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Deep Learning for Massive MIMO CSI Feedback
CsiNet is developed, a novel CSI sensing and recovery mechanism that learns to effectively use channel structure from training samples that can recover CSI with significantly improved reconstruction quality compared with existing compressive sensing (CS)-based methods.
Power Scaling of Uplink Massive MIMO Systems With Arbitrary-Rank Channel Means
It is found that regardless of the Ricean K-factor, in the case of perfect CSI, the approximations converge to the same constant value as the exact results, as the number of base station antennas grows large, while the transmit power of each user can be scaled down proportionally to 1/M.
Uplink Achievable Rate for Massive MIMO Systems With Low-Resolution ADC
In this letter, we derive an approximate analytical expression for the uplink achievable rate of a massive multiinput multioutput (MIMO) antenna system when finite precision analog-digital converters
A Unified Transmission Strategy for TDD/FDD Massive MIMO Systems With Spatial Basis Expansion Model
A spatial basis expansion model (SBEM) is built to represent the UL/DL channels with far fewer parameter dimensions, which significantly reduces the training overhead and feedback cost and enhances the spectral efficiency.
Large Intelligent Surface-Assisted Wireless Communication Exploiting Statistical CSI
Numerical results show that using the proposed phase shift design can achieve the maximum ergodic spectral efficiency, and a 2-bit quantizer is sufficient to ensure spectral efficiency degradation of no more than 1 bit/s/Hz.
Bayes-Optimal Joint Channel-and-Data Estimation for Massive MIMO With Low-Precision ADCs
A Bayes-optimal JCD estimator is developed using a recent technique based on approximate message passing that allows the efficient evaluation of the performance of quantized massive MIMO systems and provides insights into effective system design.
Performance Analysis of Mixed-ADC Massive MIMO Systems Over Rician Fading Channels
This work investigates the performance of mixed-ADC massive MIMO systems over the Rician fading channel, which is more general for the 5G scenarios like Internet of Things, and reveals the tradeoff between the achievable rate and the energy efficiency.
A Model-Driven Deep Learning Network for MIMO Detection
Numerical results show that the proposed approach can improve the performance of the iterative algorithm significantly under Rayleigh and correlated MIMO channels.
Beam Division Multiple Access Transmission for Massive MIMO Communications
This work develops asymptotically necessary and sufficient conditions for optimal downlink transmission that require only statistical channel state information at the transmitter and proposes a beam division multiple access (BDMA) transmission scheme that simultaneously serves multiple users via different beams.
Noncoherent Detections for Ambient Backscatter System
A practical transmission model for an ambient backscatter system, where a tag wishes to send some low-rate messages to a reader with the help of an ambient RF signal source, and then provide fundamental studies of noncoherent symbol detection when all channel state information of the system is unknown is formulated.