Disruptive technologies, which are caused by the cellular evolution including the Internet of Things (IoT), have significantly contributed data traffic to the mobile telecommunication network in the era of Industry 4.0. These technologies cause erroneous predictions prompting mobile operators to upgrade their network, which leads to revenue loss. Besides, the inaccuracy of network prediction also creates a bottleneck problem that affects the performance of the telecommunication network, especially on the mobile backhaul. We propose a new technique to predict more accurate data traffic. This research used a univariate Autoregressive Integrated Moving Average (ARIMA) model combined with a new disruptive formula. Another model, called a disruptive formula, uses a judgmental approach based on four variables: Political, Economic, Social, Technological (PEST), cost, time to market, and market share. The disruptive formula amplifies the ARIMA calculation as a new combination formula from the judgmental and statistical approach. The results show that the disruptive formula combined with the ARIMA model has a low error in mobile data forecasting compared to the conventional ARIMA. The conventional ARIMA shows the average mobile data traffic to be 49.19 Mb/s and 156.93 Mb/s for the 3G and 4G, respectively; whereas the ARIMA with disruptive formula shows more optimized traffic, reaching 56.72 Mb/s and 199.73 Mb/s. The higher values in the ARIMA with disruptive formula are closest to the prediction of the mobile data forecast. This result suggests that the combination of statistical and computational approach provide more accurate prediction method for the mobile backhaul networks.

The strong growth in the number of connected mobile devices has imposed new challenges in efficiently exploiting the available networks resources. Code Domain Non-Orthogonal Multiple Access (NOMA) technique appears as a tremendous efficient solution. Each device uses its assigned code to simultaneously transmit its data along with the user identifier, without any resource reservation exchange, saving precious wireless resources. However, this requires a receiver capable of blindly detecting the active users, which is highly complex. Driven by the promising superposition property of quantum architecture, the goal of this paper is to adapt and apply the quantum Grover algorithm for Active User Detection (AUD) purpose in the context of NOMA, to alleviate the search complexity. This adapted Grover's algorithm is compared with the optimal classical Maximum Likelihood (ML) AUD receivers, as well as with the basic classical Conventional Correlation Receiver (CCR). A benchmark on the probability of AUD is assessed as a function of the Signal to Noise Ratio (SNR) of the received signal. We show that our adapted Grover's algorithm is very promising in high SNR regime.

To support multiple transmissions in an optical fiber, several techniques have been studied such as Optical Code Division Multiple Access (OCDMA). In particular, the incoherent OCDMA systems are appreciated for their simplicity and reduced cost. However, they suffer from Multiple Access Interference (MAI), which degrades the performances. In order to cope with this MAI, several detectors have been studied. Among them, the Maximum Likelihood (ML) detector is the optimal one but it suffers from high complexity as all possibilities have to be tested prior to decision. However, thanks to the recent quantum computing advances, the complexity problem can be circumvented. Indeed, quantum algorithms, such as Grover, exploit the superposition states in the quantum domain to accelerate the computation. Thus, in this paper, we propose to adapt the quantum Grover's algorithm in the context of MUD, in an OCDMA system using non-orthogonal codes. We propose a way to adapt the received noisy signal to the constraints defined by Grover's algorithm. We further evaluate the probability of success in detecting the active users for different noise levels. Aside from the complexity reduction, simulations show that our proposal has a high probability of detection when the received signal is not highly altered. We show the benefits of our proposal compared to the classical and the optimal ML detector.