Transmit Power Optimization and Feasibility Analysis of Self-Backhauling Full-Duplex Radio Access Systems

In this article, we address the problem of optimal joint power and resource allocation when Non-Orthogonal Multiple Access (NOMA) and Orthogonal Frequency Multiple Access (OFDMA) are combined for a hybrid downlink multiple access. First, an optimal solution of joint power and resource allocation minimizing the transmit power consumption, is obtained by rewriting the original optimization problem into an equivalent convex one and then by solving it by means of the well-known interior-point method. Second, we analyze the properties of the optimum, which are twofold; we show that at the optimum, the order of the users is preserved from one channel to another. Then, we derive a closed-form expression for the optimal power allocation in the particular case where all users have positive non-zero transmit powers in all channels.

More data and information is being captured from systems, machines, and devices and made available to industrial information technology (IT) systems. The information is processed on-the-fly, enabling IT-based management systems to generate updated information for real-time control of the manufacturing processes. This data capturing and collection for IT systems is often referred to as the Internet of Things (IoT). When adopted to the industrial requirements, such as robustness, reliability, timeliness, and security, it is often termed as the industrial IoT (IIoT) [A1]. IIoT has attracted the attention of both the industry and academia since it is expected to enhance day-to-day activities, create new business models, products, and services, and as a broad source of research topics and ideas. Meanwhile, it is envisioned that the fifth-generation (5G) networks will be a cornerstone in future wireless industrial connectivity, and currently, there are multitude of ongoing research efforts in their design and optimization. Future industries willembrace use cases with numerous wireless-connected sensors and devices, and judging by the demand, massive machine-type communication and ultra-reliable low-latency communication (URLLC) in the literature and standardization activities, have been identified as two of the three main communication scenarios for 5G. These scenarios demand intelligent, scalable, and robust radio access techniques, network architectures, and deployment options to meet industrial demands [A2]. Therefore, more in-depth research is needed for IIoT and sensor networks in 5G-and-beyond wireless communication systems to address various challenges, including the following. 1)Transmit power control policy should be judiciously designed to improve both the spectrum efficiency and energy efficiency effectively; higher transmit powers can improve reliability but increase the interference and battery consumption. 2)Low-latency communication and computing is one of the significant challenges in 5G-and-beyond IIoT; uploading the device data to the cloud computing centers has high latency and resources waste issues in sensor networks. 3)Addressing privacy and security problems [A3] in the 5G-IIoT is fundamental to the further development and spread of 5G-IIoT. 4)Reliability and latency requirements of URLLC services, requiring less than 1-ms user plane latency and higher than 99.999% reliability, are demanding to meet, especially in time-varying industrial wireless channels. 5)Radio resource allocation, sharing, and isolation with performance guarantees under dynamic traffic conditions are critical issues for emerging IIoT applications requiring real-time support of massive connected devices. 6)5G-and-beyond IIoT networks must satisfy industrial-grade coverage, capacity, time-sensitive networking, and over-the-air time synchronization requirements [A4].

In this paper, the problem of adversarial learning is studied for a wireless powered communication network (WPCN) in which a hybrid access point (HAP) seeks to learn the transmission power consumption profile of an associated wireless transmitter. The objective of the HAP is to use the learned estimate in order to determine the transmission power of the energy signal to be supplied to its associated device. However, such a learning scheme is subject to attacks by an adversary who tries to alter the HAP's learned estimate of the transmission power distribution in order to minimize the HAP's supplied energy. To build a robust estimate against such attacks, an unsupervised Bayesian learning method is proposed allowing the HAP to perform its estimation based only on the advertised transmisson power computed in each time slot. The proposed robust learning method relies on the assumption that the device's true transmission power is greater than or equal to advertised value. Then, based on the robust estimate, the problem of power selection of the energy signal by the HAP is formulated. The HAP optimal power selection problem is shown to be a discrete convex optimization problem, and a closed-form solution of the HAP's optimal transmission power is obtained. The results show that the proposed robust Bayesian learning scheme yields significant performance gains, by reducing the percentage of dropped transmitter's packets of about 85% compared to a conventional Bayesian learning approach. The results also show that these performance gains are achieved without jeopardizing the energy consumption of the HAP.

In this paper, a multi-user (MU) satellite Multiple-Input Multiple-Output (MIMO) downlink including a multi-beam GEO satellite and multiple terrestrial users is considered. In order to meet the signal to interference plus noise ratio (SINR) requirements of different users while minimizing the satellite transmit power, the onboard precoding problem is investigated. Zero forcing (ZF) and regularized zero-forcing (RZF) precoding are first adopted and it is found that they can satisfy the SINR constraints by multiplying by a power allocation matrix. The required power allocation matrix is divided into two types: equal power factor schemes and unequal power factor schemes, and they are derived separately, where Karush-Kuhn-Tucker (KKT) conditions are used in RZF unequal power factor scheme and a heuristic algorithm is proposed to optimize the regularization factor in RZF schemes to further reduce the transmit power. Then, a semi-definite programming beamforming (SDPBF) is proposed to jointly optimize precoding and power allocation matrices. The closed-form expressions of the transmit power of the above five precoding schemes are derived and their performance is compared in the simulation part. The research results show that under the same conditions, the performance of RZF is better than that of ZF, the unequal power factor scheme is better than the equal power factor scheme, and SDPBF has the best performance. Besides, it is also found that in addition to changing the precoding method, activating more beams will further reduce the transmit power.

A power control algorithm with two prediction models based on an achievable signal-to-interference power ratio (SIR) has been proposed under a multipath Rayleigh fading environment in a code-division multiple-access (CDMA) mobile system. An achievable SIR is defined as the maximum-minimum SIR among all users at a particular time step. The corresponding mobile transmission powers are denoted as the "optimum transmission powers." In the individual power predictor (IPP) model, a linear transversal filter is assigned to each user. The output of the IPP, which is a linear combination of the optimum transmission powers of the mobile during the current and previous power measurement periods, predicts the optimum transmission power of the mobile in the next time step. In the global power predictor (GPP) model, a single predictor, constructed by a linear neural network, is used to predict the optimum transmission powers of all mobiles in the next time step. In both predictor models, the weights of the predictors are updated by using the recursive least squares algorithm. To further improve the performance, a reduced power-measurement period has been studied. Simulation results show that the proposed power control algorithm can achieve a lower outage probability and a smaller dynamic range of transmission power compared with a conventional power control scheme.

The scope of this research is to propose an adaptive machine learning approach which can help the WSN’s nodes to manage their transmission power and to improve the internode wireless communications. The optimized transmission power has benefits in terms of WSN energy consumption and RF interlink interference. The paper proposes an adaptive method of a wireless sensor node based on Multi-Layer Perceptron (MLP) network representation and machine learning. The presented in the paper approach uses the SARSA (State-Action-Reward-State-Action) algorithm which is a form of reinforcement machine learning. The aim of the new method is to improve the sensor nodes Transmission Power Management (TPM) process. This inspires many practical solutions that max-imize resource utilization and prolong the shelf life of the battery-powered wireless sensor networks.

본 논문은 IEEE 802.15.4의 MAC 계층에서 노드간 거리와 환경에 따라 적절한 전송전력 값을 설정하여 무선 모뎀의 에너지 소비를 최적화하는 방법에 대하여 제안한다. 제안된 방법은 적절한 전송전력을 결정할 때 이진검색 기법으로 빠르게 최적의 전송전력을 찾을 수 있고, 다수의 메시지 전송에 따르는 오버헤드를 최소화할 수 있다. 결정된 전송전력은 데이터 패킷을 전송할 때 사용되고, 데이터-수신확인 패킷의 교환과정에서 전파환경에 따라 변경된 네트워크 품질에 따라 동적으로 수정된다. 시뮬레이션 실험결과 본 논문에서 제안하는 방법은 IEEE 802.15.4 표준에 비해 에너지 소비량은 30% 감소하고, 단위 에너지양에 의한 데이터 전송량은 2.5배 증가하는 결과를 보였다. This paper proposes a new method to optimize energy consumption in a wireless modem by setting up a transmission power value according to the distance between nodes and circumstance in the MAC layer of IEEE 802.15.4. The proposed method can dynamically find an optimal transmission power range using the binary search scheme and minimize overhead caused by multiple message transmissions when determining the optimal transmission power. The determined transmission power is used for transmitting data packets and can be modified dynamically depending on the changes in a network environment when exchanging data packets and acknowledgement signals. The results of the simulations show 30% reduction in energy consumption while 2.5 times increase in data transmission rate per unit of energy comparing with IEEE 802.15.4 standard.

Cognitive Radio Networks appear as a probable solution for spectrum scarcity. However, establishing a balance between transmit power and interference in cognitive radio network is a challenging issue. In this paper, power allocation problem in cognitive radio is considered. Based on game theory, we modeled the problem into a repeated game with the purpose to minimize the total interference to the licensed user. Each secondary user choose their transmit power which consider both the interference to the primary user as well as to other secondary users. Simulation results for the repeated game are presented.

In this paper, we present a power allocation technique that improves the sum rate and energy efficiency without reducing the minimum data rate of an established resource block allocation technique. To achieve this, the technique is implemented in two stages where the first is for ensuring that the minimum data rate remains unchanged. The second stage is for increasing the sum rate by ensuring that more resource blocks are reused by the femtocells that reduce their power. The algorithm is iterative with changes in transmit power resulting in changes in resource block allocation. It will be shown that at the expense of some delay, the proposed technique can result in significant energy savings while maintaining the minimum data rate when compared to equal power allocation. Further, relative to optimal allocation techniques, the proposed technique results in better fairness.

Relay assignment means assigning the proper relay nodes (RN) to cell-edge users (UE) in order to exploit the spatial diversity through relay nodes and improve cell-edge performance. An optimal algorithm, called ORA, is to maximize the minimum data rate among all users. ORA is shown to find the optimal objective regardless the initial relay node. Although the algorithm is shown to converge (the minimum data rate is maximized), there exist several different RN-to-UE assignment pairs for the remaining UEs assigned to RNs. This paper is motivated to enhance the performance of the relay assignment for the remaining UEs, provided that the minimum data rate is maximized. In this paper, we propose a new strategy whose main objective is to maximize the relative performance improvement, which is a ratio of the performance improvement to original direct transmission, under the max-min constraint. Under the new assignment algorithm, not only the original optimal property of ORA is preserved, but also the performance of remaining UEs assigned to RNs is maximized. Simulation results demonstrate the effectiveness of the proposed approach.

Future wireless access will involve a variety of integrated and interworking technologies. Depending on the conditions (e.g., the specific application, the quality of service requirements, the user mobility) each session will be served through the most suited access network; following some event, there will be a variation of the access technology, which is called vertical handover. Although the use of one technology at a time is mostly envisioned, the use of multiple wireless links at the same time, hereafter called parallel transmission, is also envisioned for higher throughput. On the basis of parallel transmission, in this paper the original definition of softer vertical handover is given and an algorithm for softer vertical handover in heterogeneous wireless access networks is proposed; the achievable improvement is shown in two reference scenarios.

In third generation wireless cellular communication systems, cell sizes are expected to become smaller in order to accommodate high user density and multimedia traffic. Resource allocation schemes will have to be flexible to handle heterogeneous traffic and be dynamically adapted to the time-varying channel and user mobility. We propose such a resource management strategy. We introduce the notion of a connection group, where a mobile user simultaneously connects to multiple base stations using multiple links. The number of base stations and which base station to connect to are functions of the time-varying channel, mobility, current bandwidth need, and quality of service requirement. Once a call is admitted into the network, the resource management strategy will allow the mobile to dynamically configure its connections in such a way to minimize call dropping probability, maximize signal quality, and increase channel resource utilization efficiency. The performance of the protocol is analyzed in terms of call blocking and dropping probabilities and channel resource utilization. Results show that the proposed resource management scheme can support multimedia traffic with low call blocking and dropping probabilities and high channel utilization efficiency.

Unmanned Aerial Vehicles (UAVs) are increasingly used in the new generation of communication systems. They serve as access points, base stations, relays, and gateways to extend network coverage, enhance connectivity, or offer communications services in places lacking telecommunication infrastructure. However, optimizing UAV placement in three-dimensional (3D) environments with diverse user distributions and uneven terrain conditions is a crucial challenge. Therefore, this paper proposes a novel framework to minimize UAV transmission power while ensuring a guaranteed data rate in realistic and complex scenarios. To this end, using the particle swarm optimization evolution (PSO-E) algorithm, this paper analyzes the impact of user-truncated distribution models for suburban, urban and dense urban environments. Extensive simulations demonstrate that dense urban environments demand higher power than suburban and urban environments, with uniform user distributions requiring the most power in all scenarios. Conversely, Gaussian and exponential distributions exhibit lower power requirements, particularly in scenarios with concentrated user hotspots. The proposed model provides insight into achieving efficient network deployment and power optimization, offering practical solutions for future communication networks in complex 3D scenarios.

A resource allocation algorithm for orthogonal frequency division multiple access-based and infrastructure-based relay networks is introduced in this paper. The algorithm is applied in the downlink of a single cell. Channel state information is exploited at the transmitting base station (BS) and at the transmitting relay stations (RSs). The algorithm coordinates the allocation of subcarriers, bits, and power distributed over the BS and RSs. The goal of the resource allocation algorithm is that the sum rate in a cell is maximized. Simultaneously, each subscriber station (SS) achieves a requested data rate called minimum data rate, and a tolerated bit error probability is ensured on each link. Simulation results show that a sum rate in a cell is achieved which is near to an upper bound introduced in the paper. Even if the sum of the minimum data rates of all SSs is large, the proposed algorithm offers the minimum data rate nearly as reliable as the upper bound. Additionally, simulation results show that the proposed algorithm achieves a superior sum rate compared to the sum rate of other resource allocation algorithms.

Wireless LANs (WLANs) suffer degrading performance when operating within domestic areas due to multiple reasons such as: multi-path, fading, path loss and user mobility. To overcome this, transmission rate is usually adjusted to a more error-resistant rate. In this paper, we first present a novel rate adaptation algorithm for IEEE 802.11 that can efficiently identify the threshold frame error rate (FER) at which link adjustment is required, based on a simple throughput analysis at the MAC layer. Then, we extend our rate adaptation algorithm to support IEEE 802.11e quality of service (QoS) requirements. When a real-time stream with QoS requirements is admitted, critical constraints such as delay bound and maximum packet drop count are integrated in the selection of the most convenient transmission rate that best respects the flow requirements. Moreover, we use dynamic bandwidth allocation rather than the default transmission opportunities (TXOPs) in a way that best offers a flow the required time for retransmissions due to packet failure based on the variant loss rate present in the channel. We validate our proposed rate adaptation algorithms via simulation results where the efficiency and effectiveness of the algorithm are noticed for both best effort and QoS flows.