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Register a new userOpen-source RANs in practice: an over-the-air deployment for 5G MEC
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Added by
Juuso Haavisto
Publication date
2019
fields
Informatics Engineering
and research's language is
English
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Edge computing that leverages cloud resources to the proximity of user devices is seen as the future infrastructure for distributed applications. However, developing and deploying edge applications, that rely on cellular networks, is burdensome. Such network infrastructures are often based on proprietary components, each with unique programming abstractions and interfaces. To facilitate straightforward deployment of edge applications, we introduce OSS based RAN on OTA commercial spectrum with DevOps capabilities. OSS allows software modifications and integrations of the system components, e.g., EPC and edge hosts running applications, required for new data pipelines and optimizations not addressed in standardization. Such an OSS infrastructure enables further research and prototyping of novel end-user applications in an environment familiar to software engineers without telecommunications background. We evaluated the presented infrastructure with E2E OTA testing, resulting in 7.5MB/s throughput and latency of 21ms, which shows that the presented infrastructure provides low latency for edge applications.
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The combination of 5G and Multi-access Edge Computing (MEC) can significantly reduce application delay by lowering transmission delay and bringing computational capabilities closer to the end user. Therefore, 5G MEC could enable excellent user experience in applications like Mobile Augmented Reality (MAR), which are computation-intensive, and delay and jitter-sensitive. However, existing 5G handoff algorithms often do not consider the computational load of MEC servers, are too complex for real-time execution, or do not integrate easily with the standard protocol stack. Thus they can impair the performance of 5G MEC. To address this gap, we propose Comp-HO, a handoff algorithm that finds a local solution to the joint problem of optimizing signal strength and computational load. Additionally, Comp-HO can easily be integrated into current LTE and 5G base stations thanks to its simplicity and standard-friendly deployability. Specifically, we evaluate Comp-HO through a custom NS-3 simulator which we calibrate via MAR prototype measurements from a real-world 5G testbed. We simulate both Comp-HO and several classic handoff algorithms. The results show that, even without a global optimum, the proposed algorithm still significantly reduces the number of large delays, caused by congestion at MECs, at the expense of a small increase in transmission delay.
The IEEE 802.1 time-sensitive networking (TSN) standards aim at improving the real-time capabilities of standard Ethernet. TSN is widely recognized as the long-term replacement of proprietary technologies for industrial control systems. However, wired connectivity alone is not sufficient to meet the requirements of future industrial systems. The fifth-generation (5G) mobile/cellular technology has been designed with native support for ultra-reliable low-latency communication (uRLLC). 5G is promising to meet the stringent requirements of industrial systems in the wireless domain. Converged operation of 5G and TSN systems is crucial for achieving end-to-end deterministic connectivity in industrial networks. Accurate time synchronization is key to integrated operation of 5G and TSN systems. To this end, this paper evaluates the performance of over-the-air time synchronization mechanism which has been proposed in 3GPP Release 16. We analyze the accuracy of time synchronization through the boundary clock approach in the presence of clock drift and different air-interface timing errors related to reference time indication. We also investigate frequency and scalability aspects of over-the-air time synchronization. Our performance evaluation reveals the conditions under which 1 (mu)s or below requirement for TSN time synchronization can be achieved.
The recent and upcoming releases of the 3rd Generation Partnership Projects 5G New Radio specifications include features that are motivated by providing connectivity services to a broad set of verticals, including the automotive, rail, and air transport industries. Currently, several radio access network features are being further enhanced or newly introduced in NR to improve 5Gs capability to provide fast, reliable, and non-limiting connectivity for transport applications. In this article, we review the most important characteristics and requirements of a wide range of services that are driven by the desire to help the transport sector to become more sustainable, economically viable, safe, and secure. These requirements will be supported by the evolving and entirely new features of 5G NR systems, including accurate positioning, reference signal design to enable multi-transmission and reception points, service-specific scheduling configuration, and service quality prediction.
State-of-the-art performance for many emerging edge applications is achieved by deep neural networks (DNNs). Often, these DNNs are location and time sensitive, and the parameters of a specific DNN must be delivered from an edge server to the edge device rapidly and efficiently to carry out time-sensitive inference tasks. We introduce AirNet, a novel training and analog transmission method that allows efficient wireless delivery of DNNs. We first train the DNN with noise injection to counter the wireless channel noise. We also employ pruning to reduce the channel bandwidth necessary for transmission, and perform knowledge distillation from a larger model to achieve satisfactory performance, despite the channel perturbations. We show that AirNet achieves significantly higher test accuracy compared to digital alternatives under the same bandwidth and power constraints. It also exhibits graceful degradation with channel quality, which reduces the requirement for accurate channel estimation.
IoT systems typically involve separate data collection and processing, and the former faces the scalability issue when the number of nodes increases. For some tasks, only the result of data fusion is needed. Then, the whole process can be realized in an efficient way, integrating the data collection and fusion in one step by over-the-air computation (AirComp). Its shortcoming, however, is signal distortion when channel gains of nodes are different, which cannot be well solved by transmission power control alone in times of deep fading. To address this issue, in this paper, we propose a multi-slot over-the-air computation (MS-AirComp) framework for the sum estimation in fading channels. Compared with conventional data collection (one slot for each node) and AirComp (one slot for all nodes), MS-AirComp is an alternative policy that lies between them, exploiting multiple slots to improve channel gains so as to facilitate power control. Specifically, the transmissions are distributed over multiple slots and a threshold of channel gain is set for distributed transmission scheduling. Each node transmits its signal only once, in the slot when its channel gain first gets above the threshold, or in the last slot when its channel gain remains below the threshold. Theoretical analysis gives the closed-form of the computation error in fading channels, based on which the optimal parameters are found. Noticing that computation error tends to be reduced at the cost of more transmission power, a method is suggested to control the increase of transmission power. Simulations confirm that the proposed method can effectively reduce computation error, compared with state-of-the-art methods.
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