scholarly journals Joint User Scheduling, Relay Selection, and Power Allocation for Multi-Antenna Opportunistic Beamforming Systems

Entropy ◽  
2021 ◽  
Vol 23 (10) ◽  
pp. 1278
Author(s):  
Wen-Bin Sun ◽  
Ming-Liang Tao ◽  
Xin Yang ◽  
Tao Zhang ◽  
Chuang Han ◽  
...  
Keyword(s):  
Communication Systems ◽  
Relay Selection ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Achievable Rate ◽  
User Scheduling ◽  
Feedback Information ◽  
Fifth Generation ◽  
Np Hard Problem ◽  
Opportunistic Beamforming

Opportunistic beamforming (OBF) is a potential technique in the fifth generation (5G) and beyond 5G (B5G) that can boost the performance of communication systems and encourage high user quality of service (QoS) through multi-user selection gain. However, the achievable rate tends to be saturated with the increased number of users, when the number of users is large. To further improve the achievable rate, we proposed a multi-antenna opportunistic beamforming-based relay (MOBR) system, which can achieve both multi-user and multi-relay selection gains. Then, an optimization problem is formulated to maximize the achievable rate. Nevertheless, the optimization problem is a non-deterministic polynomial (NP)-hard problem, and it is difficult to obtain an optimal solution. In order to solve the proposed optimization problem, we divide it into two suboptimal issues and apply a joint iterative algorithm to consider both the suboptimal issues. Our simulation results indicate that the proposed system achieved a higher achievable rate than the conventional OBF systems and outperformed other beamforming schemes with low feedback information.

scholarly journals Joint Resource Allocation for Multiuser Opportunistic Beamforming Systems with OFDM-NOMA

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 809
Author(s):  
Wen-Bin Sun ◽  
Ming-Liang Tao ◽  
Ling Wang ◽  
Xin Yang ◽  
Rui-Zhe Zhou ◽  
...  
Keyword(s):  
Orthogonal Frequency Division Multiplexing ◽  
Multiple Access ◽  
Optimization Problem ◽  
Spectrum Efficiency ◽  
Computation Complexity ◽  
Transmit Power ◽  
Feedback Information ◽  
Joint Resource Allocation ◽  
Multiple Access Schemes ◽  
Opportunistic Beamforming

Opportunistic beamforming (OBF) is an effective technique to improve the spectrum efficiencies (SEs) of multiple-input-multiple-output (MIMO) systems, which can obtain multiuser diversity gains with both low computation complexity and feedback information. To serve multiple users simultaneously, many multiple-access schemes have been researched in OBF. However, for most of the multiple-access schemes, the SEs are not satisfactory. To further improve the SE, this paper proposes a downlink multiuser OBF system, where both orthogonal frequency division multiplexing (OFDM) and non-orthogonal multiple-access (NOMA) methods are applied. The closed-form expressions of the equivalent channels and SE are derived in frequency selective fading channels. Then, an optimization problem is formulated to maximize the SE, although the optimization problem is non-convex and hard to solve. To obtain the solution, we divide the optimization problem into two suboptimal issues, and then a joint iterative algorithm is applied. In the proposed optimization scheme, the subcarrier mapping ϑ, user pairing knc and allocated power Pknc are determined to maximize spectrum efficiency (SE) and reduce bit error ratio (BER). According to numerical results, the proposed method achieves approximately 5 dB gain on both SE and BER, compared to the existing beamforming methods with low feedback information. Moreover, the SE of the proposed method is approximately 2 (bps/Hz) higher than sparse code multiple-access (SCMA), when the number of waiting users and the ratio of transmit power to noise variance are respectively 10 and 20 dB. It is indicated that the proposed scheme can achieve high and low BER with the limited feedback and computation complexity, regardless of the transmit power and the number of waiting users.

An Improved Ant Colony Algorithm Based on Path Optimization Strategy for TSP

2014 ◽  
Vol 543-547 ◽  
pp. 1681-1684 ◽  
Author(s):  
Ben Can Gong ◽  
Ting Yao Jiang ◽  
Shou Zhi Xu ◽  
Peng Chen
Keyword(s):  
Optimization Problem ◽  
Ant Colony Algorithm ◽  
Optimal Solution ◽  
Ant Colony ◽  
Path Optimization ◽  
Optimization Strategy ◽  
Local Optimal Solution ◽  
Np Hard Problem ◽  
Improved Ant Colony Algorithm ◽  
High Application

Traveling salesman problem (TSP) is not only a combinatorial optimization problem but also a classical NP-hard problem, which has high application value. Ant colony algorithm (ACA) is very effective for solving TSP problem, but basic ant colony algorithm has drawbacks of low convergence rate and easily trapping in local optimal solution. An improved ant colony algorithm was proposed. It used path optimization strategy to exchange the position of cities to find the better solution for TSP. Simulation results show the improved algorithm has better optimal solution and higher efficiency.

scholarly journals Optimization of Joint Power and Bandwidth Allocation in Multi-Spot-Beam Satellite Communication Systems

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Heng Wang ◽  
Aijun Liu ◽  
Xiaofei Pan
Keyword(s):  
Bandwidth Allocation ◽  
Communication Systems ◽  
Optimization Problem ◽  
Satellite Communication ◽  
Optimal Allocation ◽  
Optimal Solution ◽  
System Capacity ◽  
Total System ◽  
Joint Power ◽  
Spot Beam

Multi-spot-beam technique has been widely applied in modern satellite communication systems. However, the satellite power and bandwidth resources in a multi-spot-beam satellite communication system are scarce and expensive; it is urgent to utilize the resources efficiently. To this end, dynamically allocating the power and bandwidth is an available way. This paper initially formulates the problem of resource joint allocation as a convex optimization problem, taking into account a compromise between the maximum total system capacity and the fairness among the spot beams. A joint bandwidth and power allocation iterative algorithm based on duality theory is then proposed to obtain the optimal solution of this optimization problem. Compared with the existing separate bandwidth or power optimal allocation algorithms, it is shown that the joint allocation algorithm improves both the total system capacity and the fairness among spot beams. Moreover, it is easy to be implemented in practice, as the computational complexity of the proposed algorithm is linear with the number of spot beams.

scholarly journals QoS-Oriented Optimal Relay Selection in Cognitive Radio Networks

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Shakeel A. Alvi ◽  
Riaz Hussain ◽  
Atif Shakeel ◽  
Muhammad Awais Javed ◽  
Qadeer Ul Hasan ◽  
...  
Keyword(s):  
Cognitive Radio ◽  
Power Consumption ◽  
Communication Systems ◽  
Relay Selection ◽  
Cognitive Radio Network ◽  
Optimal Solution ◽  
Radio Network ◽  
Objective Functions ◽  
Selection Scheme ◽  
Battery Power

A cognitive radio network can be employed in any wireless communication systems, including military communications, public safety, emergency networks, aeronautical communications, and wireless-based Internet of Things, to enhance spectral efficiency. The performance of a cognitive radio network (CRN) can be enhanced through the use of cooperative relays with buffers; however, this incurs additional delays which can be reduced by using virtual duplex relaying that requires selection of a suitable relay pair. In a virtual duplex mode, we mimic full-duplex links by using simultaneous two half-duplex links, one transmitting and the other one receiving, in such a way that the overall effect of duplex mode is achieved. The relays are generally selected based on signal-to-interference-plus-noise ratio (SINR). However, other factors such as power consumption and buffer capacity can also have a significant impact on relay selection. In this work, a multiobjective relay selection scheme is proposed that simultaneously takes into account throughput, delay performance, battery power, and buffer status (i.e., both occupied and available) at the relay nodes while maintaining the required SINR. The proposed scheme involves the formulation of four objective functions to, respectively, maximize throughput and buffer space availability while minimizing the delay and battery power consumption. The weighted sum approach is then used to combine these objective functions to form the multiobjective optimization problem and an optimal solution is obtained. The assignments of weights to objectives have been done using the rank sum (RS) method, and several quality-of-service (QoS) profiles have been considered by varying the assignment of weights. The results gathered through simulations demonstrate that the proposed scheme efficiently determines the optimal solution for each application scenario and selects the best relay for the respective QoS profile. The results are further verified by using the genetic algorithm (GA) and particle swarm optimization (PSO) techniques. Both techniques gave identical solutions, thus validating our claim.

A Method for Spatial Point-Focusing of Radio Wave Energy for Fifth-Generation Communication Systems

Vestnik MEI ◽  
2018 ◽  
Vol 5 (5) ◽  
pp. 158-165
Author(s):  
Roman S. Kulikov ◽  
◽  
Aleksandr A. Chugunov ◽  
Nikita I. Petukhov ◽  
Ivan R. Indrikov ◽  
...  
Keyword(s):  
Radio Wave ◽  
Wave Energy ◽  
Communication Systems ◽  
Spatial Point ◽  
Fifth Generation

A new algorithm for finding CRM-model coefficients

2019 ◽  
Vol 5 (3) ◽  
pp. 164-185 ◽  
Author(s):  
Alexander D. Bekman ◽  
Sergey V. Stepanov ◽  
Alexander A. Ruchkin ◽  
Dmitry V. Zelenin
Keyword(s):  
Approximate Solution ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Complex Form ◽  
Approximate Solutions ◽  
The Other ◽  
Programming Algorithm ◽  
Stochastic Algorithms ◽  
Large Set ◽  
Flow Rates

The quantitative evaluation of producer and injector well interference based on well operation data (profiles of flow rates/injectivities and bottomhole/reservoir pressures) with the help of CRM (Capacitance-Resistive Models) is an optimization problem with large set of variables and constraints. The analytical solution cannot be found because of the complex form of the objective function for this problem. Attempts to find the solution with stochastic algorithms take unacceptable time and the result may be far from the optimal solution. Besides, the use of universal (commercial) optimizers hides the details of step by step solution from the user, for example&nbsp;— the ambiguity of the solution as the result of data inaccuracy.<br> The present article concerns two variants of CRM problem. The authors present a new algorithm of solving the problems with the help of “General Quadratic Programming Algorithm”. The main advantage of the new algorithm is the greater performance in comparison with the other known algorithms. Its other advantage is the possibility of an ambiguity analysis. This article studies the conditions which guarantee that the first variant of problem has a unique solution, which can be found with the presented algorithm. Another algorithm for finding the approximate solution for the second variant of the problem is also considered. The method of visualization of approximate solutions set is presented. The results of experiments comparing the new algorithm with some previously known are given.

scholarly journals Algebraic Solution to Constrained Bi-Criteria Decision Problem of Rating Alternatives through Pairwise Comparisons

Mathematics ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 303
Author(s):  
Nikolai Krivulin
Keyword(s):  
Decision Problem ◽  
Optimization Problem ◽  
Pareto Frontier ◽  
Sufficient Conditions ◽  
Approximation Error ◽  
Optimal Solution ◽  
Algebraic Solution ◽  
Pairwise Comparisons ◽  
Logarithmic Scale ◽  
Idempotent Algebra

We consider a decision-making problem to evaluate absolute ratings of alternatives from the results of their pairwise comparisons according to two criteria, subject to constraints on the ratings. We formulate the problem as a bi-objective optimization problem of constrained matrix approximation in the Chebyshev sense in logarithmic scale. The problem is to approximate the pairwise comparison matrices for each criterion simultaneously by a common consistent matrix of unit rank, which determines the vector of ratings. We represent and solve the optimization problem in the framework of tropical (idempotent) algebra, which deals with the theory and applications of idempotent semirings and semifields. The solution involves the introduction of two parameters that represent the minimum values of approximation error for each matrix and thereby describe the Pareto frontier for the bi-objective problem. The optimization problem then reduces to a parametrized vector inequality. The necessary and sufficient conditions for solutions of the inequality serve to derive the Pareto frontier for the problem. All solutions of the inequality, which correspond to the Pareto frontier, are taken as a complete Pareto-optimal solution to the problem. We apply these results to the decision problem of interest and present illustrative examples.

scholarly journals Trajectory planning in UAV emergency networks with potential underlaying D2D communication based on K-means

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Shuo Zhang ◽  
Shuo Shi ◽  
Tianming Feng ◽  
Xuemai Gu
Keyword(s):  
Trajectory Planning ◽  
Trajectory Optimization ◽  
Communication Systems ◽  
Complete Coverage ◽  
Wireless System ◽  
Fifth Generation ◽  
Emergency Communications ◽  
Trajectory Length ◽  
Post Disaster ◽  
Joint Evaluation

AbstractAt present, unmanned aerial vehicles (UAVs) have been widely used in communication systems, and the fifth-generation wireless system (5G) has further promoted the vigorous development of them. The trajectory planning of UAV is an important factor that affects the timeliness and completion of missions, especially in scenarios such as emergency communications and post-disaster rescue. In this paper, we consider an emergency communication network where a UAV aims to achieve complete coverage of potential underlaying device-to-device (D2D) users. Trajectory planning issues are grouped into clustering and supplementary phases for optimization. Aiming at trajectory length and sum throughput, two trajectory planning algorithms based on K-means are proposed, respectively. In addition, in order to balance sum throughput with trajectory length, we present a joint evaluation index. Then relying on this index, a third trajectory optimization algorithm is further proposed. Simulation results show the validity of the proposed algorithms which have advantages over the well-known benchmark scheme in terms of trajectory length and sum throughput.

scholarly journals Quit When You Can: Efficient Evaluation of Ensembles by Optimized Ordering

2021 ◽  
Vol 17 (4) ◽  
pp. 1-20
Author(s):  
Serena Wang ◽  
Maya Gupta ◽  
Seungil You
Keyword(s):  
Optimization Problem ◽  
Optimal Solution ◽  
Combinatorial Optimization Problem ◽  
Joint Optimization ◽  
Early Stopping ◽  
Time Approximation ◽  
Approximation Bound ◽  
Evaluation Time ◽  
Speed Up ◽  
Real World Problems

Given a classifier ensemble and a dataset, many examples may be confidently and accurately classified after only a subset of the base models in the ensemble is evaluated. Dynamically deciding to classify early can reduce both mean latency and CPU without harming the accuracy of the original ensemble. To achieve such gains, we propose jointly optimizing the evaluation order of the base models and early-stopping thresholds. Our proposed objective is a combinatorial optimization problem, but we provide a greedy algorithm that achieves a 4-approximation of the optimal solution under certain assumptions, which is also the best achievable polynomial-time approximation bound. Experiments on benchmark and real-world problems show that the proposed Quit When You Can (QWYC) algorithm can speed up average evaluation time by 1.8–2.7 times on even jointly trained ensembles, which are more difficult to speed up than independently or sequentially trained ensembles. QWYC’s joint optimization of ordering and thresholds also performed better in experiments than previous fixed orderings, including gradient boosted trees’ ordering.

scholarly journals Characterization of Tiled Architecture for C-Band 1-Bit Beam-Steering Transmitarray

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1259
Author(s):  
Dmitry Kozlov ◽  
Irina Munina ◽  
Pavel Turalchuk ◽  
Vitalii Kirillov ◽  
Alexey Shitvov ◽  
...  
Keyword(s):  
Communication Systems ◽  
Beam Steering ◽  
Unit Cell ◽  
Electromagnetic Simulations ◽  
Fifth Generation ◽  
Full Wave ◽  
Array Architecture ◽  
Modulation Pattern ◽  
Good Agreement

A new implementation of a beam-steering transmitarray is proposed based on the tiled array architecture. Each pixel of the transmitarray is manufactured as a standalone unit which can be hard-wired for specific transmission characteristics. A set of complementary units, providing reciprocal phase-shifts, can be assembled in a prescribed spatial phase-modulation pattern to perform beam steering and beam forming in a broad spatial range. A compact circuit model of the tiled unit cell is proposed and characterized with full-wave electromagnetic simulations. Waveguide measurements of a prototype unit cell have been carried out. A design example of a tiled 10 × 10-element 1-bit beam-steering transmitarray is presented and its performance benchmarked against the conventional single-panel, i.e., unibody, counterpart. Prototypes of the tiled and single-panel C-band transmitarrays have been fabricated and tested, demonstrating their close performance, good agreement with simulations and a weak effect of fabrication tolerances. The proposed transmitarray antenna configuration has great potential for fifth-generation (5G) communication systems.

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