Precoding Selection Research Articles

Low Computational Complexity Precoding Selection for 5G New Radio

Low Computational Complexity Precoding Selection for 5G New Radio

Robust Frequency Selective Precoding for Downlink Massive MIMO in 5G Broadband System

Robust Frequency Selective Precoding for Downlink Massive MIMO in 5G Broadband System

The Next Generation of Beam Hopping Satellite Systems: Dynamic Beam Illumination With Selective Precoding

Beam Hopping (BH) is a popular technique considered for next-generation multi-beam satellite communication system which allows a satellite focusing its resources on where they are needed by selectively illuminating beams. While beam illumination plan can be adjusted according to its needs, the main limitation of convectional BH is the adjacent beam avoidance requirement needed to maintain acceptable levels of interference. With the recent maturity of precoding technique, a natural way forward is to consider a dynamic beam illumination scheme with selective precoding, where large areas with high-demand can be covered by multiple active precoded beams. In this paper, we mathematically model such beam illumination design problem employing an interference-based penalty function whose goal is to avoid precoding whenever possible subject to beam demand satisfaction constraints. The problem can be written as a binary quadratic programming (BQP). Next, two convexification frameworks are considered namely: (i) A Semi-Definition Programming (SDP) approach particularly targeting BQP type of problems, and (ii) Multiplier Penalty and Majorization-Minimization (MPMM) based method which guarantees to converge to a local optimum. Finally, a greedy algorithm is proposed to alleviate complexity with minimal impact on the final performance. Supporting results based on numerical simulations show that the proposed schemes outperform the relevant benchmarks in terms of demand matching performance while minimizing the use of precoding.

Neural Network-Based Fixed-Complexity Precoder Selection for Multiple Antenna Systems

In this paper, we propose a neural network-based precoder selection method for multiple antenna systems that are equipped with maximum likelihood detectors. We train a fully connected neural network by supervised learning with novel soft labels that are derived from the error probability of maximum likelihood detection. The dimension of the input data is reduced by QR decomposition of the channel matrices, thereby reducing the number of nodes of the input layer. Furthermore, the dimension reduction improves the network accuracy. The number of connections between the layers are reduced by the network pruning technique, then the survived connections are retrained to recover the degraded accuracy due to the pruning. We also optimize the regularization method, considering not only network overfitting but also pruning and retraining. Our method achieves a near optimal bit error performance of the previous sphere decoding (SD)-based symbolic algorithm, of which complexity fluctuates depending on channel matrices. Unlike the conventional SD-based method, the complexity of the proposed method is fixed by the intrinsic characteristic of neural network, which is desirable from the perspective of hardware implementation. And the fixed complexity is lowered by pruning unimportant connections of the networks. With the aid of computer simulations, we show that the fixed complexity of the proposed method is close to the average complexity of the conventional SD-based symbolic algorithm, allowing only negligible degradation of the error performance.

Knowledge Distillation-Aided End-to-End Learning for Linear Precoding in Multiuser MIMO Downlink Systems With Finite-Rate Feedback

We propose a deep learning-based channel estimation, quantization, feedback, and precoding method for downlink multiuser multiple-input and multiple-output systems. In the proposed system, channel estimation and quantization for limited feedback are handled by a receiver deep neural network (DNN). Precoder selection is handled by a transmitter DNN. To emulate the traditional channel quantization, a binarization layer is adopted at each receiver DNN, and the binarization layer is also used to enable end-to-end learning. However, this can lead to inaccurate gradients, which can trap the receiver DNNs at a poor local minimum during training. To address this, we consider knowledge distillation, in which the existing DNNs are jointly trained with an auxiliary transmitter DNN. The use of an auxiliary DNN as a teacher network allows the receiver DNNs to additionally exploit lossless gradients, which is useful in avoiding a poor local minimum. For the same number of feedback bits, our DNN-based precoding scheme can achieve a higher downlink rate compared to conventional linear precoding with codebook-based limited feedback.

Precoding Matrix Selection for LTE System

In the current 4G Long Term Evolution (LTE) system, it adopts the orthogonal frequency division multiplexing (OFDM) and multiple input multiple output (MIMO) schemes to improve the spectral efficiency and increase the data rate for the system. Besides, precoding is an effective technique in MIMO transmission for the transmitter (Tx) to use a suitable precoding matrix in accordance with the change of the channel. With the proper selected precoding matrix, MIMO system can reduce the multiple access interference and increase the received signal to noise ratio (SNR) at the receiver (Rx). In this paper, two precoding matrix selection methods are proposed via using the features of the beam patterns of the adopted precoding matrices for 3GPP LTE system. With a little performance degradation, the first proposed method has the advantage of low computational complexity and the second method can have the zero latency property compared with the conventional full search methods.

Optimal Precoder Selection for Spatially Multiplexed Multiple-Input Multiple-Output Systems With Maximum Likelihood Detection: Exploiting the Concept of Sphere Decoding

In this paper, a computationally efficient implementation technique for optimal precoder selection in spatially multiplexed (SM) multiple-input multiple-output (MIMO) systems with maximum-likelihood detection at the receiver is proposed. The techniques previously developed for suboptimal precoder selection were based on the lower bounds of the free distances of precoders to reduce the processing time. However, the use of these techniques leads to significant declines in error performance when the number of spatial streams approaches the number of receiving antennas. At the same time, to achieve optimal performance, the conventional optimal precoder selection technique can be employed; however, it has a long processing time due to exhaustive search. Thus, in this paper, we propose a precoder selection technique that maintains an optimal performance without the prohibitive processing time of the conventional optimal precoder selection. The processing time can be reduced by the following: (1) exploiting the symmetric structure of quadrature amplitude modulation (QAM) constellations, thereby reducing the search space; (2) adopting the concept of sphere decoding (SD); (3) eliminating the last stage of SD; and (4) performing an SD-like process in a selective manner. Both the optimal performance and reduction in the processing time realized by the proposed technique are confirmed via simulation.

Load-Aware Energy Efficient Adaptive Large Scale Antenna System

This paper proposes an adaptive large scale antenna system (ALSAS) for enhancing energy efficiency in low density wireless network scenarios. The proposed ALSAS comprises of two stages, a novel adaptive discontinuous transmission (ADTx) stage and an antenna array optimization (AAO) one. The basic idea is to utilize prior knowledge of the users' quality of service (QoS) requirements as well as precoding selection in the ADTx stage to maximize the transmitter hibernation periods subject to a certain complexity constraint. In the AAO stage, further power saving is achieved by reducing the number of active antenna elements subject to a certain QoS requirement. It is shown that, relative to conventional large scale antenna system (LSAS), the proposed ALSAS system achieves significant energy efficiency improvements under various scenarios. The results show that the proposed technique can provide energy efficiency improvement between 125% and 1124% in the suburban scenario, and between 196% and 952% in the rural scenario. It is also demonstrated that for rural environments with relatively small short inter-site-distance (ISD) values, ALSAS can provide up to 500% power saving for the fixed bit rate requirement case.

Minimum Euclidean distance evaluation using deep neural networks

In this paper, we propose a deep neural network (DNN) based minimum Euclidean distance evaluation method. It is known that a closed form solution of the problem to calculate the minimum Euclidean distance is not available and an exhaustive search is inevitable, which requires prohibitive complexity for practical implementation. In order to reduce the complexity, we consider a supervised machine learning approach, where the minimum Euclidean distance evaluation is modeled as a DNN regression problem. The DNN for evaluating the minimum Euclidean distance is trained with a sufficient number of training data. Numerical results are presented to show that our trained DNN provides enough accuracy to be adopted for a precoder selection problem in multiple-input multiple-output (MIMO) spatial multiplexing systems with maximum likelihood (ML) receivers.

Domain Selective Precoding in 3-D Massive MIMO Systems

Three-dimensional multiple input multiple output (3-D MIMO) with a large number of active antennas equipped in a uniformly rectangular antenna array has a significant potential in improving the system capacity. In this paper, we present an efficient two-dimensional (2-D) downlink precoding scheme for single-cell 3-D massive MIMO systems. Considering instantaneous channel state information at the base station, we show that either the elevation or azimuth domain can be used for interference cancellation. We divide the interference into two components, one of which is canceled in the elevation domain, while the other is canceled in the azimuth domain. Based on this domain selective (DS) strategy, two DS precoding algorithms are proposed for the single-path scenario based on the zero forcing and signal-to-leakage-plus-noise ratio criteria. Next, we extend our DS precoding scheme to a multi-path scenario. In the proposed algorithms, the precoding vectors of different users are determined in parallel, so as to reduce the computational complexity. Simulation results are provided to demonstrate that the proposed algorithms can achieve better spectral efficiency performance with a low computational complexity.

Dual-Codebook Precoding Selection for LTE-A System

Long-term evolution advanced (LTE-A) is currently the most popular 4G wireless communication system worldwide. LTE-A system defines eight transmit antennas in downlink and adopts the precoding matrices to reduce the effect of interference and improve the received signal quality at the receiver. In this paper, three algorithms adopting the unique overlapped angle of direction of the beams for the LTE-A precoding matrices and the Euclidean Norm relation are proposed to reduce the calculations during precoding selection. Computer simulations and experiment results show that the proposed low-complexity precoding selection method can reduce more than 70% computational complexity and still maintains a good bit error rate performance compared with literary full search method.

Multi-User Hybrid MIMO at 60 GHz Using 16-Antenna Transmitters

Given the high throughput requirement for the next generation wireless communication systems, merging millimeter wave technologies and multi-user MIMO seems a very promising strategy to achieve the required 20 Gbps. Although a full digital architecture provides the best performance and flexibility, its implementation at millimeter-wave frequencies today seems unrealistic due to the prohibitive costs and high power consumption. Hybrid analog-digital architectures, efficiently sharing beamforming operations between analog and digital domains, appear as a feasible way to implement multi-user MIMO systems at millimeter wave frequencies. While hybrid architectures have been studied intensively, an effective and flexible demonstration proving the feasibility is still missing. In this paper, we introduce the design of a millimeter wave multi-user MIMO system with hybrid analog and digital processing using 60 GHz radios equipped with phased arrays. A base station, employing two 16-antennas transmitters, serves simultaneously two user equipment devices, each with 4 antenna elements, effectively realizing spatial multiplexing. We propose a complete system design comprising the description of millimeter radio transceivers, the multi-user hybrid MIMO algorithms including a strategy for channel estimation and frequency selective precoding along with the transmission protocol. We show that the spatial multiplexing is achieved in several scenarios, most importantly in a strong interference-limited scenario.

Enhancing Security in Cognitive Radio Multicast Networks Using Interference Power

In this paper, security issue in multiple input multiple output (MIMO) multicasting system has been analyzed in the presence of a group of eavesdroppers in cognitive radio networks (CRNs). Primary base station (PBS) and secondary base station (SBS) communicate with multiple primary and secondary receivers, respectively via a precoding relay having multiple antennas. At first, considering interference the secrecy multicast capacity at the primary receivers (PRs) and the secondary receivers (SRs) has been calculated and investigated the impact of interferences on it. Then, the zero-forcing (ZF) precoding technique at the relay has been employed which enhances the secrecy multicast capacity at the PRs and SRs by zeroing the impact of interference on each other. Secondly, the existing constructive interference energy of the communication medium employing selective precoding (SP) technique at the relay has been used to improve the secrecy multicast capacity of the PRs and SRs. Finally, phase alignment precoding (PAP) technique at the relay has been introduced which uses the destructive part of interference for further increase in the secrecy multicast capacity at the PRs and SRs. It is observed that among the three precoding techniques, the best performance is achieved by using the PAP at the relay in terms of secrecy multicast capacity and secure outage probability analysis. This is due to the fact that PAP technique at the relay not only uses the constructive interference part but also it rotates the destructive interference part in such a way that the resulting interference is always instantaneously constructive. So using these precoding relays interference power can be used to enhance system performance without increasing base station power.

High Throughput and Fair Scheduling for Multi-AP Multiuser MIMO in Dense Wireless Networks

This paper considers the fair scheduling problem for dense wireless networks with access point cooperation and multiple-input-multiple-output (MIMO) links. The problem is to maximize the aggregate throughput subject to a fairness constraint that is general enough to capture many different fairness objectives. We formally specify a non-convex optimization problem that captures all aspects of the problem setting, and we propose two algorithms to approximate its solution. The first algorithm jointly optimizes the selection of user sets, MIMO precoders, and assignment of user sets to time slots. The second algorithm separately optimizes first user sets and MIMO precoders and second assignment of user sets to time slots. The first algorithm guarantees perfect fairness and produces a local optimum or a saddle point for aggregate throughput at a fairly high computational cost. The second algorithm also guarantees perfect fairness and produces optimal aggregate throughput for a given set of (possibly non-optimal) user sets while having lower computational complexity. The second algorithm also has a parameter that allows throughput and fairness to be traded off for situations where maximizing throughput is critical and approximate fairness is acceptable. Analyses are complemented by simulation results, which show that: 1) the first algorithm produces significantly higher aggregate throughput than known approaches with a running time that is practical for scenarios with up to 50 users and 2) the second algorithm produces aggregate throughput that is very close to existing heuristics while having significantly lower running time.

Precoding for Non-orthogonal Multiple Access with User Equipment Pairing

Due to the tremendous throughput requirement, current spectrum allocation scheme of 4G long term evolution-advanced system can’t satisfy the need for future communications applications. Hence, researches have proposed a non-orthogonal multiple access (NOMA) technique as a candidate for 5G access system recently. This paper proposes two precoding matrix selection methods for the NOMA system and also provides an appropriate user equipment (UE) pairing method for NOMA. Combining the UE pairing with the proposed precoding selection method, results show that system capacity for NOMA system can be further improved.

Quantized Feedback-Based Diagonal Precoding for $N \times 1$ MISO System With Generalized Orthogonal Space-Time Block Codes

In this paper, we propose an imperfect quantized feedback-based diagonal precoding scheme for generalized orthogonal space-time block codes in an $N \times 1$ multiple-input single-output (MISO) wireless communication system employing $M$ -QAM constellation. It is well known that a feedback-based precoding scheme aids channel adaptive signaling in wireless communication system and capitulates large improvements in almost all performance metrics. However, a practical feedback link is susceptible to error due to wireless channel fading and leads to wrong precoder selection. Therefore, a signal-to-noise ratio (SNR) adaptive error-tolerant weighting scheme is introduced which provides significant gain in comparison with another proposed arbitrarily fixed SNR error-tolerant weighting scheme. At first, a tight approximate closed-form expression of average symbol-error-rate (SER) and an exact analytical expression of outage probability are derived under imperfect feedback information with the help of order statistics. By minimizing the derived average SER, optimized transmit weights for the diagonal precoding matrix (based on feedback bits) are obtained. Later, a closed-form expression of the ergodic capacity with erroneous feedback bits for arbitrary MISO system is also provided by using the moment generating function approach. Further, numerical results show that the considered error-tolerant schemes outperform the uniform power allocation scheme in term of all the investigated performance metrics.

Modulation-Specific Multiuser Transmit Precoding and User Selection for One-Dimensional Signaling

Massive deployment of low data rate Internet of things and eHealth devices that require high reliability motivates the development of practical precoding and user selection techniques. In this paper, we show that throughput and communications reliability can be improved by incorporating knowledge of modulation type in the design of the multiuser transmit precoder. The transmission of low data rate one-dimensionally modulated signals in a broadcast channel is considered. The transmit precoding matrix is determined by minimizing the weighted sum of error probabilities of users. Although the proposed minimum probability of error (MPE) precoding problem is nonconvex and highly nonlinear, it is solved by the alternating minimization of two convex subproblems. A reduced-complexity version of convex MPE precoding is then introduced, which exponentially reduces the complexity of the problem. Numerical results show that the proposed precoding techniques significantly improve system performance in broadcast channels. A user selection algorithm, compatible with MPE precoding, is also proposed that selects users if their error probabilities can approach zero. Based on line packing principles in Grassmannian manifolds, it is shown that the number of selected users could potentially be more than the number of transmit antennas, which translates to supporting more simultaneous users in the shared channel compared to existing user selection methods.

Optimality of Frequency Flat Precoding in Frequency Selective Millimeter Wave Channels

Millimeter wave (mmWave) MIMO communication is a key feature of next generation wireless systems. The selection of precoders and combiners for wideband mmWave channels has involved frequency selective designs based on channel state information. In this letter, we show that under some assumptions, the dominant subspaces of the frequency domain channel matrices are equivalent. This means that semi-unitary frequency flat precoding and combining are sufficient to achieve the maximum spectral efficiency when there is not too much scattering in the channel. It also motivates the use of techniques such as compressive subspace estimation as an alternative to estimating the full channel.

ML검출 기반 공간다중화 MIMO 시스템의 UEP 프리코더 선정기술

본 논문에서는 maximum-likelihood (ML) 신호검출을 사용하는 공간다중화 multiple-input multiple-output (MIMO) 시스템을 위한 새로운 unequal error protection (UEP) 프리코더 선정기술을 제안한다. 기존 프리코더 선정기술들은 UEP를 고려하지 않고 설계되었으나, 제안된 기술은 다중안테나 UEP를 고려한다. 모의실험을 통해 제안된 기술의 적용에 따른 향상된 다중안테나 UEP 성능을 확인한다. In this paper, we propose a novel precoder selection technique for maximum-likelihood (ML) detected spatially multiplexed multiple-input multiple-output (MIMO) systems. Previous precoder selection techniques were designed without considering UEP, however the proposed technique is designed considering multi-antenna unequal error protection (UEP). Simulations demonstrate the improved multi-antenna UEP performance by the proposed technique.

Research on Pre-coding Codebook Selection Algorithm of LTE-A System

In this paper, on LTE-A system based MIMO pre-coding codebook selection algorithm are researched. Traditional pre-coding selection algorithm requires to complete the corresponding code to traverse, however improved traversal algorithm are optimized than traditional traversal algorithms, but it has high complexity. Based on the stud-y of the two kinds of pre-coding selection algorithm, we propose a modified algorithm, then the algorithm in EPA5, EVA5, ETU70 on the type of channel simulation and analysis, Simulation results show that the algorithm performance with almost no loss compared with the traditional method, while significantly lower the complexity.