MIMO Detection Research Articles

Comparison and Analysis of Sub-optimal performance of OFDM/SDMA uplink System that use Conventional Multiuser Detection Techniques

The major challenge in the practical accomplishment of OFDM/SDMA system depends on the efficient implemen- tation of a multiuser detection (MUD) technique that separates the spatially multiplexed signal streams. Several algorithms for MIMO detection for spatially-multiplexed signals have been developed till today. The MIMO MUDs that are mostly used in previous research works and practically used are minimum mean squared error (MMSE), decorrelator (ZF), Vertical-Bell laboratories layered space-time (V-BLAST), successive inter- ference cancellation (SIC) and maximum likelihood detection (MLD). MMSE and ZF are linear MIMO detection techniques, while the remaining procedures are non linear techniques. All MUD techniques in general can be explained as a solution to a quadratic optimization problem in most cases. In this work, basic principles of ZF, MMSE, ZF-OSIC, MMSE-OSIC and ML have been explained briefly and a comparison of their performances have been done. Although the detection techniques evolved so far are matured in theory, their implementation in a real-time scenario is still challenging. It is analyzed in this paper that the poor BER results of sub-optimal detection method in comparison to MLD is due to bad channel effects.

Low latency group‐sorted QR decomposition algorithm for larger‐scale MIMO systems

Sorted QR decomposition (SQRD) has been extensively adopted for various multiple-input-multiple-output (MIMO) detectors, in which the sorting process incurs severe latency when it comes to larger-scale MIMO situations. This paper proposes a group-SQRD (GSQRD) algorithm to alleviate the latency problem of general SQRD architectures for larger-scale MIMO systems. Via predictively sorting a group of 4 columns at one stage, the GSQRD could eliminate the processing latency by 41% for decomposing 16 ×16 complex-valued matrices. Additionally, this percentage even rises up to 68% for decomposing 128 ×128 matrices. To analyse the side effects, the GSQRD is applied in various MIMO detectors in a simulation link, which exhibits a negligible performance degradation for MIMO detection. Moreover, GSQRD is a hardware-friendly algorithm because the division and square root operations in GSQRD are converted to multiplications for simplifying the hardware implementation. Based on this algorithm, two corresponding hardware architectures, which contains 2 and 4 columns respectively in a sorting group, are also implemented with 65-nm CMOS technology. These architectures can work at 513 MHz to decompose 16 ×16 complex-valued matrices. The processing latencies are respectively 0.32 and 0.26 μs, superior to the state-of-art designs.

Likelihood-Based Metric for Gibbs Sampling Turbo MIMO Detection

Likelihood-Based Metric for Gibbs Sampling Turbo MIMO Detection

A Low-Complexity Double EP-Based Detector for Iterative Detection and Decoding in MIMO

We propose a new iterative detection and decoding (IDD) algorithm for multiple-input multiple-output (MIMO) based on expectation propagation (EP) with application to massive MIMO scenarios. Two main results are presented. We first introduce EP to iteratively improve the Gaussian approximations of both the estimation of the posterior by the MIMO detector and the soft output of the channel decoder. With this novel approach, denoted by double-EP (DEP), the convergence is very much improved with a computational complexity just two times the one of the linear minimum mean square error (LMMSE) based IDD, as illustrated by the included experiments. Besides, as in the LMMSE MIMO detector, when the number of antennas increases, the computational cost of the matrix inversion operation required by the DEP becomes unaffordable. In this work we also develop approaches of DEP where the mean and the covariance matrix of the posterior are approximated by using the Gauss-Seidel and Neumann series methods, respectively. This low-complexity DEP detector has quadratic complexity in the number of antennas, as the low-complexity LMMSE techniques. Experimental results show that the new low-complexity DEP achieves the performance of the DEP as the ratio between the number of transmitting and receiving antennas decreases.

Low Complexity Overloaded MIMO Detection Based on Belief Propagation with MMSE Pre-Cancellation

Low Complexity Overloaded MIMO Detection Based on Belief Propagation with MMSE Pre-Cancellation

Low complexity Massive MIMO detection algorithm based on improved LAS

Massive multiple-input multiple-output (M-MIMO) technology is a key technology for 5G communications and future mobile wireless networks. Although the likelihood ascent search (LAS) algorithm in the existing detection algorithms is relatively low in complexity, the algorithm is easy to fall into the local optimum, resulting in poor global performance. Therefore, based on this algorithm, this paper proposes an improved detection scheme based on the LAS algorithm in the reduced neighborhood. This algorithm combines the idea of a reduced neighborhood and iteratively improves the LAS algorithm. The algorithm is designed by reducing the size of the neighborhood and increasing the number of iterations. The algorithm compares the BER performance of different neighborhood parameters, and obtains a set of parameters to significantly reduce BER through simulation comparison.

Optimality of Linear MIMO Detection for 5G Systems via 1-Opt Local Search

Multiple input multiple output (MIMO) symbol detection problem belongs to non-deterministic polynomial acceptable hard combinatorial optimization (CO) class. One of the key trials in design of MIMO scheme is to develop a low complexity detection algorithm without much compromise in performance. Detection approaches proposed in the literature can be split into non-linear and linear algorithms. Vertical Bell-Labs Layered Space Time (V-BLAST) and Sphere Decoder (SD) are non-linear methods used for extracting transmitted data; whereas, Zero-Forcing (ZF) and Minimum Mean Square Error (MMSE) detections are comparatively in complex and effectual linear techniques. In this work, the heuristic 1-Opt local search method used for solving computationally hard combinatorial optimization problems is applied to the ZF and MMSE detection algorithms. First, simple MIMO decoding using ZF and MMSE is accomplished to find the estimated symbol then the transmitted symbol is calculated using the heuristic 1-opt approach by means of the estimated symbol. Simulation results demonstrate that 1-opt search when applied to the ZF and MMSE displays better bit error rate (BER) performance than the simple ZF and MMSE detectors. It is also verified through simulations that the proposed 1-Opt based detectors display better BER performance as compared to the complex nonlinear VBLAST and SD at considerably reduced complexity. Hence, the proposed detectors are appropriate for effective hardware implementations.

Learning based MIMO communications with imperfect channel state information for Internet of Things

Imperfect channel state information (CSI) may seriously worsen the system performance for classical MIMO communications. In order to overcome the impacts of imperfect CSI for Internet of things, we propose a deep convolutional neural network (DCNN) based MIMO detection algorithm, where the DCNN is trained offline and works online to refine the imperfect CSI and improve the bit error rate of the wireless systems. Two types of learning based detectors, i.e., with or without accurate CSI, are proposed in this paper to reduce the detrimental effects of imperfect CSI. The impacts of the important system parameters, such as normalized Doppler frequency and the correlation factor are evaluated in different setup scenarios. Simulation results suggest that, compared with the classical maximum likelihood detector, the proposed learning based detectors shows considerable gains.

Graph traversal aided detection in uplink MBM massive MIMO based on socio‐cognitive knowledge of swarm optimization

SummaryMedia‐based modulation (MBM) plays a crucial role in enhancing the spectral efficiency and energy efficiency of massive MIMO (mMIMO) systems for 5G and beyond wireless communications. In MBM, multiple parasitic elements, also termed as radio frequency (RF) mirrors, are placed near the transmit antennas for generating different channel fade realizations. These realizations are obtained by ON/OFF switching of RF mirrors. One of those channel fade realizations is selected (using a part of the incoming information bits) for transmitting a part of the information bits utilizing a symbol chosen from the conventional constellation set (using another part of the incoming information bits). Transmission of a symbol through one of the available channel realizations constitutes a sparse transmit vector for each user in MBM‐mMIMO. The sparse nature of transmitted symbols from multiple users and inter‐user interference makes the symbol detection in uplink MBM‐mMIMO challenging. Therefore, in this article, the problem of symbol detection in MBM‐mMIMO is analyzed from a graph‐theoretical point of view, and a graph‐traversal aided low‐complexity symbol detection algorithm is proposed inspired by socio‐cognitive learning of swarm optimization. Also, the convergence characteristic of the proposed technique is investigated theoretically. Further, an analytical expression of upper bound on bit error rate performance is derived and corroborated through simulations. Viability and robustness of the proposed technique are also justified through simulations over state‐of‐art detection techniques, under both perfect and imperfect channel state information scenarios.

An Improved Expectation Propagation Based Detection Scheme for MIMO Systems

Multiple-input Multiple-output (MIMO) technologies play an important role in modern and future wireless communication systems as they can improve the capacity without increasing the bandwidth. MIMO detection is one of the key technologies for MIMO system designs. MIMO detection schemes based on Message Passing (MP) algorithms have attracted extensive attention in recent years. The MIMO detection scheme based on Expectation Propagation (EP), which is also a kind of MP algorithms, has been proved to achieve the Bayes-optimal performance under the conditions of large system limit and compression rate threshold. However, there is improvement space for the detection performance of the EP algorithm when the conditions are not satisfied, which are the common cases in the practical applications. In this paper, when the conditions of the large system limit and compression rate threshold are not satisfied, we analyze the influences of two factors, the initial parameters selection and the moment matching strategy, on the performance of EP based detection scheme. As a result, we propose an improved EP detection scheme based on optimizing the two factors. Simulation results show that the proposed scheme outperforms the original EP detection scheme in different MIMO scenarios.

Early results on deep unfolded conjugate gradient‐based large‐scale MIMO detection

Deep learning (DL) is attracting considerable attention in the design of communication systems. This paper derives a deep unfolded conjugate gradient (CG) architecture for large-scale multiple-input multiple-output detection. The proposed technique combines the advantages of a model-driven approach in readily incorporating domain knowledge and deep learning in effective parameters learning. The parameters are trained via backpropagation over a data flow graph inspired from the iterative conjugate gradient method. We derive the closed-form expressions for the gradients for parameters training and discuss early results on the performance in a statistically identical and independent distributed channel where the training overhead is considerably low. It is worth noting that the loss function is based on the residual error that is not an explicit function of the desired signal, which makes the proposed algorithm blind. As an initial framework, we will point to the inherent issues and future directions.

An Efficient Quadratic Programming Relaxation Based Algorithm for Large-Scale MIMO Detection

Multiple-input multiple-output (MIMO) detection is a fundamental problem in wireless communications and it is strongly NP-hard in general. Massive MIMO has been recognized as a key technology in the fifth generation (5G) and beyond communication networks, which on one hand can significantly improve the communication performance, and on the other hand poses new challenges of solving the corresponding optimization problems due to the large problem size. While various efficient algorithms such as semidefinite relaxation (SDR) based approaches have been proposed for solving the small-scale MIMO detection problem, they are not suitable to solve the large-scale MIMO detection problem due to their high computational complexities. In this paper, we propose an efficient sparse quadratic programming (SQP) relaxation based algorithm for solving the large-scale MIMO detection problem. In particular, we first reformulate the MIMO detection problem as an SQP problem. By dropping the sparse constraint, the resulting relaxation problem shares the same global minimizer with the SQP problem. In sharp contrast to the SDRs for the MIMO detection problem, our relaxation does not contain any (positive semidefinite) matrix variable and the numbers of variables and constraints in our relaxation are significantly less than those in the SDRs, which makes it particularly suitable for the large-scale problem. Then we propose a projected Newton based quadratic penalty method to solve the relaxation problem, which is guaranteed to converge to the vector of transmitted signals under reasonable conditions. By extensive numerical experiments, when applied to solve large-scale problems, the proposed algorithm achieves better detection performance than a recently proposed generalized power method.

Index modulation and hierarchical signal detection approach for MIMO OFDMA

Multiple-input multiple-output (MIMO) based index modulation has the advantage of high data rate and low complexity transmission which drawn great attention recently. And on the other side due to invention of digital video broadcasting IM schemes are most widely used in recent wireless systems. In this paper, a novel low-complexity signal detector enabled multiple-input multiple-output (MIMO) detector tailored with IM systems is presented which found suitable for efficient low complexity system with tolerable error rate. The actual IM methods have no diversity gain which causes a significant reduction in the system performance. The motivation for this is that to jointly considered IM schemes in large-scale MIMO-OFDMA systems. MATLAB simulation and numerical results show that, MIMO conditions on the channel gains a novel IM scheme based large-scale multi-user (MU) multiple-input multiple-output (MIMO) systems in order to produce optimized performance results with threshold scheme the inherent QoS problem. In this paper to eliminate the hostile effects of the channel assignment of the IM, novel ML based MIMO systems with index modulation technique (ML-MIMO-IM) is proposed. Here the ML detector system is used to achieve superior performance compared to the conventional MIMO detectors. The performance of the proposed design is close to the existing MIMO scheme, while resulting in a significantly lower complexity. The efficiency of MIMO over high order constellations are verified through MATLAB BER simulation and complexity reduction is also proved to be an efficient one.

A Capacity Achieving MIMO Detector Based on Stochastic Sampling

Spatial-multiplexing multiple-input multiple-output (MIMO) systems have been developed and enhanced over the past two decades. In particular, a great amount of effort has gone towards development of capacity achieving detectors with affordable computational complexity. The developed detectors may be broadly divided into two classes: (i) deterministic sampling, such as list sphere decoding detector; and (ii) stocastic sampling, such as those based on Markov chain Monte Carlo (MCMC) search schemes. This paper proposes a novel detection scheme that is based on stochastic sampling, but is fundamentally different from the MCMC detectors. While MCMC follows a set of sequential sampling steps, hence, the sample sets obtained are highly correlated, the method proposed in this paper takes stochastic samples that are completely independent. This new approach of stochastic sampling leads to a detector with significantly reduced complexity. It also allows reduction in the detector latency.

Joint Bernoulli Filtering and MIMO Processing for Detection of Moving Targets in Shallow Ocean Environments

A joint multiple-input multiple-output (MIMO) processing framework is proposed to exploit spatial diversity and moving cues for the enhancement of target detection in a shallow ocean environment. Orthogonal signals are transmitted to illuminate different aspects of a target and beamforming operation is carried out over the received data for estimating target bearing. The target range is achieved by a replica correlation integrator where the beamformer outputs are matched with transmitted signals. After meanshift clustering algorithm is carried out over the bearing-range spectrums to generate the clutter centers, the potential trajectories of targets can be tracked by an improved Bernoulli filter with inputs of these centers. The at-sea experimental results have shown the effectiveness of the joint processing framework in MIMO detection of moving targets.

Space-Partition-Driven Learning Network for Efficient MIMO Detection

With the development of communication systems, the MIMO order of low-cost communication equipments have also increased. However, classical MIMO algorithms either have too high computational complexity or have poor performance. Some existing deep learning-based MIMO detection networks have achieved excellent results in high-order MIMO, but they have unbearable detection performance in low-order MIMO. Based on the idea of dividing the sample space, we propose MLNet. Compared with Det Net and MMNet, MLNet has made a huge leap in time-varying channels. At the same time, MLNet has four orders of magnitude less computation than existing neural network algorithms. We also propose a training method, which is not only useful for MLNet, but also improves the detection performance of DetNet.

Hermite Expansion Model and LMMSE Analysis for Low-Resolution Quantized MIMO Detection

In this paper, the Hermite polynomials are employed to study linear approximation models of narrowband multiantenna signal reception (i.e., MIMO) with low-resolution quantizations. This study results in a novel linear approximation using the second-order Hermite expansion (SOHE). The SOHE model is not based on those assumptions often used in existing linear approximations. Instead, the quantization distortion is characterized by the second-order Hermite kernel, and the signal term is characterized by the first-order Hermite kernel. It is shown that the SOHE model can explain almost all phenomena and characteristics observed so far in the low-resolution MIMO signal reception. When the SOHE model is employed to analyze the linear minimum-mean-square-error (LMMSE) channel equalizer, it is revealed that the current LMMSE algorithm can be enhanced by incorporating a symbol-level normalization mechanism. The performance of the enhanced LMMSE algorithm is demonstrated through computer simulations for narrowband MIMO systems in Rayleigh fading channels.

New approach for MIMO detection and multi-user demodulation

New approach for MIMO detection and multi-user demodulation

Low-Complexity Soft-Output MIMO Detectors Based on Optimal Channel Puncturing

Channel puncturing transforms a multiple-input multiple-output (MIMO) channel into a sparse lower-triangular form using the so-called WL decomposition scheme in order to reduce tree-based detection complexity. We propose computationally efficient soft-output detectors based on two forms of channel puncturing: augmented and two-sided. The augmented WL detector (AWLD) employs a punctured channel derived by triangularizing the true channel in augmented form, followed by left-sided Gaussian elimination. The two-sided WL detector (dubbed WLZ) employs right-sided reduction and left-sided elimination to puncture the channel. We prove that augmented channel puncturing is optimal in maximizing the lower-bound on the achievable information rate (AIR) based on a new mismatched detection model. We show that the AWLD decomposes into an MMSE prefilter and channel gain compensation stages, followed by a regular WL detector (WLD) that computes least-squares soft-decision estimates. Similarly, WLZ decomposes into a pre-processing reduction step followed by WLD. AWLD attains the same performance as the existing AIR-based partial marginalization (PM) detector, but with less computational complexity. We empirically show that WLZ attains the best complexity-performance tradeoff among tree-based detectors.

Fixed-Complexity Tree Search Schemes for Detecting Generalized Spatially Modulated Signals: Algorithms and Hardware Architectures

In the generalized spatial modulation (GenSM) multiple input multiple output (MIMO) system, each block of data bits is mapped to a set of spatially multiplexed (SMX) symbols and an index of transmit antenna combination (TAC) of active antennas. The difficulty for the GenSM MIMO receiver is to detect the SMX symbols and TAC index simultaneously. Recently, we applied the conventional sphere decoding algorithm (SDA) successively to achieve the exact maximum likelihood detection (MLD) of GenSM MIMO signals. The SDA scheme suffers from variable computational complexity and leads to hardware detectors with variable throughput rate. Instead, fixed-complexity tree search algorithms, e.g., reduced fixed sphere decoding (rFSD), with nearly MLD performance are employed to facilitate hardware implementation. Here, we propose to apply the rFSD successively and design a hardware architecture for detecting GenSM MIMO signals under the scenario of 5 transmit antennas, 2 transmit radio frequency chains, 4 receive antennas, and 64-QAM SMX symbols. The VLSI implementation results under the TSMC 90nm CMOS technology reveal that our architecture requires 276.7K gates and provides detection throughput 1.613 Gbps, while operating at 322.6 MHz. Compared with other related architectures, our architecture provides higher detection throughput rate and is of better hardware efficiency.