Trellis precoding for MIMO broadcast signaling

Channel Inversion, and its Minimum Mean Square Error (MMSE) variation, are low complexity methods for Space Division Multiple Access (SDMA) in Multiple Input Multiple Output Broadcast Channel (MIMO-BC). As the channel matrix deviates from orthogonal, these methods result in a waste of transmit power. This paper proposes a trellis precoding method (across time and space) to improve the power efficiency. Adopting a 4-state trellis shaping method from [1], the complexity of the proposed method, which is entirely at the transmitter side, is equivalent to the search in a trellis with 4N states where N is the number of transmit antennas. Numerical results are presented showing that the achievable gains, which depend on the channel realization, can be significantly higher than the traditional shaping gain which is limited to 1.53dB.     

Performance of the Zero Forcing precoding MIMO broadcast systems with channel estimation errors

In this paper, the effect of channel estimation errors upon the Zero Forcing (ZF) precoding Multiple Input Multiple Output Broadcast (MIMO BC) systems was studied. Based on the two kinds of Gaussian estimation error models, the performance analysis is conducted under different power allocation strategies. Analysis and simulation show that if the covariance of channel estimation errors is independent of the received Signal to Noise Ratio (SNR), imperfect channel knowledge deteriorates the sum capacity and the Bit Error Rate (BER) performance severely. However, under the situation of orthogonal training and the Minimum Mean Square Error (MMSE) channel estimation, the sum ca- pacity and BER performance are consistent with those of the perfect Channel State Information (CSI) with only a performance degradation.     

Frequency domain pre-equalization with transmit precoding for MIMO broadcast wireless channels

Recent research has shown that frequency domain pre-equalization (FDPE) can provide broadcast transmissions over multi-input multi-output (MIMO) frequency selective channels, where the multiple receivers need limited processing. In this paper, we consider the combination of FDPE with parallel and successive Tomlinson-Harashima Precoding (THP) and propose two novel FDPE MIMO schemes, which are referred to as FDPE-P-THP and FDPE-S-THP, respectively, based on the minimum mean square error (MMSE) criterion. The ordering algorithm in the FDPE-S-THP scheme is considered and it is shown that the system with even a randomly selected order can perform almost as well as that with the optimal one. This paper further develops an accurate theoretical performance analysis methodology for the proposed FDPE-THP schemes. Numerical results along with analytical results demonstrate the significant performance improvement of our proposed schemes compared to the conventional FDPE MIMO schemes. The channel estimation errors and channel variation effects on the proposed system are also investigated. It is shown that the performance degradation due to channel variation can be efficiently reduced by applying channel prediction.     

Capacity maximizing in massive MIMO with linear precoding for SSF and LSF channel with perfect CSI

The capacity of a massive MIMO cellular network depends on user and antenna selection algorithms, and also on the acquisition of perfect Channel State Information (CSI). Low computational cost algorithms for user and antenna selection significantly may enhance the system capacity, as it would consume a smaller bandwidth out of the total bandwidth for downlink transmission. The objective of this paper is to maximize the system sum-rate capacity with efficient user and antenna selection algorithms and linear precoding. We consider in this paper, a slowly fading Rayleigh channel with perfect acquisition of CSI to explore the system sum-rate capacity of a massive MIMO network. For user selection, we apply three algorithms, namely Semi-orthogonal user selection (SUS), Descending Order of SNR-based User Scheduling (DOSUS), and Random User Selection (RUS) algorithm. In all the user selection algorithms, the selection of Base Station (BS) antenna is based on the maximum Signal-to-Noise Ratio (SNR) to the selected users. Hence users are characterized by having both Small Scale Fading (SSF) due to slowly fading Rayleigh channel and Large-Scale Fading (LSF) due to distances from the base station. Further, we use linear precoding techniques, such as Zero Forcing (ZF), Minimum Mean Square Error (MMSE), and Maximum Ratio Transmission (MRT) to reduce interferences, thereby improving average system sum-rate capacity. Results using SUS, DOSUS, and RUS user selection algorithms with ZF, MMSE, and MRT precoding techniques are compared. We also analyzed and compared the computational complexity of all the three user selection algorithms. The computational complexities of the three algorithms that we achieved in this paper are O(1) for RUS and DOSUS, and O(M²N) for SUS which are less than the other conventional user selection methods.     

Manifold-based predictive precoding for the time-varying channel using differential geometry

Wireless Networks - For limited feedback precoding systems with the feedback delay, the accurate channel state information at the transmitter is crucial for the system performance. We first model...     

Clustered linear precoding for downlink network MIMO systems with partial CSI

We propose two novel clustered linear precoding schemes applicable to network multi‐input multi‐output systems using only partial channel state information to enhance the sum‐rate of the system. Using a channel model that decomposes a multi‐input multi‐output channel matrix into transmit and receive steering vectors and assuming that only transmit steering vectors are available at the base transmit stations, we, first, propose a regularized channel inversion precoding scheme to enhance the sum‐rate assuming only single‐antenna users are available in the system. Next, because of the limitation of regularized channel inversion to handle users with multiple receive antennas, a novel block diagonalization method is proposed. We construct the precoding matrices that jointly eliminate inter‐cell interference and maximize the sum‐rate for a given input covariance matrix. Assuming total power constraint and per‐base‐station power constraints, optimal power allocation schemes are further developed to optimize the sum‐rate. We analytically show that the sum‐rate increases linearly with the number of users when only single‐antenna users are present in the system. Numerical results show that at low signal‐to‐noise ratios, the block diagonalization precoding outperforms the regularized channel inversion in terms of the bit error rate; while at high signal‐to‐noise ratios, the regularized channel inversion provides a better performance. Copyright © 2016 John Wiley & Sons, Ltd.     

信道反馈延迟时MIMO中继系统的线性预编码算法

针对采用放大转发(Amplify and Forward, AF)中继技术的多入多出(Multiple Input Multiple Output, MIMO)系统, 考虑信道估计误差及反馈存在延迟的情况, 提出一种基于最小均方误差(Minimum Mean Squared Error, MMSE)准则的预编码设计方案.假设基站-中继端及中继端-终端的信道均存在估计误差与反馈延迟, 在基站和中继端功率都受限条件下, 以MMSE为准则, 推导得到了基站预编码矩阵、中继转发矩阵和终端解码矩阵的闭式解.数值仿真结果表明, 该方案所提出的预编码算法能有效地改善系统的误比特率与均方误差.     

BER analysis using zero-forcing linear precoding scheme for massive MIMO under imperfect channel state information

ABSTRACT

In massive MIMO systems, inter-user interference has effect on the transmitted signals, so linear precoding techniques are employed for multiuser transmission channels to remove this inter-user interference. It is very complicated to design a suitable linear precoding technique having low computational complexity, which will give an excellent Bit Error Rate (BER) performance. In this paper, we analyse BER under imperfect Channel-State-Information (CSI) using Zero-Forcing (ZF) linear precoding scheme in the downlink, massive MIMO in Time-Division-Duplex (TDD) mode. We derive the closed-form expressions for BER that are obtained from the new expressions derived for Signal-Interference-Noise Ratio (SINR), and then analysis with different parameters using numerical and Monte Carlo simulated results is carried out in MATLAB under imperfect CSI. It was found that our theoretical analysis agrees well with the simulated results. In our analysis, BER was observed in most cases to be good when the number of Base Station (BS) antennas is large enough to accommodate the served users, but when the number of users grows up, the BER performance degrades depending on how many BS antennas are capable of handling the additional users. Also, increasing the downlink transmit power was found to improve the performance, but it is not an energy-efficient way for massive MIMO.     

Transmit antenna selection for partial linear precoding MIMO schemes

A novel transmit antenna selection technique is proposed, specifically designed to maximise the benefit of partial precoding on narrowband downlink multiuser multiple input multiple output (MU-MIMO) systems employing antenna redundancy. The partial precoding utilises the constructive part of the existing interference between the MIMO sub-channels by applying partial sub-channel orthogonalisation and thus improves the signal-to-noise ratio at the receiver. In addition, a customised antenna selection criterion is employed at the transmitter, that maximises the constructive interference among the active antennas and optimises the benefits of partial precoding.     

Limited feedback-based block diagonalization for the MIMO broadcast channel

Block diagonalization is a linear preceding technique for the multiple antenna broadcast (downlink) channel that involves transmission of multiple data streams to each receiver such that no multi-user interference is experienced at any of the receivers. This low-complexity scheme operates only a few dB away from capacity but requires very accurate channel knowledge at the transmitter. We consider a limited feedback system where each receiver knows its channel perfectly, but the transmitter is only provided with a finite number of channel feedback bits from each receiver. Using a random quantization argument, we quantify the throughput loss due to imperfect channel knowledge as a function of the feedback level. The quality of channel knowledge must improve proportional to the SNR in order to prevent interference-limitations, and we show that scaling the number of feedback bits linearly with the system SNR is sufficient to maintain a bounded rate loss. Finally, we compare our quantization strategy to an analog feedback scheme and show the superiority of quantized feedback.     

MIMO中继系统中基于不完全信道信息的线性预编码算法

针对采用放大转发中继技术的多输入多输出系统,提出一种基于不完全信道状态信息的中继预编码设计方案.假设在中继端已知源-中继的全部信道状态信息,在中继-目的端的信道考虑信道估计误差及发射天线相关性,根据最小均方误差设计准则,推导获得目的端线性处理矩阵.在中继端最大发射功率约束条件下,通过理论推导求得中继端线性预编码矩阵的闭式解.数值仿真结果表明,在存在信道估计误差和天线相关性的条件下,所提方案能有效降低系统的误比特率和均方误差.     

Coordinated beamforming with limited feedback in the MIMO broadcast channel

In this paper, we propose a new joint optimization of linear transmit beamforming and receive combining vectors for the multiple-input multiple-output (MIMO) broadcast channel. We consider the transmission of a single information stream to two users with two or more receive antennas. Unlike past work in which iterative computation is required to design the beamformers, we derive specific formulations for the transmit beamformers for two active users via a power iteration and a generalized eigen analysis. To enable practical implementation, a new limited feedback algorithm is proposed that exploits the structure of the algorithm to avoid full channel quantization. The feedback overhead of the proposed algorithm is independent of the number of receive antennas. Monte Carlo simulations are used to evaluate the bit error rate and the sum rate performances of the proposed algorithm. Simulation results show that the proposed method performs close to the sum capacity of the MIMO broadcast channel even with limited feedback.     

Performance evaluation of channel inversion precoding for downlink multi-user MIMO system

In downlink multi-user multi-input multi-output (MU-MIMO) system, not every user (user equipment (UE)) can calculate accurately signal to interference and noise ratio (SINR) without prior knowledge of the other users' precoding vector. To solve this problem, this article proposes a channel inversion precoding scheme by using the lower bound of SINR and zero-forcing (ZF) algorithm. However, the SINR mismatch between lower bound SINR and actual SINR causes the inaccurateness of adaptive modulation and coding (AMC). As a result, it causes degradation in performance. Simulation results show that channel inversion precoding provides lower throughput than that of single user multi-input multi-output (SU-MIMO) at high signal-to-noise ratio (SNR) (>14 dB), due to the SINR mismatch, although the sum-rate of channel inversion precoding is higher than that of SU-MIMO at full SNR regime.     

A broadcast approach for a single-user slowly fading MIMO channel

A broadcast transmission strategy for the slowly fading Gaussian multiple-input multiple-output (MIMO) channel is introduced. This broadcast strategy is an extension of the single-input single-output (SISO) broadcast approach. Perfect channel state information (CSI) is assumed known at the receiver end only. This strategy facilitates to adapt the reliably decoded rate to the actual channel state without having any feedback link to the transmitter. Transmission of layered coded information is motivated by the theory of majorization. We derive the basic equations characterizing achievable rates of the strategy. Several ad hoc approximations to the achievable region are considered and their performance is compared with the SISO setting and the ergodic capacity. It has been demonstrated that a single-layer outage approach is reasonably efficient in the MIMO setting in terms of the average reliably decoded rate. A multiple-access channel (MAC) broadcast approach is also applied for the MIMO case, and demonstrated to be relatively efficient.     

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 desig...     

Sum capacity of MIMO Gaussian broadcast channels with channel energy constraints

The motivation of this paper is to find the class of channels that provides the best sum capacity of a MIMO Gaussian broadcast channel with both transmit power and channel energy constraints when N/sub t//spl ges/KN/sub r/, where N/sub t/, N/sub r/, and K denote the number of transmit antennas, the number of receive antennas, and the number of users in the system, respectively. The best sum capacity is achieved when the user channels are mutually orthogonal to each other. For each individual user, equal energy is distributed to all non-zero spatial eigenmodes. Further, we optimize the number of non-zero eigenmodes for all users and the optimal power distribution among users. Although, we only study the case of N/sub t//spl ges/KN/sub r/ in this paper, we conjecture that similar results still hold for N/sub t/相似文献   

一种空间相关MIMO信道下的空时预编码方案

在实际的多输入多输出（MIMO：Muhiple Input Multiple Output）移动通信系统中，信道的空间相关性通常会使得现有的空时编码方案难以获得最佳的误码性能。为了解决该问题，本文提出了一种适用于MIMO空间相关信道的空时预编码方案。该方案在完成对发射信号的空时编码后，针对发射天线的空间相关矩阵设计了合理的预编码矩阵，进而能够在保证分集增益的前提下克服相关信道的不利影响。仿真结果证实，该方案能够在一定程度上有效提高整个系统的误码性能。     

Adaptive channel partitioning and modulation for linear time-varying channels

We present a novel channel partitioning and modulation technique for linear time-varying (LTV) channels using adaptive bases of localized complex exponentials. We show that localized complex exponentials are approximate eigenfunctions of underspread LTV channels. A basis of localized complex exponentials that approximately diagonalizes the LTV channel is selected adaptively by the receiver during a training period. The basis selection process is equivalent to matching the support intervals of the localized complex exponentials to the rate of the channel time variation. The receiver sends information regarding the selected basis to the transmitter which modulates the subsequent data stream in this basis. The adaptive modulation technique performs significantly better than conventional orthogonal frequency-division multiplexing systems for rapidly varying LTV channels such as time-frequency-selective mobile radio channels.     

Maximum-throughput delivery of SVC-based video over MIMO systems with time-varying channel capacity

In this paper, we present a novel scalable video transmission strategy over multi-input multi-output (MIMO) wireless systems with time-varying channel capacity. It is a great challenge to simultaneously guarantee the QoS for video delivery and maximize the system throughput over time-varying MIMO channel. We demonstrate that, by making full use of estimated channel state information (CSI) through feedback, a cascade of adaptive operations can be designed to satisfy maximum throughput for scalable video over MIMO systems. These operations include power allocation based on water-filling (WF), adaptive channel selection (ACS), and novel throughput maximizing power reallocation (PR). The proposed ACS transmission scheme enables overall increase in data throughput among enhancement layers by adaptively launching base layer bit-stream to proper sub-channel. Then, after initial power allocation with WF and proper adaptive mode selection, we obtain the surplus power across enhancement layer sub-channels which can be reallocated to some sub-channels by the proposed PR scheme. With such power reallocation, certain enhancement layers will be able to reach new level of QAM modulation through PR so as to maximize the system data throughput. We present in this paper some detailed analysis on these adaptive operations. We also present some simulation results to demonstrate that maximum throughput video transmission over MIMO wireless systems indeed can be achieved based on scalable video coding (SVC) and a sequence of appropriately designed adaptive operations.     

Bounds on the performance of protocols for a multiple-access broadcast channel

A general model is presented for synchronous protocols that resolve conflicts among message transmissions to a multiple-access broadcast channel. An information-theoretic method is used now to show that if only finitely many types of conflicts can be distinguished by the protocol, utilization of the channel at rates approaching capacity is impossible. A random-coding argument is used to show that if the number of conflicting transmissions can be determined (which requires distinguishing infinitely many types of conflicts) then utilization of the channel at rates arbitrarily close to capacity can be achieved.