Zobrazeno 1 - 10
of 243
pro vyhledávání: '"Jing, Yindi"'
Interleaved training has been studied for single-user and multi-user massive MIMO downlink with either fully-digital or hybrid beamforming. However, the impact of channel correlation on its average training overhead is rarely addressed. In this paper
Externí odkaz:
http://arxiv.org/abs/2307.16356
In frequency division duplexing (FDD) cell-free massive MIMO, the acquisition of the channel state information (CSI) is very challenging because of the large overhead required for the training and feedback of the downlink channels of multiple coopera
Externí odkaz:
http://arxiv.org/abs/2307.10730
Publikováno v:
IEEE Wireless Communications Letters, vol. 9, no. 8, pp. 1278-1282, Aug. 2020
This letter proposes a modified non-orthogonal multiple-access (NOMA) scheme for systems with a multi-antenna base station (BS) and two single-antenna users, where NOMA transmissions are conducted only when the absolute correlation coefficient (CC) b
Externí odkaz:
http://arxiv.org/abs/2002.11185
In this paper, we study a massive multiple-input multiple-output (mMIMO) relay network where multiple source-destination pairs exchange information through a common relay equipped with a massive antenna array. The source users perform simultaneous wi
Externí odkaz:
http://arxiv.org/abs/2001.11803
Akademický článek
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We consider the use of deep neural network (DNN) to develop a decision-directed (DD)-channel estimation (CE) algorithm for multiple-input multiple-output (MIMO)-space-time block coded systems in highly dynamic vehicular environments. We propose the u
Externí odkaz:
http://arxiv.org/abs/1901.03435
Akademický článek
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Water-filling solutions play an important role in the designs for wireless communications, e.g., transmit covariance matrix design. A traditional physical understanding is to use the analogy of pouring water over a pool with fluctuating bottom. Numer
Externí odkaz:
http://arxiv.org/abs/1808.01707
Publikováno v:
IEEE Trans. Wireless Commun. vol. 18, no. 9, pp. 4368-4378, June. 2019
In this paper, a deep learning (DL)-based sphere decoding algorithm is proposed, where the radius of the decoding hypersphere is learned by a deep neural network (DNN). The performance achieved by the proposed algorithm is very close to the optimal m
Externí odkaz:
http://arxiv.org/abs/1807.03162
High power consumption and hardware cost are two barriers for practical massive multiple-input multiple-output (mMIMO) systems. A promising solution is to employ low-resolution analog-to-digital converters (ADCs). In this paper, we consider a general
Externí odkaz:
http://arxiv.org/abs/1804.07254