ﺗﺨﺼﯿﺺ ﺗﻮزﯾﻊﺷﺪه ﻓﺮوﺳﻮ و ﻓﺮاﺳﻮي ﻣﻨﺎﺑﻊ در ارﺗﺒﺎط دﺳﺘﮕﺎه ﺑﻪ دﺳﺘﮕﺎه

Distributed Downlink and Uplink Resource Allocation in D2D Communication

ﺩﺭ ﺳﺎﻣﺎﻧﻪﻫﺎﻱ ﺳﻠﻮﻟﻲ ﮐﻨﻮﻧﻲ، ﻋﻤﻠﮑﺮﺩ ﺩﺳﺘﮕﺎﻩ ﮐﺎﺭﺑﺮﺍﻥ ﻣﻮﺟﻮﺩ ﺩﺭ ﻣﺮﺯ ﺳﻠﻮﻝ ﺑﻪ ﺩﻟﻴﻞ ﮐﻴﻔﻴﺖ ﺿﻌﻴﻒ ﺍﺭﺗﺒﺎﻁ، ﺁﺳﻴﺐ ﻣــﻲﺑﻴﻨــﺪ. ﺣــﺎﻝ ﺁﻧﮑــﻪ ﺍﻳﻦ ﺍﺭﺗﺒﺎﻃﺎﺕ ﺑﻪ ﺗﻌﺪﺍﺩ ﺑﻴﺸﺘﺮﻱ ﺑﻠﻮﮎ ﻣﻨﺎﺑﻊ ﻭ ﺗﻮﺍﻥ ﺍﻧﺘﻘﺎﻟﻲ ﻧﻴﺰ ﻧﻴﺎﺯ ﺩﺍﺭﺩ. ﺑﺮﺍﻱ ﮐﺎﻫﺶ ﺗﻌﺪﺍﺩ ﺑﻠﻮﮎﻫﺎﻱ ﻓﺮﮐﺎﻧﺴﻲ ﻭ ﺗﻮﺍﻥ ﺍﻧﺘﻘﺎﻟﻲ، ﺍﻳﻦ ﻣﻘﺎﻟﻪ ﺑﻪ ﺑﺮﺭﺳــﻲ ﺍﺭﺗﺒﺎﻁ ﺩﺳﺘﮕﺎﻩ ﺑﻪ ﺩﺳﺘﮕﺎﻩ ﺩﺭ ﺣﺎﻟﺖﻫﺎﻱ ﻓﺮﻭﺳﻮ ﻭ ﻓﺮﺍﺳﻮﻱ ﺳﺎﻣﺎﻧﻪﻫﺎﻱ ﻣﺨﺎﺑﺮﺍﺕ ﺳﻠﻮﻟﻲ ﻣﻲﭘﺮﺩﺍﺯﺩ. ﺑﻪﻣﻨﻈﻮﺭ ﺑﻬﻴﻨﻪﺳــﺎﺯﻱ ﺍﺗﺼــﺎﻝ ﮐــﺎﺭﺑﺮﺍﻥ ﻣﺨﺘﻠــﻒ ﺩﺭ ﺷﺒﮑﻪ ﺑﻪ ﻣﻌﻨﺎﻱ ﻳﺎﻓﺘﻦ ﺑﻬﺘﺮﻳﻦ ﺍﺭﺗﺒﺎﻁ )ﮐﻢﺗﺮﻳﻦ ﻣﻴﺰﺍﻥ ﺗﻮﺍﻥ ﻣﺼﺮﻓﻲ( ﻳﮏ ﮐﺎﺭﺑﺮ ﺑﺎ ﺍﻳﺴﺘﮕﺎﻩ ﭘﺎﻳﻪ ﮐﻪ ﻣﻤﮑﻦ ﺍﺳﺖ ﺍﺯ ﻃﺮﻳﻖ ﺍﺭﺗﺒﺎﻁ ﺑﺎ ﮐﺎﺭﺑﺮﺍﻥ ﺩﻳﮕﺮ ﻭ ﻳــﺎ ﺍﺭﺗﺒﺎﻁ ﻣﺴﺘﻘﻴﻢ ﺑﺎ ﺍﻳﺴﺘﮕﺎﻩ ﭘﺎﻳﻪ ﺑﺮﻗﺮﺍﺭ ﺷﻮﺩ ﻭ ﺑﺎ ﻫﺪﻑ ﮐﻤﻴﻨﻪ ﮐﺮﺩﻥ ﮐﻞ ﺗــﻮﺍﻥ ﺍﻧﺘﻘــﺎﻝﻳﺎﻓﺘــﻪ، ﺍﺯ ﺭﻭﺵﻫــﺎﻱ ﺑﻬﻴﻨﻪﺳــﺎﺯﻱ ﻣﺘﻔــﺎﻭﺗﻲ ﻣﺎﻧﻨــﺪ ﺑﻬﻴﻨﻪﺳــﺎﺯﻱ ﺟﺴﺘﺠﻮﻱ ﮔﺮﺍﻧﺸﻲ، ﺑﻬﻴﻨﻪﺳﺎﺯﻱ ﺍﺯﺩﺣﺎﻡ ﺫﺭﺍﺕ، ﺑﻬﻴﻨﻪﺳﺎﺯﻱ ﻭﺭﺍﺛﺘﻲ ﻭ ﺭﺍﻫﺒﺮﺩ ﺗﻮﺯﻳﻊﺷﺪﻩ ﻣﺒﺘﻨﻲ ﺑﺮ ﻳــﺎﺩﮔﻴﺮﻱ Q ﺑــﺎ ﺍﺳــﺘﻔﺎﺩﻩ ﺍﺯ ﺗﺼــﻤﻴﻢﮔﻴﺮﻱ ﺑﻴﺸــﻴﻨﻪ ﻫﻤﻮﺍﺭ ﺑﻬﺮﻩ ﮔﺮﻓﺘﻪ ﻣﻲﺷﻮﺩ. ﻧﺘﺎﻳﺞ ﻋﺪﺩﻱ ﮐﺎﻫﺶ ﺗﻮﺍﻥ ﺣﺪﻭﺩ 30 ﺩﺭﺻﺪ ﺭﺍ ﺑﺮﺍﻱ ﺍﻳﻦ ﺍﺭﺗﺒﺎﻃــﺎﺕ ﺑــﺎ ﭘﻴﭽﻴــﺪﮔﻲ ﻣﺤﺎﺳــﺒﺎﺗﻲ ﮐﻤﺘــﺮ ﺑــﺎ ﺍﺳــﺘﻔﺎﺩﻩ ﺍﺯ ﺭﻭﺵ ﻳﺎﺩﮔﻴﺮﻱ Q ﻧﺴﺒﺖ ﺑﻪ ﺣﺎﻟﺘﻲ ﮐﻪ ﺑﻪﻃﻮﺭ ﻣﺮﺳﻮﻡ ﺗﻤﺎﻡ ﮐﺎﺭﺑﺮﺍﻥ ﺍﺗﺼﺎﻝ ﺧﻮﺩ ﺭﺍ ﺍﺯ ﻃﺮﻳﻖ ﺍﻳﺴﺘﮕﺎﻩ ﭘﺎﻳﻪ ﻭ ﺑﻪﺻﻮﺭﺕ ﻣﺘﻤﺮﮐﺰ ﺑــﺎ ﭘﻴﭽﻴــﺪﮔﻲ ﻣﺤﺎﺳــﺒﺎﺗﻲ ﺑــﺎﻻ ﺑﺮﻗﺮﺍﺭ ﻣﻲﺳﺎﺯﻧﺪ، ﺑﻴﺎﻥ ﻣﻲﺩﺍﺭﺩ.

In current cellular systems, the performance of active users' devices at the cell edge suffers from the poor link quality. However, these connections also requires more resource blocks and transmission power. In order to reduce the number of resource blocks and transmission power, this paper discusses device to device communication in downlink and uplink cases of cellular communication systems. In order to optimize the connections of different network users, which means finding the best user’s connection to a base station (minimum power consumption), which may be established through communication with other users or direct connection with the base station, and to minimize the total transmission power, different optimization methods such as gravitational search optimization, particle swarm optimization, genetic optimization algorithm and distributed strategy based on Q learning and softmax decision making methods are used. The numerical results show a power reduction of around 30 percent for these distributed communications with less computational complexity using the Q learning method compared to the case in which all users traditionally connect through the base station in a centralized way with high computational complexity.