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Introduction to Multiplexing , Multiplexing PPT in short
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- 2. Definition and types MULTIPLEXIN G Multiplexing isa technique that combines multiple signals for transmission over a single data link. It enables efficient use of resources and increased data transmission capacity. Types of Multiplexing • Space Division Multiplexing (SDM) SDM involves physically separating channels. • Time Division Multiplexing (TDM) TDM allocates specific time slots to different channels. • Frequency Division Multiplexing (FDM) FDM separates signals into different frequency bands.
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- 4. TIME DIVISION MULTIPLEXING (TDM) Time DivisionMultiplexing (TDM) allocates fixed time slots for multiple data streams to share a single communication channel. It interleaves data from different sources by assigning each source a unique time slot within a predefined cycle. TDM is commonly used in telecommunications for voice and data transmission over a single physical medium, optimizing resource utilization through temporal segmentation.
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- 6. FREQUENCY DIVISION MULTIPLEXING (FDM) Frequency Division Multiplexing(FDM) allocates distinct frequency bands to multiple signals for simultaneous transmission. It enables multiple users or channels to share a communication medium by dividing the available frequency spectrum. FDM is commonly used in radio, television broadcasting, and traditional wired telephone systems for efficient utilization of bandwidth.
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- 8. SPACE DIVISION MULTIPLEXING (SDM) Space DivisionMultiplexing (SDM) concurrently transmits multiple data streams over separate physical paths within a shared medium. SDM boosts communication capacity by utilizing parallel transmission paths, such as separate fiber strands or antennas. It addresses increasing data demands by enabling simultaneous transmission of data streams, offering higher throughput and efficiency.
- 9. OF MULTIPLEXING ADVANTAGES 1 3 2 Increased Data Transmission Capacity ImprovedBandwidth Efficiency Enhanced Network Performance Enables efficient utilization of available resources. Optimizes the use of the available data link bandwidth. Maximizes the network's potential for data transmission.
- 10. OF MULTIPLEXING APPLICATIONS 1 2 Telecommunications Supports efficient voiceand data transmission over networks. Data Centers Enables high-capacity data transfer and storage capabilities. Broadcasting Combines multiple audio/video signals into a single broadcast, enhancing spectral efficiency. 4 3 Satellite Communication Maximizes bandwidth utilization in satellite systems, facilitating diverse applications like TV broadcasting and internet access.
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Editor's Notes
- #5 there is no central authority or control. Each node operates independently and makes decisions based on local information. Distributed algorithms often involve concurrent execution of multiple processes or nodes. Distributed algorithms involve techniques like distribution Distributed transactions, consensus protocols, and replication to maintain a consistent view of data across nodes. Nodes in a distributed system can fail due to hardware or software issues, and the algorithm should continue to operate correctly despite these failures. it is designed to work efficiently and able to contain a growing number of nodes without degradation in performance
- #7 there is no central authority or control. Each node operates independently and makes decisions based on local information. Distributed algorithms often involve concurrent execution of multiple processes or nodes. Distributed algorithms involve techniques like distribution Distributed transactions, consensus protocols, and replication to maintain a consistent view of data across nodes. Nodes in a distributed system can fail due to hardware or software issues, and the algorithm should continue to operate correctly despite these failures. it is designed to work efficiently and able to contain a growing number of nodes without degradation in performance
- #9 there is no central authority or control. Each node operates independently and makes decisions based on local information. Distributed algorithms often involve concurrent execution of multiple processes or nodes. Distributed algorithms involve techniques like distribution Distributed transactions, consensus protocols, and replication to maintain a consistent view of data across nodes. Nodes in a distributed system can fail due to hardware or software issues, and the algorithm should continue to operate correctly despite these failures. it is designed to work efficiently and able to contain a growing number of nodes without degradation in performance