Time-division Duplex Massive MIMO Research Articles

Channel Estimation and Prediction in a Pilot-Less Massive MIMO TDD Using Non-Coherent DMPSK

A novel time division duplex massive MIMO technique is proposed based on performing a pilot-less channel estimation in the uplink (UL) utilizing reconstructed differentially encoded data. Spatial multiplexing and differentially encoded data is applied both in the UL and in the downlink (DL). In this system, a reference signal is the first one of the differentially encoded streams in the UL and DL, and the pilots for data estimation are avoided while maintaining spatial multiplexing capabilities. To improve the channel estimation we propose to use a linear Wiener filter and we also propose different symbols placing strategies in an OFDM grid. We also propose a detection improvement of the UL data utilizing the predicted channels. We perform an analysis of the MSE of the blind channel estimation using the differentially encoded data and analyze the symbol-error-rate for both the UL and the DL when channel aging is considered. The analysis is corroborated via numerical results and the proposed scheme is shown to outperform its pilot-based counterpart.

Thompson Sampling-Based Antenna Selection With Partial CSI for TDD Massive MIMO Systems

Antenna selection (AS) is a promising technology that can reduce the implementation complexity and hardware cost of a massive MIMO system, in which a part of the attainable antennas are selected and connected to the radio frequency chains in each time slot. In this article, we propose a low-complexity AS algorithm to maximize the downlink achievable rate in the time-division duplexing massive MIMO system, which is based on online Thompson sampling technique and significantly reduces the pilot overhead required for channel estimation with only partial channel state information. We prove that the distribution-dependent upper bound of the proposed algorithm is sub-linear as a function of time slot by introducing the concept of regret. We also develop three discounting factors to accommodate the large-scale variations across antennas. Numerical results show that the downlink achievable rate can be greatly improved with our proposed scheme as compared to other typical ones.

Beyond 5G: Leveraging Cell Free TDD Massive MIMO Using Cascaded Deep Learning

This letter deals with the calibration of Time Division Duplexing (TDD) reciprocity in an Orthogonal Frequency Division Multiplexing (OFDM) based Cell Free Massive MIMO system where the responses of the (Radio Frequency) RF chains render the end to end channel non-reciprocal, even though the physical wireless channel is reciprocal. We further address the non-availability of the uplink channel estimates at locations other than pilot subcarriers and propose a single-shot solution to estimate the downlink channel at all subcarriers from the uplink channel at selected pilot subcarriers. We propose a cascade of two Deep Neural Networks (DNN) to achieve the objective. The proposed method is easily scalable and removes the need for relative reciprocity calibration based on the cooperation of antennas, which usually introduces dependency in Cell Free Massive MIMO systems.

Eradication of pilot contamination and zero forcing precoding in the multi‐cell TDD massive MIMO systems

In massive multiple-input multiple-output (M-MIMO) systems, the base station (BS) estimates the downlink and uplink channels using the uplink pilot training in conjunction with channel reciprocity property of time division duplex (TDD) operation. However, this channel estimation is contaminated due to the use of non-orthogonal pilot sequences for the uplink training in neighbouring cells that results in inter-cell interference. This study proposes an effective pilot contamination elimination scheme along with zero forcing precoding for multi-cell TDD M-MIMO systems. In the proposed scheme, each BS is assigned with a specific orthogonal variable spreading factor (OVSF) code row and a set of Zadoff–Chu (ZC) sequences are used for uplink pilot training. Then, the set of ZC sequences is multiplied element-wise at each BS with its OVSF code row to generate the orthogonality among the pilot sequences across the neighbouring cells. The proposed method can promise uncontaminated channel estimation and interference free downlink transmission without requiring the knowledge of the channels’ second-order statistics and without increasing the training overhead by a factor equal to the number of interfering cells. The simulation results authenticate the effectiveness and superiority of the proposed scheme over other schemes.

Downlink training scheme for massive MIMO systems

A novel downlink training scheme is proposed for time-division duplex massive MIMO systems. Different from conventional solutions, including the cell-specific reference signal in LTE, the UE-specific reference signal in LTE-A and the commonly used method without the downlink training sequence, the cell-specific demodulation reference signal is proposed by utilising the inherent asymptotic orthogonality, which is shared by all users within one cell and used for the estimation of effective channel coefficients. The training overhead is reduced and the downlink spectral efficiency can be improved considerably.

Achievable Rates of FDD Massive MIMO Systems With Spatial Channel Correlation

It is well known that the performance of frequency-division-duplex (FDD) massive MIMO systems with i.i.d. channels is disappointing compared with that of time-division-duplex (TDD) systems, due to the prohibitively large overhead for acquiring channel state information at the transmitter (CSIT). In this paper, we investigate the achievable rates of FDD massive MIMO systems with spatially correlated channels, considering the CSIT acquisition dimensionality loss, the imperfection of CSIT and the regularized-zero-forcing linear precoder. The achievable rates are optimized by judiciously designing the downlink channel training sequences and user CSIT feedback codebooks, exploiting the multiuser spatial channel correlation. We compare our achievable rates with TDD massive MIMO systems, i.i.d. FDD systems, and the joint spatial division and multiplexing (JSDM) scheme, by deriving the deterministic equivalents of the achievable rates, based on popular channel models. It is shown that, based on the proposed eigenspace channel estimation schemes, the rate-gap between FDD systems and TDD systems is significantly narrowed, even approached under moderate number of base station antennas. Compared to the JSDM scheme, our proposal achieves dimensionality-reduction channel estimation without channel pre-projection, and higher throughput for moderate number of antennas and moderate to large channel coherence time, though at higher computational complexity.