Metode Interference cancellation Yang Efisien Pada Jaringan Nirkabel Area Tubuh

Jaringan Nirkabel Area Tubuh (Wireless Body Area Network) adalah sensor yang berada pada tubuh manusia yang bisa langsung berkomunikasi kepada perangkat penerima secara nirkabel. Aplikasi yang digunakan dari WBAN ini adalah untuk membantu memudahkan dalam bidang kesehatan untuk pasien agar data pengukuran di tubuh bisa langsung diterima oleh dokter agar segera diperiksa. Pada kanal WBAN sangat rentan terhadap gangguan, yaitu gangguan berupa Intersymbol Interference (ISI) maupun Multiple Access Interference (MAI). Kesalahan dalam pengiriman data pasien akan sangat membahayakan. Diperlukan metode pengurangan gangguan untuk mengatasi masalah tersebut. Successive Interference Cancellation banyak diterapkan untuk mengatasi masalah gangguan yang terjadi pada sistem MIMO. Pada tugas akhir ini didapatkan skema yang paling efisien untuk mengurangi eror akibat interferensi pada kanal WBAN yaitu dengan menggunakan sistem komunikasi MIMO Alamouti dan menambahkan equalizer Zero Forcing dan Successive Interference Cancellation menghasilkan nilai BER 0,0001 pada EbNo=14. =================================================================================== Wireless Body Area Network (Wireless Body Area Network) is a sensor located on the human body that can directly communicate to the receiving device wirelessly. Applications that use of WBAN is to help facilitate in the field of health for the patient so that the measurement data in the body can be directly received by the doctor to be checked immediately. In WBAN canal, very susceptible to interference, which is interference in the form of Intersymbol Interference (ISI) and Multiple Access Interference (MAI). Errors in the delivery of patient data would be very dangerous. Noise reduction methods are needed to resolve the issue. Successive Interference Cancellation widely applied to overcome the problem of disruption of the MIMO system. In this final project obtained the most efficient scheme for reducing errors due to interference on a WBAN channel by using MIMO Alamouti communication system and add Zero Forcing Equalizer with Successive Interference Cancellation and generate value of BER 0.0001 for EbNo=14

Survey of Spectrum Sharing for Inter-Technology Coexistence

Increasing capacity demands in emerging wireless technologies are expected to be met by network densification and spectrum bands open to multiple technologies. These will, in turn, increase the level of interference and also result in more complex inter-technology interactions, which will need to be managed through spectrum sharing mechanisms. Consequently, novel spectrum sharing mechanisms should be designed to allow spectrum access for multiple technologies, while efficiently utilizing the spectrum resources overall. Importantly, it is not trivial to design such efficient mechanisms, not only due to technical aspects, but also due to regulatory and business model constraints. In this survey we address spectrum sharing mechanisms for wireless inter-technology coexistence by means of a technology circle that incorporates in a unified, system-level view the technical and non-technical aspects. We thus systematically explore the spectrum sharing design space consisting of parameters at different layers. Using this framework, we present a literature review on inter-technology coexistence with a focus on wireless technologies with equal spectrum access rights, i.e. (i) primary/primary, (ii) secondary/secondary, and (iii) technologies operating in a spectrum commons. Moreover, we reflect on our literature review to identify possible spectrum sharing design solutions and performance evaluation approaches useful for future coexistence cases. Finally, we discuss spectrum sharing design challenges and suggest future research directions

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig