Hybrid Vector Perturbation Precoding: The Blessing of Approximate Message Passing

Lyu, Shanxiang; Ling, Cong

doi:10.1109/tsp.2018.2877205

Cited by 39 publications

(19 citation statements)

References 43 publications

(113 reference statements)

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“…The noise spectral density and transmission bandwidth are set to -170 dBm/Hz and 1 MHz, respectively. For comparison, we examine the performance of the conventional LS-BOMP [11], MMSE-BOMP [29], and approximate message passing (AMP) algorithm [30]. When generating nonzero values in the LDS codebook, we use an i.i.i.…”

Section: A Simulation Setupmentioning

confidence: 99%

Deep Neural Network-Based Active User Detection for Grant-Free NOMA Systems

Kim

Ahn

Shim

2020

IEEE Trans. Commun.

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As a means to support the access of massive machine-type communication devices, grant-free access and non-orthogonal multiple access (NOMA) have received great deal of attention in recent years. In the grant-free transmission, each device transmits information without the granting process so that the basestation needs to identify the active devices among all potential devices. This process, called an active user detection (AUD), is a challenging problem in the NOMA-based systems since it is difficult to identify active devices from the superimposed received signal. An aim of this paper is to put forth a new type of AUD based on deep neural network (DNN). By applying the training data in the properly designed DNN, the proposed AUD scheme learns the nonlinear mapping between the received NOMA signal and indices of active devices. As a result, the trained DNN can handle the whole AUD process, achieving an accurate detection of the active users. Numerical results demonstrate that the proposed AUD scheme outperforms the conventional approaches in both AUD success probability and computational complexity.By exploiting the fact that only a few active devices in a cell transmit the information concurrently (see Fig. 1), the AUD problem can be readily formulated as a sparse recovery problem [8], [9]. Since the transmit vector is sparse, compressed sensing (CS) technique has been popularly employed [10], [11]. In [8], the AUD problem is modeled as a single measurement vector (SMV) problem and MPA is used to solve the problem. In this CS-based AUD scheme, basestation detects devices based on the correlation between the received signal and device specific sequence. However, performance of the CS-based AUD is not that appealing when the columns of a system matrix (a.k.a. sensing matrix) are highly correlated and sparsity (the number of nonzero elements) of the underlying input vector increases. In fact, in the practical NOMAbased transmission, correlation among the NOMA sequences and also device activity (sparsity) are relatively high so that the CS-based AUD might not be effective. Indeed, it has been shown that the performance of the sparse recovery algorithm is degraded significantly when the mutual correlation and sparsity increase [11]. Therefore, it is of importance to come up with a new type of AUD scheme suitable for the overloaded yet less sparse access scenarios.An aim of this paper is to pursue an entirely different approach to detect active users in the grant-free NOMA scenario. For an efficient and accurate AUD, we exploit the deep neural network (DNN), a learning-based tool to approximate the complicated and nonlinear function.Over the years, DNN has been successfully applied in numerous applications such as image classification [12], machine translation [13], automatic speech recognition [14], and Go game [15].Recently, DNN has been also applied to various wireless systems such as multiple-input and multiple-output (MIMO) detection, wireless scheduling, and direction-of-arrival (DoA) estimation [16]. In these w...

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Section: A Simulation Setupmentioning

confidence: 99%

Deep Neural Network-Based Active User Detection for Grant-Free NOMA Systems

Kim

Ahn

Shim

2020

IEEE Trans. Commun.

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“…We shall adopt this notion and observe the influence of LR on the plain ZFP and the ZF-THP approaches, instead of using the potentially ambiguous terminology of lattice reduction aide broadcast precoding [164], the latter of which essentially constitutes extensions to the vector perturbation scheme we will cover later. In essence, approximate lattice precoding heuristically solves the closest vector problem with preprocessing (CVPP), the solution of which can be further used as preprocessing for other schemes, such as the approximate message passing algorithm of [165]. However, the strict-sense CVPP in lattice theory typically exploits the geometry of the Voronoi cell and utilizes exponential-sized memory/buffer for very high dimensional lattices [166], whereas LRMUP algorithms simply acquire a good basis to work with for low to medium dimensional lattices.…”

Section: B Approximate Lattice Precodingmentioning

confidence: 99%

Far-End Crosstalk Mitigation for Future Wireline Networks Beyond G.mgfast: A Survey and an Outlook

Zhang

et al. 2020

IEEE Access

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With the escalating demand for high speed, high reliability, low latency, low cost and ubiquitous connectivity, the telecommunications industry is entering a new era where the ultimate optimality of the current wireline-wireless access network has to be achieved. Regarding the current wireline network paradigm, dominated by the copper-based digital subscriber lines (DSL) technology, multi-Gigabit data rate is the ambitious design objective at the customer end for the forthcoming ITU-T G.mgfast standard. In order to prepare for the new challenges in the era of total network convergence, both the wireline and the wireless community must be able to think beyond their respective conventions and learn from each other if necessary. Overall, the current DSL-based wireline network architecture is prone to the mutual interference resulting in far-end crosstalk (FEXT). The newly expanded 424/848 MHz spectrum of the ambitious G.mgfast project introduces far higher FEXT than that over the current 212/30 MHz G.fast/VDSL2 band. Additionally, the coexistence of multiple standards will also cause 'alien' FEXT. In these cases, using the plain zero forcing precoding (ZFP) will no longer attain a sufficiently high performance. However, as shown in the field of wireless communications, using lattice reduction as a signal space remapping technique significantly improves the performance of traditional multiuser detectors (MUD) and of the respective multiuser precoders (MUP). These promising techniques have largely remained unexploited in commercial wireless communications, due to their complexity in the face of the rapidly fluctuating wireless channels. In this survey, we present an overview of the state-of-the-art in wireline access network and an outlook for recent technological advances in the holistic 'wireline + wireless' access network in the context of network convergence, focusing on the dominant challenge of FEXT mitigation in future DSL networks. Against this background, we investigate both the family of linear precoding and of the Tomlinson-Harashima precoding (THP) schemes conceived for classic DSL. Furthermore, we present a tutorial on the family of lattice reduction aided MUPs, as well as quantifying their expected performances in realistic DSL scenarios. As a by-product, we also demonstrate the duality between MUP and MUD, in the hope that the fifty years' history of MUD could be used to accelerate the development of efficient near-optimal MUPs for future DSL. Under the recommended operating conditions of the 212 MHz profile of G.fast, our benchmark comparisons indicate that the lattice reduction aided techniques are very powerful compared to conventional schemes. In particular, their good performances do not rely on optimized spectrum balancing, and they are also shown to be relatively robust against channel state information (CSI) estimation error, under the assumption of perfectly time-invariant DSL channels.

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“…In AMP algorithm [23] ZF/SIC is used for initial guess of maximal error distance which is practically very close to small scale sphere decoding and certain probabilities also exist in which large dimensions contain slight error distance. Ternary distribution is used [32] for such scenarios. Empirical study has also proved that maximum portion of errors can be removed by using ternary distribution {−1, 0, 1} for PX (x q ).…”

Section: Algorithm 1 the Amp Algorithmmentioning

confidence: 99%

“…In [31], it is shown that the calculation of the posterior mean function (known as threshold function [28]) of AMP algorithm for small values of noise variance will become numerically unstable. In [32], two threshold functions have been designed to mitigate or overcome the computational complexities of AMP algorithm. The AMP algorithm with threshold function using ternary distribution is known as AMPT whereas, the one with threshold function using Gaussian distribution is known as AMPG [32].…”

Section: Introductionmentioning

confidence: 99%

“…In [32], two threshold functions have been designed to mitigate or overcome the computational complexities of AMP algorithm. The AMP algorithm with threshold function using ternary distribution is known as AMPT whereas, the one with threshold function using Gaussian distribution is known as AMPG [32]. We have thoroughly examined the AMP algorithm and evaluated the performances of both versions with different threshold.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Massive MIMO Detection for 5G Communication Systems via Hybrid n-Bit Heuristic Assisted-VBLAST

et al. 2019

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Being increasingly spectral and energy efficient, massive multiple-input multiple-output (MIMO) is envisaged as a potential technology for fifth generation (5G) wireless communication networks. Radio spectrum has become a scarce resource in wireless communications and consequently imposes excessive cost on the high data rate transmission. Several linear and non linear detection techniques such as Zero-Forcing (ZF), Minimum Mean Square Error (MMSE), and Vertical Bell-Labs Layered Space Time (VBLAST) have been introduced. The purpose of such schemes is to mitigate the signal detection problems which are based on trade-offs between the bit error rate (BER) performance and computational complexities. The challenge in the design of massive-MIMO systems is developing less complex and efficient detection algorithms. The problem in building a receiver for massive-MIMO is to de-correlate the spatial signatures on the receiver antenna array. In this paper, we propose a novel algorithm viz: Hybrid n-Bit Heuristic Assisted-VBLAST (HHAV) to perform an optimum decoding. We have simulated this structure in dynamic Rayleigh fading channel. We have also evaluated the AMP algorithm with two threshold functions which include AMP with ternary distribution (AMPT) and AMP with Gaussian distribution (AMPG). Numerical results confirm that HHAV algorithm performs significantly better than the in vogue aforementioned detection systems as introduced in recent years.

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Hybrid Vector Perturbation Precoding: The Blessing of Approximate Message Passing

Cited by 39 publications

References 43 publications

Deep Neural Network-Based Active User Detection for Grant-Free NOMA Systems

Deep Neural Network-Based Active User Detection for Grant-Free NOMA Systems

Far-End Crosstalk Mitigation for Future Wireline Networks Beyond G.mgfast: A Survey and an Outlook

Optimal Massive MIMO Detection for 5G Communication Systems via Hybrid n-Bit Heuristic Assisted-VBLAST

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