A LINEAR PRECODING STRATEGY BASED ON PARTICLE SWARM OPTIMIZATION IN MULTICELL COOPERATIVE TRANSMISSION

An optimal linear precoding scheme based on Particle Swarm Optimization(PSO),which aims to maximize the system capacity of the cooperative transmission in the downlink channel,is proposed for a multicell multiuser single input single output system.With such a scheme,the optimal precoding vector could be easily searched for each user according to a simplified objective function.Simulation results show that the proposed scheme can obtain larger average spectrum efficiency and a better Bit Error Rate(BER) performance than Zero Forcing(ZF) and Minimum Mean Square Error(MMSE) algorithm.

Linear Precoder Design for Correlated Rician Fading Channel Under Imperfect CSI

In this paper,we have modeled a linear precoder for indoor multiuser multiple input multiple output(MU-MIMO)system with imperfect channel state information(CSI)at transmitter.The Rician channel is presumed to be mutually coupled and spatially,temporarily correlated.The imperfection with CSI is primarily due to the channel estimation error at receiver and feedback delay amidst the receiver and transmitter in CSI transmission.Along with,the insufficient spacing between the antenna at transmitter and receiver persuades mutual coupling(MC)among the array elements.In addition,the MIMO channel is presumed to be jointly correlated(Weichselberger correlation model).When we look back on the existing precoder design,it considered spatial correlation alone disregarding joint correlation of antenna array elements.With all above assumption,we have designed a linear precoder which minimizes mean squared error(MSE)subjected to total transmit power constraint for MUMIMO system.The simulation results proven that proposed precoder shows substantial enhancement in bit error rate(BER)performance in comparison with the existing technique.The mathematical analysis corroborates the simulation results.

Lattice-reduction-aided MMSE precoding for correlated MIMO channels and performance analysis

The lattice-reduction （LR） has been developed to im- prove the performance of the zero-forcing （ZF） precoder in multiple input multiple output （MIMO） systems. Under the assumptions of uncorrelated flat fading channel model and perfect channel state information at the transmitter （CSIT）, an LR-aided ZF precoder is able to collect the full transmit diversity. With the complex Lenstra- Lenstra-Lov^sz （LLL） algorithm and limited feedforward structure, an LR-aided linear minimum-mean-square-error （LMMSE） pre- coder for spatial correlated MIMO channels and imperfect CSIT is proposed to achieve lower bit error rate （BER）. Assuming a time division duplexing （TDD） MIMO system, correlated block flat fad- ing channel and LMMSE uplink channel estimator, it is proved that the proposed LR-aided LMMSE precoder can also obtain the full transmit diversity through an analytical approach. Furthermore, the simulation results show that with the quadrature phase shift keying （QPSK） modulation at the transmitter, the uncoded and coded BERs of the LR-aided LMMSE precoder are lower than that of the traditional LMMSE precoder respectively when Eb-No is greater than 10 dB and 12 dB at all correlation coefficients.

Efficient Gauss-Seidel Precoding with Parallel Calculation in Massive MIMO Systems

A number of requirements for 5G mobile communication are satisfied by adopting multiple input multiple output(MIMO)systems.The inter user interference(IUI)which is an inevitable problem in MIMO systems becomes controllable when the precoding scheme is used.In this paper,the horizontal Gauss-Seidel(HGS)method is proposed as precoding scheme in massive MIMO systems.In massive MIMO systems,the exact inversion of channel matrix is impractical due to the severe computational complexity.Therefore,the conventionalGauss-Seidel(GS)method is used to approximate the inversion of channel matrix.The GS has good performance by using previous calculation results as feedback.However,the required time for obtaining the precoding symbols is too long due to the sequential process of GS.Therefore,the HGS with parallel calculation is proposed in this paper to reduce the required time.The rows of channel matrix are eliminated for parallel calculation inHGSmethod.In addition,HGSuses the ordered channelmatrix to prevent performance degradation which is occurred by parallel calculation.The HGS with proper number of parallelly computed symbols has better performance and reduced required time compared to the traditional GS.

Efficient coding schemes with power allocation using space-time-frequency spreading

An efficient spaee-time-frequency （STF） coding strategy for multi-input multi-output orthogonal frequency division multiplexing （MIMO-OFDM） systems is presented for high bit rate data transmission over frequency selective fading channels. The proposed scheme is a new approach to space-time-frequency coded OFDM （ODFDM） that combines OFDM with space-time coding, linear precoding and adaptive power allocation to provide higher quality of transmission in terms of the bit error rate performance and power efficiency. In addition to exploiting the maximux diversity gain in frequency, time and space, the proposed scheme enjoys high coding advantages and low-complexity decoding. The significant performance improvement of our design is confirned by corroborating numerical simulations.

An Enhanced Jacobi Precoder for Downlink Massive MIMO Systems

Linear precoding methods such as zero-forcing(ZF)are near optimal for downlink massive multi-user multiple input multiple output(MIMO)systems due to their asymptotic channel property.However,as the number of users increases,the computational complexity of obtaining the inverse matrix of the gram matrix increases.Forsolving the computational complexity problem,this paper proposes an improved Jacobi(JC)-based precoder to improve error performance of the conventional JC in the downlink massive MIMO systems.The conventional JC was studied for solving the high computational complexity of the ZF algorithm and was able to achieve parallel implementation.However,the conventional JC has poor error performance when the number of users increases,which means that the diagonal dominance component of the gram matrix is reduced.In this paper,the preconditioning method is proposed to improve the error performance.Before executing the JC,the condition number of the linear equation and spectrum radius of the iteration matrix are reduced by multiplying the preconditioning matrix of the linear equation.To further reduce the condition number of the linear equation,this paper proposes a polynomial expansion precondition matrix that supplements diagonal components.The results show that the proposed method provides better performance than other iterative methods and has similar performance to the ZF.

Semi-Blind Channel Estimation Algorithm for OFDM Systems

A semi-blind channel estimation algorithm based on subspace approach for orthogonal frequency division multiplexing（OFDM） systems over the frequency-selective channel is proposed. A linear preeoding is applied on each block before the IFFT operation and a low-rank structure is created in the received signal. Then subspace properties can be exploited to identify the channel up to a scalar ambiguity. The residual scalar ambiguities eliminated by inserting pilots into data stream. Simulation results illustrate the performance of the proposed semi-blind algorithm.

Secrecy sum-rate analysis of massive MIMO systems under dual-threat attacks using normalization methods

Massive Multiple Input Multiple Output(MIMO)has been considered as an emerging technology to enhance the spectral and energy efficiency for the upcoming wireless communication systems.This paper derives a closedform approximation for the Ergodic Achievable Secrecy Sum-Rate(EASSR)by considering the joint impact of eavesdroppers and jammers.Two widely used linear precoding techniques,Zero-Forcing(ZF)and Maximum Ratio Transmission(MRT),were used in conjunction with matrix and vector normalization to analyze the secrecy performance.Closed-form expressions are used to explain how the secrecy performance is affected when using the ZF and MRT precoding in the eavesdropping and jamming attack models.We also analyze and compare the performances of different combinations of normalization method and precoding technique in various scenarios.From the analytical expressions and simulation results,we observe that the vector and matrix normalization perform better for the ZF precoding than for the MRT precoding in high Signal-to-Noise Ratio(SNR)scenarios.However,in low SNR,the MRT with matrix normalization outperforms the ZF with vector normalization regardless of the number of users in the system.Further,we observe that the MRT fails to serve more than two users in high SNR scenario.Numerical results obtained from Monte Carlo simulation are used to corroborate the accuracy of the asymptotic secrecy analysis.

Optimal precoding for full-duplex base stations under strongly correlated self-interference channels

We study the optimal precoding for a full-duplex (FD) system, where one FD multi-antenna base station (BS) respectively transmits to and receives from two half-duplex single-antenna mobile users (MUs) on the same time slot and frequency band. At the FD BS, the received signal from the desired MU is severely affected by the extremely strong self-interference (SI) from its transmit antennas to the receive antennas. In the presence of residual SI after imperfect SI cancellation, the downlink transmission rate maximization problem subject to a targeted uplink rate is formulated as a non-convex optimization problem to characterize the achievable rate region for the considered system. Considering the case in which the SI channel is strongly correlated, the above problem is transformed into a convex problem by exploiting the rank-one property of the SI channel, which can be solved efficiently. Finally, numerical results validate the effectiveness of the proposed scheme.

Convex-optimization-based precoding for MIMO downlinks

In this paper, the design of linear leakage-based precoders is considered for multiple-input multiple-output （MIMO） downlinks. Our proposed scheme minimizes total transmit power under each user＇s signal-to-leakage-plus-noise ratio （SLNR） constraint. When the base station knows perfect channel state information （CSI）, suitable reformulation of design problem allows the successful application of semidefinite relaxation （SDR） techniques. When the base station knows imperfect CSI with limited estimation errors, the design problem can be solved using semidefinite program （SDP）. At the same time, it can dynamically allocate each user＇s SLNR threshold according to each user＇s channel state, so it is more feasible than other similar S1NR-based precoding methods. Simulation results show that using large SLNR thresholds, the proposed design has better bit error rate （BER） performance than maximal-SLNR precoding method at high signal-to-noise ratio （SNR）. Moreover, when the base station knows imperfect channel state information, the proposed precoder is robust to channel estimation errors, and has better BER preformance than other similar SINR-based precoding methods.

Robust Precoding Schemes for Multi-User MISO-OFDM Downlink with Limited Time-Domain Feedback

In multi-user multiple-input single-output orthogonal frequency-division multiplexing （MISO-OFDM） downlinks with limited feedback, both linear precoders （LP） and Tomlinson-Harashima precoders （THP） experience performance degradation due to inaccurate channel state information at the transmitter （CSIT）. This analysis treats the downlink channels as random quantities and exploits their second order statistics in robust precoding schemes to correct the errors introduced in the feedback procedure. The time-domain channel vectors are found to reduce the feedback overhead more than the frequency-domain vectors. A compression and restoration method and a codebook design are also given to obtain compact feedback quantities. Simulations show that the robust LP and THP are superior to the previous methods with tradeoffs possible between performance and feedback overhead.

Joint linear precoder and power allocation design for uplink MIMO systems with limited feedback

In this article, the authors consider joint design of a linear precoder and power allocation for uplink multiuser multiple input multiple output （MIMO） communication systems with limited feedback to improve the bit error rate （BER） performance for all users. Precoder selection from the codebook set is directly based on the exact BER performance, instead of other suboptimal criteria, to achieve the optimal precoder matrix, but closed-form expressions may not exist in the view of power allocation based directly on the BER criterion. From this perspective, the authors propose the joint transmitter optimization algorithm for the consideration of precoder design, with total power constraint for asymptotic MBER （AMBER） criterion. In this AMBER criterion, a closed-form solution has been derived for power allocation with an optimal precoder. The simulation results show that the proposed joint design algorithm can achieve a much better performance than precoding with uniform power allocation and only consideration of power allocation.