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.

In the Internet-of-Things (IoT) and artificial intelligence (AI), complete implementations are dependent largely on the speed of the fifth generation (5G) networks. However, successive interference cancellation (SIC) in non-orthogonal multiple access (NOMA) of the 5G mobile networks can be still decoding latency and receiver complexity in the conventional SIC NOMA scheme. Thus, in order to reduce latency and complexity of inherent SIC in conventional SIC NOMA schemes, we propose a rate-lossless non-SIC NOMA scheme. First, we derive the closed-form expression for the achievable data rate of the asymmetric 2PAM non-SIC NOMA, i.e., without SIC. Second, the exact achievable power allocation interval of this rate-lossless non-SIC NOMA scheme is also derived. Then it is shown that over the derived achievable power allocation interval of user-fairness, rate-lossless non-SIC NOMA can be implemented. As a result, the asymmetric 2PAM could be a promising modulation scheme for rate-lossless non-SIC NOMA of 5G networks, under user-fairness.

Recently, the sixth generation (6G) networks have become tremendous research topics. Intelligent reflecting surface (IRS) technologies have been envisioned, to increase spectrum and energy efficiency for the fifth generation (5G) mobile networks, towards the sixth generation (6G) communications. In this paper, especially for the strongest channel gain user, we investigate the bit-error rate (BER) of non-orthogonal multiple access (NOMA) systems with intelligent reflecting surface (IRS). First, we derive a BER expression in a closed-form of Q functions. Second, we investigate the BER performance improvement of IRS NOMA systems over NOMA systems versus the power allocation. Moreover, we analyze the BER performance improvement of IRS NOMA systems over NOMA systems versus the number of IRS devices. In results, NOMA equipped with IRS technologies could play an important role in the paradigm shift from 5G mobile networks to 6G mobile networks.

As the sixth generation (6G) promising technique, intelligent reflecting surface (IRS) has recently attracted much attention. The IRS based wireless communication is expected to deploy the upcoming 6G mobile networks, to increase energy and spectrum efficiency in the fifth generation (5G) wireless networks. In this paper, we compare the bit-error rate (BER) performances for phase-shift schemes of IRS non-orthogonal multiple access (NOMA). First, we derive a BER expression for the equalizing phase-shift scheme in IRS-NOMA networks. Then we compare the BER of the equalizing phase-shift scheme to that of the identical phase-shift scheme in IRS-NOMA networks, and show the BER improvement of the equalizing phase-shift scheme IRS NOMA over the identical phase-shift scheme IRS NOMA. Furthermore, we also validate the proposed analytical BER for the equalizing phase-shift scheme in IRS-NOMA by Monte Carlo simulations, and demonstrate that they well match each other.

Towards the sixth generation (6G) mobile networks, spectrum and energy efficiency of non-orthogonal multiple access (NOMA) transmissions in the fifth generation (5G) wireless system have been improved by intelligent reflecting surface (IRS) technologies. However, the reflecting devices of an IRS tend to be correlated because they are placed close on the surface each other. In this paper, we present an analysis on the correlated IRS in NOMA cellular networks. Specifically, we consider the bit-error rate (BER) performances for correlated-IRS in NOMA networks. First, based on the central limit theorem, we derive an approximate analytical expression of the BER for correlated-IRS NOMA systems, by using the second moment of the channel gain. Then we validate the proposed analytical BER by Monte Carlo simulations, and show that they are in good agreement. In addition, we also show numerically the BER improvement of the correlated-IRS NOMA over the conventional independent-IRS NOMA.

To improve spectrum and energy efficiency in the fifth generation (5G) wireless channels, intelligent reflecting surface (IRS) transmissions have been envisioned, possibly towards the sixth generation (6G) networks. In this paper, we analyze the bit-error rate (BER) performance of intelligent reflecting surface (IRS) assisted non-orthogonal multiple access (NOMA) systems. First, we derive a closed-form expression of the BER in terms of Q functions. Then we analyze the BER improvement of the IRS NOMA system over the conventional NOMA system with respect to the power allocation. Furthermore, we also demonstrate numerically the BER improvement of the IRS NOMA network over the conventional NOMA network in respect of the number of reflecting devices.

Recently, to improve the performance of the strongest channel gain user in non-orthogonal multiple access (NOMA) with a non-uniform source and symmetric superposition coding (SSC), a novel bit-to-symbol (BTS) mapping have been proposed. However, only the performance of the user with the stronger channel gain was analyzed. Thus, we compare the bit-error rate (BER) of the new BTS scheme with that of uniform sources, especially for the user with weakest channel gain. First, we show that the performance of the novel BTS scheme for the user with weakest channel gain also improves, compared to that of the uniform sources. Furthermore, the signal-to-noise (SNR) gain of the new BTS scheme over the uniform sources is calculated. As a consequence, the novel BTS scheme would improve the performance of the user with weakest channel gain as well as that with the stronger channel gain for SSC NOMA with a non-uniform source.

In this paper, to improve further the bit-error rate (BER) performance of the stronger channel user in non-uniform source non-orthogonal multiple access (NOMA) with symmetric superposition coding (SSC), we propose a smart bit-to-symbol (BTS) mapping of SSC. First, the analytical expression for the total allocated power of the proposed BTS mapping scheme is derived, and then we show that the BER of the proposed BTS mapping scheme improves further, compared to that of the existing BTS mapping scheme. Moreover, based on the simulations, the signal-to-noise (SNR) gain of the proposed BTS mapping scheme over the existing BTS mapping scheme is calculated. In result, the proposed BTS mapping could be a candidate scheme for non-uniform source SSC NOMA with the SNR gain.

Lempel-Ziv coding is one of the most famous source coding schemes. The output of this source coding is usually a non-uniform code, which requires additional source coding, such as arithmetic coding, to reduce a redundancy. However, this additional source code increases complexity and decoding latency. Thus, this paper proposes the optimal maximum a-posteriori (MAP) receiver for non-uniform source non-orthogonal multiple access (NOMA) with symmetric superposition coding (SSC). First, we derive an analytical expression of the bit-error rate (BER) for non-uniform source NOMA with SSC. Then, Monte Carlo simulations demonstrate that the BER of the optimal MAP receiver for the non-uniform source improves slightly, compared to that of the conventional receiver for the uniform source. Moreover, we also show that the BER of an approximate analytical expression is in a good agreement with the BER of Monte Carlo simulation. As a result, the proposed optimal MAP receiver for non-uniform source could be a promising scheme for NOMA with SSC, to reduce complexity and decoding latency due to additional source coding.

In the Internet of Thing (IoT) framework, massive machine-type communications (MMTC) have required large spectral efficiency. For this, non-orthogonal multiple access (NOMA) has emerged as an efficient solution. Recently, a non-successive interference cancellation (SIC) NOMA scheme has been implemented without loss. This lossless NOMA without SIC is achieved via correlated superposition coding (SC), in contrast to conventional independent SC. However, conventional minimum high-correlated SC for only 2-user NOMA schemes was investigated in the lossless 2-user non-SIC NOMA implementation. Thus, this paper investigates a 3-user low-correlated SC scheme, especially for an inflated achievable sum rate, with a design of 3-user low-correlated SC. First, we design the 3-user low-correlated SC scheme by taking the minimum sum rate between 3–user SIC NOMA and 3-user non-SIC NOMA, both with correlated SC. Then, simulations demonstrate that the low correlation in the direction of the first user’s power allocation inflates the sum rate in the same direction, compared to that of conventional minimum high-correlated SC NOMA, and such inflation due to low correlation is also observed similarly, in the direction of the second user’s power allocation. Moreover, we also show that the two low correlations of the first and second users inflates doubly in the both directions of the first and second users’ power allocations. As a result, the proposed 3-user low-correlated SC could be considered as a promising scheme, with the inflated sum rate in the future fifth-generation (5G) NOMA networks.