Exact(1)
In this paper, a novel CS-NOMA scheme for MMTC in 5G was proposed to enable joint detection of active users and their data.
Similar(59)
An improved version of the algorithm in [5, 6] was later introduced in [7] to enable joint detection and tracking of targets with unknown and randomly changing aspect.
In our recent work [23], we propose a novel CS-based NOMA scheme for the CDMA and /or the OFDM systems in which CS-MUD is deployed to enable joint activity and data detection without knowledge of the activity information of the users in advance.
In order to enable joint activity and data detection without requiring the activity knowledge of users, the CS-MUD is deployed in the BS to recover the sparse signal x in (3).
To address these challenges, we propose a novel compressed sensing (CS -based non-orthogonal multiple aCS -basedMA) scheme, called CS-NOMA, which introduces low coherence spreading (LCS) signatures to enable joint activity and data detection without requiring the activity informultiplef users in accesse.
Consequently, a joint detection of a very large number of users can be prohibitive.
The recent advances in neuroimaging computing methods also enabled joint analysis of the multimodal data.
Moreover, TANOR enables joint exploration of algorithmic and architectural variations in realizing efficient hardware accelerators.
The CS-MUD enables joint activity and data detection, which facilitates a reliable detection of direct random access.
In addition, the proposed CS-NOMA scheme enables joint activity users and data detection through CS-MUD, even if the CSI is not perfect.
The differences enable the detection of nanoscale motion.
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