scholarly journals Power Allocation for Reducing PAPR of Artificial-Noise-Aided Secure Communication System

2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Tao Hong ◽  
Geng-xin Zhang
Keyword(s):  
Power Allocation ◽  
Nonconvex Optimization ◽  
Communication System ◽  
Secure Communication ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Average Power ◽  
Finite Alphabet ◽  
Artificial Noise ◽  
Communication Signals

The research of improving the secrecy capacity (SC) of wireless communication system using artificial noise (AN) is one of the classic models in the field of physical layer security communication. In this paper, we consider the peak-to-average power ratio (PAPR) problem in this AN-aided model. A power allocation algorithm for AN subspaces is proposed to solve the nonconvex optimization problem of PAPR. This algorithm utilizes a series of convex optimization problems to relax the nonconvex optimization problem in a convex way based on fractional programming, difference of convex (DC) functions programming, and nonconvex quadratic equality constraint relaxation. Furthermore, we also derive the SC of the proposed signal under the condition of the AN-aided model with a finite alphabet and the nonlinear high-power amplifiers (HPAs). Simulation results show that the proposed algorithm reduces the PAPR value of transmit signal to improve the efficiency of HPA compared with benchmark AN-aided secure communication signals in the multiple-input single-output (MISO) model.

scholarly journals Nonconvex and game theory optimization for resource allocation in wireless communications

2021 ◽  
Author(s):  
Kandasamy Illanko
Keyword(s):  
Wireless Communication ◽  
Power Allocation ◽  
Communication Systems ◽  
Optimization Problem ◽  
Polynomial Complexity ◽  
Optimization Problems ◽  
Stackelberg Game ◽  
Mathematical Optimization ◽  
Simultaneous Optimization ◽  
Theoretical Development

Designing wireless communication systems that efficiently utilize the resources frequency spectrum and electric power, leads to problems in mathematical optimization. Most of these optimization problems are difficult to solve because the objective functions are nonconvex. While some problems remain unsolved, the solutions proposed in the literature for the others are of somewhat limited use because the algorithms are either unstable or have too high a computational complexity. This dissertation presents several stable algorithms, most of which have polynomial complexity, that solve five different nonconvex optimization problems in wireless communication. Two centralized and two distributed algorithms deal with the power allocation that maximizes the throughput in the Gaussian interference channel (GIC)with various constraints. The most valuable of these algorithms, the one with the minimum rate constraints became possible after a significant theoretical development in the dissertation that proves that the throughput of the GIC has a new generalized convex structure called invexity. The fifth algorithm has linear complexity, and finds the power allocation that maximizes the energy efficiency (EE) of OFDMA transmissions, for a given subchannel assignment. Some fundamental results regarding the power allocation are then used in the genetic algorithm for determining the subchannel allocation that maximizes the EE. Pricing for channel subleasing for ad-hoc wireless networks is considered next. This involves the simultaneous optimization of many functions that are interconnected through the variables involved. A composite game, a strategic game within a Stackelberg game, is used to solve this optimization problem with polynomial complexity. For each optimization problem solved, numerical results obtained using simulations that support the analysis and demonstrate the performance of the algorithms are presented.

scholarly journals Nonconvex and game theory optimization for resource allocation in wireless communications

2021 ◽  
Author(s):  
Kandasamy Illanko
Keyword(s):  
Wireless Communication ◽  
Power Allocation ◽  
Communication Systems ◽  
Optimization Problem ◽  
Polynomial Complexity ◽  
Optimization Problems ◽  
Stackelberg Game ◽  
Mathematical Optimization ◽  
Simultaneous Optimization ◽  
Theoretical Development

Designing wireless communication systems that efficiently utilize the resources frequency spectrum and electric power, leads to problems in mathematical optimization. Most of these optimization problems are difficult to solve because the objective functions are nonconvex. While some problems remain unsolved, the solutions proposed in the literature for the others are of somewhat limited use because the algorithms are either unstable or have too high a computational complexity. This dissertation presents several stable algorithms, most of which have polynomial complexity, that solve five different nonconvex optimization problems in wireless communication. Two centralized and two distributed algorithms deal with the power allocation that maximizes the throughput in the Gaussian interference channel (GIC)with various constraints. The most valuable of these algorithms, the one with the minimum rate constraints became possible after a significant theoretical development in the dissertation that proves that the throughput of the GIC has a new generalized convex structure called invexity. The fifth algorithm has linear complexity, and finds the power allocation that maximizes the energy efficiency (EE) of OFDMA transmissions, for a given subchannel assignment. Some fundamental results regarding the power allocation are then used in the genetic algorithm for determining the subchannel allocation that maximizes the EE. Pricing for channel subleasing for ad-hoc wireless networks is considered next. This involves the simultaneous optimization of many functions that are interconnected through the variables involved. A composite game, a strategic game within a Stackelberg game, is used to solve this optimization problem with polynomial complexity. For each optimization problem solved, numerical results obtained using simulations that support the analysis and demonstrate the performance of the algorithms are presented.

scholarly journals Research on PDMA communication system based on Complete Complementary Sequence

2020 ◽  
Author(s):  
Shufeng Li ◽  
Baoxin Su ◽  
Libiao Jin
Keyword(s):  
Power Allocation ◽  
Spread Spectrum ◽  
Communication System ◽  
Multiple Access ◽  
Large Scale ◽  
Average Power ◽  
Spectrum Efficiency ◽  
System Model ◽  
Complementary Sequence ◽  
Allocation Algorithm

Abstract Pattern division multiple access (PDMA) is a new non-orthogonal multiple access (NOMA) technology. It is proposed to meet the challenge of 5G large-scale connectivity and high-frequency spectral efficiency. Compared with traditional orthogonal multiple access (OMA), PDMA can support more users through the allocation of non-orthogonal resources. Due to perfect aperiodic correlation, complete complementary sequence (CCS) greatly improves the spectrum efficiency of the system. It has been widely used in wireless communication and radar sensing, and it still has research value in 5G. In order to apply the advantages of CCS to NOMA communication system, this paper proposes a system model of CCS spread spectrum coding based on PDMA. CCS is used as spread spectrum code to improve the performance of PDMA communication system. At the same time, on the basis of spread spectrum technology, this paper analyzes the average power allocation algorithm and water-filling power allocation algorithm, and a dynamic power allocation algorithm based on the transmission rate and practical application is proposed. The simulation results show that the system model can effectively improve the performance of the system.

scholarly journals Global Optimization for the Sum of Certain Nonlinear Functions

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Mio Horai ◽  
Hideo Kobayashi ◽  
Takashi G. Nitta
Keyword(s):  
Global Optimization ◽  
Branch And Bound ◽  
Nonconvex Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Global Optimization Problem ◽  
Nonlinear Functions ◽  
Nonconvex Optimization Problems ◽  
Second Derivatives ◽  
Fractional Function

We extend work by Pei-Ping and Gui-Xia, 2007, to a global optimization problem for more general functions. Pei-Ping and Gui-Xia treat the optimization problem for the linear sum of polynomial fractional functions, using a branch and bound approach. We prove that this extension makes possible to solve the following nonconvex optimization problems which Pei-Ping and Gui-Xia, 2007, cannot solve, that the sum of the positive (or negative) first and second derivatives function with the variable defined by sum of polynomial fractional function by using branch and bound algorithm.

scholarly journals Power Allocation for Secrecy-Capacity-Optimization-Artificial-Noise Secure MIMO Precoding Systems under Perfect and Imperfect Channel State Information

2021 ◽  
Vol 11 (10) ◽  
pp. 4558
Author(s):  
Yebo Gu ◽  
Bowen Huang ◽  
Zhilu Wu
Keyword(s):  
Channel Estimation ◽  
Power Allocation ◽  
Optimization Problem ◽  
Estimation Error ◽  
Channel Estimation Error ◽  
Artificial Noise ◽  
Secrecy Capacity ◽  
Transmission Power ◽  
Channel State ◽  
Imperfect Channel Estimation

In this paper, we consider the physical layer security problem of the wireless communication system. For the multiple-input, multiple-output (MIMO) wireless communication system, secrecy capacity optimization artificial noise (SCO−AN) is introduced and studied. Unlike its traditional counterpart, SCO−AN is an artificial noise located in the range space of the channel state information space and thus results in a significant increase in the secrecy capacity. Due to the limitation of transmission power, making rational use of this power is crucial to effectively increase the secrecy capacity. Hence, in this paper, the objective function of transmission power allocation is constructed. We also consider the imperfect channel estimation in the power allocation problems. In traditional AN research conducted in the past, the expression of the imperfect channel estimation effect was left unknown. Still, the extent to which the channel estimation error impacts the accuracy of secrecy capacity computation is not negligible. We derive the expression of channel estimation error for least square (LS) and minimum mean squared error (MMSE) channel estimation. The objective function for transmission power allocation is non-convex. That is, the traditional gradient method cannot be used to solve this non-convex optimization problem of power allocation. An improved sequence quadratic program (ISQP) is therefore applied to solve this optimization problem. The numerical result shows that the ISQP is better than other algorithms, and the power allocation as derived from ISQP significantly increases secrecy capacity.

scholarly journals Efficiency of quantum vs. classical annealing in nonconvex learning problems

2018 ◽  
Vol 115 (7) ◽  
pp. 1457-1462 ◽  
Author(s):  
Carlo Baldassi ◽  
Riccardo Zecchina
Keyword(s):  
Nonconvex Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Transverse Field ◽  
Learning Problems ◽  
Optimal Solutions ◽  
Local Minima ◽  
Quantum Annealing ◽  
Nonconvex Optimization Problems ◽  
Classical Energy

Quantum annealers aim at solving nonconvex optimization problems by exploiting cooperative tunneling effects to escape local minima. The underlying idea consists of designing a classical energy function whose ground states are the sought optimal solutions of the original optimization problem and add a controllable quantum transverse field to generate tunneling processes. A key challenge is to identify classes of nonconvex optimization problems for which quantum annealing remains efficient while thermal annealing fails. We show that this happens for a wide class of problems which are central to machine learning. Their energy landscapes are dominated by local minima that cause exponential slowdown of classical thermal annealers while simulated quantum annealing converges efficiently to rare dense regions of optimal solutions.

scholarly journals Research on PDMA communication system based on Complete Complementary Sequence

2020 ◽  
Author(s):  
Shufeng Li ◽  
Baoxin Su ◽  
Libiao Jin
Keyword(s):  
Power Allocation ◽  
Spread Spectrum ◽  
Communication System ◽  
Multiple Access ◽  
Large Scale ◽  
Transmission Rate ◽  
Average Power ◽  
System Model ◽  
Complementary Sequence ◽  
Allocation Algorithm

Abstract Pattern division multiple access (PDMA) is a new non-orthogonal multiple access (NOMA) technology. It is proposed to meet the challenge of 5G large-scale connectivity and high-frequency spectral efficiency. Compared with traditional orthogonal multiple access (OMA), PDMA can support more users through the allocation of non-orthogonal resources. Due to perfect aperiodic correlation, complete complementary sequences (CCS) still has research value in 5G. In order to apply the advantages of CCS to NOMA communication system, this paper proposes a system model of CCS spread spectrum coding based on PDMA. CCS is used as spread spectrum code to improve the performance of PDMA communication system. At the same time, on the basis of spread spectrum technology, this paper analyzes the average power allocation algorithm and water-filling power allocation algorithm, and a dynamic power allocation algorithm based on the transmission rate and practical application is proposed. The simulation results show that the system model can effectively improve the performance of the system.

scholarly journals Research on PDMA communication system based on complete complementary sequence

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Shufeng Li ◽  
Baoxin Su ◽  
Libiao Jin
Keyword(s):  
Power Allocation ◽  
Spread Spectrum ◽  
Communication System ◽  
Multiple Access ◽  
Large Scale ◽  
Average Power ◽  
Spectrum Efficiency ◽  
System Model ◽  
Complementary Sequence ◽  
Allocation Algorithm

Abstract Pattern division multiple access (PDMA) is a new non-orthogonal multiple access (NOMA) technology. It is proposed to meet the challenge of 5G large-scale connectivity and high-frequency spectral efficiency. Compared with traditional orthogonal multiple access (OMA), PDMA can support more users through the allocation of non-orthogonal resources. Due to perfect aperiodic correlation, complete complementary sequence (CCS) greatly improves the spectrum efficiency of the system. It has been widely used in wireless communication and radar sensing, and it still has research value in 5G. In order to apply the advantages of CCS to NOMA communication system, this paper proposes a system model of CCS spread spectrum coding based on PDMA. CCS is used as spread spectrum code to improve the performance of PDMA communication system. At the same time, on the basis of spread spectrum technology, this paper analyzes the average power allocation algorithm and water-filling power allocation algorithm, and a dynamic power allocation algorithm based on the transmission rate and practical application is proposed. The simulation results show that the system model can effectively improve the performance of the system.

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