Communication networks are expanding rapidly and becoming increasingly complex. As a consequence, the conventional rule-based algorithms or protocols may no longer perform at their best efficiencies in these networks. Machine learning (ML) has recently been applied to solve complex problems in many fields, including finance, health care, and business. ML algorithms can offer computational models that can solve complex communication network problems and consequently improve performance. This paper reviews the recent trends in the application of ML models in communication networks for prediction, intrusion detection, route and path assignment, Quality of Service improvement, and resource management. A review of the recent literature reveals extensive opportunities for researchers to exploit the advantages of ML in solving complex performance issues in a network, especially with the advancement of softwaredefined networks and 5G.

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Fiber-Wireless (FiWi) network can provide abundant bandwidth capacity and mobility to the end-users. It also eliminates the need of having complete tedious end-to-end fiber installation from the central office to the users, which saves tremendous capital expenditure. However, FiWi is still progressing. Researchers worldwide are still developing experimental works for improvement on the network reliability, quality-of-services and security. Almost all recently proposed testbed designed for FiWi are using hardware that lacks in programmability feature, making it challenging to implement any protocols and algorithms. A testbed must be flexible, scalable and reprogrammable so that various experiments and testing can be implemented easily for testing purposes. In this paper, a reprogrammable FiWi testbed using software-defined radio (SDR) is proposed. One of the most prominent SDR available in the market is Universal Software Radio Peripheral (USRP). It is chosen to be used in this paper as it is equipped with a user-friendly programming platform; LabVIEW. To test the testbed's reprogrammability feature, two algorithms are implemented for proof-ofconcept; collision avoidance and dynamic bandwidth allocation. The collision avoidance algorithm is implemented in the wireless side of the testbed using the concept of Carrier Sensing Multiple Access/Collision Avoidance. At the fiber domain, a dynamic bandwidth allocation-limited scheduling is incorporated in the testbed. The results show that algorithms implemented in the testbed are in-line with the expected results. It proves that the testbed can be used for future algorithm testing for research purposes.