Signal-to-interference-plus-noise Ratio Targets 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.

Multiobjective Beamforming Power Control for Robust SINR Target Tracking and Power Efficiency in Multicell MU-MIMO Wireless System

This study proposes a multiobjective beamforming power control scheme to achieve the robust quality of service (QoS), the optimal signal-to-interference-plus-noise ratio (SINR) target tracking and the minimum average power consumption simultaneously in multicell multiuser MIMO (multicell MU-MIMO) wireless system with imperfect channel. The advantages of the proposed multiobjective beamforming power control optimization method are threefold. First, the proposed power control scheme can simultaneously achieve three objectives as follows: 1) the optimal H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">∞</sub> robust SINR target tracking performance, 2) the optimal H <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> SINR target tracking performance, and 3) the minimum average power consumption in the multicell MU-MIMO wireless system. The proposed multiobjective optimization problem (MOP) is equivalently derived as aMOP by minimizing their corresponding upper bounds under the constraint of a set of linear matrix inequalities (LMIs). Second, without the requirement of statistical information and the norm-bounded condition of the channel uncertainty, the effect of inter-cell interference, intra-cell interference, channel uncertainty, round trip delay, and noise can be efficiently attenuated by the proposed robust SINR target tracking method. Third, through the proposed LMIs-constrained multiobjective evolution algorithm (MOEA), the designer can obtain Pareto optimal solutions efficiently with a good distribution along the Pareto front to achieve the proposed multiobjective power control design. Several simulation examples with the proposed multiobjective control scheme are produced with favourable results under the guarantee of the theoretical analysis.

A robust energy-efficient power control algorithm for cognitive radio networks

To achieve energy efficiency is very important for future cognitive radio networks since we need less power consumption and much more transmitted information. In this paper, we propose a power allocation scheme with robust energy efficiency consideration for appropriately guaranteeing target signal to interference plus noise ratio (SINR) requirement for cognitive users and the received interferences at primary receivers below a threshold. A time-varying interference threshold protection factor and a protection margin to the SINR targets are introduced for the above purpose. This problem is formulated as a fraction programming problem solved by an iterative algorithm based on Lagrange dual approach by convex transformation. Simulation results show the validity of the proposed algorithm on both energy efficiency and robustness under channel gain disturbance.

Power Optimization in Random Wireless Networks

In this paper, we analyze the problem of power control in large, random wireless networks that are obtained by “erasing” a finite fraction of nodes from a regular $d$ -dimensional lattice of $N$ transmit-receive pairs. In this model, which has the important feature of a minimum distance between transmitter nodes, we find that when the network is infinite, power control is always feasible below a positive critical value of the users’ signal-to-interference-plus-noise ratio (SINR) target. Drawing on tools and ideas from statistical physics, we show how this problem can be mapped to the Anderson impurity model for diffusion in random media. In this way, by employing the so-called coherent potential approximation method, we calculate the average power in the system (and its variance) for 1-D and 2-D networks. This approach is equivalent to traditional techniques from random matrix theory and is in excellent agreement with the numerical simulations; however, it fails to predict when power control becomes infeasible. In this regard, even though infinitely large systems are always unstable beyond a critical value of the users’ SINR target, finite systems remain stable with high probability even beyond this critical SINR threshold. We calculate this probability by analyzing the density of low lying eigenvalues of an associated random Schrodinger operator, and we show that the network can exceed this critical SINR threshold by at least $ \mathop {{\mathcal O}}\nolimits ((\log N)^{-2/d})$ before undergoing a phase transition to the unstable regime. Finally, using the same techniques, we also calculate the tails of the distribution of transmit power in the system and the rate of convergence of the Foschini–Miljanic power control algorithm in the presence of random erasures.

Downlink User Capacity of Massive MIMO Under Pilot Contamination

Pilot contamination has been regarded as a main limiting factor of time division duplexing (TDD) massive multiple-input-multiple-output (Massive MIMO) systems, as it will make the signal-to-interference-plus-noise ratio (SINR) saturated. However, how pilot contamination will limit the user capacity of downlink Massive MIMO, i.e., the maximum number of users whose SINR targets can be achieved, has not been addressed. This paper provides an explicit expression of the Massive MIMO user capacity in the pilot-contaminated regime where the number of users is larger than the pilot sequence length. This capacity expression characterizes a region within which a set of SINR requirements can be jointly satisfied. The size of this region is fundamentally limited by the pilot sequence length. Furthermore, the scheme for achieving the user capacity, i.e., the uplink pilot training sequences and downlink power allocation, has been identified. Specifically, the generalized Welch bound equality sequences are exploited and it is shown that the power allocated to each user should be proportional to its SINR target. With this capacity-achieving scheme, the SINR requirement of each user can be satisfied and energy-efficient transmission is achieved in the large-antenna-size (LAS) regime. The comparison with two non-capacity-achieving schemes highlights the superiority of our proposed scheme in terms of achieving higher user capacity. Furthermore, for the practical scenario with a finite number of antennas, the actual antenna size required to achieve a significant percentage of the asymptotic performance has been analytically quantified.

Joint Beamforming and Power Allocation for MIMO Relay Broadcast Channel With Individual SINR Constraints

In this paper, system design for the multi-input multi-output (MIMO) relay broadcast channel with individual signal-to-interference-plus-noise ratio (SINR) constraints at the mobile stations (MS) is considered. By exploring the structure of downlink (DL) uplink (UL) duality at either the base station (BS) or the relay station (RS), we propose two schemes of joint power allocation and beamforming design at the BS and the RS. The problem of existence of feasible solutions under practical power constraints at the BS and the RS with given SINR targets is considered first. Then the problem of sum power minimization is considered. Each design problem can be solved efficiently using optimal joint power allocation and beamforming under the framework of convex optimization. We also show that with subchannel pairing at the RS, the transmission power can be reduced by channel compensation at either hop. Finally, an extension to more general multi-hop applications is provided to further improve the power efficiency.

Secrecy Wireless Information and Power Transfer With MISO Beamforming

The dual use of radio signal for simultaneous wireless information and power transfer (SWIPT) has recently drawn significant attention. To meet the practical requirement that the energy receiver (ER) operates with significantly higher received power as compared to the conventional information receiver (IR), ERs need to be deployed in more proximity to the transmitter than IRs in the SWIPT system. However, due to the broadcast nature of wireless channels, one critical issue arises that the messages sent to IRs can be eavesdropped by ERs, which possess better channels from the transmitter. In this paper, we address this new physical-layer security problem in a multiuser multiple-input single-output (MISO) SWIPT system where one multi-antenna transmitter sends information and energy simultaneously to an IR and multiple ERs, each with one single antenna. Two problems are investigated with different practical aims: the first problem maximizes the secrecy rate for the IR subject to individual harvested energy constraints of ERs, while the second problem maximizes the weighted sum-energy transferred to ERs subject to a secrecy rate constraint for IR. We solve these two non-convex problems optimally by a general two-stage procedure. First, by fixing the signal-to-interference-plus-noise ratio (SINR) target for ERs or IR, we obtain the optimal transmit beamforming and power allocation solution by applying the technique of semidefinite relaxation (SDR). Then, each of the two problems is solved by a one-dimension search over the optimal SINR target for ERs or IR. Furthermore, for each problem, suboptimal solutions of lower complexity are proposed.

Power control in two-tier femtocell networks

In a two tier cellular network - comprised of a central macrocell underlaid with shorter range femtocell hotspots - cross-tier interference limits overall capacity with universal frequency reuse. To quantify near-far effects with universal frequency reuse, this paper derives a fundamental relation providing the largest feasible cellular Signal-to-Interference-Plus-Noise Ratio (SINR), given any set of feasible femtocell SINRs. We provide a link budget analysis which enables simple and accurate performance insights in a two-tier network. A distributed utility- based SINR adaptation at femtocells is proposed in order to alleviate cross-tier interference at the macrocell from cochannel femtocells. The Foschini-Miljanic (FM) algorithm is a special case of the adaptation. Each femtocell maximizes their individual utility consisting of a SINR based reward less an incurred cost (interference to the macrocell). Numerical results show greater than 30% improvement in mean femtocell SINRs relative to FM. In the event that cross-tier interference prevents a cellular user from obtaining its SINR target, an algorithm is proposed that reduces transmission powers of the strongest femtocell interferers. The algorithm ensures that a cellular user achieves its SINR target even with 100 femtocells/cell-site (with typical cellular parameters) and requires a worst case SINR reduction of only 16% at femtocells. These results motivate design of power control schemes requiring minimal network overhead in two-tier networks with shared spectrum.