Satellite Teletraffic Engineering

Answers

1. 1

   Teletraffic engineering in satellite communications is the application of probability theory and queueing theory to design, plan, and manage satellite networks to efficiently handle communication traffic. Its primary objectives are to:

   • Maximize utilization of satellite resources (transponders, bandwidth)
   • Minimize call blocking and service degradation
   • Ensure quality of service (QoS) requirements are met
   • Optimize cost-performance trade-offs in network design
2. 2

   An Erlang is a dimensionless unit of traffic intensity, representing the continuous use of one voice path. In satellite teletraffic engineering, it's calculated as:

   A = λ × h

   where A is traffic in Erlangs, λ is the average call arrival rate (calls/hour), and h is the average call holding time (hours/call). For example, if 30 calls arrive per hour with an average duration of 3 minutes (0.05 hours), the traffic load is 1.5 Erlangs.

3. 3

   From a teletraffic perspective:

   • GEO (Geostationary Earth Orbit): High latency (~250ms round trip) affects interactive services; wide coverage reduces handover needs but creates concentrated traffic patterns.
   • MEO (Medium Earth Orbit): Moderate latency (~110ms); requires more satellites for coverage, introducing handover traffic management challenges.
   • LEO (Low Earth Orbit): Low latency (~20ms) improves user experience; requires complex handover management and routing due to rapid satellite movement.
4. 4

   The Erlang B formula calculates the probability that a call is blocked due to all circuits being busy:

   P_b = (A^N / N!) / (Σ_{i=0}^{N} A^i / i!)

   where P_b is blocking probability, A is traffic in Erlangs, and N is number of channels. In satellite network planning, it's used to determine the required number of transponder channels for a given traffic load and target blocking probability.

5. 5

   Grade of Service (GoS) is a measure of the probability that a user will be denied service due to network congestion. In satellite communications, it's typically specified as:

   • Call blocking probability (e.g., P.01 = 1% blocking)
   • Call setup delay
   • Post-dial delay

   For voice services, GoS is often specified as P.01 or P.02 during the busy hour. For data services, additional parameters like packet loss rate and delay jitter may be included.

6. 6

   Multiple access techniques impact teletraffic management differently:

   • FDMA: Fixed bandwidth allocation requires careful frequency planning; traffic variations can lead to inefficient use if channels are pre-allocated.
   • TDMA: Dynamic time slot allocation allows statistical multiplexing gains; requires precise synchronization and guard times between slots.
   • CDMA: Soft capacity limits where more users degrade service gradually; allows overloading but requires power control to manage interference.
7. 7

   Traffic intensity measures the average number of simultaneous calls during a period. It directly relates to satellite transponder utilization:

   Utilization = Traffic Intensity (Erlangs) / Number of Channels

   High utilization maximizes revenue but increases blocking probability. Satellite operators typically design for 70-85% utilization during busy hours, balancing efficiency with acceptable GoS. Transponder utilization must also account for guard bands (FDMA) and guard times (TDMA).

8. 8

   The busy hour is the 60-minute period with the highest traffic load. It's critical in satellite teletraffic analysis because:

   • Network capacity must be designed to handle busy hour traffic while meeting GoS requirements
   • Satellite resources are dimensioned based on busy hour Erlangs
   • Revenue calculations often use busy hour traffic as a key metric
   • Performance guarantees in SLAs are typically specified for busy hour conditions
9. 9

   Propagation delay in GEO systems (~250ms round trip) significantly affects teletraffic engineering:

   • Increases call setup time, affecting perceived quality
   • Reduces efficiency of interactive protocols (e.g., TCP requires adjustments)
   • Affects voice echo control requirements
   • Impairs certain signaling protocols designed for terrestrial delays
   • Requires larger buffers for data services to maintain throughput

   These factors must be considered in traffic models and QoS parameters for GEO systems.

10. 10

    The Poisson traffic model assumes:

    • Calls arrive randomly and independently
    • Call arrivals follow a Poisson distribution
    • Call holding times follow a negative exponential distribution
    • Infinite number of sources

    Key parameters: arrival rate (λ), service rate (μ), and number of servers (N). Applicable to satellite systems for:

    • Modeling large user populations accessing a satellite transponder
    • Voice traffic where calls are independent events
    • Systems with many more potential users than available channels
11. 11

    Call blocking probability is the probability that a call attempt fails due to all available channels being occupied. It relates to the number of channels through the Erlang B formula:

    As the number of channels (N) increases:

    • For the same traffic load, blocking probability decreases
    • More channels allow higher traffic loads at the same blocking probability
    • There are economies of scale: doubling channels more than doubles capacity at fixed blocking probability

    In satellite transponders, channels may be frequency slots (FDMA), time slots (TDMA), or code allocations (CDMA).

12. 12

    The trade-off between bandwidth efficiency and traffic capacity involves:

    • Higher bandwidth efficiency (bits/Hz) allows more data per transponder but often requires complex modulation (e.g., 64-QAM) that needs higher SNR, reducing coverage area and potentially increasing blocking in fringe areas.
    • Robust modulation (e.g., QPSK) provides better coverage and lower blocking but uses bandwidth less efficiently.
    • Forward Error Correction (FEC) adds overhead but reduces retransmissions, effectively increasing capacity for error-prone links.
    • Capacity can be increased by accepting higher blocking rates or by implementing prioritization schemes.

    Optimal design balances these factors based on service requirements and user distribution.