اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدSimultaneous Downlink Data Transmission and Uplink Channel Estimation with Reduced Complexity Full Duplex MIMO Radios
108
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
In this paper, we study Full Duplex (FD) Multiple-Input Multiple-Output (MIMO) radios for simultaneous data communication and control information exchange. Capitalizing on a recently proposed FD MIMO architecture combining digital transmit and receive beamforming with reduced complexity multi-tap analog Self-Interference (SI) cancellation, we propose a novel transmission scheme exploiting channel reciprocity for joint downlink beamformed information data communication and uplink channel estimation through training data transmission. We adopt a general model for pilot-assisted channel estimation and present a unified optimization framework for all involved FD MIMO design parameters. Our representative Monte Carlo simulation results for an example algorithmic solution for the beamformers as well as for the analog and digital SI cancellation demonstrate that the proposed FD-based joint communication and control scheme provides 1.4x the downlink rate of its half duplex counterpart. This performance improvement is achieved with 50% reduction in the hardware complexity for the analog canceller than conventional FD MIMO architectures with fully connected analog cancellation.
قيم البحث
اقرأ أيضاً
Accurate downlink channel information is crucial to the beamforming design, but it is difficult to obtain in practice. This paper investigates a deep learning-based optimization approach of the downlink beamforming to maximize the system sum rate, wh
en only the uplink channel information is available. Our main contribution is to propose a model-driven learning technique that exploits the structure of the optimal downlink beamforming to design an effective hybrid learning strategy with the aim to maximize the sum rate performance. This is achieved by jointly considering the learning performance of the downlink channel, the power and the sum rate in the training stage. The proposed approach applies to generic cases in which the uplink channel information is available, but its relation to the downlink channel is unknown and does not require an explicit downlink channel estimation. We further extend the developed technique to massive multiple-input multiple-output scenarios and achieve a distributed learning strategy for multicell systems without an inter-cell signalling overhead. Simulation results verify that our proposed method provides the performance close to the state of the art numerical algorithms with perfect downlink channel information and significantly outperforms existing data-driven methods in terms of the sum rate.
In this paper, we study Simultaneous Communication of Data and Control (SCDC) information signals in Full Duplex (FD) Multiple-Input Multiple-Output (MIMO) wireless systems. In particular, considering an FD MIMO base station serving multiple single-a
ntenna FD users, a novel multi-user communication scheme for simultaneous DownLink (DL) beamformed data transmission and UpLink (UL) pilot-assisted channel estimation is presented. Capitalizing on a recent FD MIMO hardware architecture with reduced complexity self-interference analog cancellation, we jointly design the base stations transmit and receive beamforming matrices as well as the settings for the multiple analog taps and the digital SI canceller with the objective to maximize the DL sum rate. Our simulation results showcase that the proposed approach outperforms its conventional half duplex counterpart with 50% reduction in hardware complexity compared to the latest FD-based SCDC schemes.
Although the hardware complexity of the analog self-interference canceller in full duplex Multiple Input Multiple Output (MIMO) designs does not necessarily scale with the number of transceiver antennas, exploiting the benefits of analog cancellation
in massive MIMO systems with hundreds of antenna elements is still quite impractical. Hybrid Analog and Digital (A/D) beamforming architectures have been lately considered as a candidate technology for realizing massive MIMO transceivers with very large number of antenna elements, but with much fewer numbers of Radio Frequency (RF) chains. In this paper, we present a novel architecture for full duplex hybrid A/D beamforming transceivers including multi-tap analog cancellation with reduced number of taps and simple multiplexers for efficient signal routing among the transceiver RF chains. Capitalizing on the proposed transceiver architecture, we present a joint design of analog cancellation and A/D beamforming with the objective to maximize the achievable full duplex rate performance. Representative millimeter wave simulation results demonstrate the effectiveness of the proposed architecture and algorithmic framework for enabling simultaneous uplink and downlink communications with reduced complexity analog self-interference cancellation.
Reconfigurable intelligent surfaces (RISs) have recently received widespread attention in the field of wireless communication. An RIS can be controlled to reflect incident waves from the transmitter towards the receiver; a feature that is believed to
fundamentally contribute to beyond 5G wireless technology. The typical RIS consists of entirely passive elements, which requires the high-dimensional channel estimation to be done elsewhere. Therefore, in this paper, we present a semi-passive large-scale RIS architecture equipped with only a small fraction of simplified receiver units with only 1-bit quantization. Based on this architecture, we first propose an alternating direction method of multipliers (ADMM)-based approach to recover the training signals at the passive RIS elements, We then obtain the global channel by combining a channel sparsification step with the generalized approximate message passing (GAMP) algorithm. Our proposed scheme exploits both the sparsity and low-rankness properties of the channel in the joint spatial-frequency domain of a wideband mmWave multiple-input-multiple-output (MIMO) communication system. Simulation results show that the proposed algorithm can significantly reduce the pilot signaling needed for accurate channel estimation and outperform previous methods, even with fewer receiver units.
This paper considers the application of reconfigurable intelligent surfaces (RISs) (a.k.a. intelligent reflecting surfaces (IRSs)) to assist multiuser multiple-input multiple-output (MIMO) uplink transmission from several multi-antenna user terminals
(UTs) to a multi-antenna base station (BS). For reducing the signaling overhead, only partial channel state information (CSI), including the instantaneous CSI between the RIS and the BS as well as the slowly varying statistical CSI between the UTs and the RIS, is exploited in our investigation. In particular, an optimization framework is proposed for jointly designing the transmit covariance matrices of the UTs and the RIS phase shift matrix to maximize the system global energy efficiency (GEE) with partial CSI. We first obtain closed-form solutions for the eigenvectors of the optimal transmit covariance matrices of the UTs. Then, to facilitate the design of the transmit power allocation matrices and the RIS phase shifts, we derive an asymptotically deterministic equivalent of the objective function with the aid of random matrix theory. We further propose a suboptimal algorithm to tackle the GEE maximization problem with guaranteed convergence, capitalizing on the approaches of alternating optimization, fractional programming, and sequential optimization. Numerical results substantiate the effectiveness of the proposed approach as well as the considerable GEE gains provided by the RIS-assisted transmission scheme over the traditional baselines.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
