Intelligent Transportation Systems (ITS) will become an essential part of every city in the near future. They should support various vehicle-to-everything (V2X) applications that improve road safety or even enable autonomous driving. Recently, the European Telecommunications Standards Institute (ETSI) introduced a multi-access (mobile) edge computing concept as a promising solution to satisfy the V2X delay and computational requirements. Based on this concept, the tasks generated by V2X applications can be offloaded to servers at the edge of the radio access network (RAN). There is a need for a task offloading algorithm that minimizes the ITS operator expenses connected with the servers deployment and maintenance while satisfying the requirements of the V2X applications. Most of the existing papers in the literature do not pay much attention to queuing delays at servers. In this paper, the queuing delays are analyzed by considering a general-type task computational time distribution. A non-linear optimization problem is formulated to minimize the ITS operator expenses subject to delays and computational resources constraints. The flexibility is also improved by considering that a delay constraint is satisfied with a given probability. To solve this problem, a method for linearization of the problem is proposed, and consequently, an algorithm based on Integer Linear Programming (ILP) is designed. A heuristic algorithm called Cost-effective Heuristic Algorithm for Task offloading (CHAT) is also introduced that provides close to optimal results and has much lower computational complexity than the ILP algorithm. The efficiency of the CHAT algorithm is studied in several scenarios in terms of the computational time, delays, and the total server energy consumption as the cost function. The results show that the CHAT algorithm satisfies the requirements of the V2X applications in all the considered scenarios and reduces the ITS operator expenses over twice compared with other algorithms proposed in the literature.

Providing Ultra-Reliable Low-Latency Communications (URLLC) is one of the key challenges for 5G systems, which requires the redesign of both radio access technologies and the architectures. While 3GPP and the research community focus mainly on radio low layer functionality and architectural changes that enable URLLC, this paper proposes a novel cross-layer framework that extends the already standardized 5G solutions to support a very massive number of users/flows in the system. The framework consists of a scheduler, a congestion avoidance algorithm and a new traffic management algorithm, which together achieve the maximum capacity of 5G systems for URLLC, i.e., they maximize the load at which the reliability and latency requirements are satisfied for at least 99% of the users. Extensive performance evaluation results demonstrate that the proposed framework achieves the network capacity very close to the upper bound.

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.

Two amendments to IEEE 802.11 have recently been published: 802.11aa and 802.11ae. Both enhance Quality of Service (QoS) provisioning in Wi-Fi networks by providing support for multicast transmission, enhanced audio video streaming, coping with inter-network interference, and improved prioritization of management frames. The proposed solutions either extend mechanisms already existing in the standard or introduce new ones. Therefore, it is important for researchers to understand the new functionalities. To this end we provide the first description of these latest mechanisms: we present the motivation behind them, explain their design principles, provide examples of usage, and comment on compatibility issues. Finally, we identify new research challenges related to the two new amendments.

Ultra-Reliable Low Latency Communications (URLLC) is an important feature of 5G networks which cannot be enabled without redesigning the architecture of cellular networks. While most activities in 3GPP are focused on developing low-layer functionality, such as modulation and coding schemes, OFDMA numerology, fast hybrid automatic repeat request, in the paper, we study the influence of upper-layer functionality - specifically, resource allocation and traffic management algorithms - on URLLC performance. Although such algorithms cannot enable ultra-high reliability and ultra-low latency, with accurate system level simulations we show that the proper choice of such algorithms can significantly improve network capacity for URLLC traffic.