# Presentation: 288875

## Eytan Modiano
Massachusetts Institute of Technology

**Massachusetts Institute of Technology**

**Architectures and Protocols for Next Generation Satellite Networks**

## Technical Challenges

**Network**

**DLC**

**Physical**

** **

**Appl**

**Mod/Demod**
**Variable R**

**FEC**

**ARQ**

**Media Access**
**Control**

**Router/Buffer**

**Transport**

**Net Management**

*** ******Dynamic Capacity***
*** ******Agile beams***
*** ******MAC***

***Dynamic routing: ****deterministic & stochastic*
***Heterogeneous network:**** Satcom, fiber, wireless*
***Differentiated services:**** cost-based, time-deadline, ...*

**Congestion**
**& Flow**
**Control**

**Gateways**

**Convergence Layer**
**-----------------**
**Agent-**
**Mediator**

***Satellite resources   extremely precious***

**Technical Challenges**

## Opportunities for dynamic resource allocation

**DLC**

**Physical**

**Appl**

**Mod/Demod**
**Variable R**

**FEC**

**ARQ**

**Media Access**
**Control**

**Transport**

**Opportunities for dynamic resource allocation**

** ****Physical Layer: Weather induced variable ****capacity channel**
***- Power management***
***- Variable rate modulation and coding***
***- Adaptive packet size***
** ****Media access control with agile antenna beams**** **
***	******- Optimal beam scheduling***
*** ******	******- Beam pattern design ***
*** ******Network layer routing and flow control***
***- ******Energy efficient routing***
***- Time-varying link capacity***
***- Limited on-board buffers***

## Variable Rate Modulation

**QPSK**

**8-PSK**

**16-PSK**

**BPSK**

***LIDS***

**Variable Rate Modulation**

## Scheduling with limited buffers

**Satellite to ground**
**Bursty traffic**
**Arbitrary arrival distribution**
**Channel rates vary**
**K transmitters onboard satellite**

_**Preliminary Results**_
**Scheduling of transmissions**
**K = 1  => Fastest-Rate-First Policy minimize unfinished work**** **
**K > 1  ?**** **
**Serving the fastest rate downlink not always optimal**

- DOWNLINK
- BEAMS

## Sub-optimality of fastest first scheduling

**Example 3 queues and 2 servers**
**Fastest-first **
**Slot 1: serve queues 1 & 2**
**Slot 2: serve queue 3 **
**Slot 3: serve queue 3 **** **
**Notice that during slots 2 and 3 only one server is used!**
**Alternative algorithm**
**Slot 1: serve queues 1 & 3**
**Slot 2: serve queues 2 & 3 **

**Must take queue length into account !**

## Instability of fastest queue first

**When packets arrive to either of the top two queues they are served first**
**With probability p****2**** no server can serve last queue**
**As long as arrival rate exceeds 1 - p****2**** last queue is unstable**
**Notice that when p < 1, if we assign one server to always serve queue 3 and the other to alternate between 1 and 2, all queues would be stable**

**Two servers, Bernoulli arrivals**

**Must take queue length into account !**

## Stable Algorithms 

**Longest queue first**
**Algorithm allocates the K servers to the K longest queues**
**Theorem:  Longest queue first is stable, for all arrival rates within the stability region**
**Proof:  Using Lyapunov stability**

**Stability Region:**

**Longest (K) queues first achieves 100% throughput**

## Extensions

**Time varying service rates **
** ********1**** = ********2**** = .. = ********N**
**On-off queues**
**Longest (K) queues first minimizes average buffer occupancy**
**Power Limited systems**
** ********i (t) ****is a convex function of the power allocated to the ith server (P****i****)**
**Total power allocated to servers is limited (                  )**
**Allocate power to**

## Energy efficient routing

**Limited energy onboard satellites**
**Limited energy input from solar arrays**
**Finite battery capacity**
**Energy drain**
**Onboard processing (CPU, etc.)**
**Transmission**
**Downlink transmission**
**Uplink processing**
**Crosslinks to other satellites**
**Energy efficient routing**
**Downlink transmission dominates cost**
**Optimal energy allocation**
**Given limited energy, how do we choose which customers (calls) to serve **
**Different rewards for serving customers**
**Should we save energy for a potential future customer with a higher reward?**

## Satellite energy allocationsingle satellite case

**Satellite orbits earth**
**Different demand levels at different locations**
**Different rewards for serving the demands  ($/unit energy)**
**Energy input (from sun) depends on location (fixed schedule)**
**Finite battery capacity**
**Satellite can serve a request only if it has enough energy**
**Objective: maximize total rewards by properly choosing which customer to serve**
**Divide orbit into discrete time slots (locations)**
**At each location we are given a demand and reward for serving that demand**
**Demand and reward may not be known in advance (fixed or random)**

## Dynamic programming formulation

**Dynamic programming equations**** **
**J****k**** is concave **
**Threshold algorithms optimal**
**If reward > threshold then consume **
**Otherwise save energy for later stage**

## Example: unlimited demand

*E**max* = 5

*E**max* = 30

*E**max* = 200

- Notice the impact of battery capacity *E**max*

## Summary

**Present satellite systems are bent-pipes (transponders)**
**Designed for voice**
**Can be “forced” to work with data, but inefficient**
**Satellite processing can significantly improve performance **
**Especially for bursty data applications**
**Research on multiple fronts from physical layer to transport layer**** **
**Adaptive modulation and coding**
**Multiple access**
**Beam scheduling**
**Flow control**
**Resource allocation**