributed detectors in terms of bit-error rate.

The combination of orthogonal frequency modulation (OFDM) and multiple-input multiple-output (MIMO) techniques plays an important role in modern communication systems. In order to meet the growing throughput demands, future MIMO-OFDM receivers are expected to utilize a massive number of antennas, operate in dynamic environments, and explore high frequency bands, while satisfying strict constraints in terms of cost, power, and size. An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implement controllable compression in acquisition. In this work we study the application of DMAs for MIMO-OFDM receivers operating with bit-constrained analog-to-digital converters (ADCs). We present a model for DMAs which accounts for the configurable frequency selective profile of its metamaterial elements, resulting in a spectrally flexible hybrid structure. We then exploit previous results in task-based quantization to show characterized the achievable OFDM recovery accuracy for a given DMA configuration in the presence of bit-constrained ADCs, and propose methods for adjusting the DMA parameters based on channel state information. Our numerical results demonstrate that by properly exploiting the spectral diversity of DMAs, notable performance gains are obtained over existing designs of conventional hybrid architectures, demonstrating the potential of DMAs for realizing high performance massive antenna arrays of reduced cost and power consumption.

The combination of orthogonal frequency modulation (OFDM) and multiple-input multiple-output (MIMO) systems plays an important role in modern communication systems. In order to meet the growing throughput demands, future MIMO-OFDM receivers are expected to utilize a massive number of antennas, operate in dynamic environments, and explore high frequency bands, while satisfying strict constraints in terms of cost, power, and size. An emerging technology to realize massive MIMO receivers of reduced cost and power consumption is based on dynamic metasurface antennas (DMAs), which inherently implement controllable compression in acquisition. In this work we study the application of DMAs for MIMO-OFDM receivers operating with bit-constrained analog-to-digital converters (ADCs). We exploit previous results in task-based quantization to show how DMAs can be configured to improve recovery in the presence of constrained ADCs, and propose an algorithm for adjusting the DMA parameters based on channel state information. Our numerical results demonstrate that the DMA-based receiver is capable of accurately recovering OFDM signals, and that its performance is comparable to receivers operating without bit limitations, while being significantly less costly and more power efficient.

The order-of-magnitude increase in the dimension of antenna arrays, which forms extra-large-scale massive multiple-input-multiple-output (MIMO) systems, enables substantial improvement in spectral efficiency, energy efficiency, and spatial resolution. However, practical challenges, such as excessive computational complexity and excess of baseband data to be transferred and processed, prohibit the use of centralized processing. A promising solution is to distribute baseband data from disjoint subsets of antennas into parallel processing procedures coordinated by a central processing unit. This solution is called subarray-based architecture. In this work, we extend the application of expectation propagation (EP) principle, which effectively balances performance and practical feasibility in conventional centralized MIMO detector design, to fit the subarray-based architecture. Analytical results confirm the convergence of the proposed iterative procedure and that the proposed detector asymptotically approximates Bayesian optimal performance under certain conditions. The proposed subarray-based EP detector is reduced to centralized EP detector when only one subarray exists. In addition, we propose additional strategies for further reducing the complexity and overhead of the information exchange between parallel subarrays and the central processing unit to facilitate the practical implementation of the proposed detector. Simulation results demonstrate that the proposed detector achieves numerical stability within few iterations and outperforms its counterparts.

The use of low-resolution analog-to-digital converters can significantly reduce power consumption and hardware cost. However, their resulting severe nonlinear distortion makes achieving reliable data transmission challenging. For orthogonal frequency division multiplexing (OFDM) transmission, the orthogonality among subcarriers is destroyed. This invalidates conventional OFDM receivers relying heavily on this orthogonality. In this paper, we move on to quantized OFDM (Q-OFDM) prototyping implementation based on our previous achievement in optimal Q-OFDM detection. First, we propose a novel Q-OFDM channel estimator by extending the generalized Turbo (GTurbo) framework formerly applied for optimal detection. Specifically, we integrate a type of robust linear OFDM channel estimator into the original GTurbo framework, and derive its corresponding extrinsic information to guarantee its convergence. We also propose feasible schemes for automatic gain control, noise power estimation, and synchronization. Combined with the proposed inference algorithms, we develop an efficient Q-OFDM receiver architecture. Furthermore, we construct a proof-of-concept prototyping system and conduct over-the-air (OTA) experiments to examine its feasibility and reliability. This is the first work that focuses on both algorithm design and system implementation in the field of low-resolution quantization communication. The results of the numerical simulation and OTA experiment demonstrate that reliable data transmission can be achieved.

Orthogonal frequency division multiplexing (OFDM) has been widely used in communication systems operating in the millimeter wave (mmWave) band to combat frequency-selective fading and achieve multi-Gbps transmissions, such as the IEEE 802.15.3c and the IEEE 802.11ad. For mmWave systems with ultra high sampling rate requirements, the use of low-resolution analog-to-digital converters (ADCs) (i.e., 1-3 bits) ensures an acceptable level of power consumption and system costs. However, orthogonality among subchannels in the OFDM system cannot be maintained because of the severe nonlinearity caused by low-resolution ADC, which renders the design of data detector challenging. In this paper, we develop an efficient algorithm for optimal data detection in the mmWave OFDM system with low-resolution ADCs. The analytical performance of the proposed detector is derived and verified to achieve the fundamental limit of the Bayesian optimal design. On the basis of the derived analytical expression, we further propose a power allocation (PA) scheme that seeks to minimize the average symbol error rate. In addition to the optimal data detector, we also develop a feasible channel estimation method, which can provide high-quality channel state information without significant pilot overhead. Simulation results confirm the accuracy of our analysis and illustrate that the performance of the proposed detector in conjunction with the proposed PA scheme is close to the optimal performance of the OFDM system with infinite-resolution ADC.