Application of compressive sensing in sparse spatial channel recovery for beamforming in mmWave outdoor systems

Mimo for millimeter-wave wireless communications: beamforming, spatial multiplexing, or both?

“…Recently, to exploit the spatial characteristics of the mmWave channel, new techniques based on compressive sensing (CS) have been proposed [17]. CS is a signal processing technique for efficiently acquiring and reconstructing a signal by taking advantage of the signal's sparseness or compressibility, and solving underdetermined linear systems.…”

“…Therefore, CS can be efficiently used to estimate the sparse spatial propagation channel and identify the AoD. This method requires only a fraction of the codebook-based overhead and provides an accurate estimate of the AoD [17]. Given knowledge of the user's spatial channel, analog BF can be applied through the application of antenna phase weights.…”

Practical Demonstration of Limited Feedback Beamforming for mmWave Systems

“…Tailored towards exploiting features such as reduced dimensionality or the sparsity found in mmWave channels, new techniques based on CS have recently been proposed [11]. CS is a signal processing technique for efficiently acquiring and reconstructing a signal by taking advantage of the signal's sparsity and by solving an underdetermined linear system.…”

“…z + " system [14], where z is sparse; therefore, CS can be efficiently used to estimate the sparse spatial propagation channel and to identify the AoD. This method requires only a fraction of the codebook-based overhead and provides an accurate estimate of the AoD [11].…”

“…In this work we derive BF weights to maximise the Signal-to-Noise-Ratio (SNR). Compared to the length of the codebook, which is of the order of the number of antenna elements (~), the number of required CS measurements is proportional to ( ) [11]. More information on the theoretic background and application of CS for sparse time and spatial signal estimation can be found in [9][10][11].…”