Results 81 to 90 of about 2,806 (201)
This work proposes COANET (crayfish optimization algorithm‐based route optimization for IoV Networks), which is an innovative bio‐inspired framework based on the crayfish optimization algorithm. COANET's core utilized the crayfish behaviours of foraging, competition and summer resort to allow the dynamic balancing of exploration and exploitation during
Ghassan Husnain +5 more
wiley +1 more source
CA‐PPO: Collision‐Aware Reinforcement Learning for Grant‐Free NOMA in Wireless Federated Learning
This paper proposes a collision‐aware proximal policy optimisation (CA‐PPO) framework for joint clustering and power allocation in grant‐free NOMA–enabled wireless federated learning systems. By explicitly accounting for random access collisions, SIC decoding failures and energy–latency constraints, the proposed approach stabilises learning under ...
Emmanuel Atebawone +5 more
wiley +1 more source
ABSTRACT Smart cities represent transformative urban ecosystems, leveraging advanced technologies to address challenges posed by rapid urbanisation, energy efficiency demands, and sustainability goals. Despite extensive research, a comprehensive and integrated literature analysis combining AI‐based communication, energy management, cybersecurity, and ...
Maaz Tahir Malik +5 more
wiley +1 more source
MMTC Convention & Exhibition Center (Green Architecture)
Along with the development of the economic industry related to various activities such as tourism and attractions, several strategies were implemented for global competition, such as increasing tourism, marketing and investment activities, as well as ...
Handayani, Medina
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Learning Random Access Schemes for Massive Machine-Type Communication With MARL
This paper investigates various multi-agent reinforcement learning (MARL) techniques for designing grant-free random access (RA) schemes suitable for low-complexity, low-power battery-operated devices in massive machine-type communication (mMTC ...
Muhammad Awais Jadoon +3 more
doaj +1 more source
This study introduces a meta‐federated deep reinforcement learning (Meta‐FedDRL) framework to address dynamic resource allocation (DRA) challenges in ultra‐dense and heterogeneous 5G and beyond network slicing. The proposed approach enables distributed learning while preserving data confidentiality and enhancing generalization.
Faustin Samafou +4 more
wiley +1 more source
Faktor-Faktor Yang Mempengaruhi Pendapatan Pedagang Sayur Mayur Di Pasar Raya MMTC Medan
Penelitian ini bertujuan untuk mengetahui apa saja faktor-faktor yang mempengaruhi pendapatan pedagang sayur mayur di Kota Medan. Penelitian ini dilaksanakan pada bulan Mei-Juli 2019.
Sihura, Kalvin Vianus
core +1 more source
The growing number of connected devices in high-density environments poses serious challenges for accommodating and managing these devices across different network slicing services, such as ultra-reliable low-latency communication (URLLC) and massive ...
Suhare Solaiman
doaj +1 more source
Breaking Orthogonality in Uplink With Randomly Deployed Sources
The requirement of the upcoming sixth-generation (6G) wireless communication systems to significantly elevate the services of enhanced mobile broadband (eMBB) and massive machine-type communications (mMTC) necessitates the design and investigation of ...
Apostolos A. Tegos +5 more
doaj +1 more source
Analisis Tarikan Perjalanan Menuju Pasar Basah MMTC Dijalan Williem Iskandar Pasar V
Tarikan perjalanan adalah jumlah pergerakan perjalanan yang terjadi menuju ke lokasi tertentu setiap satuan waktu. Tarikan perjalanan ini berhubungan dengan penentuan jumlah perjalanan keseluruhan yang dibangkitkan oleh sebuah kawasan.
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