SIMULASI CLUSTER FORMATION PROCEDURE DAN COOPERATIVE CLUSTER FORMATION METHOD PADA DEVICE-TO-DEVICE (D2D) COMMUNICATION UNTUK MENCAPAI EFISIENSI ENERGI

ABSTRAK Komunikasi Device-to-Device (D2D) diharapkan dapat menjadi solusi dari berbagai isu komunikasi seluler. Salah satunya adalah isu efisiensi energi. Biaya konsumsi energi yang semakin mahal mendorong operator seluler untuk menekan penggunaan energi. Selain itu, borosnya energi batere pada device pengguna merupakan masalah yang tidak dapat diabaikan. Sistem cluster pada komunikasi D2D diharapkan dapat memberikan solusi terhadap masalah ini. Dengan menggunakan MATLAB, dibuat simulasi dan analisis efisiensi energi untuk metode clustering dan cooperative clustering dari komunikasi Device-to-Device (D2D) pada teknologi LTE-A. Dilakukan pengujian terhadap metode-metode mutakhir dalam komunikasi D2D (metode clustering, cooperative clustering, dan cluster head rotation) untuk melihat hasil efisiensi energi dari metode- metode tersebut. Simulasi- simulasi yang dilakukan meliputi: pengaruh perbedaan skenario, pengaruh perbedaan rate, dan pengaruh perbedaan jumlah device per cluster pada efisiensi energi. Pada simulasi pertama, dilakukan analisis terhadap dampak dari perbedaan jumlah device terhadap tingkat konsumsi energi. Dari hasil simulasi tersebut dapat terlihat bahwa tingkat konsumsi energi meningkat secara linear terhadap tingkat pertambahan jumlah device. Dari hasil simulasi tersebut juga dapat terlihat bahwa sel yang menggunakan metode cooperative clustering mengabiskan energi paling sedikit. Sebagai contoh, didapatkan hasil bahwa skenario yang menggunakan metode cooperative clustering, menghabiskan 23% energi yang lebih kecil dibandingkan skenario yang menggunakan metode clustering. Pada simulasi kedua, dilakukan analisis terhadap dampak dari variasi tingkat data transfer rate terhadap tingkat konsumsi energi. Hasil yang didapatkan menunjukkan bahwa tingkat konsumsi enegi akan menurun secara eksponensial seiring dengan kenaikan tingkat data transfer rate. Untuk simulasi terakhir, dilakukan analisis terhadap dampak dari variasi jumlah cluster member pada setiap cluster. Hasil simulasi ini menunjukkan bahwa untuk sel yang menggunakan metode clustering, variasi dari jumlah cluster member cenderung tidak berpengaruh pada konsumsi energi. Namun, untuk sel yang menggunakan metode cooperative clustering, setiap pertambahan satu cluster member akan meningkatkan tingkat konsumsi energi sebesar 25%. Kata kunci : Device-to-Device, LTE- Advance, Short range(SR), Long range(LR)

Device Discovery Schemes for Energy-efficient Cluster Head Rotation in D2D

 In this paper, novel device discovery approaches for the Cluster Head Rotation, which is a state-of-the-art method for the Device-to-Device communication, are proposed. The device discovery is the process to detect and to include new devices in the Device-to-Device communication. The proposed device discovery is aimed to attain energy efficiency for the communication devices. We propose two schemes for the device discovery: eNB-assisted and independent device discovery. Compared to previous work, the proposed device discovery is utilizing the cluster head rotation method, to achieve better energy efficiency. In this work, several simulations were performed and discussed for both schemes. In the first simulation, the device energy consumption is examined. After that, the number of devices that get rejected is studied. The device discovery processes in multi cluster head scenario, which is Cluster Head Rotation, are examined in this paper. The result of the simulation shows that eNB-assisted device discovery can provide better energy efficiency. Also, the number of rejected devices of the eNB-assisted device discovery is slightly lower than independent device discovery

Selective Green Device Discovery for Device-to-Device Communication

The D2D communication is expected to improve devices’ energy-efficiency, which has become a major requirement of the future wireless network. Before the D2D communication can be performed, the device discovery between devices must be done. The previous works usually only assumed one mode of device discovery, i.e. either use network-assisted (with network supervision) or independent (without network supervision) device. Therefore, we propose a selective device discovery for device-to-device (D2D) communication that can utilize both device discovery modes and maintain devices’ energy-efficiency. Different from previous works, our proposed method selects the best device discovery mode to get the best energy-efficiency. Moreover, to further improve the energy-efficiency, our proposed method also deployed in D2D cluster with multiple cluster heads. The proposed method selects the most suitable mode using thresholds (cluster energy consumption and new device acceptance) and cluster energy expectation. Our experiment result indicates that the proposed method provides lowest energy consumption per new accepted device while compared with schemes with full network-assisted and independent device discovery in low numbers of new device arrival (for the number of new devices arrival = 1 ~ 3)

Quantum Machine Learning for Next-G Wireless Communications: Fundamentals and the Path Ahead

A comprehensive coverage of the state-of-the-art in quantum machine learning (QML) methodologies, with a unique perspective on their applications for wireless communications, is presented. The paper begins by delving into the fundamental principles of quantum computing, and then goes through different operations and techniques that are involved in QML deployments. Subsequently, it provides an in-depth look at various methods peculiar to quantum computing, such as quantum search algorithms, and discusses their potentials towards maximizing the performance of wireless systems. The integration of quantum-based learning models into the existing machine learning methodologies, such as within the frameworks of unsupervised learning and reinforcement learning, are then examined. Taking the viewpoint of wireless communications, diverse studies in the literature that employ QML-based optimization methods are also highlighted. Finally, to ensure the applicability and feasibility of QML for optimizing wireless systems, potential solutions for deployment challenges are addressed