Why does statistical multiplexing have higher efficiency?

Concept: Multiplexing techniques share one physical link among multiple data streams. Static schemes such as frequency-division multiplexing (FDM) and synchronous (fixed) time-division multiplexing (TDM) pre-allocate a fixed frequency band or a fixed, recurring time slot to every channel, whether or not that channel currently has data to send. Statistical multiplexing (asynchronous / statistical TDM) instead allocates the shared bandwidth or time slots dynamically, on demand, only to the channels that currently have data ready to transmit.

Application: Consider N users sharing one link, where on average only a fraction of them are transmitting at any instant.

1. Under fixed/synchronous TDM, every one of the N users gets its own recurring slot each cycle even if it has nothing to send, so those slots go empty and link capacity is wasted.

2. Under statistical multiplexing, the multiplexer looks only at users with data ready and hands them the next available slot; idle users are simply skipped, so no slot capacity is wasted on silence.

3. Because slots are reused for whichever active user needs them, the same physical link capacity can carry more effective traffic, giving higher throughput for the same bandwidth.

Cross-check: Matching this back to the options confirms the answer and rules out the rest.

• Reserving bandwidth for each user even when idle is fixed/static allocation — the opposite behaviour, and exactly why static schemes are less efficient by comparison.

• Compressing all data is a separate technique (data compression) that reduces data volume; it does not change how the shared channel capacity is divided among users.

• Using fixed time slots describes synchronous TDM, not the demand-driven scheme the question asks about.

So the higher efficiency of statistical multiplexing comes from allocating bandwidth dynamically based on demand, so idle users never waste reserved capacity.