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The listen before talk (LBT) mechanism is often used in dynamic spectrum access (DSA) schemes, which requires secondary users (SUs) to perform spectrum sensing before accessing a channel so as to avoid transmission collisions with primary users (PUs). In the scenario of DSA with multiple PU channels, channel sensing order according to the idle probabilities of PU channels is important for SUs to improve the spectrum efficiency. However, conventional DSA schemes are sluggish in updating the estimates of idle probabilities sequentially, which hinders their application in highly dynamic channels (with time-varying idle probabilities). To overcome this issue, we propose a change detection algorithm with a binary hypothesis testing of Schwarz Information Criterion (SIC), and present an SIC based Thompson Sampling Algorithm (SIC-TSA) to promptly update the estimates of idle probabilities. Moreover, the collision probabilities among SUs are analyzed. Numerical results are provided to show tha ....
Orthogonal time frequency space (OTFS) technique, which modulates data symbols in the delay-Doppler (DD) domain, presents a potential solution for supporting reliable information transmission in high-mobility vehicular networks. In this paper, we study the issues of DD channel estimation for OTFS in the presence of fractional Doppler. We first propose a channel estimation algorithm with both low complexity and high accuracy based on the unitary approximate message passing (UAMP), which exploits the structured sparsity of the effective DD domain channel using hidden Markov model (HMM). The empirical state evolution (SE) analysis is then leveraged to predict the performance of our proposed algorithm. To refine the hyperparameters in the proposed algorithm, we derive the update criterion for the hyperparameters through the expectation-maximization (EM) algorithm. Finally, Our simulation results demonstrate that our proposed algorithm can achieve a significant gain over various baseline sc ....
Grant-free random access is promising in achieving massive connectivity with sporadic transmissions in massive machine type communications (mMTC) for internet of things (IoT) applications, where the hand-shaking between the access point (AP) and users is skipped, leading to high multiple access efficiency. In grant-free random access, the AP needs to identify the active users and perform channel estimation and signal detection. Conventionally, pilot signals are required for the AP to achieve user activity detection and channel estimation before active user signal detection, which may still result in substantial overhead and latency. In this paper, to further reduce the overhead and latency, we investigate the problem of grant-free random access without the use of pilot signals in a millimeter wave (mmWave) multiple input and multiple output (MIMO) system, where the AP performs blind joint user activity detection, channel estimation and signal detection (UACESD). We show that the blind ....
This paper studies the channel estimation for wideband multiple-input multiple-output (MIMO) systems equipped with hybrid analog/digital transceivers operating in the millimeter-wave (mmWave) or terahertz (THz) bands. By exploiting the low-rank property of the concatenated channel matrix of the delay taps, we formulate the channel estimation problem as a low-rank matrix sensing (LRMS) problem and solve it using a low-complexity generalized conditional gradient-alternating minimization (GCG-ALTMIN) algorithm. This LRMS-based solution can accommodate different precoder/combiner and training structures. In addition, it does not require knowledge about the array responses at the transceivers, in contrast to most existing solutions allowing low training overhead. Furthermore, a preconditioned conjugate gradient (PCG) algorithm-based implementation and a low-rank matrix completion (LRMC) formulation are proposed to further reduce the computational complexity. In order to enhance the channel ....