This two-part paper considers an uplink massive device communication scenario
in which a large number of devices are connected to a base-station (BS), but
user traffic is sporadic so that in any given coherence interval, only a subset
of users are active. The objective is to quantify the cost of active user
detection and channel estimation and to characterize the overall achievable
rate of a grant-free two-phase access scheme in which device activity detection
and channel estimation are performed jointly using pilot sequences in the first
phase and data is transmitted in the second phase. In order to accommodate a
large number of simultaneously transmitting devices, this paper studies an
asymptotic regime where the BS is equipped with a massive number of antennas.
The main contributions of Part I of this paper are as follows. First, we note
that as a consequence of having a large pool of potentially active devices but
limited coherence time, the pilot sequences cannot all be orthogonal. However,
despite the non-orthogonality, this paper shows that in the asymptotic massive
multiple-input multiple-output (MIMO) regime, both the missed device detection
and the false alarm probabilities for activity detection can always be made to
go to zero by utilizing compressed sensing techniques that exploit sparsity in
the user activity pattern. Part II of this paper further characterizes the
achievable rates using the proposed scheme and quantifies the cost of using
non-orthogonal pilot sequences for channel estimation in achievable rates.

本文研究在大量设备连接基站的场景下，利用压缩感知和非正交导频序列技术来实现设备活动检测，通过两阶段的访问方案实现数据传输，进而实现了上行大规模设备通信中用户暂态性的高效利用，并且在大规模 MIMO 系统下使得错过检测和虚警概率可以被刻意的控制到 0。