Target Signal-to-interference-plus-noise Ratio Research Articles

Hybrid Whale Optimization Algorithm for Energy Efficient Cognitive Radio Network

The general use of wireless sensor devices and their importance in terms of deployment cost, size, and user friendly have given an increase to many different applications of cognitive radio networks. In cognitive radio networks, energy efficiency is a very important problem. In literature, many different methods are developed by researchers which are not able to produce the best results. Hence in this paper, a hybrid technique is developed to attain the energy efficiency deliberation intended for properly maintaining objective signal to interference plus noise ratio (SINR) need for the received interferences and cognitive users at a receiver of primary under a reference value. In the proposed methodology, interference threshold protection margin and protection factor to the SINR target is developed. The hybrid WOFA algorithm can be a mixing of the Whale Optimization Algorithm (WOA) and Firefly Algorithm (FA). In the WOA algorithm, updating positions of the whales are achieved with the utilization of the FA algorithm. The proposed method is designed in the MATLAB platform and it is compared with the existing approaches of WOA by robustness in addition to energy efficiency under the disturbance of channel gain. The proposed method is analyzed with the two different cases such as Target SINR ratio condition is 3.52dB and Target SINR ratio condition is 5.11dB respectively.

The SINR Meta Distribution in Poisson Cellular Networks

This work studies the signal-to-interference-plus-noise ratio (SINR) meta distribution (MD) in cellular networks with a focus on the Poisson model. Firstly, we show that for stationary base station point processes, arbitrary fading, and power-law path loss with exponent α, the base station density λ and the noise power σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> impact the SINR MD only through η\pmb \triangleq λ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">α/2</sup> /σ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , termed the network signal-to-noise ratio (NSNR). Next, we show that for Poisson cellular networks, the SINR MD can be written as g(x)θ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2/α</sup> when the target SINR θ and the target reliability x jointly satisfy a constraint. We derive this constraint and the integral of g(x). Lastly, we discuss several extensions of the results to more general models and architectures.

Intelligent Reflecting Surface Enabled Secure Cooperative Transmission for Satellite-Terrestrial Integrated Networks

In this paper, we propose an intelligent reflecting surface (IRS) empowered secure cooperative transmission strategy for satellite-terrestrial integrated networks. In such networks, an IRS is deployed near the satellite user to reflect the common-spectrum friendly interference from the terrestrial network to help protect the satellite downlink transmission from being eavesdropped. We seek to minimize the target signal-to-interference-plus-noise-ratio (SINR) at the eavesdropper required to guarantee the reliable communication at the terrestrial network user and to limit the maximum tolerable interference at the satellite user. Then, an alternation optimization scheme is developed. Simulation results verify that the proposed IRS enabled cooperative jamming scheme can reduce the target SINR at the eavesdropper significantly, thus achieving a great secrecy gain.

Capacity Gain of Full Duplex Self-Backhauling and Opportunistic Full Duplex Self-Backhauling

This paper investigates the capacity gain of full duplex (FD) self-backhauling over the half duplex (HD) counterpart in downlink cellular systems, and then proposes an opportunistic FD scheme. The first step is to derive the capacity gain of FD self-backhauling over the HD counterpart as a function of the system parameters in a computationally tractable form. Unlike previous studies on the capacity of FD self-backhauling, a practical constraint is imposed so that the end-to-end link achieves a target signal-to-interference-plus-noise ratio (SINR) for a successful link connection. Also, a condition under which FD is more beneficial than HD is derived. Specifically, the minimum allowable self interference cancellation factor and the maximum allowable cell radius for the benefit of FD are derived as an explicit function of system parameters. Their dependencies on the system parameters are investigated and compared with counterparts derived for single-hop point-to-point links. Lastly, to overcome the weak points of FD and HD, this paper proposes a user-wise opportunistic FD (OFD) self-backhauled scheme. To this end, an instantaneous channel gain-based FD/HD switching rule is derived from a condition under which FD cannot instantaneously satisfy the target SINR. We confirm the substantial capacity gain of the proposed user-wise instantaneous OFD over a cell-wise static duplex switching scheme, as well as the fixed duplex (HD or FD) schemes. When compared to the static duplex switching scheme, the proposed OFD is most beneficial at the boundary of HD-preferred and FD-preferred regions. Based on this, a system-level guideline is proposed for selecting the most practical scheme among OFD, FD, and HD by trading off capacity gain and switching overhead. Extensive simulation results are provided to confirm the correctness of the derivations and the related investigations.

Optimality Conditions of Performance-Guaranteed Power Minimization in MIMO Networks: A Distributed Algorithm and Its Feasibility

A distributed approach is proposed to the problem of signal-to-interference-plus-noise-ratio (SINR)-guaranteed power minimization (SGPM) for multicell multiuser (MCMU) multiple-input multiple-output (MIMO) systems. Unlike prior SGPM approaches, the proposed technique is based on solving necessary and sufficient optimality conditions, which are derived by decomposing the original problem into forward and backward (FB) subproblems, while ensuring the strong duality of each subproblem. The proposed distributed SGPM algorithm makes use of FB adaptation and Jacobi recursion, respectively, for iterative filter design and power allocation. A sufficient condition for the feasibility of the proposed distributed algorithm is analyzed, based on the matrix inverse-positive theory. Unlike the existing fully distributed FB filter update algorithms, the proposed approach guarantees target SINR performance as well as its convergence to a stationary point. Simulation results illustrate the enhanced power efficiency with the performance guarantees of the proposed method compared to the existing distributed techniques.

Power Allocation in Cellular Networks Based on Outage Probability and Normalized SINR

In this paper, power allocation in cellular networks is proposed based on the outage probability and normalized signal to interference plus noise ratio (SINR). Upper and lower bounds on the outage probability are determined using the normalized SINR considering path loss, shadowing, and fading. The problems of minimizing the user power subject to outage probability and target SINR constraints are then considered as power allocation problems. These problems are solved using Perron-Frobenius theory and geometric programming (GP). The objectives are to efficiently provide users with flexible date rates and reduce the outage probability and user transmit power. Typically, only the path loss is considered in determining the outage probability whereas path loss, multipath fading and lognormal shadowing are considered in this paper along with the interference from other users. Results are presented which show that the proposed power allocation schemes provide better performance than the target SINR tracking power control (TPC), opportunistic power control (OPC), and temporary removal and feasibility check power control (DFC) algorithms.

Non-Cooperative Power Control Game in D2D Underlying Networks with Variant System Conditions

In this paper, the problem of power control using a game theoretic approach based on sigmoid cost function is studied for device-to-device (D2D) communications underlying cellular networks. A non-cooperative game, where each D2D transmitter and a cellular user select their own transmit power level independently, is analyzed to minimize their user-serving cost function and achieve a target signal to interference-plus-noise-ratio (SINR) requirement. It is proved analytically that the Nash equilibrium point of the game exists and it is unique under certain constraints. Numerical results verify the analysis and demonstrate the effectiveness of the proposed game with variant system conditions, such as path loss exponents, target SINR, interference caused by the cellular user, pricing coefficients, and sigmoid control parameter.

Prioritised and selective power control in cellular wireless networks

Power control is used in cellular communications systems to reduce power consumption and satisfy as many users as possible by managing the mutual interference between users. In signal to interference plus noise ratio (SINR) tracking power control (TPC) schemes, all users are required to adjust their power levels for each iteration, which is inefficient. In this study, a prioritised and selective uplink power control scheme is proposed. Priority user requirements are satisfied first, and then as many normal users (NUs) as possible are satisfied with their target SINRs. In addition, if an NU is currently satisfied with its target SINR, it is not required to update its transmit power level. Conversely, NUs who are not satisfied update their power levels. Simulation results are presented, which show that the proposed scheme outperforms dynamic target SINR TPC and variable target SINR TPC in terms of power consumption and efficiency. In addition, the proposed scheme is better than target SINR TPC and opportunistic power control as the number of users increases.

A Framework for Automated Cellular Network Tuning With Reinforcement Learning

Tuning cellular network performance against always occurring wireless impairments can dramatically improve reliability to end users. In this paper, we formulate cellular network performance tuning as a reinforcement learning (RL) problem and provide a solution to improve the performance for indoor and outdoor environments. By leveraging the ability of Q-learning to estimate future performance improvement rewards, we propose two algorithms: (1) closed loop power control (PC) for downlink voice over LTE (VoLTE) and (2) self-organizing network (SON) fault management. The VoLTE PC algorithm uses RL to adjust the indoor base station transmit power so that the signal to interference plus noise ratio (SINR) of a user equipment (UE) meets the target SINR. It does so without the UE having to send power control requests. The SON fault management algorithm uses RL to improve the performance of an outdoor base station cluster by resolving faults in the network through configuration management. Both algorithms exploit measurements from the connected users, wireless impairments, and relevant configuration parameters to solve a non-convex performance optimization problem using RL. Simulation results show that our proposed RL based algorithms outperform the industry standards today in realistic cellular communication environments.

Minimization of PAPR for DFT-Spread OFDM With BPSK Symbols

In this paper, the problem of reducing the peak-to-average power ratio (PAPR) of a discrete Fourier transform spread orthogonal frequency-division multiplexing signal is considered for the transmission of BPSK symbols. In particular, a joint optimization of the constellation-rotation angle and the pulse-shaping vector is conducted with or without the allocation of additional sub-carriers. To avoid the exponential complexity required to evaluate the PAPR, a computationally efficient upper bound on the worst-case PAPR is derived and adopted as the objective function. Unlike conventional upper bounds, the proposed upper bound exploits the structure in the relative phase among symbol waveforms and incorporates the constellation-rotation effect. The signal-to-interference-plus-noise ratio (SINR) at the output of a constellation-derotating matched filter is also derived along with a generalized Nyquist criterion and adopted as the inequality constraint function. Then, joint optimizations are conducted through numerical search for various signal parameters and target SINRs. Surprisingly, the optimal rotation angle is neither 0 nor $\pi /2$ . It is shown that the proposed combinations significantly outperform the existing combinations of the rotation angle and the shaping vector when the signal is amplified by a nonlinear power amplifier.

A robust energy efficiency power allocation algorithm in cognitive radio networks

In order to solve the problem that traditional energy efficiency power allocation algorithms usually require the assumption of constant or perfect channel state information in cognitive radio networks (CRNs), which may lead to performance degradation in real systems with disturbances or uncertainties, we propose a robust energy efficiency power allocation algorithm for underlay cognitive radio (CR) systems with channel uncertainty in consideration of interference power threshold constraint and minimum target SINR requirement constraint. The ellipsoid sets are used to describe the channel uncertainty, and a constrained fractional programming for the allocation is transformed to a convex optimization problem by worst-case optimization approach. A simplified version of robust energy efficiency scheme by a substitutional constraint having lower complexity is presented. Simulation results show that our proposed scheme can provide higher energy efficiency compared with capacity maximization algorithm and guarantee the signal to interference plus noise ratio (SINR) requirement of each cognitive user under channel uncertainty.

Efficient joint power and admission control in underlay cognitive networks using Benders’ decomposition method

In this paper, the joint power and admission control (JPAC) problem in cognitive radio networks is studied. This problem is decomposed into two subproblems, by utilizing the Benders’ decomposition theory, which are efficiently addressed. Specifically, the former is a simple linear optimization problem which a closed-form expression for its optimal solution is derived, and the latter is solved via an iterative distributed power control algorithm. Furthermore, we use the outcomes of the decomposed two subproblems to propose a sequential searching JPAC algorithm with a novel removal metric whereas minimal number of SUs are sequentially removed. In an infeasible system, where the minimum target signal-to-interference-plus-noise-ratios (SINRs) of all primary and secondary users are not simultaneously reachable, our proposed JPAC algorithm guarantees protecting all primary users while the maximal number of SUs are admitted and supported with their target SINRs. Not only our proposed algorithm does converge to an equilibrium, but also outperforms existing algorithms in terms of average outage ratio and average aggregate power, as demonstrated through the extensive simulations.

On the Stability of Distributed Power Control Algorithms Under Imperfect Estimation of Channel and Interference

The distributed power control algorithms available in this paper assume perfect estimation of interference power at the receivers and often, the availability of perfect local channel state information (CSI). However, perfect estimation of interference power or acquisition of channel information is impractical. In this paper, we consider a generic class of distributed power control algorithms and analyze the resulting performance loss when faced with errors in CSI and/or interference level estimation. We approximate such power control algorithms with linearized state space dynamics wherein the errors in the estimation of received interference or the CSI, can be modeled as additive observation noise. Based on the proposed model, we derive lower bounds on the norm of the errors in the transmit power vector and receive signal-to-interference-plus-noise ratio (SINR) vector. Our analysis suggests that when the target SINR vector approaches the Pareto-optimal bound, the errors in the transmit vectors could grow without bound. As a consequence, to maintain a performance loss within an acceptable range, either much longer estimation times or lower target SINRs are required. Our numerical results confirm the validity of our analysis and the validity of the proposed bounds.

Coverage probability analysis for Full-Duplex relay aided Device-to-Device communications networks

The coverage probability of both the cellular users and the Device-to-Device (D2D) users are analyzed. We assume that the cellular users are able to communication with the Base Station (BS) either by relying on the assistance of Full-Duplex (FD) mode relays or via direct user-to-BS links with high-enough Signal-to-Interference-plus-Noise-Ratio (SINR). Note that the FD-mode devices are capable of simultaneously operating in two modes, i.e. the D2D mode and the cooperative relay mode, with the sum power consumption at these devices kept constant. The closed-form expressions for coverage probability of both tier users are derived. After that, numerical analyses are provided, showing that the coverage probability of the both the cellular and the D2D users can be substantially influenced by a variety of parameters, including the power allocation factor of the relays, the density of users, and the self-interference imposed on the FD mode relays, etc. Furthermore, in the D2D enabled networks, it is shown that the FD relay aided transmission is beneficial to enhancing the coverage probability of the cellular users if the target SINR is lower than 5 dB.

Interference-Aware Uplink Power Control in 3GPP LTE-A HetNet

The latest evolution of cellular network, i.e., the heterogeneous network (HetNet) is a promising technique for facing the explosive data demands that the macro-only network can barely meet. However, unplanned small cell deployments, along with an aggressive frequency reuse scheme, generate severe interference between adjacent cells, degrading system performance. In the conventional homogeneous network, fractional power control (FPC), which consists of open-loop power control (OL-PC) and closed-loop power control (CL-PC), is used to determine adequate transmit power of user equipment (UE) to avoid inter-cell interference. However, setting the UE transmit power requires delicate awareness of uplink interference to properly avoid the severe and complicated inter-cell interference in an uplink HetNet environment. In this paper, we propose an interference-aware uplink power control scheme that considers outgoing and incoming interference in each cell in order to fully take the interference situation of the HetNet into account while setting the OL-PC parameter. By using the proposed scheme, uplink interference existing in a HetNet environment can evidently be reduced; therefore, the total cell capacity of both the macro- and the femtocell is increased by 25.6 %. Furthermore, the simulation results of a CL-PC on a 10 ms basis show that the received signal to interference-plus-noise ratio (SINR) of OL-PC with CL-PC is spread less around the target SINR compared to the case of OL-PC alone. Therefore, it is verified that the CL-PC supports the received SINR of OL-PC to meet the target SINR by compensating the channel variation caused by the fading effect.

Power headroom report-based uplink power control in 3GPP LTE-A HetNet

In a 3rd Generation Partnership Project Long Term Evolution-Advanced (3GPP LTE-A) uplink, user equipment (UE) has a maximum transmission power limit defined by the UE power class. Generally, the cell edge UE has a higher probability to be constrained by the maximum transmission power level owing to the compensation of the large pathloss. When the UE transmission power is constrained by the maximum level, allocating a higher number of physical resource blocks (PRBs) than the UE power capability can afford will reduce the transmission power to be allocated per PRB, resulting in inefficient use of power resources. To avoid this power inefficiency, the uplink transmission power can be controlled according to the number of PRBs allocated using the power headroom report-based power efficient resource allocation (PHR-PERA) scheme proposed in this paper. Furthermore, adaptive open-loop power control (OL-PC) based on the signal-to-interference-plus-noise ratio (SINR) and the uplink interference is used to improve the cell capacity. By the uplink power control employing the proposed PHR-PERA scheme, the macro and femto UE throughputs were increased by 49.9 and 5 %, respectively, compared to the case of conventional fractional power control (FPC). Additional gains of 21.9 and 4.8 % for macro and femto UE throughputs, respectively, were achieved by adaptive OL-PC. The performance of fast closed-loop power control (CL-PC) based on the received SINR is also evaluated in this paper. The simulation results demonstrate that CL-PC supports OL-PC by compensating the fading effect for the UE uplink SINR to meet the target SINR.

Coverage Probability and Achievable Rate Analysis of FFR-Aided Multi-User OFDM-Based MIMO and SIMO Systems

Expressions are derived for the coverage probability and average rate of both multi-user multiple input multiple output (MU-MIMO) and single input multiple output (SIMO) systems in the context of a fractional frequency reuse (FFR) scheme. In particular, given a reuse region of $\frac{1}{3}$ (FR3) and a reuse region of 1 (FR1) as well as a signal-to-interference-plus-noise-ratio (SINR) threshold $S_{th}$ , which decides the user assignment to either the FR1 or FR3 regions, we theoretically show that: $1)$ the optimal choice of $S_{th}$ which maximizes the coverage probability is $S_{th} = T$ , where $T$ is the target SINR required for ensuring adequate coverage, and $2)$ the optimal choice of $S_{th}$ which maximizes the average rate is given by $S_{th}= T^{\prime}$ , where $T^{\prime}$ is a function of the path loss exponent, the number of antennas and of the fading parameters. The impact of frequency domain correlation amongst the OFDM sub-bands allocated to the FR1 and FR3 cell-regions is analysed and it is shown that the presence of correlation reduces both the coverage probability and the average throughput of the FFR network. Furthermore, the performance of our FFR-aided MU-MIMO and SIMO systems is compared. Our analysis shows that the (2 $\times$ 2) MU-MIMO system achieves 22.5% higher rate than the (1 $\times$ 3) SIMO system and for lower target SINRs, the coverage probability of a (2 $\times$ 2) MU-MIMO system is comparable to a (1 $\times$ 3) SIMO system. Hence the former one may be preferred over the latter. Our simulation results closely match the analytical results.

Exploiting Regional Differences: A Spatially Adaptive Random Access

In this paper, we discuss the potential for improvement of the simple random access scheme by utilizing local information such as the received signal-to-interference-plus-noise-ratio (SINR). We propose a spatially adaptive random access (SARA) scheme in which the transmitters in the network utilize different transmit probabilities depending on the local situation. In our proposed scheme, the transmit probability is adaptively updated by the ratio of the received SINR and the target SINR. We investigate the performance of the spatially adaptive random access scheme. For the comparison, we derive an optimal transmit probability of ALOHA random access scheme in which all transmitters use the same transmit probability. We illustrate the performance of the spatially adaptive random access scheme through simulations. We show that the performance of the proposed scheme surpasses that of the optimal ALOHA random access scheme and is comparable with the CSMA/CA scheme.

A Game Theory Approach for Efficient Power Control and Interference Management in Two-tier Femtocell Networks based on Local Aain

In the recent years, femtocell technology has received a considerable attention due to the ability to provide an efficient indoor wireless coverage as well as enhanced capacity. However, under the spectrum sharing between femtocell user equipment (FUEs) and the owner of spectrum macrocell user equipment (MUEs), both may experience higher uplink interference to each other. This paper proposes a novel distributed power control algorithm for the interference management in two-tier femtocell networks. Due to the assignment of licensed radio frequency to the outdoor macrocell users, the access priority of MUEs should be higher than FUEs. In addition, the quality of service (QoS) of MUEs that is expressed in the target signal-to-interference-plus-noise ratio (SINR) must always be achieved. On the other hand, we consider an efficient QoS provisioning cost function for the low-tier FUEs. The proposed algorithm requires only local information and converges even in cases where the frontiers of available power serve the target SINRs impossible. The advantage of the algorithm is the ability to implement in a distributed manner. Simulation results show that the proposed algorithm based on our cost function provides effective resource allocation and substantial power saving as compared to the traditional algorithms.

Distributed Beamforming for Macro Diversity and Power Control With Large Arrays in Spatial Correlated Channels

Downlink beamforming plays a key role in massive multiple-input multiple-output (MIMO) systems to provide reasonable signal-to-interference-plus-noise ratio (SINR) in the presence of intercell interference (ICI). Due to an excessive backhaul overhead, limited cooperative beamforming is usually desirable. In this paper, we consider distributed beamforming that can exploit macro diversity with limited cooperation for cell-edge users where multiple base stations (BSs) transmit signals to those users simultaneously. Bounds on the SINRs are derived to see the impact of the spatial correlation. Since distributed beamforming itself cannot guarantee quality of service (QoS) requirements for given target SINRs with limited cooperation, based on derived closed-form expressions for bounds on SINRs, joint power control is considered with two different approaches. One is optimal, but central processing is required. The other approach is suboptimal, while it can be implemented as a distributed power control algorithm.