Related papers: Sparsity-Based Channel Estimation Exploiting Deep …
Deep learning has demonstrated the important roles in improving the system performance and reducing computational complexity for $5$G-and-beyond networks. In this paper, we propose a new channel estimation method with the assistance of deep…
In this paper, we study joint antenna activity detection, channel estimation, and multiuser detection for massive multiple-input multiple-output (MIMO) systems with general spatial modulation (GSM). We first establish a double-sparsity…
In order to unlock the full advantages of massive multiple input multiple output (MIMO) in the downlink, channel state information (CSI) is required at the base station (BS) to optimize the beamforming matrices. In frequency division duplex…
In wireless communication systems, the use of multiple antennas at both the transmitter and receiver is a widely known method for improving both reliability and data rates, as it increases the former through transmit or receive diversity…
Massive MIMO is widely considered as a key enabler of the next generation 5G networks. With a large number of antennas at the Base Station, both spectral and energy efficiencies can be enhanced. Unfortunately, the downlink channel…
Reliability is becoming increasingly important for many applications envisioned for future wireless systems. A technology that could improve reliability in these systems is massive MIMO (Multiple-Input Multiple-Output). One reason for this…
For downlink massive multiple-input multiple-output (MIMO) operating in time-division duplex protocol, users can decode the signals effectively by only utilizing the channel statistics as long as channel hardening holds. However, in a…
In this paper, a framework of beamspace channel estimation in millimeter wave (mmWave) massive MIMO system is proposed. The framework includes the design of hybrid precoding and combining matrix as well as the search method for the largest…
Wireless-controlled robots, cars and other critical applications are in need of technologies that offer high reliability and low latency. Massive MIMO, Multiple-Input Multiple-Output, is a key technology for the upcoming 5G systems and is…
Perfect channel estimation is very hard, time/ power consuming, and expensive; so it is not preferred (e.g. in mobile) communication systems. This paper seeks for new, cheap, low complexity, deep learning based solution. Several new…
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…
Reconfigurable antennas that can dynamically change their operation state exhibit excellent adaptivity and flexibility over traditional antennas, and MIMO arrays that consist of multifunctional and reconfigurable antennas (MRAs) are…
Millimeter-wave (mmWave) channels, which occupy frequency ranges much higher than those being used in previous wireless communications systems, are utilized to meet the increased throughput requirements that come with 5G communications. The…
Channel estimation is of great importance in realizing practical intelligent reflecting surface-assisted multi-user communication (IRS-MC) systems. However, different from traditional communication systems, an IRS-MC system generally…
With a significant increase in area throughput, Massive MIMO has become an enabling technology for fifth generation (5G) wireless mobile communication systems. Although prototypes were built, an openly available dataset for channel impulse…
For an orthogonal frequency-division multiplexing (OFDM) system over a doubly selective (DS) channel, a large number of pilot subcarriers are needed to estimate the numerous channel parameters, resulting in low spectral efficiency. In this…
Orthogonal time frequency space (OTFS) modulation outperforms orthogonal frequency division multiplexing (OFDM) in high-mobility scenarios. One challenge for OTFS massive MIMO is downlink channel estimation due to the large number of base…
Channel estimation at millimeter wave (mmWave) is challenging when large antenna arrays are used. Prior work has leveraged the sparse nature of mmWave channels via compressed sensing based algorithms for channel estimation. Most of these…
Massive MIMO basestations, operating with frequency-division duplexing (FDD), require the users to feedback their channel state information (CSI) in order to design the precoding matrices. Given the powerful capabilities of deep neural…
Large-scale multiple-input multiple-output (MIMO) holds great promise for the fifth-generation (5G) and future communication systems. In near-field scenarios, the spherical wavefront model is commonly utilized to accurately depict the…