相关论文: Sparse Recovery for Holographic MIMO Channels: Lev…
Accurate channel estimation in orthogonal time frequency space (OTFS) systems with massive multiple-input multiple-output (MIMO) configurations is challenging due to high-dimensional sparse representation (SR). Existing methods often face…
In this paper, we study the problem of sparse channel estimation via a collaborative and fully distributed approach. The estimation problem is formulated in the angular domain by exploiting the spatially common sparsity structure of the…
Millimeter-wave massive multiple-input multiple-output (MIMO) can use a lens antenna array to considerably reduce the number of radio frequency (RF) chains, but channel estimation is challenging due to the number of RF chains is much…
Channel estimation poses significant challenges in millimeter-wave massive multiple-input multiple-output systems, especially when the base station has fewer radio-frequency chains than antennas. To address this challenge, one promising…
The beam squint effect, which manifests in different steering matrices in different sub-bands, has been widely considered a challenge in millimeter wave (mmWave) multiinput multi-output (MIMO) channel estimation. Existing methods either…
Advancements in emerging technologies, e.g., reconfigurable intelligent surfaces and holographic MIMO (HMIMO), facilitate unprecedented manipulation of electromagnetic (EM) waves, significantly enhancing the performance of wireless…
This paper investigates the problem of estimating sparse channels in massive MIMO systems. Most wireless channels are sparse with large delay spread, while some channels can be observed having sparse common support (SCS) within a certain…
To address the challenges of high-dimensional channel estimation and underutilized spatial correlations among users in holographic MIMO (HMIMO) systems, this paper proposes a joint graph-cut algorithm for multi-user channel estimation in…
Channel acquisition is one of the main challenges for the deployment of reconfigurable intelligent surface (RIS) aided communication systems. This is because an RIS has a large number of reflective elements, which are passive devices with…
Emerging technologies, such as holographic multiple-input multiple-output (HMIMO) and stacked intelligent metasurface (SIM), are driving the development of wireless communication systems. Specifically, the SIM is physically constructed by…
We consider the problem of estimating sparse communication channels in the MIMO context. In small to medium bandwidth communications, as in the current standards for OFDM and CDMA communication systems (with bandwidth up to 20 MHz), such…
We propose equalization-based data detection algorithms for all-digital millimeter-wave (mmWave) massive multiuser multiple-input multiple-out (MU-MIMO) systems that exploit sparsity in the beamspace domain to reduce complexity. We provide…
The interest in variable selection for clustering has increased recently due to the growing need in clustering high-dimensional data. Variable selection allows in particular to ease both the clustering and the interpretation of the results.…
Accurate channel impulse response (CIR) is required for coherent detection and it can also help improve communication quality of service in next-generation wireless communication systems. One of the advanced systems is multi-input…
We propose a simple and efficient time-series clustering framework particularly suited for low Signal-to-Noise Ratio (SNR), by simultaneous smoothing and dimensionality reduction aimed at preserving clustering information. We extend the…
We analyze a multiple-input multiple-output (MIMO) radar model and provide recovery results for a compressed sensing (CS) approach. In MIMO radar different pulses are emitted by several transmitters and the echoes are recorded at several…
In mmWave massive multiple-input multiple-output (mMIMO) systems, hybrid digital/analog beamforming has been recognized as an economic means to overcome the severe mmWave propagation loss. To facilitate beamforming for mmWace mMIMO, there…
In modern communication systems, channel state information is of paramount importance to achieve capacity. It is then crucial to accurately estimate the channel. It is possible to perform SISO-OFDM channel estimation using sparse recovery…
We consider the problem of channel estimation for millimeter wave (mmWave) systems, where, to minimize the hardware complexity and power consumption, an analog transmit beamforming and receive combining structure with only one radio…
Massive MIMO has been regarded as one of the key technologies for 5G wireless networks, as it can significantly improve both the spectral efficiency and energy efficiency. The availability of high-dimensional channel side information (CSI)…