IEEE Journal of Selected Topics in Signal Processing

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The last few years have witnessed a tremendous growth of the demand for wireless services and a significant increase of the number of mobile subscribers. A recent data traffic forecast from Cisco reported that the global mobile data traffic reached 1.2 zettabytes per year in 2016, and the global IP traffic will increase nearly threefold over the next 5 years.

A novel scheme called dual-polarized spatial media-based modulation (DP-SMBM), which combines judiciously the media-based modulation (MBM), spatial modulation (SM), and dual-polarized (DP) antennas, is proposed in this paper. The DP-SMBM introduces a new DP domain to convey additional information without occupying extra physical space, effectively enhancing the transmission rate and alleviating the finite space issue.

In this paper we propose a family of index modulation systems which can operate with low-power consumption and low operational complexity for multi-user communication. This is particularly suitable for non-time sensitive Internet of Things (IoT) applications such as telemetry, smart metering, and soon.

Millimeter wave technology is an essential component of most solutions that address the coverage and throughput demands of next-generation cellular networks. To overcome the high propagation losses however, it is necessary to deploy large antenna arrays for spatial localization of energy by beamforming. 

Energy-efficient, highly integrated lens antenna arrays (LAAs) have found widespread applications in wideband millimeter wave or terahertz communications, localization and tracking, and wireless power transfer. Accurate estimation of angle-of-arrival (AoA) is key to those applications, but has been hindered by a spatial-wideband effect in wideband systems. 

This paper presents a time-frequency masking based online multi-channel speech enhancement approach that uses a convolutional recurrent neural network to estimate the mask. The magnitude and phase components of the short-time Fourier transform coefficients for multiple time frames are provided as an input such that the network is able to discriminate between the directional speech...

This paper presents a time-frequency masking based online multi-channel speech enhancement approach that uses a convolutional recurrent neural network to estimate the mask. The magnitude and phase components of the short-time Fourier transform coefficients for multiple time frames are provided as an input such that the network is able to discriminate between the directional speech...

The seven papers in this special issue cover various far-field speech processing techniques including speech enhancement, separation and recognition, and their integration. In most of the methods, multichannel speech processing is an essential component to achieve state-of-the-art performance.

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