The open radio access network (RAN) aims to bring openness and intelligence to the traditional closed and proprietary RAN technology and offer flexibility, performance improvement, and cost-efficiency in the RAN’s deployment and operation. This paper provides a comprehensive survey of the Open RAN development. We briefly summarize the RAN evolution history and the state-of-the-art technologies applied to Open RAN. The Open RAN-related projects, activities, and standardization is then discussed. We then summarize the challenges and future research directions required to support the Open RAN. Finally, we discuss some solutions to tackle these issues from the open source perspective.

Narrowband Internet of Things (NB-IoT) is a new access technology introduced by 3GPP. This paper presents an analytical model to estimate the access success probability and average access delay of the random access channels by considering maximum number of preamble transmissions, size of backoff windows, and number of sub-carriers in each coverage enhancement (CE) levels. A joint optimization technique is proposed to configure the parameters to maximize the access success probability under a target delay constraint. The accuracy of the analysis and the effectiveness of the proposed optimization technique are verified by computer simulations and benchmarked with exhaustive search. The result shows that the proposed optimization is able to find the optimal configuration under various conditions.

Machine-type communication (MTC) is a crucial service for next-generation cellular networks. Mass access to the network by MTC devices may result in the overload of radio access networks (RANs) and degrade the service quality of human-to-human communication. Group paging is one of the mechanisms proposed to alleviate the RAN-overload problem. This paper presents an analytical model based on a recursive contending-users estimation (RCE) method proposed in the literature to derive the performance metrics of collision probability, access success probability, average access delay, statistics of preamble transmissions, statistics of access delay, and utilization of random-access opportunities (RAOs) for group paging with various combinations of group sizes and reserved radio resources in a paging access interval. The optimal group size and required RAOs are subsequently derived based on the given target access success probability. Numerical results demonstrate that the proposed model can accurately estimate the performance of group paging.

This paper presents a combinatorial model and approximation formulas to estimate the average number of successful and collided users for a one-shot random access in finite-user multichannel slotted ALOHA systems. The proposed model and approximation can be used to evaluate the performance of group paging for machine-type communication (MTC) in 3GPP LTE. Numerical results demonstrate the applicable range of the approximation formulas and the accuracy of the derived performance metrics.

IoT devices are deployed in a building to instantly collect electricity load usage for next hour load consumption forecasting so that the operation of the building can be properly managed. However, due to the hardware or system error, data missing or overflow might occur. Noise might also be added into the collected data. Under this circumstance, the accuracy of next hour load forecasting would degrade which in turn decreases the building operation performance. In this paper, we propose an hourly load forecasting framework combining Data Preparation and LSTM, namely LSTM-DP, by considering data pre-processing, feature engineering and Long Short-Term Memory (LSTM). In LSTM-DP, the collected data is firstly processed by interpolation and Savitzky Golay filter, therefore the pattern of load consumption can be properly extracted by LSTM for next hour load forecasting. Moreover, we adopt two-stack LSTM to better determine the relationships among the time series information. We study the real data collected from three buildings of a company in Asia to investigate the performance of next hour load forecasting, and the results show the proposed LSTM-DP outperforms others.