Scalable Synchronous User Activity Detection for 6G Massive Access

Haiyou Guo,Tao Tao, Liyu Cai

2023 IEEE 98TH VEHICULAR TECHNOLOGY CONFERENCE, VTC2023-FALL(2023)

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摘要
In this paper, we create a scalable user activity detection (UAD) scheme for massive access. The proposed solution provides a holistic design from signature sequence design and its transmission method at device side to the reception method at base station side. We design a set of constant-modulus signature sequences for accurate, fast and scalable UAD. The designed sequences are the nonorthogonal complex sinusoid sequence. Different devices can be discriminated by assigning distinct digital frequency, which allows for unique association between user and signature sequence and collision avoidance. Taking advantage of the precoding strategies based on phase compensation or pre-equalization, the UAD problem can be casted as a linear inverse problem of nonnegative underdetermined system. At the heart of which, a sparse nonnegative vector can be solved by a nonnegative least square method, the support of which preserves the activity information exactly. In this way, base station can readily identify any possible combination of active users out of the entire set as long as the sequence length more than the number of actual active users, no matter how the total number of devices scales up.
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关键词
user activity detection,massive random access,sequence design,sparsity detection,nonnegative least square
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