82 research outputs found
Beamforming for bidirectional MIMO full duplex under the joint sum power and per antenna power constraints
No full textHybrid beamforming and combining for millimeter wave full duplex massive MIMO interference channel
No full textHybrid beamforming for bidirectional massive MIMO full duplex under practical considerations
No full textPer-link parallel and distributed hybrid beamforming for multi-user multi-cell massive MIMO millimeter wave full duplex
No full text
Intelligent Reflecting Surfaces Assisted Millimeter Wave MIMO Full Duplex Systems
Full text linkpeer reviewedIn this paper, we propose to remove the analog stage of hybrid beamforming (HYBF) in the millimeter wave (mmWave) full-duplex (FD) systems. Such a solution is highly desirable as the analog stage suffers from high insertion loss and high power consumption. Consequently, the mmWave FD nodes can operate with a fewer number of antennas, instead of relying on a massive number of antennas, and to tackle the propagation challenges of the mmWave band we propose to use near-field intelligent reflecting surfaces (NF-IRSs). The objective of the NF-IRSs is to simultaneously and smartly control the uplink (UL) and downlink (DL) channels while assisting in shaping the SI channel: this to obtain very strong passive SI cancellation. A novel joint active and passive beamforming design for the weighted sum-rate (WSR) maximization for the NF-IRSs-assisted mmWave point-to-point FD system is presented. Results show that the proposed solution fully reaps the benefits of the IRSs, only when they operate in the NF, which leads to considerably higher gains compared to the conventional massive MIMO (mMIMO) mmWave FD and half duplex (HD) systems
Parallel and distributed hybrid beamforming for multicell millimeter wave MIMO full duplex
No full textMulti-stage/hybrid BF under limited dynamic range for OFDM FD backhaul with MIMO SI nulling
No full textDeep Reinforcement Learning for Backscatter Communications: Augmenting Intelligence in Future Internet of Things
No full textBackscatter communication (BC) technology offers sustainable solutions for
next-generation Internet-of-Things (IoT) networks, where devices can transmit
data by reflecting and adjusting incident radio frequency signals. In parallel
to BC, deep reinforcement learning (DRL) has recently emerged as a promising
tool to augment intelligence and optimize low-powered IoT devices. This article
commences by elucidating the foundational principles underpinning BC systems,
subsequently delving into the diverse array of DRL techniques and their
respective practical implementations. Subsequently, it investigates potential
domains and presents recent advancements in the realm of DRL-BC systems. A use
case of RIS-aided non-orthogonal multiple access BC systems leveraging DRL is
meticulously examined to highlight its potential. Lastly, this study identifies
and investigates salient challenges and proffers prospective avenues for future
research endeavors
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