Downlink User Power Allocation and Joint Rate Maximization in UAV-Relayed Assisted SWIPT-NOMA System

In this paper, in order to improve the performance of 5G wireless communication system and save power consumption to achieve the optimal power distribution and maximize the total user rate in a multi-user cluster of non-orthogonal multiple access (NOMA) system downlink. a SWIPT-NOMA system assisted by dynamic unmanned aerial vehicle (UAV) relay is constructed. The UAV relay dynamic programming under two-hop communication was firstly studied, and the global optimal power allocation strategy for downlink users of SWIPT-NOMA system is found. Finally, the optimal relay selection algorithm was used to maximize the total user rate, which was verified by Monto Carlo simulation. The simulation results show that the performance of the proposed system model is better than that of the traditional FDMA system scheme in terms of outage probability, energy consumption and total user rate under different distribution scenarios for the multi-user cluster deployed with dynamic UAV relay system.

RB Allocation Scheme for eMBB and URLLC Coexistence in 5G and Beyond

In this paper, we studied resource allocation in a dynamic multiplexing scenario of two 5G services, namely, enhanced mobile broadband (eMBB) and ultrareliable low-latency communication (URLLC). Considering that eMBB services focus on high data rates while URLLC is very strict in terms of latency and reliability, we formulate a joint eMBB/URLLC resource allocation problem. To solve the formulated problem, in particular, we propose a joint resource allocation algorithm that can satisfy both eMBB user rate and URLLC interrupt probability requirements. We propose an RB allocation scheme that takes into account the needs of eMBB and URLLC users. The program consists of two phases as follows: (1) establish an RB allocation for eMBB users and (2) assign the minislot for URLLC users and calculate the transmission power of URLLC users that ensures the reliability constraint. We also realize and verify the algorithm through the simulation.

Capacity Analysis of Non-Orthogonal Multiple Access for Uplink and Downlink

Non-orthogonal multiple access (NOMA) is intended to be used for the next generation 5G cellular networks. In this paper, the expressions for the channel capacities for symmetric and asymmetric NOMA networks have been analysed. The performance measure of user spectral efficiency and the sum-rate bounds, for the NOMA and the existing orthogonal multiple access (OMA) networks have been compared. Furthermore, analysis of user rate and capacity of NOMA network has been carried out and it is observed that the NOMA capacity region varies as a function of the power allocation factor. The corresponding models have been developed for both uplink and downlink, and simulated with MATLAB. The NOMA performance with imperfect Successive Interference Cancellation (SIC) decoding is also analysed for the downlink. It is also verified that the transmit power may be increased to counter the spectral efficiency reduction due to imperfect SIC.

Guaranteeing User Rates With Reinforcement Learning in 5G Radio Access Networks

The user experience constitutes an important quality metric when delivering high-definition video services in wireless networks. Failing to provide these services within requested data rates, the user perceived quality is strongly degraded. On the radio interface, the packet scheduler is the key entity designed to satisfy the users' data rates requirements. In this chapter, a novel scheduler is proposed to guarantee the bit rate requirements for different types of services. However, the existing scheduling schemes satisfy the user rate requirements only at some extent because of their inflexibility to adapt for a variety of traffic and network conditions. In this sense, the authors propose an innovative framework able to select each time the most appropriate scheduling scheme. This framework makes use of reinforcement learning and neural network approximations to learn over time the scheduler type to be applied on each momentary state. The simulation results show the effectiveness of the proposed techniques for a variety of data rates' requirements and network conditions.

Exploring the effectiveness of a COVID-19 contact tracing app using an agent-based model

A contact-tracing strategy has been deemed necessary to contain the spread of COVID-19 following the relaxation of lockdown measures. Using an agent-based model, we explore one of the technology-based strategies proposed, a contact-tracing smartphone app. The model simulates the spread of COVID-19 in a population of agents on an urban scale. Agents are heterogeneous in their characteristics and are linked in a multi-layered network representing the social structure—including households, friendships, employment and schools. We explore the interplay of various adoption rates of the contact-tracing app, different levels of testing capacity, and behavioural factors to assess the impact on the epidemic. Results suggest that a contact tracing app can contribute substantially to reducing infection rates in the population when accompanied by a sufficient testing capacity or when the testing policy prioritises symptomatic cases. As user rate increases, prevalence of infection decreases. With that, when symptomatic cases are not prioritised for testing, a high rate of app users can generate an extensive increase in the demand for testing, which, if not met with adequate supply, may render the app counterproductive. This points to the crucial role of an efficient testing policy and the necessity to upscale testing capacity.

Male Contraceptive Behavior in Rajshahi District of Bangladesh

This article examines the actual scenario of male involvement either as a user of contraceptive methods or supportive reproductive partners of women on contraceptive use in Rajshahi district of Bangladesh. The analysis shows that, knowledge and use of contraception are low in our study area. Comparative figures of contraceptive practice between men and women found a large disparity. Findings reveal that the user rate of all male contraceptive methods (e.g., condom at 17 percent) is still low compared to that of female methods (pill at 33.8 percent), which shows that men like to impose contra ceptive responsibility on women. Identification of socio-economic and demographic differentials affecting contraceptive behavior of men is performed through the technique of logistic regression. This paper elucidates that education, residence, fertility preference, mass media facility and knowledge about AIDS are the significant factors to influence contraceptive practices among men. Regarding the factors shunning the contraceptive practice we found that men’s opposition to use contraceptive methods is as most significant. The next reason for not using is related to contraceptive method.

Exploring the effectiveness of a COVID-19 contact tracing app with an agent-based model

A contact-tracing strategy has been deemed necessary to contain the spread of COVID-19 following the relaxation of lockdown measures. Using an agent-based model, we explore one of the technology-based strategies proposed, a contact-tracing smartphone app. The model simulates the spread of COVID-19 in a population of agents on an urban scale. Agents are heterogeneous in their characteristics and are linked in a multi-layered network representing the social structure - including households, friendships, employment and schools. We explore the interplay of various adoption rates of the contact-tracing app, different levels of testing capacity, and behavioural factors to assess the impact on the epidemic. Results suggest that a contact tracing app can contribute substantially to reducing infection rates in the population when accompanied by a sufficient testing capacity or when the testing policy prioritises symptomatic cases. As user rate increases, prevalence of infection decreases. With that, when symptomatic cases are not prioritised for testing, a high rate of app users can generate an extensive increase in the demand for testing, which, if not met with adequate supply, may render the app counterproductive. This points to the crucial role of an efficient testing policy and the necessity to upscale testing capacity.