Signal-to-noise Ratio Maximization Research Articles

Hybrid Beamforming and Relay Selection for End-to-End SNR Maximization in Single-User Multi-Relay MIMO Systems.

This paper proposes a novel hybrid beamforming and relay selection scheme for spectral efficiency maximization in a non-regenerative multi-relay multi-input multi-output (MIMO) system. The analog beamforming component in the radio-frequency (RF) domain must follow an element-wise constant modulus constraint, which makes the underlying design problem mathematically intractable and therefore, it is quite challenging to obtain the global optimal solution. To address this problem, phase-only precoding/combining matrices are derived by maximizing the end-to-end received signal-to-noise ratio (SNR) under transmit power constraint at the source and each relay node. This task is achieved by decomposing the original complicated optimization problem into two independent components. The first component designs the RF precoder/combiner at source and relay nodes by maximizing the received SNR at relay nodes. While the second component attempts to derive the analog precoder/combiner at relay nodes and destination by maximizing the received SNR at the destination. Digital baseband processing matrices are obtained by deriving the closed-form expression, which minimizes interference among different sub-channels. Finally, the relay selection is made by maximizing the overall SNR from the source to the destination. Computer simulations reveal that the performance of the proposed algorithm is close to its fully digital counterpart and approximately 6% higher than the specified relay-assisted hybrid beamforming techniques. Moreover, the proposed method achieves more than 15% higher performance in a sparse scattering environment when compared with the given relay selection techniques.

Water experiment for assessing vibroacoustic beamforming gain for acoustic leak detection in a sodium-heated steam generator

A technique based on vibration measurements has been developed to detect water leaking into sodium in view to improving the monitoring of steam generators. Leak-induced vibrations can be masked by background noise. This problem can be overcome by employing a beamforming technique to increase the signal-to-noise ratio (SNR). In order to study the feasibility and efficiency of this technique for the present configuration, experimental investigations were performed on a mock-up composed of a straight cylindrical pipe coupled to a hydraulic circuit by two flanges. A sound emitter introduced in the pipe simulates the source to be detected while the background noise vibrations are controlled by flow speed. Beamforming is applied to the signals measured by an array of accelerometers mounted externally on the pipe. Two different types of beamforming are considered: the conventional (Bartlett) type and an advanced type based on SNR maximization (MaxSNR). After analysing the vibroacoustic behaviour of the mock-up, the article focuses on the efficiency of the two beamforming treatments for narrow and broad bands.

Pilot Spoofing Attack by Multiple Eavesdroppers

In this paper, we investigate the design of a pilot spoofing attack (PSA) carried out by multiple single-antenna eavesdroppers (Eves) in a downlink time-division duplex system, where a multiple antenna base station (BS) transmits confidential information to a single-antenna legitimate user. During the uplink channel training phase, multiple Eves collaboratively impair the channel acquisition of the legitimate link, aimed at maximizing the wiretapping signal-to-noise ratio (SNR) in the subsequent downlink data transmission phase. Two different scenarios are investigated: 1) the BS is unaware of the PSA and 2) the BS attempts to detect the presence of the PSA. For both scenarios, we formulate wiretapping SNR maximization problems. For the second scenario, we also investigate the probability of successful detection and constrain it to remain below a pre-designed threshold. The two resulting optimization problems can be unified into a more general non-convex optimization problem, and we propose an efficient algorithm based on the minorization–maximization (MM) method and the alternating direction method of multipliers (ADMM) to solve it. The proposed MM-ADMM algorithm is shown to converge to a stationary point of the general problem. In addition, we propose a semi-definite relaxation (SDR) method as a benchmark to evaluate the efficiency of the MM-ADMM algorithm. Numerical results show that the MM-ADMM algorithm achieves near-optimal performance and is computationally more efficient than the SDR-based method.

Sparse Array Quiescent Beamformer Design Combining Adaptive and Deterministic Constraints

In this paper, we examine sparse array quiescent beamforming for multiple sources in interference-free environment. To maximize the output signal-to-noise ratio (SNR), the beamformer design comprises two intertwined stages, the determination of beamforming weights and the reconfiguration of array structure. The SNR maximization may produce high sidelobe levels, making the receiver vulnerable to interferences. We consider the problem of achieving maximum SNR beamforming subject to specified quiescent pattern constraints and, as such, combine both adaptive and deterministic approaches for sparse array configurations. We employ two convex relaxation methods and an iterative linear fractional programming algorithm to solve the nonconvex antenna selection problem for sparse array beamformers. Simulation examples demonstrate that the array configuration plays a vital role in determining the beamforming performance in interference-free scenarios.

Quantum Bee Colony Optimization and Non-dominated Sorting Quantum Bee Colony Optimization Based Multi-relay Selection Scheme

In cooperative multi-relay networks, the relay nodes which are selected are very important to the system performance. How to choose the best cooperative relay nodes is an optimization problem. In this paper, multi-relay selection schemes which consider either single objective or multi-objective are proposed based on evolutionary algorithms. Firstly, the single objective optimization problems of multi-relay selection considering signal to noise ratio (SNR) or power efficiency maximization are solved based on the quantum bee colony optimization (QBCO). Then the multi-objective optimization problems of multi-relay selection considering SNR maximization and power consumption minimization (two contradictive objectives) or SNR maximization and power efficiency maximization (also two contradictive objectives) are solved based on non-dominated sorting quantum bee colony optimization (NSQBCO), which can obtain the Pareto front solutions considering two contradictive objectives simultaneously. Simulation results show that QBCO based multi-relay selection schemes have the ability to search global optimal solution compared with other multi-relay selection schemes in literature, while NSQBCO based multi-relay selection schemes can obtain the same Pareto front solutions as exhaustive search when the number of relays is not very large. When the number of relays is very large, exhaustive search cannot be used due to complexity but NSQBCO based multi-relay selection schemes can still be used to solve the problems. All simulation results demonstrate the effectiveness of the proposed schemes.

Noise adjusted version of generalized principal component analysis

Principal component analysis (PCA) is a well-known tool in image processing, especially in dimension reduction schemes. Since using PCA is based on the vector representation of the image, the spatial locality of pixels in the image is not considered. However, generalized PCA (GPCA) could be applied to images in two dimensional spaces. Both schemes do not consider the noisy case of signals. Noise adjusted PCA (NAPCA) tries tond new coordinates for signal representation based on signal to noise ratio (SNR) maximization. In this paper we generalized noise adjusted GPCA to benet the advantage of GPCA and SNR maximization case of NAPCA in two dimensional spaces. The experimental results on the huge databases show its reliability.

SNR Maximization Hashing

We propose a novel robust hashing algorithm based on signal-to-noise ratio (SNR) maximization to learn compact binary codes, where the SNR metric is used to select a set of projection directions, and one hash bit is extracted from each projection direction. We first motivate this approach under a Gaussian model for the underlying signals, in which case maximizing SNR is equivalent to minimizing the robust hashing error probability. A globally optimal solution can be obtained by solving a generalized eigenvalue problem. We also develop a multibit per projection algorithm to learn longer hash codes when the number of high-SNR projections is limited. The proposed algorithms are tested on both synthetic and real data sets, showing significant performance gains over existing hashing algorithms.

Robust Joint Source-Relay-Destination Design Under Per-Antenna Power Constraints

This paper deals with joint source-relay-destination beamforming (BF) design for an amplify-and-forward (AF) relay network. Considering the channel state information (CSI) from the relay to the destination is imperfect, we first aim to maximize the worst case received SNR under per-antenna power constraints. The associated optimization problem is then solved in two steps. In the first step, by revealing the rank-one property of the optimal relay BF matrix, we establish the semi-closed form solution of the joint optimal BF design that only depends on a vector variable. Based on this result, in the second step, we propose a low-complexity iterative algorithm to obtain the remaining unknown variable. We also study the problem for minimizing the maximum per-antenna power at the relay while ensuring a received signal-to-noise ratio (SNR) target, and show that it reduces to the SNR maximization problem. Thus the same methods can be applied to solve it. The differences between our result and the existing related work are also discussed in details. In particular, we show that in the perfect CSI case, our algorithm has the same performance but with much lower cost of computational complexity than the existing method. Finally, in the simulation part, we investigate the impact of imperfect CSI on the system performance to verify our analysis.

Beamforming for Multiuser MIMO-OFDM Interference Channels With Multipath Diversity

This paper presents three beamforming designs for multiuser multiple-input and multiple-output with orthogonal frequency-division multiplexing, where the transmit and receive beamformers are obtained iteratively with closed-form steps. In the first case, the transmit (Tx) beamformers are set and the receive (Rx) beamformers are calculated. It works by projecting the Tx beamformers into a null space of appropriate channels. This eliminates one interference term for each user. Then the Rx beamformer for each user maximizes its instantaneous signal-to-noise ratio (SNR) while satisfying an orthogonality condition to eliminate the remaining interferences. The second case is jointly optimizing the Tx and Rx beamformers from constrained SNR maximization. It uses the results from the first case. The third case is also for joint optimization of Tx-Rx beamformers but combines constrained SNR and signal-to-interference-plus-noise ratio maximization. The minimum number of antennas required is derived as part of the formulation. All cases can include a linear constellation precoder for extracting multipath diversity. Finally, the required feedback rates are derived and compared to existing beamforming methods. Using the standardized statistical channel model for IEEE 802.11n, our simulations demonstrate faster beamforming, improved error performance, and the ability to extract multipath diversity which is not possible in the least-square approach.

Enhancing the Efficiency of Constrained Dual-Hop Variable-Gain AF Relaying Under Nakagami-<formula formulatype="inline"> <tex Notation="TeX">$m$</tex></formula> Fading

This paper studies power allocation for performance constrained dual-hop variable-gain amplify-and-forward (AF) relay networks in Nakagami- m fading. In this context, the performance constraint is formulated as a constraint on the end-to-end signal-to-noise-ratio (SNR) and the overall power consumed is minimized while maintaining this constraint. This problem is considered under two different assumptions of the available channel state information (CSI) at the relays, namely full CSI at the relays and partial CSI at the relays. In addition to the power minimization problem, we also consider the end-to-end SNR maximization problem under a total power constraint for the partial CSI case. We provide closed-form solutions for all the problems which are easy to implement except in two cases, namely selective relaying with partial CSI for power minimization and SNR maximization, where we give the solution in the form of a one-variable equation which can be solved efficiently. Numerical results are then provided to characterize the performance of the proposed power allocation algorithms considering the effects of channel parameters and CSI availability.

Power Allocation Strategies for Fixed-Gain Half-Duplex Amplify-and-Forward Relaying in Nakagami-m Fading

In this paper, we study power allocation strategies for a fixed-gain amplify-and-forward relay network employing multiple relays. We consider two optimization problems for the relay network: 1) maximizing the end-to-end signal-to-noise ratio (SNR) and 2) minimizing the total power consumption while maintaining the end-to-end SNR over a threshold value. We investigate these two problems for two relaying protocols of all-participate (AP) relaying and selective relaying and two cases of feedback to the relays, namely full and limited. We show that the SNR maximization problem is concave and that the power minimization problem is convex for all protocols and feedback cases considered. We obtain closed-form expressions for the two problems in the case of full feedback and solve the problems through convex programming for limited feedback. Numerical results show the benefit of having full feedback at the relays for both optimization problems. However, they also show that feedback overhead can be reduced by having only limited feedback to the relays with only a small degradation in performance.

Multi‐relay selection schemes based on evolutionary algorithm in cooperative relay networks

SUMMARYIn cooperative relay networks, the selected relay nodes have great impact on the system performance. In this paper, a multi‐relay selection schemes that consider both single objective and multi‐objective are proposed based on evolutionary algorithms. First, the single‐objective optimization problems of the best cooperative relay nodes selection for signal‐to‐noise ratio (SNR) maximization or power efficiency optimization are solved based on the quantum particle swarm optimization (QPSO). Then the multi‐objective optimization problems of the best cooperative relay nodes selection for SNR maximization and power consumption minimization (two contradictive objectives) or SNR maximization and power efficiency optimization (also two contradictive objectives) are solved based on a non‐dominated sorting QPSO, which can obtain the Pareto front solutions of the problems considering two contradictive objectives simultaneously. The relay systems can select one solution from the Pareto front solutions according to the trade‐off of SNR and power consumption (or the trade‐off of SNR and power efficiency) to take part in the cooperative transmission. Simulation results show that the QPSO‐based multi‐relay selection schemes have the ability to search global optimal solution compared with other multi‐relay selection schemes in literature. Simulation results also show that the non‐dominated sorting QPSO‐based multi‐relay selection schemes obtain the same Pareto solutions as exhaustive search, but the proposed schemes have a very low complexity. Copyright © 2013 John Wiley & Sons, Ltd.

SNR Maximization through Relay Selection in Cognitive Radio Networks

In this study, we apply Artificial Bee Colony (ABC) optimization to the problem of relay selection and transmit power allocation to selected relays in non-regenerative Relay Assisted Cognitive Radio Networks (RCRNs). The objective of our proposed scheme is to maximize the Signal-to-Noise Ratio (SNR) at the secondary destination while keeping the interference offered to the primary user below a certain threshold level. Simulation results verify the effectiveness of the proposed algorithm.

A Study on the Optimal Receiver Impedance for SNR Maximization in Broadband PLC

We consider the design of the front-end receiver for broadband power line communications. We focus on the design of the input impedance that maximizes the signal-to-noise ratio (SNR) at the receiver. We show that the amplitude, rather than the power, of the received signal is important for communication purposes. Furthermore, we show that the receiver impedance impacts the amplitude of the noise term. We focus on the background noise, and we propose a novel description of the noise experienced at the receiver port of a PLC network. We model the noise as the sum of four uncorrelated contributions, that is, the active, resistive, receiver, and coupled noise components. We study the optimal impedance design problem for real in-home grids that we assessed with experimental measurements. We describe the results of the measurement campaign, and we report the statistics of the optimal impedance. Hence, we study the best attainable performance when the optimal receiver impedance is deployed. We focus on the SNR and the maximum achievable rate, and we show that power matching is suboptimal with respect to the proposed impedance design approach.

Signal-to-noise ratio optimization for detecting bacteria with interdigitated microelectrodes

Impedimetric sensors can be used for the detection of biological targets, such as bacteria. In this paper, 3D finite-element simulations show that random distributions of bacteria on interdigitated microelectrode sensor surfaces restrict their limit of detection (LOD). We demonstrate the strong influence of the bacteria diameter, surface coverage and electrode geometry on both the sensitivity and the signal-to-noise ratio (SNR). Particularly, we optimize the microelectrode design to maximize the SNR and hence the LOD, and establish that the optimal electrode gap is approximately four times the bacteria diameter. Furthermore, we show that the electrode width and thickness should respectively be minimized and maximized in the considered range with regards to current technological limitations. The paper finally highlights and discusses, from the designer point of view, the critical design tradeoff between SNR and sensitivity maximization.

Bacteria Detection with Interdigitated Microelectrodes: Noise Consideration and Design Optimization

Impedimetric biosensors are promising for label-free, real-time, sensitive and selective detection of bacteria. However, these sensors do typically not detect below 103 colony forming unit (CFU) per ml in absence of dielectrophoresis and labels. This work shows that the noise source due to random distributions of bacteria on the biosensor surface strongly restricts the limit of detection (LOD) for an interdigitated microelectrode (IDE) configuration. 3D finite-element simulations also indicate that the bacteria diameter and the surface coverage influence both the sensitivity and the signal-to-noise ratio (SNR). Optimization of the IDE design suggests that the SNR is maximized, i.e., the LOD is minimized, as the electrode gap is approximately five times the bacteria diameter and the electrode width and thickness are minimized and maximized with regards to current technological limitations, respectively. The paper finally highlights the critical design trade-off between SNR and sensitivity maximization.

Capacity and Complexity Trade-offs in MIMO Analog–Digital Combining Systems

Analog---digital combining is an alternative solution to decrease the hardware complexity of an all-digital combining system. We analyze the analog weights from the perspectives of maximizing analog signal-to-noise ratio (SNR) and effective channel capacity $${(\tilde{C})}$$ . We propose simple sub-optimal algorithms to calculate receive analog weights in both open loop (OL) and closed loop (CL) systems, and compare the proposed schemes with an all-digital combining system, in terms of $${\tilde{C}}$$ . For both criteria, a small number of additional antennas is required to achieve the same mean capacity as an all-digital combining system in an urban micro model, e.g., 1 additional antenna in selection diversity and combining is required to achieve the same capacity as in a 2 × 4 all-digital CL system at low SNR. The additional number of antennas is smaller in CL than in OL. The effective channel capacity based on the SNR maximization criterion is similar to that based on the $${\tilde{C}}$$ maximization criterion, but the computational complexity the analog weights calculation is lower for the SNR maximization criterion.

Robust Signal Detection Under Model Uncertainty

In detecting a deterministic signal in the presence of Gaussian noise, the receiver operating characteristic (ROC) curve determined by a linear detector with slope maximizing the signal-to-noise ratio (SNR) and with varying threshold characterizes limits of detection performance. In this note, we consider the problem of detecting a signal in the presence of uncertainty in the signal itself and the noise covariance. We show that the classical result can be generalized to robust signal detection with a convex uncertainty model: the ROC curve determined by a linear detector with slope maximizing the worst-case SNR gives limits of detection performance in the worst-case sense. The worst-case SNR maximization problem can be solved using convex optimization, so robust ROC analysis is tractable.