Asynchronous Code-Division Random Access Using Convex Optimization
Lorne Applebaum, Waheed U. Bajwa, Marco F. Duarte, and Robert, Calderbank

TL;DR
This paper introduces a novel asynchronous non-orthogonal code-division random access scheme combined with a convex optimization-based multiuser detection algorithm, significantly improving user capacity without requiring channel knowledge.
Contribution
It presents a convex optimization-based MUD algorithm that handles asynchronous user activity without delay or channel information, outperforming traditional methods.
Findings
Outperforms orthogonal signaling in user capacity
Does not require user delay or channel state information
Works efficiently with random and algebraic codewords
Abstract
Many applications in cellular systems and sensor networks involve a random subset of a large number of users asynchronously reporting activity to a base station. This paper examines the problem of multiuser detection (MUD) in random access channels for such applications. Traditional orthogonal signaling ignores the random nature of user activity in this problem and limits the total number of users to be on the order of the number of signal space dimensions. Contention-based schemes, on the other hand, suffer from delays caused by colliding transmissions and the hidden node problem. In contrast, this paper presents a novel pairing of an asynchronous non-orthogonal code-division random access scheme with a convex optimization-based MUD algorithm that overcomes the issues associated with orthogonal signaling and contention-based methods. Two key distinguishing features of the proposed MUD…
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