Forcing Precoding Research Articles

Energy Efficiency Optimization of Cell-Free Massive MIMO with Zero Forcing Precoding

Energy Efficiency Optimization of Cell-Free Massive MIMO with Zero Forcing Precoding

Numtrical Evaluation of Optimal Precoding Algorithm and Zero Forcing Precoding in MU-MISO Channel with Channel Aging

In paper the numerical investigation of the impact of delayed channel state information (CSI) due to user movement and caused channel aging on the performance of multiuser precoder in downlink MISO system. The time variant CSI is obtained by the least squares channel estimation. We consider Zero Forcing algorithm and numerical optimization based solution of calculating precoder vectors maximizimg sum rate of multiuser system. For numerical simulation the QuaDRiGa channel model reflecting the real propagation conditions for moving users is used. The obtained performance of multiuser Zero Forcing and optimization based beamforming in spatially correlated channel are compared based on the empirical cumulative density function of the sum rate of multiple users.

Downlink Massive MU-MIMO With Successively-Regularized Zero Forcing Precoding

In this letter, we consider linear precoding for downlink massive multi-user (MU) multiple-input multiple-output (MIMO) systems. We propose the novel successively-regularized zero forcing (SRZF) precoding, which exploits successive null spaces of the MIMO channels of the users, along with regularization, to control the inter-user interference (IUI) and to enhance performance and robustness to imperfect channel state information (CSI) at the base station (BS). We study the IUI characteristics of the proposed SRZF precoding for perfect and imperfect CSI at the BS. Furthermore, via computer simulations, we compare the sum rate of SRZF precoding with those of several baseline schemes including conventional and regularized zero forcing (ZF) precoding. Our simulation results reveal that, for massive MIMO systems with inter-user channel correlations, the proposed SRZF precoding significantly outperforms the considered baseline schemes for both perfect and imperfect CSI at the BS.

Improved Iterative Inverse Matrix Approximation Algorithm for Zero Forcing Precoding in Large Antenna Arrays

Precoding algorithms are used in massive multiple-input multiple-output (mMIMO) communication systems to ensure effective signal transmission. The zero-forcing (ZF) is one of the most common linear precoding algorithms used in MIMO and mMIMO systems. ZF precoding is complex to implement because it requires direct matrix inversion of the gram matrix. Iterative algorithms have been proposed for approximating matrix inversions. However, iterative algorithms require initial conditions and pre-computations to converge to the optimal transmitted signal vector. This paper proposes a new improved iterative algorithm that guarantees convergence under any circumstances without dependency on any optimized initial parameter or condition. The proposed algorithm is based on a three-step iterative and iterative generalized inverse matrix approximation algorithm. The proposed algorithm was verified using a new correlated channel model that included mutual coupling effects and gain and phase variances caused by radio frequency elements at a base station (BS). The computational complexity of the proposed algorithm was then computed. This study analyzes and compares the bit error rate (BER) performance of the proposed algorithm with that of prominent existing algorithms. Moreover, the sum-rate performance of the proposed algorithm was analyzed. Simulations were performed under both correlated and uncorrelated channel conditions, for comparison and analysis. The simulation results demonstrate that the proposed algorithm outperforms the compared algorithms in terms of the convergence and convergence rates.

Mitigating pilot contamination in Rician faded massive MIMO 5G systems using enhanced zero forcing precoding and ring partitioning

In this paper, pilot contamination, in massive multiple-input multiple-output (MIMO) system is reduced to a significant extent by using base station (BS) rotation, user (UE) scheduling and enhanced Zero Forcing (e-ZF) precoding. Cellular network is divided, based on the angle, and partitioned into rings for grouping mobile UEs. Grouping is done by creating specifically designed policies using three significant network parameters. Neural network is used for scheduling grouped UEs and then UEs are assigned with optimal pilots. Hybrid particle swarm optimization with fuzzy (PSOF) logic detects optimal pilot in accordance to channel quality and additive white gaussian noise (AWGN). Simulations are performed in MATLAB-R2017b and performance is experimentally evaluated in terms of achievable rate, noise ratio and spectral efficiency in Rician Fading environment. Comparative results are evaluated with respect to conventional precoding techniques as dirty paper coding (DPC), block diagonalization (BD), matched filter (MF) and Tomlinson Harashima (TH) precoding.

Complex Regularized Zero Forcing Precoding for Massive MIMO Systems

In this paper, an energy efficient precoding scheme is proposed for massive multiple-input multiple-output (MIMO) systems. The proposed precoding scheme outperforms the conventional zero forcing (ZF) precoder in terms of the power consumption and the achievable transmission rate by including a complex valued regularization parameter such that the energy efficiency is improved. The proposed energy efficient complex regularized zero forcing precoder is analytically evaluated, verified by simulations and compared to the ZF precoder. The obtained results confirm the robustness of the proposed precoder in massive MIMO systems.

Physical-Layer Security With Optical Generalized Space Shift Keying

Spatial modulation (SM) is a promising technique that reduces inter-channel interference while providing high power efficiency and detection simplicity. In order to ensure the secrecy of SM, precoding and friendly jamming are widely adopted in the literature. However, neither of those methods can take advantage of SM. In this paper, a novel spatial constellation design (SCD) technique is proposed to enhance the physical layer security (PLS) of optical generalized space shift keying (GSSK), which can retain some benefits of SM. Due to the lack of small-scale fading, the quasi-static characteristics of the optical channel is used to tailor the received signal at the legitimate user’s (Bob’s) side. The PLS of the system is guaranteed by the appropriate selection of the power allocation coefficients for randomly activated light emitting diodes (LEDs). With the aid of Bob’s channel state information at the transmitter, the bit error ratio (BER) of Bob is minimized while the BER performance of the potential eavesdroppers (Eves) is significantly degraded. Monte-Carlo simulation results show that the proposed SCD-zero forcing precoding (ZFP) forces Eve to experience a BER of around 0.5 by outperforming both the conventional and ZFP based GSSK for all practical signal-to-noise-ratio regimes and Bob-Eve separations.

Wireless Environment Aware Adaptive Scheduling Technique For Cellular Networks

<p>It is now well known that employing channel knowledge based on signaling techniques in wireless mobile ad-hoc networks (MANET) system can yield large improvements in almost all performance metric. Here we proposed the adaptive scheduling, in which the work done is based upon the bandwidth information of channel to provide better quality of service (‘QoS’) to the cell-edge mobile stations. Channel information is critical based on which scheduling is carried out. The bandwidth channel information contains estimation delay, the pilot channel noise and pilot contamination. Afterwards, Zero Forcing precoding methodology has applied for removing the interference at user nodes, destination nodes and gateway side. By extending the characteristics of ZF, the modified Zero Forcing (MZF) has proposed to achieve higher throughput rate and higher spectrum efficiency. The achievable-rates of the ZF and MZF has derived under the comprehensive model of imperfect bandwidth information.</p>

Novel Multi-cell Precoding Schemes for TDD Massive MIMO Systems

Two novel multi-cell precoding schemes, maximum ratio combining precoding with base station cooperation (MRC-BSC) and zero forcing precoding with BS cooperation (ZF-BSC), are proposed for time division duplex massive multiple-input multiple-output system. By making full use of other cells (not target cell) channel estimate information and changing the matrix structure of classical single-cell maximum ratio combining (MRC) precoding and zero forcing (ZF) precoding, proposed precoding schemes are obtained to reduce inter-cell interference with low complexity. The expressions of lower bound per-user achievable rate of proposed precoding schemes for different uplink pilot reuse factors situations are derived through theoretic analysis. Simulation results demonstrate that the proposed MRC-BSC and ZF-BSC can always achieve higher rates than single-cell MRC precoding and ZF precoding. For each precoding scheme, the downlink achievable rate can be improved greatly when enlarging the uplink pilot reuse factor. In addition, the proposed precoding schemes does not affect (or even improve) other cells' performance while improving the target cell's performance.

Optimal User Loading in Massive MIMO Systems with Regularized Zero Forcing Precoding

We consider a downlink massive multiple-input multiple output system employing regularized zero-forcing precoding. We derive the asymptotic signal-to-leakage-plus-noise ratio (SLNR) as both the number of antennas and the number of users go to infinity at a fixed ratio. Focusing on spatially uncorrelated channels with homogeneous large scale fading gains, we show that the SLNR is asymptotically equal to signal-to-interference-plus-noise ratio, which allows us to optimize the user loading for spectral efficiency. The results show that the optimal user loading varies depending on the channel signal-to-noise ratio (SNR). As the SNR increases, the optimal user loading decreases at low SNR, but increases at high SNR.

Physical Layer Network Coding Based on Integer Forcing Precoded Compute and Forward

In this paper, we address the implementation of physical layer network coding (PNC) based on compute and forward (CF) in relay networks. It is known that the maximum achievable rates in CF-based transmission is limited due to the channel approximations at the relay. In this work, we propose the integer forcing precoder (IFP), which bypasses this maximum rate achievability limitation. Our precoder requires channel state information (CSI) at the transmitter, but only that of the channel between the transmitter and the relay, which is a feasible assumption. The overall contributions of this paper are three-fold. Firstly, we propose an implementation of CF using IFP and prove that this implementation achieves higher rates as compared to traditional relaying schemes. Further, the probability of error from the proposed scheme is shown to have up to 2 dB of gain over the existent lattice network coding-based implementation of CF. Secondly, we analyze the two phases of transmission in the CF scheme, thereby characterizing the end-to-end behavior of the CF and not only one-phase behavior, as in previous proposals. Finally, we develop decoders for both the relay and the destination. We use a generalization of Bezout’s theorem to justify the construction of these decoders. Further, we make an analytical derivation of the end-to-end probability of error for cubic lattices using the proposed scheme.