Signal To Inference Plus Noise Ratio Research Articles

Robust Beamforming and Power Minimization Design in MISO Health Monitoring System

In this paper, we study the power splitting (PS) scheme for each user in a multiple-input single-output (MISO) health monitoring system, while considering simultaneous wireless information and power transmission (SWIPT). We construct an optimization problem with robust power minimization constrained by the signal-to-inference-plus-noise ratio (SINR) and energy harvesting (EH). For channel uncertainty, this problem is a non-convexity and is not easy to solve and it can be translated into semidefinite program (SDP) by utilizing S-Procedure. Then the above optimization problem is combined with different channel uncertainty models, and the Bernstein-type inequality and Gaussian error function are introduced to further simplify the optimization problem. Simulation results prove the performance of the proposed robust scheme and point out its effective help for the development of modern telemedicine systems.

Energy-Efficient Optimization in Multi-Sensor WBAN With Multi-Antenna AP

This paper optimizes the energy efficiency performance by joint time and beamforming weights allocation in a wireless body area network (WBAN), which consists of a multi-antenna access point (AP), in particular, the AP intends to broadcast radio frequency (RF) power to several single-antenna on-body sensors and receives physiological information from them synchronously. Different path loss and shadowing values are considered in each sensor's channel model to be more practical. In this paper, maximal ratio transmission (MRT) technique and zero-forcing (ZF) technique are applied in RF energy transmission and information decoding, respectively. Considering the signal-to-inference-plus-noise ratio (SINR) threshold of each sensor, the total rate-energy ratio is formulated as a nonconvex optimization problem in each time block, we transform the nonconvex optimization problem into a convex optimization problem and solved it by convex optimization tools. We investigate the energy efficiency performance and reliability performance of the optimal solutions, and numerical results demonstrate the effectiveness of the optimal solutions.

Energy‐efficient power allocation algorithm in cognitive radio networks

Most of traditional power allocation algorithms are often based on maximum capacity technology (MCT) in cognitive radio networks (CRNs) at the expense of higher energy consumption. The optimisation of energy efficiency power allocation schemes are important performance in green communication. This study investigates the energy efficient power allocation for orthogonal frequency division multiplexing based CRNs in underlay mode. The authors’ scheme is obtained by optimising an objective function consisting of the users’ performance degradation and the network interference, in the same time to track time-varying target of signal to inference plus noise ratio (SINR) under maximum transmit power for each cognitive user and interference power constraint from primary user. A convex optimisation problem is formulated where the tradeoff between energy consumption and transmission capacity is considered, and a distributed algorithm is developed to solve this problem. Simulation results show that the authors’ proposed algorithm can guarantee an acceptable target SINR for all cognitive users and significantly improves energy efficiency compared with throughput per Joule and MCT schemes. Furthermore, the proposed algorithm with the introduced appropriate weight parameters can get higher transmission capacity.

Robust beamforming with block diagonalisation for MIMO interference channels

The authors focus on robust beamforming design for multiple-input multiple-output (MIMO) interference channels with imperfect channel state information (CSI) which is described as a bounded CSI error model. With all transmissions restricted to single data stream, the problem of maximising the minimum signal-to-inference-plus-noise ratio (SINR) subject to individual power constraint at each transmitter is considered as the max–min fairness transceiver design. Specifically, the authors propose a robust beamforming algorithm named as block diagonalisation assisted (BDA) approach for solving the max–min problem. First, block diagonalisation precoding is applied to simplify the original max–min SINR problem. Then, an iterative algorithm is proposed with alternating process to solve the simplified problem, and the convergence behaviour and computational complexity are analysed. Simulation results show the convergence of the BDA algorithm and demonstrate the robustness of the proposed transceiver design through the bit error rate and the minimum SINR.

Robust beamforming and power splitting design in MISO SWIPT downlink system

In this study, the authors consider simultaneous wireless information and power transfer (SWIPT) in a multiple-input-single-output (MISO) downlink system, where power splitting scheme is considered for each user of this system. Since channel state information of each user cannot be available at the transmitter, robust beamforming for SWIPT in the MISO downlink system is presented by incorporating with different types of channel uncertainty models. The authors first formulate the robust power minimisation problem subject to the signal-to-inference-plus-noise ratio (SINR) and energy harvesting (EH) constraints by incorporating two Gaussian channel uncertainties. The original problem is not convex in terms of channel uncertainties, and cannot be solved efficiently. The authors employ the well-known Bernstein-type inequality and Gaussian error function to make probability based constraints tractable, respectively, in order to recast the original problem as the convex form. Moreover, the robust power minimisation problem with the probability based SINR and EH constraints is formulated by incorporating random distribution with known error mean and covariance matrix. By exploiting conditional value-at-risk functional and semi-definite relaxation, this optimisation problem is relaxed as the convex form. Finally, numerical results are provided to validate the performance of these proposed robust schemes.

Robust Time Reversal-based Transmit Optimization for Green Heterogeneous Networks

Time reversal (TR) beamforming, considered as one of most prominent linear precoders, has showed the feasibility in conventional communications. In this paper, we propose the TR-based transmit optimization for heterogeneous networks (HetNets) where multiple femtocells apply TR precoders over frequency selective channels, with the signal-to-inference-plus-noise ratio (SINR) and interference constraints at single-antenna femtocell user devices. In multi-femtocell network environments, we exhibit that TR outperforms the well-known zero-forcing beamforming. Particularly, we focus on the practical case of imperfect channel estimation at femtocell base stations. The robust optimization methodology is hence devised by taking into account the average effect of channel estimation error (CEE) on system performance. Under the assumption of estimation error in time-varying channels, we derive the exact closed-form expressions of the desired signal, inter-symbol interference (ISI), inter-user interference (IUI), co- and cross-layer interference terms, respectively. These derivations allow us to tackle the proposed robust algorithm by convex optimization techniques. In final, numerical results are shown to confirm the validation of our proposal. DOI: http://dx.doi.org/10.5755/j01.eee.21.3.10431

Narrow-Band Interference Suppression in Impulse-Radio Ultrawideband Systems

For impulse-radio ultrawideband (IR-UWB) communications, pulse overlap often occurs in indoor dense multipath environments, where the time separation between adjacent received multipaths is typically less than one pulsewidth. Moreover, due to its low power spectral density (PSD) and its overlay usage model, the IR-UWB signal is also vulnerable to narrow-band interference (NBI) of a high power level. Taking the effect of pulse overlap into account, this paper presents an optimal prerake/postrake UWB transceiver design to counteract NBI. The prerake/postrake uses rake structures at both the transmitter and the receiver. The optimum weights for the prerake and postrake are derived by maximizing the signal-to-inference-plus-noise ratio (SINR). Furthermore, an adaptive scheme is proposed to achieve the optimal solution iteratively. Simulations show that the proposed prerake/postrake and the adaptive scheme can significantly eliminate the adverse effect of NBI and thus perform well even in the presence of severe NBI.

SINR based Vertical Handoff Algorithm between GPRS and Wi-Fi Networks

Next generation wireless network is foreseen as the combination of heterogeneous wireless networks capable of providing enhanced services to mobile users. Vertical handoff is a crucial issue in providing service to mobile users, in a heterogeneous network. To maintain continuous service during vertical handoff period, the handoff procedure should consider the noise and interference in the networks. In this article, we have proposed an algorithm based on the received signal to inference plus noise ratio (SINR) for handoff between GPRS and Wi-Fi networks. Here SINR from Wi-Fi network is converted to the equivalent SINR of the GPRS network and vice-versa, so that the handoff algorithm can have the knowledge of achievable bandwidths in both the networks. This helps in taking a handoff decision. Simulation study on handoff between GPRS and Wi-Fi networks using QualNet showed that consideration of received SINR during the vertical handoff period maintains better system throughput than considering received signal strength (RSS) as handoff criteria.