Earticle

The lower transmission power capability of internet of things (IoT) devices in millimeter-wave (mmWave) networks necessitates the activation of beamforming techniques by employing multiple antennas. To ensure robust directional transmissions at mmWave frequencies, it is essential to accurately align narrow beams with the dominant channel direction. However, directional communications necessitate aligning the beam with the channel direction, resulting in significant transmit power consumption. We aim to reduce the transmit power required to achieve a desired beam-alignment performance, taking into account the power limitations of IoT devices.We propose an analytical framework for optimizing resource utilization in the hierarchical beam alignment algorithm, designed to progressively reduce the size of the beamformer candidate set. Based on the analytical studies, we develop a resource allocation algorithm that determines the optimal transmit power and constructs the optimal set of beamformer candidates at each beam alignment stage. Numerical simulations demonstrate that the proposed power allocation algorithm effectively reduces the transmit power to meet the performance targets for IoT networks.

In this letter, we propose two power allocation strategies, named Optimal Power Allocation (OPA) and Equal Intercept Probability (EIP), for dual-hop cooperative decode-and-forward (DF) relaying networks under constraint of intercept probability (IP). Particularly, with knowledge of channel statistics of the eavesdropping links, the transmitters such as source and relays adjust their transmit power to control the value of IP by a desired level. A max?min relay selection method is applied to enhance the e2e outage probability (OP). We derive expressions of transmit power for the source and the selected relay. Then, Monte Carlo simulations were performed to verify analytical results.

In this paper, we study secure energy efficiency (SEE) for simultaneous wireless information and power transfer (SWIPT) in a distributed antenna system (DAS) based IoT network. We consider a system in which both the legitimate user (Bob) and the eavesdropper (Eve) rely on the energy harvested from the received radio signal. Firstly, with perfect channel state information (CSI), we formulate the maximization of SEE as a constrained optimization problem. In this problem, we aim to highlight the advantage of SWIPT in exploiting the Eve’s energy harvesting requirement. This is achieved by defining a charge constraint in the SEE optimization problem which ensures that the Eve is deprived of its only energy source. Next, considering the fact that perfect CSI is hard to achieve in practice, we characterize the system performance in terms of the outage probability (OP) of SEE. For the given SWIPT-in-DAS setup, we derive the closed form expression for the OP of SEE and with the help of numerical results, we study the effect of transmit power levels, number of distributed antenna (DA) ports and the PS ratio of devices. To the best of our knowledge, this is the first attempt to define the OP of SEE for SWIPT-in-DAS.

In this letter, we propose an optimal joint bandwidth and power allocation method for heterogeneous wireless networks (HetNets) by maximizing the users’ quality of experience (QoE), which is a widely used subjective metric reflecting the users’ satisfaction of the multimedia service. By taking two factors into consideration simultaneously: Rate Factor (RF) and Energy Factor (EF), we can measure the QoE of end-users effectively. An optimal QoE-driven allocation algorithm is further developed for maximizing the end-users’ QoE utility subject to the constraints of transmit bandwidth, power and data rate. The simulation results demonstrate the theoretical analysis.

In the heterogeneous network (HetNet) employing downlink non-orthogonal multiple access (NOMA), we focus on the non-convex optimization problem to optimize the spectral efficiency (SE) while the users satisfy the quality-of-service (QoS) requirement. In the previous work, the optimal joint successive interference cancellation and power allocation (JSPA) algorithm for maximizing SE is proposed to solve the mixed-integer non-linear programming (MINLP) problem in NOMA-enabled HetNet. However, the optimal solution requires exponential complexity by the number of base stations (BSs). Therefore, we present a deep neural network (DNN)-based algorithm for JSPA to reduce the complexity. In particular, to deal with the MINLP-based JSPA problem, we reformulate it into an equivalently simple problem that optimizes only the power consumption of BSs. Then, we introduce the unsupervised DNN-based method for JSPA to handle the simplified problem. The presented scheme yields improved SE and outage performance compared with traditional DNN-based methods. Additionally, we propose a user selection scheme with low complexity to enhance the SE of the proposed DNN-based power allocation. Through simulations, we illustrate that the suggested DNN-based scheme can attain SE performance similar to that of the optimal scheme.

In this paper, we propose a deep learning approach for solving power allocation problems in massive MIMO networks. We use signal-to-interference-plus-noise-ratio (SINR) and signal-to-leak-plus-noise ratio (SLNR) criteria for linear precoder design to define the max?min and max-prod power allocation challenges. The power allocation process to each user equipment in the base station coverage takes a long time and is inefficient, hence numerous base stations are deployed to serve multiple user equipments. As a result, we develop a deep neural network (DNN) framework in which the user’s equipment position is utilized to train the deep model, which is then used to forecast the ideal power distribution depending on the user’s location. Compared to the traditional optimization approach, the DNN design helps to obtain the optimal solution of the power allocation problem within a short time via a quick-inference process. Simulation results show that the SINR criterion outperforms the SLNR one. Meanwhile, deep learning achieves excellent results in forecasting power allocation with an accuracy of 85% for the max?min strategy and 99% for the max-product approach.

We propose resource allocation schemes for a multi-carrier non-orthogonal multiple access (MC-NOMA) system with the consideration of energy harvesting. The resource allocation schemes includes an optimization scheme for maximizing the total system capacity, and an optimization scheme for maximizing the fairness among users, such that both schemes guarantee minimum data rate and harvested energy to each user. Because NOMA is one of the key technologies for enhancing system capacity, we apply a NOMA scheme to an Energy Harvesting Wireless Network (EHWN) to increase the capacity whilst at the same time supplying sufficient harvested energy and data rate for each user. The proposed resource allocation schemes are based on a block coordinate descent (BCD) algorithm that can effectively solve problems in which the objective function is not jointly concave. The performance of each proposed scheme is evaluated using simulations, and we show that the capacity maximization and fairness schemes outperform orthogonal multiple access (OMA) schemes in terms of capacity and fairness, respectively.

이진 변조 비직교 다중접속에서 1+1 용량 영역을 달성하는 주제에 대한, 다시 말해, 총 전력은 얼마나 필요한가와 이때 전력은 어떻게 할당해야 되는가에 대한 연구가 다소 미흡하다. 본 논문에서는, 허용 가능한 손실 범위 안에서, 2PAM 비직교 다중 접속의 1+1 용량 영역을 달성할 수 있는 평균 총 전송 전력을 고찰한다. 다음으 로, 충분한 평균 총 전력을 기반으로 1+1 용량 영역을 달성할 수 있는 전력 할당 계수를 계산한다. 그리고, 수치적 결과를 통해서 0.008 미만의 허용 가능한 손실 범위 안에서, 근접 1+1 용량 영역이 달성됨을 보여준다. 또한, 수치상으로 근접 1+1 용량 영역을 달성하는 양 사용자의 전력 할당 계수를 계산한다. 결론적으로 2PAM 비직교 다중 접속이 근접 1+1 용량 영역에서 동작하기 위해, 적절한 전력 할당과 함께, 적당한 총 전력이 비직교 다중접 속 설계에서 계산될 수 있다.

In binary-modulation non-orthogonal multiple access (NOMA), there has been rare researches for the 1+1 capacity region to be achieved; how much total power is required and what power allocation is assigned for this total power. In this paper, the average total transmitted power to achieve 1+1 capacity region of binary pulse amplitude modulation (2PAM) NOMA is investigated, with a tolerable loss. Then, based on the sufficient average total transmitted power, we calculate the power allocation coefficient to achieve 1+1 capacity region. Furthermore, it is shown by numerical results that with the tolerable loss less than 0.008, near 1+1 capacity region is achieved. We also calculate numerically the power allocation coefficient for both users to achieve near 1+1 capacity region. As a result, for 2PAM NOMA to operate near 1+1 capacity region, proper total power with appropriate power allocation could be calculated in design of NOMA systems.

드론을 활용한 분야는 점점 많아지고 있지만, 실제 드론의 임무 수행은 내장된 배터리를 통하여 활동하므로 드론의 내장된 배터리 용량에 따라 드론의 행동과 시간의 제약이 따른다. 이러한 드론의 자율 비행이나 임무 수행을 하는 드론의 운행 시간에 대한 단점을 보완하기 위해서 드론 배터리 용량을 늘리거나 배터리 충전을 위한 연구가 진행되 었다. 최근 드론 배터리 충전을 위한 방식으로 배터리 교환 방식과 무선 전력 충전방식에 관한 연구가 일부 진행이 되고 있다. 이중 무선 전력 충전방식은 하나의 드론을 충전하는 시스템은 개발이 되어 있지만, 다수의 드론을 충전 하는 방법에 대한 표준과 충전 방법에 관한 연구는 아직 활발하게 진행되고 있지는 않다. 무선 충전의 효율을 높이 기 위해서는 해당 무선 채널을 원하는 사용자에게 효율적으로 배분할 수 있는 최적화 문제를 해결하는 것이 우선이 다. 유선 충전 시 전력 할당 최적화 문제를 풀기 위해서 유전자 알고리즘을 이용하여 자동차 배터리 충전에 적용한 일부 연구는 있지만, 드론에 해당 연구를 적용하기에는 여러 차이점이 존재한다. 본 논문에서는 마이크로 유전 알고 리즘을 적용하여 무선 전력 충전의 전송 효율에 관여하는 무선 채널 설정 방법을 통해 다수의 드론을 충전할 방법을 제시한다. 시뮬레이션을 통해서 본 연구에서 제안하는 방법이 다수의 드론을 충전할 수 있는 무선 채널 배분의 효율 을 높일 수 있음을 보였다.

Although the increased use of drones or Unmanned Aerial Vehicles(UAVs) for the commercial and military field, the drone's built-in battery capacity will limit the drone's behavior and time. To achieve a fully autonomous flight or mission complete, recently, some researches have been conducted on increasing drone battery capacity or charging the battery method. Some of these studies have recently been conducted on battery switching and wireless charging methods to charge drone batteries. A system for charging a single drone is developed, but the standard for charging multiple drones and the method for charging them are not yet actively studied; however, research on the wireless charging method is not actively conducted. To improve the efficiency of wireless charging, it is first necessary to solve the optimization problem of efficiently allocating the corresponding wireless channel to the user who wants them. Although some studies have been applied to car battery charging using genetic algorithms to solve the problem of power allocation optimization in wired charging, there are many differences in applying the research to drones. In this study, applying the Micro Genetic Algorithm(μ-GA), we present a method for charging multiple drones through the wireless channel setup method involved in the transmission efficiency of wireless power charging. The feasibility of the proposed algorithm was checked using simulation. Numerical results are presented to demonstrate the performance of the proposed method.

온실가스 배출권의 할당에 관한 기존의 연구들을 보면 경매나 무상할당방식의 장단점 또는 형평성 및 경제성 측면에서 국내 가능성 여부 등을 조사한 연구들은 많았으나, 온실가스 배출특성을 고려한 할당방식에 대한 연구사례는 많지 않았다. 이에 따 라 본 연구에서는 특정 배출시설에 대하여 배출권 할당방법을 마련하고 실제 배출권 할당을 설계하는 과정에서 적용 가능성이 있는지 살펴보고자 하였다. 본 연구에서는 온실가스 배출량이 많은 발전시설에 대하여 연료별로 구분하여 할당계수와 할당량 을 산정하고, 이를 통해 배출시설의 특성을 고려한 2가지 방법의 할당기준을 적용하여 할당량을 산정하였으며 주요 결과는 다 음과 같다. 첫째, 발전시설을 연료별로 구분하여 배출시설의 특성을 고려하여 할당계수를 마련하고 이를 이용해 배출권을 할 당한 결과 배출권의 과다 할당문제가 발생하지 않았다. 특히, 연구에서 사용된 2011년도의 배출량과 비교할 경우에는 배출권 할당량이 2011년도 배출량에 비해 22% 정도 적음에 따라 배출시설들은 배출권 거래와 시설 개선 등을 통해 온실가스를 감축 하여야 한다. 둘째, 동일 종류의 배출시설이며 동일 연료를 사용함에도 시설의 효율과 관리 노하우 정도에 따라 온실가스 배 출량이 서로 상이한 특성을 고려하여 할당계수를 산정하여 배출권을 할당한 결과 배출권의 구매 또는 판매가 가능한 배출권이 생산됨을 알 수 있다. 이는 배출권 거래가 가능해지기 위해 기본적으로 필요한 배출권 거래 가능량이 발생하는 것으로서 배출 권에 대한 수요와 공급의 토대를 제공한다. 또한, 해당 사업장으로 하여금 배출권 매매를 통해서만 할당량을 준수하게 하지 않 고 연료 전환이나 자체 시설에 대한 효율개선 노력을 통해 온실가스 배출량을 감축하도록 유도할 수 있다. 셋째, 비용을 투자 하여 다른 시설에 비해 단위 연료 사용량 당 온실가스 배출량이 적은 시설의 경우 조기 행동(early action)에 대한 보상을 요구 할 수 있는데, 본 연구에서 제시한 할당계수 산정방법을 이용하는 경우에는 조기행동에 대한 보상도 가능한 것을 알 수 있다. 이는 배출권을 할당함에 있어 논란이 될 수 있는 형평성 해결 과정에 논리적 근거를 제공할 수 있을 것으로 보인다.

This study analyzes the allocation method of the Emission Trading Scheme around the world and suggests an allocation method which may be applied to our country especially for power plants. In this study, the allocation of tradable permits was calculated by multiplying the allocation factor and activities of the emission facilities. The allocation factor is the sum of the carbon dioxide emissions per maximum activities during the baseline year (the recent 3 years). As a result, firstly, the over-allocation problem which other countries encountered at the primary stage when they started the Emission Trading Scheme may not happen. Secondly, this method may pave the way for the emission trading because it provides supply and demand of the tradable permits. Thirdly, this method can induce the stakeholders into making other efforts such as fuel change, efficiency improvement etc. excluding emission trading because the amount of allocation is about 22% less than the most recent emission. Lastly, this method may give compensation to the facilities which made actions to cut back on carbon dioxide earlier than other facilities. Their emission factor is less than the allocation factor, so their allocation amount is more than their recent emissions.

Recently, the issue of excessive energy consumption in wireless communications has become increasing critical, and the energy harvesting as a renewable energy resource, has received extensive attractions. In this paper, a wireless sensor network (WSN) is considered, where the source-destination pair communicates via an energy harvesting relay links. We study the problem of the harvested energy distribution among the source, relay and destination nodes. An effective power allocation scheme is developed which exploits the decode-and-forward (DF) relaying strategy and channel state information. The outage probability is analyzed and simulation results show that the outage performance for two sub-channels is always performs well in the cases of different threshold target data rate. Moreover, the effect of the different radio of the optimal sub-channel gain and Rayleigh channel gain on the outage performance is evaluated.

Traditional beamforming and power control algo-rithms in cognitive radio (CR) are based on the assumption of perfect channel state information (CSI) however; this may lead to performance degradation in realistic systems. In this paper, the problem of joint beamforming and power control is investigated in underlay CR networks with imperfect CSI. Our objective is to maximize the sum utility of secondary users (SUs) under the primary users (PUs) interference power constraints and the transmission power constraint of SUs. First, the joint beamforming and power control problem is formulated under game theory framework, where the SUs compete with each other over the beamforming vectors and transmission power level made available by the PUs. Moreover, the channel uncertainty is described using ellipsoid sets and the interference power constraints can be converted into robust interference power constraints. Besides, Nash equilibrium (NE) is considered as the solution of this game. Finally, simulation results show that the proposed scheme can converge to a locally optimal pair of beamforming vector and transmission power level in the presence of channel uncertainty.

MIMO system performance mainly by correlation of spatial interference presence and space, In order to overcome these effects, it is necessary by pre-coding technology to achieve, Here we mainly use block diagonalization algorithm both linear pre-coding for analysis and further integration of the average power allocation algorithm and power allocation algorithm MATLAB simulation. Through research and data analysis, system performance block diagonalization algorithm is better than under zero forcing algorithms. When the same number of transmit antennas, the number of receive antennas are the same, the average power and power calculation algorithm with SNR increases, can effectively channel capacity to achieve the desired optimum value.

This paper focuses on the optimal relay selection and power allocation issue, which studies the communication between SS (Secondary Source) and two SDs (Secondary Destinations) through single relay in CR （cooperative cognitive communication） system. Sufficiently takes the multi-antenna relay, primary-secondary users coexisting, and AF (Amplify and Forward) relay mode as the researching background, and takes the constraints caused by the primary user’s interference and the max transmission power into account, the author proposes to maximize the system throughput by the selection of cooperative relay and the allocation of optimal power. Furthermore, the author also puts forward the approximate expressions of the optimal power allocation, and figures out the optimal solution by the Lagrange multiplier, whose property can be manifested by the simulation and comparison results.

In this paper, a multicast network in the presence of an untrusted relay is investigated. A three-phase AF-based cooperative jamming with power allocation is proposed to enhance the system secrecy rate. Based on time division based cooperative multicast (TDCM) protocol, the source uses part of its available power to broadcast pre-defined jamming signals in order to create interference at the untrusted relay, while the relay amplifies the linearly combined two received signals and then re-transmits it to the destination. Optimum power allocation policy involving the allocation between the information and jamming signals at the source and between two combined signal factors at the relay to maximize the achievable worse secrecy rate or sum-rate are derived and analyzed. Numerical results are provided to demonstrate our analytical results and reveal that compared with the two benchmarks schemes, the proposed scheme can obtain significantly higher positive secrecy rate for the same transmit power budget, especially in the case of maximizing secrecy sum-rate.

The outage probability (OP) and average symbol error probability (ASEP) performance of multiple-mobile-relay-based mobile-to-mobile (M2M) networks with decode-and-forward (DF) relaying over N-Nakagami fading channels is investigated in this paper. The power allocation problem is formulated to determine how the overall transmit power should be shared between broadcasting and relaying phases for performance optimization. Then the OP and ASEP performance under different conditions is evaluated through numerical simulations to verify the analysis. The simulation results show that the fading coefficient, the number of cascaded components, the relative geometrical gain, the power-allocation parameter, and the number of mobile relays have an important influence on the OP and ASEP performance.

This paper proposes the joint optimization of power allocation, subcarrier assignment and relay selection strategy for cooperative OFDM system to maximize the total capacity. We use Lagrangeandual approach for relaxing some available constraints and also make use of the simple greedy algorithm to perform lesser iterations to converge. Extensive experiments with realistic data prove that the subgradient dual method is approriate to dynamic resource allocation.

A power allocation scheme for the secondary user when the primary user employs the full duplex relay (FDR) is proposed in cognitive radio system. When the FDR is deployed in primary system, the primary relay and receiver suffers the interference from the secondary transmitter in order to share the spectrum of the primary user. Therefore, the power allocation scheme to satisfy the interference constraint is needed for the spectrum sharing. Thus, we propose a new power allocation scheme to satisfy the quality of service (QoS) of he primary user. Since the proposed power allocation scheme does not need the nstantaneous channel state information (CSI), the proposed scheme not only has feedback burden, but also can be robust the outdated CSI environment.