Channel state-Adaptive techniques for Throughput Enhancement in Wireless Broadband Networks I. Koutsopoulos and L. Tassiulas Presented by Vladimir Bychkovskiy

Contents Motivation System Model Resource Allocation Algorithms Modulation Control Modulation and Power Control Simulation Results Critique

Motivation High projected demand for High speed connections Flexibility Ease of deployment Fiber is fast, but End-users may be mobile Wiring “last-mile” may be very expensive Solution: Make “last-mile” wireless!

System Model System setup M base stations N users ( at fixed and arbitrary locations) Single carrier frequency T f Perfect synchronization among carrier frames is assumed Time-division duplexing (TDD) downlink and uplink

System Model Modulation level User bit rate requirements r i  Number of channels n i (timeslots)  Modulation level b i (bits/symbol)  Symbol rate (symbols/sec) K symbols/timeslot for simplicity Main Idea of the algorithm Allow assignment of different modulation levels in different channels.

Modulation Control Problem formulation Modulation level for a user is determined by SNR  High modulation does not allow for reuse It does not enhance total system capacity  Low modulation allows to pack more users into a channel It “may increase” total system capacity Problem:  Identify a set of cochannel users which achieves maximum total throughput for each channel  Alternatively: minimize the total number of channels

Modulation Control (cont’d) Resource Allocation Algorithm Pack as many users as possible Attempt to keep high modulation level Greedy approach Insert a user that creates the least interference Once cochannel set is determined select highest possible modulation level For L available modulation levels the running time is O(LNM 2 )

Modulation and Power Control Why bother with power control? Modulation level variation  Increases robustness to interference  Increases reuse  DOES NOT reduce the amount of cochannel interference Addition of power control on base station  Can reduce cochannel interference

Modulation and Power Control Resource Allocation Algorithm Same objectives as last time Greedy Approach (again) Perform adaptation of modulation level and power of each channel separately Attempt to allocate all users to channels  If SNR requirements are not satisfied, attempt to adjust power Running time of the algorithm O(LN 2 M 2 )

Simulation Setting 8x8 km grid 16 base stations Random and Fixed positions TDMA/TDD access L(d)=L(d0)+10*k*log (d/d0)

Simulation Comparative study Adaptive Modulation Adaptive Modulation and Power Classical Power Control with Fixed Modulation (Zandar)

Simulation

Critique Pros Novel approach to resource allocation Complete mathematical model (for fixed users) Cons Perfect synchronization assumption Centralized algorithm High computation requirements Mobile users problem is not addressed Result:  Algorithm may not scale well