Plus-noise Ratio Research Articles

Robust Hybrid Beamforming for Outage-Constrained Multigroup Multicast mmWave Transmission With Phase Shifter Impairments

This article investigates robust hybrid beamforming design for outage-constrained multigroup multicast millimeter-wave (mmWave) transmission. The phase shifter impairments incorporated in the analog beamformer can impose negative damage on the Quality of Service (QoS) of users. To address this imperfect hardware problem, phase shifter impairments are modeled, and robust hybrid beamforming is formulated to maximize the worst-case signal-to-interference–plus-noise ratio (SINR). The digital beamformer and analog beamformer are jointly optimized based on an alternating optimization (AO) approach, where both the outage probability of users and the average transmit power at the base station (BS) are derived into closed-form expressions in each iteration. Moreover, the semidefinite programming (SDP) approach is resorted to handling the hybrid beamforming design. Based on the penalty and the successive convex approximation (SCA) approaches, we further propose a penalty-SCA-SDP-AO (PSSA) algorithm to address the constant-modulus constraint caused by phase shifters and the rank-one constraint introduced by the semidefinite form. Simulation results show that compared to the conventional nonrobust design, our robust hybrid beamforming design generally improves 40% nonoutage performance at least.

Performance Analysis and Optimal Cooperative Cluster Size for Randomly Distributed Small Cells Under CAN Protocol

The ease of imposing multicell coordination mechanisms to enhancing system spectrum efficiency (SE) and performance estimate is one of the key advantages of cloud/centralized control area networks (CAN). Enormous number of cooperative cells should theoretically result in a higher SE, but they may also cause considerable delays due to extra channel state information (CSI) feedback and joint processing computing needs at the cloud data centre, resulting in performance deficit. I partition the network into numerous clusters of cooperating tiny cells and create a throughput optimization problem to study the impact of delays on throughput gains. As a function of cluster size, I figure various delay factors and the network's sum-rate, treating cluster size as the fundamental optimization variable. For both linear and planar network installations, I treat both base station and user geometric locations as random variables in my study. On the basis of the homogeneous Poisson point processing (PPP)model, the output SINR (signal-to-interference- plus-noise ratio) and ergodic sum-rate are calculated. The sum-rate optimization problem is formulated and solved in terms of cluster size. The suggested analytical framework may be used to precisely evaluate the performance of practical cloud-based small cell networks via clustered cooperation, according to simulated study.

Distributed Radar Target Detection Based on RF-SSA in Non-Gaussian Noise

Distributed radar target detection in non-Gaussian noise, modeled as the sum of K-distributed clutter plus thermal noise, is considered in this paper. The conventional target techniques, e.g., constant false-alarm rate (CFAR), scatterer density-dependent generalized likelihood ratio test (SDD-GLRT), and energy integration (EI) detectors, have limited performance. On the other hand, since radar target detection can be considered a classification task, deep learning techniques have been widely applied as radar detectors in recent years, but such techniques require a larger amount of training samples to prevent overfitting, which is time-consuming. To balance detection efficiency and accuracy, this paper proposes an improved random forest algorithm based on the sparrow search algorithm (RF-SSA). First, we propose a mixed method of 3DT space-time adaptive processing and wavelet denoising (3DT-WD) to improve the output signal-to-clutter plus-noise ratio. Then, the SSA is applied to the RF algorithm to adaptively obtain the optimal parameters of the detection model. The simulation results show that the proposed RF-SSA ensures higher detection performance than the other classical methods, showing a gain of about 2 dB at the same detection probability. Moreover, the detection results of the real data further confirm the superiority of the proposed RF-SSA.

Halton-Chaos and ALPSO power allocation methods for passive optical CDMA networks

The allocation of minimum power with guarantee of minimum rate in passive optical network optical code division multiple access (PON-OCDMA) networks is analyzed considering heuristic optimization methods such as Particle Swarm Optimization (PSO) and four variants. The base of the power-rate adaptive PON-OCDMA is the multi-length 2-D wavelength-hopping/time-spreading code called multiple-length extended wavelength-hopping prime code (MLEWHPC) to support the services of different media in accordance with the bit-rate and quality of service (QoS). The Halton-Chaos-β-PSO (HC-PSO) algorithm is proposed for solving the optimal power and rate allocation in order to maximize the EE of the optical system, subject to QoS restrictions based on the signal-to-interference plus-noise ratio (SINR) in each class of optical user and minimum required power. The proposed HC-PSO and the analytical–iterative–heuristic approach based on the augmented Lagrangian and PSO (ALPSO) method demonstrate promising performance-complexity trade-off, specially for high dimensionality OCDMA systems. The proposed hybrid analytical-evolutionary resource allocation method is corroborated by numerical simulation results, considering the tuning of heuristic evolutionary input parameters, which have demonstrated to improve the EE of the optical system when the power-rate allocation procedure is deployed dynamically in real time applications.

Power Control for Cognitive Users of Perception Layer in Complex Industrial CPS Based on DQN

Wireless communication is a significant auxiliary technology of data transmission for industrial Cyber-Physical system (CPS). While for the complex industrial scenario of coal mine with long and narrow laneway, lifetime of wireless perception nodes is a potential and nonnegligible problem for safety production. In order to deal with this problem, a power control algorithm based on deep Q network (DQN) is adopted to train micro base station (MBS) by two steps so that the MBS can learn an optimal policy to help the cognitive users (CUs) communicate with a proper transmit power. Firstly, the selection range of transmit power for CUs is calculated by the lower bound of Signal-to-Interference plus-Noise Ratio (SINR) to guarantee the transmission condition of both users. Then, the power control problem is modeled as a Markov Decision Process (MDP) with unknown transition function, where the energy consumption is decreased by giving the upper bound of CUs’ SINR or threshold of transmit power in formulation of reward. In modeled MDP, the system state, which collected by primary users (PUs) and fed back to MBS, is reduced dimension by method of principal component analysis and then treated as the input of DQN. After that, DQN is used to train a power control optimal policy by minimizing the loss function. Simulation results demonstrate that the proposed power control algorithm based on DQN has a good performance that the average transition step and energy utility are 3.56 and 1580h, which is better than the existing solutions.

Novel Parameter Estimation and Radar Detection Approaches for Multiple Point-Like Targets: Designs and Comparisons

In this work, we develop and compare two innovative strategies for parameter estimation and radar detection of multiple point-like targets. The first strategy, which appears here for the first time, jointly exploits the maximum likelihood approach and Bayesian learning to estimate targets' parameters including their positions in terms of range bins. The second strategy relies on the intuition that for high signal-to-interference plus-noise ratio values, the energy of data containing target components projected onto the nominal steering direction should be higher than the energy of data affected by interference only. The adaptivity with respect to the interference covariance matrix is also considered exploiting a training data set collected in the proximity of the window under test. Finally, another important innovation aspect concerns the adaptive estimation of the unknown number of targets by means of the model order selection rules.

Interference cancellation in co‐located MIMO radars using waveform optimisation in signal dependent clutter

In this study, two iterative design algorithms for multiple-input multiple-output (MIMO) radars with co-located antennas with point targets are proposed. In these algorithms, by joint design of the receiver filter and transmit waveform using linear combination of orthogonal waveforms, the authors aim to maximise the signal-to-interference plus-noise ratio (SINR) in the presence of signal dependent interference. In the first proposed algorithm, in each iteration, transmit waveforms and receive filter are designed in closed form to decrease the computational complexity. In the second method, by adding constant envelope criteria, the final waveform would be a linear combination of orthogonal waveforms and because of using a constant envelope, combining coefficients have equal magnitudes and different phases. Therefore, it is more practical for hardware implementation in comparison to the first proposed method. Simulation results show that both proposed methods have better SINR performances compared with other methods proposed in MIMO radar literature. The outperformance of the second proposed method with respect to phased arrays shows that by only using phase shifted combination of orthogonal waveforms, better performance in comparison to phased array radars can be achieved.

Robust optimization for AN-aided wireless information and power transfer for secure heterogeneous networks

In this paper, under the ellipsoidal channel covariance errors, we investigate robust optimization for artificial noise (AN)-aided wireless information and power transfer with secrecy outage constraint in a two-tier wireless-powered heterogeneous network (HeNet). In this considered network, one femtocell consisting of multiple femtocell users (FUs) is deployed under the coverage of one macrocell consisting of multiple macrocell users (MUs), where FUs can receive information and harvest energy from the femtocell base station (FBS) simultaneously. Meanwhile, multiple eavesdroppers attempt to wiretap the confidential messages intended for FUs. To secure the information and power transfer, AN is injected into the downlink signal of the FBS simultaneously. Moreover, while satisfying the constraints on the signal-to-interference plus-noise ratio requirement at each MU, energy harvesting and secrecy outage probability requirement at each FU the transmit beamforming and AN vectors of the macrocell base station (MBS) and FBS are jointly designed to minimize the total transmit power (MTP). Due to the secrecy outage constraint and channel state information (CSI) errors, this MTP problem is non-convex and contains infinite constraints, which cannot be solved directly. Resorting to the first-order Taylor expansion, Lagrange dual theory and the semi-definite relaxation (SDR) technique, we reformulate the original problem into a solvable form. Then, based on the convex reformulations, we further propose a successive convex approximation (SCA) algorithm for iteratively obtaining the optimal design. Simulation results are finally presented and demonstrate the effectiveness and security of our proposed robust design.

Blind adaptive constrained constant modulus algorithms based on unscented Kalman filter for beamforming

This work proposes constrained constant modulus unscented Kalman filter (CCM-UKF) algorithm and its low-complexity version called reduced-rank constrained constant modulus unscented Kalman filter (RR-CCM-UKF) algorithm for blind adaptive beamforming. In the generalized sidelobe canceller (GSC) structure, the proposed algorithms are devised according to the CCM criterion. Firstly, the cost function of the constrained optimization problem is transformed to suit the Kalman filter-style state space model. Then, the optimum weight vector of the beamformer can be estimated by using the recursive formulas of UKF. In addition, the a priori parameters of UKF (system and measurement noises) are processed adaptively in the implementation. Simulation results demonstrate that the proposed algorithms outperform the existing methods in terms of convergence speeds, output signal-tointerference- plus-noise ratios (SINRs), mean-square deviations (MSDs) and robustness against steering mismatch.

MSE Balancing in the MIMO BC: Unequal Targets and Probabilistic Interference Constraints

Second order cone (SOC) restrictions have been widely employed in the design of wireless communication systems to recast precoding optimizations in convex form. This paper highlights the flexibility of this framework to handle various transmit power and interference limitations also in the multistream multiple-input multiple-output broadcast channel with imperfect channel state information at the transmitter. In particular, we propose an iterative solution for mean square error (MSE) balancing with such constraints via the Lagrangian dual formulation. The MSE balancing criteria, contrary to signal-to-interference plus-noise ratio and rate criteria, exhibit an interesting property when considering different targets for the receivers. Users with low target values are strictly prioritized in comparison to users with large MSE targets that can even be switched off. Therefore, only a subset of the receivers is served at the solution with stringent limitations for the precoders. An example of such stringent limitations are small power constraints and probabilistic interference temperature constraints that set a measurable threshold on the generated interference in a cognitive radio system. We provide four different approaches to approximate these probabilistic constraints with deterministic SOC constraints, and compare their accuracy.

Statistical Resolution Limit Analysis of Two Closely Spaced Signal Sources Using Rao Test

High computational complexity and difficulty in taking an analytical expression of detection statistics are some of the problems encountered when using the statistical resolution limit (SRL) analysis in the generalized likelihood ratio test (GLRT). A Rao test-based method is used in this paper to analyze the 1-D SRL of two closely spaced signals, which can simplify the derivation of detection statistics, reduce the computational complexity, and improve the detection performance in some scenarios. In the proposed approach, two cases are considered based on the interference sources that are either involved or not in the received signals. Moreover, in each case, the detection statistic is derived by assuming the known or unknown variance of the added noise, respectively. For the performance comparison, the GLRT detection statistic is also identified. The relationship between the SRL and the required minimum signal-to-noise ratio/signal-to-interference-plus-noise ratio is also obtained to resolve the two adjacent signal sources. Then, the detection performance and computational complexity are evaluated. The simulation results are used to verify the effectiveness and superiority of the Rao test.

Multi-Objective Resource Allocation Scheme for D2D Multicast with QoS Guarantees in Cellular Networks

Device-to-device (D2D) multicast communication can greatly improve the spectrum utilization in a content delivery scenario. However, the co-channel interference and power consumption brought by D2D bring new challenges. All the D2D multicast groups expect to achieve a higher system capacity with less extra energy cost. In this paper, we investigate the uplink resource allocation issue when D2D multicast groups share the resources with other cellular uses (CUs), while guaranteeing a certain level of quality of service (QoS) to CUs and D2D users. Firstly we address a flexible tradeoff framework in which the system power consumption and the system capacity (i.e., the number of admitted D2D links) are assigned with different weight factors so that these two objectives are jointly considered. Then we propose an efficient resource optimization scheme, which comprises sub-channel allocation and signal-to-interference- plus-noise ratio (SINR) assignment. Numerical results validate the effectiveness of the proposed framework, and demonstrate the advantages in dealing with the proposed multi-objective optimization problem.

Outage Probability of Maximal Ratio Combiner in the Presence of Adjacent Cell Interference and Adjacent Channel Interference

Transmission of information in wireless communications is subjected to multipath fading, adjacent channel interference, and adjacent cell interference. The aim of this paper is to study the analysis of maximal ratio combiner (MRC) in the presence of adjacent cell interference and adjacent channel Interference using outage probability. MRC is a well known signal processing technique in which the received signal at each antenna is weighted by their individual signal-to-interference plus-noise ratios and are coherently combined. Imperfect estimation of the desired user's channel fading coefficients will be analyzed and the channel fading coefficients will be assumed to be Gaussian distributed. The estimation error will be measured with the help of correlation coefficients between the true fading values and their estimates.

Ultra-dense small cell planning using cognitive radio network toward 5G

Mobile communication is facing new challenges to the soaring traffic demand of numerous user devices; thus, the notion of the small cell has been proposed and realized in recent years. However, licensed spectrum has been occupied by various underlying access technologies, so the deployment of small cells needs a sophisticated planning algorithm. In this article, we provide an overview of reconfigurable radio and small cell technologies, then introduce the tentative network architecture for 5G. Two planning approaches (i.e., genetic-based and graphbased) are proposed that accommodate cognitive radio technology to improve user throughput by eliminating communication interference. Since cognitive radio networking provides frequency allocation with cognition cycle for better spectral efficiency, we tackle the deployment of ultradense small cells and consider the coordination of unlicensed spectrum at the same time. Results show that the proposed algorithms with spectrum cognition improve network performance in terms of throughput and signal-to-interference- plus-noise ratio. Specifically, the genetic- based algorithm increases 232 percent in throughput and 150 percent in signal-to-interference- plus-noise ratio compared to the graphbased algorithm. Finally, we conclude this article by discussing potential challenges and opportunities.

Exact Performance Analysis of AF Based Hybrid Satellite-Terrestrial Relay Network with Co-Channel Interference

This paper considers the effect of co-channel interference on hybrid satellite-terrestrial relay network. In particular, we investigate the problem of amplify-and-forward (AF) relaying in hybrid satellite-terrestrial link, where the relay is interfered by multiple co-channel interferers. The direct link between satellite and terrestrial destination is not available due to masking by surroundings. The destination node can only receive signals from satellite with the assistance of a relay node situated at ground. The satellite-relay link is assumed to follow the shadowed Rice fading, while the channels of interferer-relay and relay-destination links experience Nakagami-m fading. For the considered AF relaying scheme, we first derive the analytical expression for the moment generating function (MGF) of the output signal-to-interference- plus-noise ratio (SINR). Then, we use the obtained MGF to derive the average symbol error rate (SER) of the considered scenario for M-ary phase shift keying (M-PSK) constellation under these generalized fading channels.

Design of phase codes with interval staggering for Doppler ambiguous target detection

The design of radar coherent pulse trains encoded with interval staggering for ambiguous Doppler target detection in clutter is addressed. To alleviate the Doppler ambiguous problem, an interpulse phase coding optimisation model is formulated in terms of staggered interval sampling, while maximising the output signal-tointerference- plus-noise ratio for improved target detection. Based on a reformulation of this optimisation problem as a non-convex quadratically constrained quadratic program (QCQP), a semidefinite relaxation algorithm for this QCQP is presented. Numerical results demonstrate the effectiveness of this proposed approach.

On the Achievable User Number of the Downlinks in Cellular-Based Machine-to-Machine Communications

In cellular-based machine-to-machine (M2M) networks, supporting large number of machine-type communications (MTC) users (devices) has become a critical challenge in both the uplink and downlink channels. In this paper, we focus on the downlink beamforming using zero-forcing dirty paper coding (DPC). In order to characterize the system's ability of user admission, we consider the achievable user number, which is defined as the number of users whose signal-to-interference plus-noise ratios exceed a target threshold. Due to the complexity of the optimal scheme, we propose two algorithms with random user scheduling and greedy user scheduling in maximizing the achievable user number by dynamical power assignment. Using the joint distribution of effective channel gains, we derive the achievable user number of both the scheduling schemes. An upper bound on the achievable user number of the greedy scheme is then derived which is shown to be tight when there are a large number of users. From numerical results, we show that both of the schemes enjoy a linear increase in the achievable user number as the number of transmitter antennas increases. The performance of the greedy scheduling scheme is close to the optimal scheduling scheme.

基于VSINR的分布式多小区MU-MIMO预编码矩阵设计

In this paper, we propose a distributed precoder design algorithm based on virtual signal-to-interference- plus-noise ratio (VSINR) for multi-cell multi-user multiple-input multiple-output (MU-MIMO) system. As the problem of finding the optimal precoder to maximize the system throughput is non-convex and non-trivial, it is important to find low-complexity solutions. Motivated by the recent results in the distributed signal-to-leakageplus- noise ratio (SLNR) model, we aim to establish equivalence between the distributed VSINR maximization problem and the original centralized throughput maximization problem by designing the proper correction factor, and the correction factor is constructed to satisfy the equivalence condition that the sub-gradients w.r.t the precoder of the VSINR model and the system throughput model become zero at the same time. Then we can build a VSINR model which is proved to be Pareto optimal, thus the entire system throughput can be optimized by using precoders based on an appropriate distributed VSINR model. Finally we can distributedly solve the corresponding Pareto optimal precoding matrix problem based on local CSI (CSI, channel state information) and a closed-form solution of optimal transmitter precoder can be derived. Simulation results show that the proposed precoder can enhance the system throughput with varying transmitting SNRs, active users and interference factors, and outperform other existing distributed precoder design algorithms such as ZF, MRT and SLNR algorithms. Besides, the iteration in our proposed algorithm converges fast within 3 iterations, the approximate optimum can be achieved, and within 20 iterations, and the global optimum can be obtained. Therefore, our proposed algorithm can bring a significant increase to the average cell throughput while effectively saving the system overhead.

Time-varying AR modeling and adaptive IIR notch filter for anti-jamming DSSS receiver

Using Time-Varying AR (TVAR) model and adaptive notch filter is a new method for the non-stationary jammer suppression in Direct Sequence Spread Spectrum (DSSS). The performance of TVAR model for Instantaneous Frequency (IF) estimation will be affected by some factors such as basis functions. Focusing on this problem, the optimal basis function of TVAR model for the IF estimation of the LFM signal is obtained in this paper. Besides the depth and width of notching, the phase properties of notch filter affect the Signal-to-Interference plus-Noise Ratio (SINR) of correlation output to the narrowband jammer suppression in DSSS, in response to the problem the closed solution of correlation output SINR improvement has been derived when a single frequency jammer passes through direct IIR notch filter, and its performance has been compared with those of five coefficient FIR filters. Later, a novel method for LFM jammer suppression based on Fourier basis TVAR model and direct IIR notch filter is proposed. The simulation results show the effectiveness of the proposed method.

Distributed transmit power allocation for multihop cognitive-radio systems

In this paper, we consider a relay-assisted wideband cognitive-radio (CR) system under the assumption that the frequency band chosen by the CR relay network for unlicensed spectrum usage overlaps with one or more bands dedicated to primary (e.g., licensed) narrowband links. Our objective is to optimize the performance of the CR system while limiting the interference in direction of the primary receivers, without requiring any adaptation of the transmitted signal spectra at the cognitive nodes. To this end, we study appropriate transmit power allocation (TPA) strategies among the cognitive relays. We first investigate the optimal centralized (OC) TPA solution and show that it can be formulated as a linear program. Since the OC-TPA solution requires a considerable amount of information exchange between the cognitive nodes, we develop two distributed TPA schemes, namely (i) a fully decentralized (FD) TPA scheme and (ii) a distributed feedback-assisted (DFA) TPA scheme. The FD-TPA scheme aims at maximizing the output signal-to-interference- plus-noise ratio (SINR) at the destination node of the CR network according to a best-effort strategy. It requires neither feedback information from the destination node nor an exchange of channel state information between the cognitive relays. The DFA-TPA scheme, on the other hand, utilizes feedback information from the destination node, in order to achieve a predefined target output SINR value, while minimizing the overall transmit power spent by the relays. Analytical and simulation-based performance results illustrate that notable performance improvements compared to non-cooperative transmission (i.e., without relay assistance) are achieved by the proposed schemes, especially when more than two hops are considered. In particular, the proposed distributed TPA schemes typically perform close to the OC-TPA solution.