295 research outputs found
Successful Recovery Performance Guarantees of SOMP Under the L2-norm of Noise
The simultaneous orthogonal matching pursuit (SOMP) is a popular, greedy
approach for common support recovery of a row-sparse matrix. However, compared
to the noiseless scenario, the performance analysis of noisy SOMP is still
nascent, especially in the scenario of unbounded noise. In this paper, we
present a new study based on the mutual incoherence property (MIP) for
performance analysis of noisy SOMP. Specifically, when noise is bounded, we
provide the condition on which the exact support recovery is guaranteed in
terms of the MIP. When noise is unbounded, we instead derive a bound on the
successful recovery probability (SRP) that depends on the specific distribution
of the -norm of the noise matrix. Then we focus on the common case when
noise is random Gaussian and show that the lower bound of SRP follows
Tracy-Widom law distribution. The analysis reveals the number of measurements,
noise level, the number of sparse vectors, and the value of mutual coherence
that are required to guarantee a predefined recovery performance.
Theoretically, we show that the mutual coherence of the measurement matrix must
decrease proportionally to the noise standard deviation, and the number of
sparse vectors needs to grow proportionally to the noise variance. Finally, we
extensively validate the derived analysis through numerical simulations
Sensing User's Activity, Channel, and Location with Near-Field Extra-Large-Scale MIMO
This paper proposes a grant-free massive access scheme based on the
millimeter wave (mmWave) extra-large-scale multiple-input multiple-output
(XL-MIMO) to support massive Internet-of-Things (IoT) devices with low latency,
high data rate, and high localization accuracy in the upcoming sixth-generation
(6G) networks. The XL-MIMO consists of multiple antenna subarrays that are
widely spaced over the service area to ensure line-of-sight (LoS)
transmissions. First, we establish the XL-MIMO-based massive access model
considering the near-field spatial non-stationary (SNS) property. Then, by
exploiting the block sparsity of subarrays and the SNS property, we propose a
structured block orthogonal matching pursuit algorithm for efficient active
user detection (AUD) and channel estimation (CE). Furthermore, different
sensing matrices are applied in different pilot subcarriers for exploiting the
diversity gains. Additionally, a multi-subarray collaborative localization
algorithm is designed for localization. In particular, the angle of arrival
(AoA) and time difference of arrival (TDoA) of the LoS links between active
users and related subarrays are extracted from the estimated XL-MIMO channels,
and then the coordinates of active users are acquired by jointly utilizing the
AoAs and TDoAs. Simulation results show that the proposed algorithms outperform
existing algorithms in terms of AUD and CE performance and can achieve
centimeter-level localization accuracy.Comment: Submitted to IEEE Transactions on Communications, Major revision.
Codes will be open to all on https://gaozhen16.github.io/ soo
- …