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In this paper we study an intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) network where the direct link between the base station (BS) and one of the users is blocked and the IRS is deployed to serve the blocked user. The IRS designs under both the ideal IRS with continuous phase shifting and the non-ideal IRS with discrete phase shifting are considered. For both cases, by leveraging the isotropic random vector and the Laguerre series, we derive insightful results and closed-form expressions on the performance measures including the average required transmit power, the outage probability, and the diversity order. Our analytical results show that the transmit power scales down linearly with the BS antenna number and quadratically with the IRS element number. The diversity order equals the smaller of the BS antenna number and the IRS element number with a scaling coefficient. Our results also reveal the effect of the phase quantization resolution to the system performance when non-ideal IRS is used. Numerical results are provided to validate the accuracy of our analysis and the non-ideal IRS with four or more bits for quantization is shown to achieve nearly the same performance as the ideal IRS.

A popular technique of designing multiple-input multiple-output (MIMO) communication systems relies on optimizing the positive semidefinite covariance matrix at the source. In this paper, a unified MIMO optimization framework based on the Karush-Kuhn-Tucker (KKT) conditions is proposed. In this framework, with the aid of matrix optimization theory, Theorem 1 presents a generic optimal transmit covariance matrix for MIMO systems with diverse objective functions subject to various power constraints and different levels of channel state information (CSI). Specifically, Theorem 1 fundamentally reveals that for a diverse family of MIMO systems, the optimal transmit covariance matrices associated with different objective functions under various power constraints can be derived in a unified generic water-filling-like form. When applying Theorem 1 to the case of multiple general power constraints, we firstly equivalently transform multiple power constraints into a single counterpart by introducing multiple weighting factors based on Pareto optimization theory. The optimal weighting factors can be found by the proposed modified subgradient method. On the other hand, for the imperfect MIMO system with statistical CSI errors, we firstly address the non-convexity of the robust optimization problem by following the idea of alternating optimization. Finally, our numerical results verify the optimal solution structure in Theorem 1 and the global optimality of the proposed modified subgradient method, as well as demonstrate the performance advantages of the proposed alternating optimization algorithm.

In this paper, we analyze the average transmit power of non-orthogonal multiple access (NOMA) systems with requirements on the user signal-to-interference-plus-noise-ratios (SINRs), and propose new transmission schemes and user clustering algorithms to reduce the average transmit power. First, for systems with a multi-antenna base station (BS) and a single cluster of two single-antenna users, the average transmit power with SINR constraints is analyzed for channel-alignment-based NOMA and multi-user beamforming with matched filter (MF) beamformers. Properties of the average transmit power are obtained with respect to the alignment threshold and the BS antenna number. It is shown that using either scheme alone without the alignment consideration leads to unbounded average transmit power. With the observation that the two schemes have distinct preferred regions of channel alignment, hybrid transmissions of NOMA and multi-user beamforming are proposed for power saving and outage avoidance. Further, for systems with more than two users, clustering algorithms are developed to group users into multiple two-user clusters with respect to the minimization of the total transmit power for NOMA and hybrid schemes. Simulation results are provided to validate our theoretical results. In addition, the proposed transmission schemes and clustering algorithms are shown to achieve significant saving in the average transmit power.

This letter proposes a modified non-orthogonal multiple-access (NOMA) scheme for systems with a multi-antenna base station (BS) and two single-antenna users, where NOMA transmissions are conducted only when the absolute correlation coefficient (CC) between the user channels exceeds a threshold and the BS uses matched-filter (MF) precoding along the user with the stronger average channel gain. We derive the average minimal transmit power to guarantee the signal-to-interference-plus-noise-ratio (SINR) levels of both users. Our results show that the average minimal power grows logarithmically in the reciprocal of the CC threshold and a non-zero threshold is necessary for the modified NOMA scheme to have finite average minimal transmit power. Further, for the massive MIMO scenario, we derive the scaling laws of the average transmit power and outage probability with respect to the antenna numbers, as well as their tradeoff law. Simulation results are shown to validate our theoretical results., Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

This paper analyzes the performance of multi-user full-duplex (FD) massive multiple-input multiple-output (MIMO) systems with low resolution analog-to-digital converters (ADCs) and digital-to-analog converters (DACs) under Rician fading channels. The maximum ratio combining and maximum ratio transmission are used at the base station (BS) for the uplink and downlink, respectively. By leveraging on the additive quantization noise model, tight closed-form approximations of the uplink and downlink achievable rates are obtained for both perfect and imperfect channel state information cases. The results show the impact of the Rician $K$ -factor, ADC/DAC resolution, loop interference, and inter-user interference of the systems. In addition, we adopt the power scaling law to show that to achieve a fixed level of the signal-to-interference-plus-noise ratio, the transmit power of each user and the BS can be scaled down proportionally to the inverse of the BS antenna number. Moreover, we compare the performance of the FD mode and the half-duplex mode, and study the trade-off between the achievable rate and BS energy efficiency. Numerical results show that the use of low-resolution ADCs/DACs can significantly improve the BS energy efficiency with only small reduction in the achievable rate of the FD system.

This letter is on the multi-user massive multi-input multi-output (MIMO) uplink with the mixed analog-to-digital converter (ADC) architecture and the zero-forcing (ZF) receiver at the base station (BS). Under imperfect channel state information (CSI), a closed-form approximation of the spectral efficiency (SE) is derived for a general resolution profile. Further, simplified SE results are provided for two common asymptotic massive MIMO scenarios. In the SE analysis, to overcome the difficulty induced by the mixed-ADC structure, new techniques are developed in calculating the average noise powers caused by the CSI error and the quantization.

Massive multiple-input-multiple-output (MIMO) system inevitably faces the hardware cost and energy efficiency problem due to its large number of antennas at the base station (BS). The use of low-resolution analog-to-digital converters (ADCs), e.g., typical 1-bit ADCs, can effectively reduce the system cost. In this paper, we consider a massive MIMO uplink with ternary/three level ADCs. The design of typical linear combiner based detectors is given along with their analytical symbol-error-rate (SER) performance results. Analytical and simulation results show that 1) ternary ADCs can effectively compensate the SER performance gap between 1-bit and full-resolution ADCs; 2) optimal design of ternary ADCs for SER minimization can be referred to the existing design for quantization error minimization; 3) ternary ADCs perform better than 2-bit ADCs in energy efficiency. Thus, for some low-cost scenarios where implementing 2-bit ADCs for each antenna in massive MIMO may be even unaffordable, ternary ADC can be a good choice.

Extreme learning machine (ELM) is commonly adopted and best known for its extremely fast learning capability and notable performance. In this paper, a multiple split-complex ELM (Multi-SCELM) regressor based equalization and detection method is proposed for OFDM systems. This method combines ELM regressors for equalization and minimum-distance based symbol slicers for symbol detection. Furthermore, the proposed Multi-SCELM is extended to fully complex ELM (CELM) for channel equalization and detection. Simulations demonstrate that compared to existing ELM based methods, the proposed one owns the advantages of lower computational complexity, higher detection accuracy, stronger activation function adaptability, shorter training length and better subchannel number adaptability especially in strong frequency selective channels. Compared to the benchmark MMSE method, the proposed method has minor performance degradation but significant reduction in computational complexity.

In this letter, for outage performance orientated large intelligent surfaces (LISs)-assisted point to point wireless systems with severely blocked direct link and Rayleigh fading channels,we first propose a jointly interleaved training and transmission design. Then a semi-closed form expression is derived for the average training overhead. And it is shown to be upper bounded by the minimum between the LIS size and a value explicitly dependent on the target receiver signal-to-noise-ratio (SNR). The upper bound gives the condition on the target SNR for achieving overhead saving compared to the full CSI scheme. And the overhead saving increases linearlywith the LIS size for constant target SNR. Non-negligible overhead saving is still available even though one increases the target SNR with larger LIS, e.g., as the square of the LIS size for fully exploiting the beamforming gain. Finally, we indicate the impact of practical phase quantization on the training and feedback overhead. Simulations verify these results and show that the proposed scheme can significantly reduce the training overhead without performance loss compared to the full CSI scheme.

In this paper, a deep learning (DL)-based sphere decoding algorithm is proposed, where the radius of the decoding hypersphere is learned by a deep neural network (DNN). The performance achieved by the proposed algorithm is very close to the optimal maximum likelihood decoding (MLD) over a wide range of signal-to-noise ratios (SNRs), while the computational complexity, compared to existing sphere decoding variants, is significantly reduced. This improvement is attributed to the DNN’s ability of intelligently learning the radius of the hypersphere used in decoding. The expected complexity of the proposed DL-based algorithm is analytically derived and compared with existing ones. It is shown that the number of lattice points inside the decoding hypersphere drastically reduces in the DL-based algorithm in both the average and worst-case senses. The effectiveness of the proposed algorithm is shown through the simulation for high-dimensional multiple-input multiple-output (MIMO) systems, using high-order modulations.

We address the problem of secure wireless communications over an untrusted full-duplex (FD) relay based on the source jamming scheme. The optimal power allocation between the confidential signal and the jamming signal is derived to maximize the secrecy rate. Then, the corresponding secrecy outage probability (SOP) and the average secrecy rate (ASR) are analyzed. A tight approximation and an asymptotic result are further obtained for the single-antenna destination case both in simple forms. The large-antenna destination case is also analyzed rigorously. Further discussion reveals that transmit-power dependent self-interference has significant negative impact on the secrecy performance.

An uplink multi-user massive Multi-input multi-output (MIMO) system is considered with a mixed Analog-to-digital converter (ADC) architecture under imperfect Channel state information (CSI). A closed-form approximation for the Spectral efficiency (SE) is derived for a general mixed ADC structure with any resolution profile. To achieve a balance between the SE and receive Energy efficiency (EE) of the system, the ADC resolution profile optimization problem that maximizes a linear combination of the SE and the Base station (BS) receive power consumption is formulated. An algorithm based on gradient search is proposed whose complexity is linear in the number of BS antennas. Numerical results verify that the proposed ADC resolution design largely outperforms the two-level structure especially in the lower SE region and provides more choices than the uniform-ADC architecture for resolving the SE-EE tradeoff.

This paper investigates the relay selection (RS) problem for multi-hop full-duplex relay networks where multiple source–destination (SD) pairs compete for the same pool of relays, under the attack of multiple eavesdroppers. To enhance the physical-layer security, within a given coherence time, our objective is to jointly assign the available relays at each hop to different SD pairs to maximize the minimum secrecy rate among all pairs. Two RS schemes, optimal RS and suboptimal RS (SRS), are proposed for two-hop networks based on global channel state information (CSI) and only SD pairs CSI, respectively. Since all users can communicate within the same coherence time, our joint RS schemes are important for the user-fairness and ultra-reliable low-latency communications. To evaluate the performance, the exact secrecy outage probability of the SRS scheme is derived under two residual self-interference models. The asymptotic analysis shows that the SRS scheme achieves full diversity. A relay-based jamming scheme is also proposed by using unassigned relays for user communications. Finally, the two-hop RS schemes and the analysis are extended to the general multi-hop network with multiple eavesdroppers. The numerical results reveal interesting fundamental trends where the proposed schemes can significantly enhance the secrecy performance.

Nowadays, Leakage detection is of great importance as pipelines are the major means of transporting hydrocarbon fluids and gases. In this paper, a novel two-stage detection method is introduced to differentiate normal, leakage and transient conditions of pipelines. In this method, feature vectors are constructed from the flow difference and pressure using leakage characteristics, and are normalized with the modified hyperbolic-tangent estimator. An artificial neural network is used in the first stage of detection to differentiate normal and abnormal conditions with the feature vectors as the inputs. In the second detection stage, a simple logic is used to distinguish leakage and transient from abnormal time-windows. In addition, a pre-set leak-size tolerance is used to trigger alarms for detected leakage time-windows. The results for the cases of using different machine learning methods and varying leak-size tolerances are given. The method has been shown to have higher detection performance and less false alarms in comparison with the line balance and Kantorovich distance methods.

In this paper, a statistical-based sphere decoding with increasing radius search (S-SD-IRS) algorithm is proposed, where the radiuses of the decoding hyperspheres are determined based on the statistical properties of the communication channel and additive noise. We show that the probability density functions (PDFs) of the q lowest squared distances in the closest lattice point problem can be approximated by Gumbel distributions with different parameters. Based on the obtained PDFs and by considering the characteristics of the fading channels and additive noise, we choose the radiuses for sphere decoding more efficiently than the conventional methods that ignore the characteristics of system. The performance achieved by the proposed algorithm is very close to the optimal maximum likelihood decoding (MLD) over a wide range of signal-to-noise ratios (SNRs), while the computational complexity, compared to existing sphere decoding variants, is significantly reduced. It is shown that the average number of lattice points inside the decoding hyperspheres drastically reduces in the proposed S-SD-IRS algorithm.

This paper investigates the power consumption and user-clustering of a multi-cluster multi-input single-output non-orthogonal multiple access (MISO-NOMA) systems. First, we derive the average transmit power of a two-user cluster, given constraints on the signal-to-interference-plus-noise-ratios (SINRs) of users and a threshold on the alignment of channel directions. Then the asymptotic behavior of the average transmit power under several scenarios are studied to show the scaling of the transmit power with respect to the base station (BS) antenna number and the alignment threshold. After that, a two-layer user clustering algorithm is proposed to improve the power efficiency by selecting the head and tail users in each cluster sequentially. Simulation results validate the correctness of our theoretical results. Further, the proposed user-cluster algorithm is shown to achieve significant saving on the transmit power.

In this paper, a decision-directed (DD)-channel estimation (CE) algorithm by employing deep learning (DL) is proposed for high mobility vehicular environments. The proposed algorithm relies on DL for channel prediction without prior knowledge about channel statistics, such as the channel covariance matrix and its time variation. Therefore, it does not require any prior Doppler rate estimation, which is highly complicated in high mobility environments. Based on the performed simulations, comparing to the Kalman filter-based DD-CE algorithm, our DL-based algorithm results in more reliable communications. It has been shown that comparing to the minimum mean square error (MMSE)-based DD-CE algorithm, our DL-based algorithm imposes much lower complexity to the system and the performance degradation is small compared to the MMSE-based DD-CE algorithm that requires the exact value of the Doppler rate.

To support reliable transmission of data at high rate in time-varying fading channels, adaptive transmission is required, where transmitter and receiver adjust their transmission and reception mode to the dynamics of the channel. The receiver, based on its channel estimation and prediction, decides the optimal link adaptation and feeds this back to the transmitter. In this paper, we develop a deep learning (DL)-based link adaptation algorithm for highly dynamic communication links, where adaptive transmission parameters are decided for $l\gt1$ forward time steps without a priori knowledge on channel statistics. Compared to conventional solutions, our approach reduces the feedback requirements from the receiver to the transmitter by a factor of l which significantly reduces the complexity. This achievement comes at no additional cost on the data rate and/or bit error rate.

This paper analyzes the outage probability for the uplink of multi-user massive multi-input-multi-output systems with a mixed analog-to-digital converter (ADC) architecture, in which the base station (BS) is equipped with ADCs of different resolution levels. Maximum-ratio combining (MRC) is used at the BS. By deriving the distribution of the user-interference power and statistical properties of other components in the signal-to-interference-plus-noise-ratio (SINR), a tight closed-form approximation for the outage probability is obtained for a general mixed ADC structure with any resolution profile. Then, two methods for the ADC resolution profile optimization are proposed considering both the outage probability and the BS energy consumption. The first method uses low-complexity incremental search to minimize the BS energy consumption for given outage probability constraint. The other method is based on multi-objective optimization and adopts a discrete-variation of the classic non-dominated sorting genetic algorithm II (NSGA-II). Numerical results are presented to validate the outage probability results. Furthermore, it is shown that the two proposed mixed-resolution ADC designs largely outperform a two-level ADC structure and provide more choices than the uniform ADC structure for resolving the tradeoff between outage probability and BS energy consumption.

Zero-forcing (ZF) precoding plays an important role for massive MIMO downlink due to its near optimal performance. However, the high computation cost of the involved matrix inversion hinders its application. In this paper, we adopt the first order Neumann series (NS) for a low-complexity approximation. By introducing a relaxation parameter jointly with one selected user's interference to others into the precondition matrix, we propose the identity-plus-column NS (ICNS) method. By further exploiting the multi-user diversity gain via choosing the user with the largest interference to others, the ordered ICNS method is also proposed. Moreover, the sum-rate approximations of the proposed ICNS method and the competitive existing identity matrix based NS (INS) method are derived in closed-form, based on which the performance loss of ICNS due to inversion approximation compared with ideal ZF and its performance gain over INS are explicitly analyzed for three typical massive MIMO scenarios. Finally, simulations verify our analytical results and also show that the proposed two designs achieve better performance-complexity tradeoff than ideal ZF and existing low-complexity ZF precodings for practical large antenna number, correlated channels and not-so-small loading factor., Comment: 30 Pages, 9 figures, submitted to IEEE Transactions on Communications

In this paper, we study the beam-based training design jointly with the transmission design for hybrid massive antenna single-user (SU) and multiple-user (MU) systems where outage probability is adopted as the performance measure. For SU systems, we propose an interleaved training design to concatenate the feedback and training procedures, thus making the training length adaptive to the channel realization. Exact analytical expressions are derived for the average training length and the outage probability of the proposed interleaved training. For MU systems, we propose a joint design for the beam-based interleaved training, beam assignment, and MU data transmissions. Two solutions for the beam assignment are provided with different complexity-performance tradeoff. Analytical results and simulations show that for both SU and MU systems, the proposed joint training and transmission designs achieve the same outage performance as the traditional full-training scheme but with significant saving in the training overhead., 16 Pages (double column), 11 figures. This work has been accepted by the IEEE Journal of Selected Topics in Signal Processing (JSTSP), Special Issue on Hybrid Analog - Digital Signal Processing for Hardware-Efficient Large Scale Antenna Arrays. This version is different from the former one due to the revisions made for the comments of 1st and 2nd round review

This paper considers the uplink of multiuser massive multi-input multi-output systems with a mixed analog-to-digital converter (ADC) architecture, in which the base station (BS) is equipped with ADCs of different resolution levels. While higher resolution, ADCs reduce the quantization error to improve the performance; they also incur higher energy. This paper studies the receive energy efficiency (EE) and the ADC resolution profile design under the maximum-ratio combining receiver. First, closed-form approximations for the spectral efficiency (SE) and receive EE are derived for a general mixed ADC structure. Then, the ADC resolution profile optimization problem that maximizes the receive EE with respect to SE requirement is formulated. An algorithm based on decremental searching and dynamic programming is proposed whose complexity is linear in the number of BS antennas. Numerical results verify that with the proposed design, the mixed-ADC receiver can have nearly the same SE performance as the all full-resolution one but with considerably lighter burden of energy consumption.

This paper conducts sensitivity analysis and sensitivity-based design for linear filter alarm monitoring systems. Based on a derivative-based local sensitivity measure, models are proposed to assess the sensitivity of the system detection errors to changes in the trip point and to uncertainties in the collected data. Then, analytical expressions are derived to quantitatively evaluate the sensitivity of a general linear alarm filter with unknown data distributions. Subsequently, a new sensitivity-based linear filter design method is formulated to minimize a weighted sum of the detection errors subject to upper bounds on the system sensitivities. Extensive simulations with both Gaussian and industrial data are conducted to verify the analytical results and to show trade-offs between the detection errors and sensitivities of linear filter alarm system.

This paper provides a comprehensive scaling law and performance analysis for multi-user massive multiple-input-multiple-output (MIMO) relay networks, where the relay is equipped with a massive antenna array and uses maximal-ratio combining/maximal-ratio transmission (MRC/MRT) for low-complexity processing. Imperfect channel state information (CSI) is considered for both source-relay and relay-destination channels. First, a sum-rate lower bound is derived, which manifests the effect of system parameters, including the numbers of relay antennas and users, the CSI quality, and the transmit powers of the sources and the relay. Via a general scaling model on the parameters with respect to the relay antenna number, the asymptotic scaling law of the signal-to-interference-plus-noise-ratio (SINR) is obtained, which shows quantitatively the tradeoff of the network parameters. In addition, a sufficient condition on the parameter scalings for the SINR to be asymptotically deterministic is given, which covers existing results on such analysis as special cases. Then, the scenario where the SINR increases linearly with the relay antenna number is studied. The sufficient and necessary condition on the parameter scaling for this scenario is proved. It is shown that in this case, the interference power is not asymptotically deterministic, and then, the average bit error rate is analyzed.

Mean-squared-error (MSE) is one of the most widely used performance metrics for the designs and analysis of multi-input-multiple-output (MIMO) communications. Weighted MSE minimization, a more general formulation of MSE minimization, plays an important role in MIMO transceiver optimization. While this topic has a long history and has been extensively studied, existing treatments on the methods in solving the weighted MSE optimization are more or less sporadic and non-systematic. In this paper, we firstly review the two major methodologies, Lagrange multiplier method and majorization theory based method, and their common procedures in solving the weighted MSE minimization. Then some problems and limitations of the methods that were usually neglected or glossed over in existing literature are provided. These problems are fundamental and of critical importance for the corresponding MIMO transceiver optimizations. In addition, a new extended matrix-field weighted MSE model is proposed. Its solutions and applications are discussed in details. Compared with existing models, this new model has wider applications, e.g., nonlinear MIMO transceiver designs and capacity-maximization transceiver designs for general MIMO networks., 34 Pages, 4 figures

As a key infrastructure of Internet of Things (loT), wireless sensor networks (WSN) can be utilized in a wide range of applications. The prediction based data fusion methods provide effective tools to reduce the amount of data transmissions while maintaining prediction accuracy. Recently a grey prediction model (GM) combining optimally-pruned extreme learning machine (OPELM) data fusion method has been proposed and shown to have good performance. However, the existing GM- OPELM method performs model training and broadcasting before each prediction, resulting in high complexity and energy consumption. In this paper the conditional training based GM (CT-GM) and GM-OPELM (CT-GM-OPELM) are proposed. By introducing an error threshold, the algorithms only perform model training when the prediction error is beyond the threshold. Compared with existing GM and GM-OPELM methods, the CT- GM and CT-GM-OPELM methods not only can achieve the higher rate of acceptable prediction and better time efficiency but also has significant reduction in the energy consumption on model training and transmissions.

This paper is on the uplink communications of single-cell multi-user massive multi-input-multi-output (MIMO) systems with a mixture of 1-bit analog-to-digital converters (ADCs) and full-resolution ADCs. A new low-complexity linear combining scheme is proposed for the receiver at the base station (BS) to take into account the difference of the two ADC types. A symbol-wise decoding is derived to separately decode the information of each user and an analytical approximation is obtained for the symbol error rate (SER) of the proposed combining and decoding. Simulation results show that the proposed scheme achieves significant performance improvement compared to the traditional maximum-ratio-combining (MRC) receiver.

This paper considers a multiple-input multiple-output (MIMO) multi-way relay network (MWRN) where users exchange their information via a multi-way relay equipped with a large-scale antenna array, i.e., massive MIMO multi-way relay. Further, each antenna at the relay station is assumed to have a pair of low-resolution analog-to-digital converters (ADCs) to reduce the energy consumption and hardware cost at the relay. Lloyd-max algorithm is used to find the mean-squared error (MSE) optimum quantization labels and thresholds for the ADCs. With perfect channel state information (CSI) and zero-forcing (ZF) beam-forming for both reception and transmission at the relay, a closed-form approximation for the average achievable rate of each pair of users is derived with the help of Bussgang's decomposition. The results enable us to understand the achievable rate behavior with respect to system parameters, and especially to quantify the performance degradation caused by low-resolution ADCs. Numerical results verify the validity of Bussgang's theorem in our case, and that the derived result is an accurate performance predictor of the network. Further, both analytical and theoretical results reveal that the effect of ADC resolutions on the rate performance is as significant as the relay and users' transmit powers.

We consider the use of deep neural network (DNN) to develop a decision-directed (DD)-channel estimation (CE) algorithm for multiple-input multiple-output (MIMO)-space-time block coded systems in highly dynamic vehicular environments. We propose the use of DNN for $k$ -step channel prediction for space-time block code (STBC), and show that deep learning (DL)-based DD-CE can remove the need for Doppler rate estimation in fast time-varying quasi stationary channels, where the Doppler rate varies from one packet to another. Doppler rate estimation in this kind of vehicular channels is remarkably challenging and requires a large number of pilots and preambles, leading to lower power and spectral efficiency. We train two DNNs which learn the real and imaginary parts of the MIMO fading channels over a wide range of Doppler rates. We demonstrate that by these DNNs, DD-CE can be realized with only priori knowledge about Doppler rate range and not the exact value. For the proposed DD-CE algorithm, we also analytically derive the maximum likelihood (ML) decoding algorithm for STBC transmission. The proposed DL-based DD-CE is a promising solution for reliable communication over vehicular MIMO fading channels without accurate mathematical models. This is because DNNs can intelligently learn the statistics of the fading channels. Our simulation results show that the proposed DL-based DD-CE algorithm exhibits lower error propagation compared to existing DD-CE algorithms which require perfect knowledge of the Doppler rate.

This letter analyzes the user-interference and outage probability for single-cell multi-user massive multi-input-multi-output (MIMO) systems with matched-filter (MF) precoding. Existing performance studies on massive MIMO systems have focused on the sum-rate by deriving the asymptotic deterministic equivalence. In this work, we treat the user-interference as random, and derive a tight closed-form approximation for the distribution of the interference power. This enables the analysis of the outage probability. The derived results are shown to have accurate match with the simulation.

This paper investigated the uplink of multi-user massive multi-input multi-output (MIMO) systems with a mixed analog-to-digital converter (ADC) receiver architecture, in which some antennas are equipped with costly full-resolution ADCs and others with less expensive low-resolution ADCs. A closed-form approximation of the achievable spectral efficiency (SE) with the maximum-ratio combining (MRC) detector is derived. Based on this approximated result, the effects of the number of base station antennas, the transmit power, the proportion of full-resolution ADCs in the mixed-ADC structure, and the number of quantization bits of the low-resolution ADCs are revealed. Results showcase that the achievable SE increases with the number of BS antennas and quantization bits, and it converges to a saturated value in the high user power regime or the full ADC resolution case. Most important, this work efficiency verifies that for massive MIMO, the mixed-ADC receiver with a small fraction of full-resolution ADCs can have comparable SE performance with the receiver with all full-resolution ADCs but at a considerably lower hardware cost., Comment: 11 pages, 3 figures

Water-filling solutions play an important role in the designs for wireless communications, e.g., transmit covariance matrix design. A traditional physical understanding is to use the analogy of pouring water over a pool with fluctuating bottom. Numerous variants of water-filling solutions have been discovered during the evolution of wireless networks. To obtain the solution values, iterative computations are required, even for simple cases with compact mathematical formulations. Thus, algorithm design is a key issue for the practical use of water-filling solutions, which however has been given marginal attention in the literature. Many existing algorithms are designed on a case-by-case basis for the variations of water-filling solutions and/or with complex logics. In this paper, a new viewpoint for water-filling solutions is proposed to understand the problem dynamically by considering changes in the increasing rates on different subchannels. This fresh viewpoint provides useful mechanism and fundamental information in finding the optimization solution values. Based on the new understanding, a novel and comprehensive method for practical water-filling algorithm design is proposed, which can be used for systems with various performance metrics and power constraints, even for systems with imperfect channel state information (CSI)., 16 pages, 3 figures. This manuscript is submitted to IEEE Transactions on Signal Processing

This paper studies the relay selection (RS) problem for full-duplex (FD) relay networks with multiple source-destination (SD) pairs under the attack of colluding eavesdroppers. Based on available channel state information (CSI), both optimal relay selection (ORS) and suboptimal relay selection (SRS) schemes are considered to maximize the minimum secrecy rate among all pairs in order to enhance the physical-layer security. The secrecy performance of the more practical SRS scheme is then evaluated in terms of intercept probability and diversity order. The SRS achieves full diversity when the gains of the main-to- eavesdropper and the main-to-interference channels increase asymptotically.

Zero-forcing (ZF) precoding plays an important role for massive MIMO downlink due to its near optimal performance in high signal-to- noise (SNR) region. However, the high computation cost of the involved matrix inversion hinders its application in practical large- scale systems. In this paper, we adopt the first order Neumann series (NS) expansion for a low-complexity approximation of matrix inversion. Compared to existing NS based schemes, we introduce a relaxation parameter jointly with one user's channel interference to others into the precondition matrix and propose the identity-plus- column NS (ICNS) method. By further exploiting the multi-user diversity gain via choosing the user with largest interference to others, the ordered ICNS method is also proposed. Moreover, the closed-form sum-rate approximation of the ICNS method is derived. Simulations verify our analytical results and the advantage of the proposed schemes over other existing low-complexity ZF precodings for massive MIMO systems with correlated channels and not-so-small loading factor.

In this paper, we study the beam-based training design for the single-user (SU) hybrid massive antenna system based on outage probability performance. First, an interleaved training design is proposed where the feedback is concatenated with the training procedure to monitor the training status and to have the training length adaptive to the channel realization. Then, the average training length and outage probability are derived for the proposed interleaved training and SU transmission. Analytical results and simulations show that the proposed interleaved scheme achieves the same outage performance as the traditional full-training scheme but with significant saving in the training overhead.

Non-orthogonal multiple access (NOMA), as the newest member of the multiple access family, is envisioned to be an essential component of 5G mobile networks. The combination of NOMA and multi-antenna multi-input multi-output (MIMO) technologies exhibits a significant potential in improving spectral efficiency and providing better wireless services to more users. In this article, we introduce the basic concepts of MIMO-NOMA and summarize the key technical problems in MIMO-NOMA systems. Then, we explore the problem formulation, beamforming, user clustering, and power allocation of single/multi-cluster MIMO-NOMA in the literature along with their limitations. Furthermore, we point out an important issue of the stability of successive interference cancellation (SIC) that arises using achievable rates as performance metrics in practical NOMA/MIMO-NOMA systems. Finally, we discuss incorporating NOMA with massive/millimeter wave MIMO, and identify the main challenges and possible future research directions in this area., 14 pages (single column), 4 figures. This work has been accepted by the IEEE Wireless Communications, the special issue of non-orthogonal multiple access for 5G

Beamforming is a powerful technique which has been widely used in signal processing, radar and sonar, biomedical, and particularly in communications, where the basic idea is to optimally process signals received over different antennas, or the signals which are to be transmitted over different paths by adjusting the signal amplitudes and phases to form a strong beam toward the direction of interest, and at the same time, to avoid receiving or creating interference. In the past decade, with booming research in cooperative relay communications, a new type of beamforming, namely network beamforming, has appeared and developed to fulfill the needs of the ever-evolving wireless systems. A network beamformer is implemented at neither the transmitter nor the receiver but at the intermediate relays to adaptively process signals and conduct the tasks of both the receive beamformer and the transmit beamformer. In this chapter, a systematic overview of various network beamforming approaches and solutions are presented for multi-relay cooperative networks with a wide variety of configurations, including one-way and two-way communications, single-user and multi-user cases, flat-fading and frequency-selective channel models, and networks with perfect and partial channel state information.

High power consumption and hardware cost are two barriers for practical massive multiple-input multiple-output (mMIMO) systems. A promising solution is to employ low-resolution analog-to-digital converters (ADCs). In this paper, we consider a general mMIMO multi-way relaying system with a multi-level mixed-ADC architecture, in which each antenna is connected to an ADC pair of an arbitrary resolution. By leveraging on Bussgang's decomposition theorem and Lloyd-Max algorithm for quantization, tight closed-form approximations are derived for the average achievable rates of zero-forcing (ZF) relaying considering both perfect and imperfect channel state information (CSI). To conquer the challenges caused by multi-way relaying, the complicated ZF beam-forming matrix, and the general mixed-ADC structure, we develop a novel method for the achievable rate analysis using the singular-value decomposition (SVD) for Gaussian matrices, distributions of the singular values of Gaussian matrices, and properties of Haar matrices. The results explicitly show the achievable rate behavior in terms of the user and relay transmit powers and the numbers of relay antennas and users. Most importantly, it quantifies the performance degradation caused by low-resolution ADCs and channel estimation error. We demonstrate that the average achievable rate has an almost linear relation with the square of the average of quantization coefficients pertaining to the ADC resolution profile., Comment: Part of this work on the performance analysis of mMIMO multi-way relay networks with low-resolution uniform-ADC structures has been presented at the IEEE International Conference on Communications (ICC) 2019 [1]

In this paper, we study the power allocation problem in a multiuser cooperative network, where users help each other's transmissions to an access point. We use iterative double auction (IDA) game to model the interaction among the users and the access point. In each iteration of this game, the users first submit bids for buying other users' power and asks for selling its own power, and then, the access point determines the power allocation based on users' bids and asks. We propose a distributed algorithm for the implementation of the IDA-based power allocation. We also show that the proposed algorithm achieves a weighted sum signal-to-noise ratio (sum-SNR)-optimal solution. Simulation results are conducted to verify the performance of the proposed algorithm.

In this work, we study joint user-relay selection and association in multi-user multi-relay cooperative wireless relay uplinks with multi-antenna nodes. For non-regenerative and altruistic relays we propose a low-complexity joint scheme, which simultaneously selects multiple relays and users for cooperation as well as assigns the selected users to different selected relays for service. The proposed scheme is sub-optimal and utilizes only the channel gains between the nodes, which leads to reduced feedback and overhead in comparison to schemes that require full channel knowledge. Furthermore, the complexity of the scheme scales linearly as the product of the total number of relays, the total number of users and the number of selected users. Simulation results demonstrate the superiority of the proposed joint scheme compared to a scheme with neither user-relay selection nor user-relay association and another scheme with user-relay association, but no user-relay selection. The favorable performance and low-complexity of the proposed scheme make it very attractive for possible implementation in emerging broadband wireless relay networks (e.g., LTE-Advanced).

The ever increasing demands for mobile network access have resulted in a significant increase in bandwidth usage. By improving the system spectral efficiency, multi-way relay networks (MWRNs) provide promising approaches to address this challenge. In this paper, we propose a novel linear beamforming design, namely partial zero-forcing (PZF), for MWRNs with a multiple-input-multiple-output (MIMO) relay. Compared to zero-forcing (ZF), PZF relaxes the constraints on the relay beamforming matrix such that only partial user-interference, instead of all, is canceled at the relay. The users eliminate the remaining interferences through self-interference and successive interference cancellation. A sum-rate maximization problem is formulated and solved to exploit the extra degrees-of-freedom resulted from PZF. Simulation results show that the proposed PZF relay beamforming design achieves significantly higher network sum-rates than the existing linear beamforming designs.

This paper conducts the performance sensitivity analysis for linear alarm filters. With a derivative-based local sensitivity measure, models are proposed to assess the sensitivity of the system detection errors to changes in the trip point and uncertainties in the collected data. Then, sensitivity formulations for a general linear alarm filter framework with unknown data distributions are studied. Extensive simulations with Gaussian and Gamma data using two typical linear filters, the moving average (MA) filter and the linear weighted (LW) filter, are conducted to verify the analytical results and to show the trade-offs among different system parameters.

This paper investigates the relay selection (RS) problem in networks with multiple users and multiple common amplify-and-forward (AF) relays. Considering the overall quality-of-service of the network, we first specify our definition of optimal RS for multiple-user relay networks. Then an optimal RS (ORS) algorithm is provided, which is a straightforward extension of an RS scheme in the literature that maximizes the minimum end-to-end receive signal-to-noise ratio (SNR) of all users. The complexity of the ORS is quadratic in both the number of users and the number of relays. Then a suboptimal RS (SRS) scheme is proposed, which has linear complexity in the number of relays and quadratic complexity in the number of users. Furthermore, diversity orders of both the ORS and the proposed SRS are theoretically derived and compared with those of a naive RS scheme and the single-user RS network. It is shown that the ORS achieves full diversity; while the diversity order of the SRS decreases with the the number of users. For two-user networks, the outage probabilities and array gains corresponding to the minimum SNR of the RS schemes are derived in closed forms. It is proved that the advantage of the SRS over the naive RS scheme increases as the number of relays in the network increases. Simulation results are provided to corroborate the analytical results.

In this paper, we consider a multi-user single-relay wireless network, where the relay facilitates transmissions of the users' signals to the destination. We study the relay power allocation among the users, and use bargaining theory to model the negotiation among the users on relay power allocation. By assigning a bargaining power to each user to indicate its transmission priority, we propose an asymmetric Nash bargaining solution (NBS)-based relay power allocation scheme. We also propose a distributed implementation for this solution, where each user only requires its local channel state information (CSI). We analytically investigate the impact of the bargaining powers on the relay power allocation and show that via proper selection of the bargaining powers, the proposed power allocation can achieve a balance between the network sum-rate and the user fairness. Then we generalize the NBS-based power allocation and its distributed implementation to multi-user multi-relay networks. Simulation results are shown to compare the proposed power allocation with sum-rate-optimal power allocation and even power allocation. The impact of the bargaining powers on the power allocation is also demonstrated via simulations.

This paper studies relay selection schemes for two-way amplify-and-forward (AF) relay networks. For a network with two users that exchange information via multiple AF relays, we first consider a single-relay selection (SRS) scheme based on the maximization of the worse signal-to-noise ratio (SNR) of the two end users. The cumulative distribution function (CDF) of the worse SNR of the two users and its approximations are obtained, based on which the block error rate (BLER), the diversity order, the outage probability, and the sum-rate of the two-way network are derived. Then, with the help of a relay ordering, a multiple-relay selection (MRS) scheme is developed. The training overhead and feedback requirement for the implementation of the relay selection schemes are discussed. Numerical and simulation results are provided to corroborate the analytical results.

This paper deals with optimal joint user power control and relay distributed beamforming for two-way relay networks, where two end-users exchange information through multiple relays, each of which is assumed to have its own power constraint. The problem includes the design of the distributed beamformer at the relays and the power control scheme for the two end-users to optimize the network performance. Considering the overall two-way network performance, we maximize the lower signal-to-noise ratio (SNR) of the two communication links. For single-relay networks, this maximization problem is solved analytically. For multi-relay networks, we propose an iterative numerical algorithm to find the optimal solution. While the complexity of the optimal algorithm is too high for large networks, two sub-optimal algorithms with low complexity are also proposed, which are numerically shown to perform close to the optimal technique. It is also shown via simulation that for two-way networks with both single relay and multiple relays, proper user power control and relay distributed beamforming can significantly improve the network performance, especially when the power constraints of the two end-users in the networks are unbalanced. Our approach also improves the power efficiency of the network largely.

This paper proposes a low-complexity precoding scheme for the downlink of single-cell two-user massive multi-input-multi-output (MIMO) systems, which can be seen as a combination of matched-filter (MF) precoding with extra user power adjustment. In this scheme, the base station (BS) antennas are grouped into two sets according to their channels, then each set has a main user to serve and a power coefficient is introduced to adjust the weighting between main user and the other user. Via theoretical analysis on the statistical properties of the signal power and interference power, the achievable sum-rate is derived. In addition, the power adjustment coefficient optimization with respect to the sum-rate maximization is solved. Our results show that the proposed precoding outperforms the standard MF precoding.

In this paper, we propose a threshold-system-based detector (TD) for detecting a known deterministic signal in independent non-Gaussian noise whose probability density function (pdf) is unknown but is symmetric and unimodal. The optimality of the proposed TD is proved under the assumptions of white noise, small signal, and a large number of samples. While previous TD designs need accurate information of the noise pdf, the proposed TD is independent of the noise pdf, and thus is robust to the noise pdf. The detection probability and the receiver operating characteristic (ROC) of the proposed TD are analyzed both theoretically and numerically. It is shown that even without knowing the noise pdf, the proposed TD has close performance to the optimal detector designed with the noise pdf information. It also performs significantly better than the matched filter (MF) when the noise pdf has heavy tails. The practical implementation, robustness to both the noise pdf and the signal, and region of validity of the proposed TD are also investigated.

This paper considers a multiuser single-relay wireless network, where the relay gets paid for helping users forward signals, and the users pay to receive the relay service. We study the relay power allocation and pricing problems and model the interaction between the users and the relay as a two-level Stackelberg game. In this game, the relay, which is modeled as the service provider and the leader of the game, sets the relay price to maximize its revenue, whereas the users are modeled as customers and followers who buy power from the relay for higher transmission rates. We use a bargaining game to model the negotiation among users to achieve a fair allocation of relay power. Based on the proposed fair relay power allocation rule, the optimal relay power price that maximizes the relay revenue is derived analytically. Simulation shows that the proposed power allocation scheme achieves higher network sum rate and relay revenue than the even power allocation. Furthermore, compared with the sum-rate-optimal solution, simulation shows that the proposed scheme achieves better fairness with comparable network sum rate for a wide range of network scenarios. The proposed pricing and power allocation solutions are also shown to be consistent with the laws of supply and demand.

In this paper, channel training and coherent decodings under channel estimation error are investigated for relay networks with one single-antenna transmitter, R single-antenna relays, and one R-antenna receiver. A two-stage training scheme is proposed to estimate both the relay-receiver and the transmitter-relay channels at the receiver, which are commonly required in amplify-and-forward (AF) relay networks. We use distributed space-time coding (DSTC) for data transmission and investigate the effect of channel estimation errors on network performance. Two coherent decodings are considered: mismatched decoding in which channel estimations are treated as if perfect, and matched decoding in which estimation error is taken into consideration. We show that for full diversity, with mismatched decoding, at least 3R symbol intervals are required for training; while with matched decoding, R+2 symbol intervals for training are enough. The complexities of the decoding schemes are investigated. To achieve a balance between performance and complexity, an adaptive decoding scheme is proposed. Simulated error rates are shown to justify the analytical results.