2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom)
During the last decade, the number of devices connected to the Internet by Wi-Fi has grown significantly. A high density of both the client devices and the hot spots posed new challenges related to providing the desired quality of service in the current and emerging scenarios. To cope with the negative effects caused by network densification, modern Wi-Fi is becoming more and more centralized. To improve network efficiency, today many new Wi-Fi deployments are under control of management systems that optimize network parameters in a centralized manner. In the paper, for such a cloud management system, we develop an algorithm which aims at maximizing energy efficiency and also keeps fairness among clients. For that, we design an objective function and solve an optimization problem using the branch and bound approach. To evaluate the efficiency of the developed solution, we implement it in the NS-3 simulator and compare with existing solutions and legacy behavior.
Ultra reliable low latency communications (URLLC) is one of the most promising and demanding services in 5G systems. This service requires very low latency of less than 1 - 10 ms and very high transmission reliability: the acceptable packet loss ratio is about 10 -5 . To satisfy such strict requirements, many issues shall be solved. This paper focuses on the link adaptation problem, i.e., the selection of a modulation and coding scheme (MCS) for transmission based on the received channel quality indicator (CQI) reports. On the one hand, link adaptation should select a robust MCS to provide high reliability. On the other hand, it should select the highest possible MCS to reduce channel resource consumption. The paper shows that even for one URLLC user, link adaptation is still a challenging problem, especially in highly-variant channels. To solve this problem, a conservative link adaptation algorithm is designed. The algorithm estimates the strongest channel degradation at the time moment of the actual packet transmission and selects an MCS taking into account the worst degradation. The obtained results show that the proposed algorithm is efficient in terms of both the packet loss ratio and the channel resource consumption.
In the middle of 2018, 3GPP approved the first version of the novel radio access technology for 5G systems called New Radio (NR). One of the most important novelties of NR compared to the existing Long-Term Evolution technology is the ability to configure physical layer parameters. In particular, for each user a base station can adaptively select duration of a slot used for data transmission. In this paper, we show that the new feature can be used for improving performance of web services. We propose an algorithm which adaptively selects slot duration depending on the amount of user data enqueued at the base station. Performance evaluation of the proposed algorithm with NS-3 simulator shows significant improvement of web pages download rate.
Being of high importance, real-time applications, such as online gaming, real-time video streaming, virtual reality, and remote-control drone and robots, introduce many challenges to the developers of wireless networks. Such applications pose strict requirements on the delay and packet loss ratio, and it is hardly possible to satisfy them in Wi-Fi networks that use random channel access. The article presents a novel approach to enable real-time communications by exploiting an additional radio. This approach was recently proposed by us in the IEEE 802.11 Working Group and attracted much attention. To evaluate its gain and to study how real-time traffic coexists with the usual one, a mathematical model is designed. The numerical results show that the proposed approach allows decreasing the losses and delays for the real-time traffic by orders of magnitude, while the throughput for the usual traffic is reduced insignificantly in comparison to existing networks.