Spatio-Temporal processing for Optimum Uplink-Downlink WCDMA Systems

The capacity of a cellular system is limited by two different phenomena, namely multipath fading and multiple access interference (MAl). A Two Dimensional (2-D) receiver combats both of these by processing the signal both in the spatial and temporal domain. An ideal 2-D receiver would perform joint space-time processing, but at the price of high computational complexity. In this research we investigate computationally simpler technique termed as a Beamfom1er-Rake. In a Beamformer-Rake, the output of a beamfom1er is fed into a succeeding temporal processor to take advantage of both the beamformer and Rake receiver. Wireless service providers throughout the world are working to introduce the third generation (3G) and beyond (3G) cellular service that will provide higher data rates and better spectral efficiency. Wideband COMA (WCDMA) has been widely accepted as one of the air interfaces for 3G. A Beamformer-Rake receiver can be an effective solution to provide the receivers enhanced capabilities needed to achieve the required performance of a WCDMA system. We consider three different Pilot Symbol Assisted (PSA) beamforming techniques, Direct Matrix Inversion (DMI), Least-Mean Square (LMS) and Recursive Least Square (RLS) adaptive algorithms. Geometrically Based Single Bounce (GBSB) statistical Circular channel model is considered, which is more suitable for array processing, and conductive to RAKE combining. The performances of the Beam former-Rake receiver are evaluated in this channel model as a function of the number of antenna elements and RAKE fingers, in which are evaluated for the uplink WCDMA system. It is shown that, the Beamformer-Rake receiver outperforms the conventional RAKE receiver and the conventional beamformer by a significant margin. Also, we optimize and develop a mathematical formulation for the output Signal to Interference plus Noise Ratio (SINR) of a Beam former-Rake receiver. In this research, also, we develop, simulate and evaluate the SINR and Signal to Noise Ratio (Et!Nol performances of an adaptive beamforming technique in the WCDMA system for downlink. The performance is then compared with an omnidirectional antenna system. Simulation shows that the best perfom1ance can be achieved when all the mobiles with same Angle-of-Arrival (AOA) and different distance from base station are formed in one beam

Massive MIMO channel prediction using recurrent neural networks

Massive MIMO has been classified as one of the high potential wireless communication technologies due to its unique abilities such as high user capacity, increased spectral density, and diversity among others. Due to the exponential increase of connected devices, these properties are of great importance for the current 5G-IoT era and future telecommunication networks. However, outdated channel state information (CSI) caused by the variations in the channel response due to the presence of highly mobile and rich scattering is a major problem facing massive MIMO systems. Outdated CSI occurs when the information obtained about the channel at the transmitter changes before transmission. This leads to performance degradation of the network. In this work, we demonstrate a low complexity channel prediction method using neural networks. Specifically, we explore the power of recurrent neural network utilizing long-short memory cells in analyzing time series data. We review various neural network-based channel prediction methods available in the literature and compare complexity and performance metrics. Results indicate that the proposed methods outperform conventional systems by tremendously lowering the complexity associated with channel prediction.This work is funded by the scientific and technological research council of Turkey (TÜBITAK) under grand 119E392

Exploiting the spatio-temporal channel properties of multiple antenna systems

The spatio-temporal channel properties of multiple antenna systems are exploited to obtain new approaches to localization and channel prediction. It is shown that a mobile station can be localized in multipath environments under the explicit consideration of scatterers. Thus, unlike conventional localization systems, the scatterers are used as an aid in localization. Moreover, it is shown that channel prediction in multiple antenna systems can be performed using linear prediction filters. This result is used to propose optimal and computationally inexpensive suboptimal channel predictors

Spatio-Temporal processing for Optimum Uplink-Downlink WCDMA Systems

The capacity of a cellular system is limited by two different phenomena, namely multipath fading and multiple access interference (MAl). A Two Dimensional (2-D) receiver combats both of these by processing the signal both in the spatial and temporal domain. An ideal 2-D receiver would perform joint space-time processing, but at the price of high computational complexity. In this research we investigate computationally simpler technique termed as a Beamfom1er-Rake. In a Beamformer-Rake, the output of a beamfom1er is fed into a succeeding temporal processor to take advantage of both the beamformer and Rake receiver. Wireless service providers throughout the world are working to introduce the third generation (3G) and beyond (3G) cellular service that will provide higher data rates and better spectral efficiency. Wideband COMA (WCDMA) has been widely accepted as one of the air interfaces for 3G. A Beamformer-Rake receiver can be an effective solution to provide the receivers enhanced capabilities needed to achieve the required performance of a WCDMA system. We consider three different Pilot Symbol Assisted (PSA) beamforming techniques, Direct Matrix Inversion (DMI), Least-Mean Square (LMS) and Recursive Least Square (RLS) adaptive algorithms. Geometrically Based Single Bounce (GBSB) statistical Circular channel model is considered, which is more suitable for array processing, and conductive to RAKE combining. The performances of the Beam former-Rake receiver are evaluated in this channel model as a function of the number of antenna elements and RAKE fingers, in which are evaluated for the uplink WCDMA system. It is shown that, the Beamformer-Rake receiver outperforms the conventional RAKE receiver and the conventional beamformer by a significant margin. Also, we optimize and develop a mathematical formulation for the output Signal to Interference plus Noise Ratio (SINR) of a Beam former-Rake receiver. In this research, also, we develop, simulate and evaluate the SINR and Signal to Noise Ratio (Et!Nol performances of an adaptive beamforming technique in the WCDMA system for downlink. The performance is then compared with an omnidirectional antenna system. Simulation shows that the best perfom1ance can be achieved when all the mobiles with same Angle-of-Arrival (AOA) and different distance from base station are formed in one beam

Compressive Sampled CSI Feedback Method Based on Deep Learning for FDD Massive MIMO Systems

Accurate downlink channel state information (CSI) is required to be fed back to the base station (BS) in frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) systems in order to achieve maximum antenna diversity and multiplexing. However, downlink CSI feedback overhead scales with the number of transceiver antennas, a major hurdle for practical deployment of FDD massive MIMO systems. To solve this problem, we propose a compressive sampled CSI feedback method based on deep learning (SampleDL). In SampleDL, the massive MIMO channel matrix is sampled uniformly in time/frequency dimension before being fed into neural networks (NNs), which will reduce the computational resource/time at user equipment (UE) as well as enhance the CSI recovery accuracy at the BS. Both theoretical analysis and normalized mean square errors (NMSE) results confirm the advantages of the proposed method in terms of time complexity and recovery accuracy. Besides, a suitable CSI feedback period is explored by link level simulations, which aims to further reduce the overhead of CSI feedback without degrading the communication quality