Long-Term Proportional Fair QoS Profile Follower Sub-carrier Allocation Algorithm in Dynamic OFDMA Systems Arijit Ukil, Ja...
Agenda <ul><li>Motivation </li></ul><ul><li>Wireless Channel Dynamics </li></ul><ul><li>Multi User diversity </li></ul><ul...
<ul><li>We Want More </li></ul>Apart from Voice Traffic, applications require high data rate and variable QoS >  e-mail > ...
Targets of LTE <ul><li>Peak data rate </li></ul><ul><ul><li>100 Mbps DL/ 50 Mbps UL  </li></ul></ul><ul><li>Mobility </li>...
IEEE 802.16 QoS
Challenges <ul><li>Limited Resources :  Capacity-limited medium </li></ul><ul><li>Traffic patterns, user locations, consta...
Channel Dynamics Wireless Channel is time-varying and frequency-selective Multipath fading provides high peaks to exploit ...
Multi User Diversity In a large system with users fading independently, there is likely to be a user with a good channel c...
Why another degree of freedom? By principal, a  well designed communication system should take the available degrees of fr...
How time-diversity gain can be achieved Time Diversity technique fundamentally consists of retransmitting the corrupted in...
QoS awa re OFDMA  Sub -carrier allocation <ul><li>The purpose of Resource Allocation is to intelligently allocate the limi...
Problem <ul><li>Optimum instantaneous sub-carrier allocation is not possible in real time because of high computational co...
Long Term Proportional Fair Resource Allocation
System Model <ul><li>Single cell multi-user OFDMA system with FRF=1 </li></ul><ul><li>Conditions: </li></ul><ul><li>Sub-ca...
Proportional Fair Optimization <ul><li>Objective is to maximize the sum rate over time with a feedback on users’ already a...
Traditional PF is instantaneous decision maker <ul><li>Traditional Proportional Fair Optimization is based on instantaneou...
Long Term Proportional Fair   <ul><li>Objective is to  </li></ul>
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OFDMA resource allocation in 4G wireless networks

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OFDMA resource allocation in 4G wireless networks

  1. 1. Long-Term Proportional Fair QoS Profile Follower Sub-carrier Allocation Algorithm in Dynamic OFDMA Systems Arijit Ukil, Jaydip Sen, Debasish Bera Innovation Lab, Convergence and Wireless Technology, Kolkata, India
  2. 2. Agenda <ul><li>Motivation </li></ul><ul><li>Wireless Channel Dynamics </li></ul><ul><li>Multi User diversity </li></ul><ul><li>Why another degree of freedom? </li></ul><ul><li>QoS-aware Resource Allocation Problem </li></ul><ul><li>Long Term Proportional Fair Resource Allocation </li></ul><ul><li>Application </li></ul><ul><li>Simulation Results </li></ul><ul><li>Conclusion </li></ul>
  3. 3. <ul><li>We Want More </li></ul>Apart from Voice Traffic, applications require high data rate and variable QoS > e-mail > multimedia messaging > Internet browsing > video conferencing > audio and video streaming > e-commerce > mobile TV
  4. 4. Targets of LTE <ul><li>Peak data rate </li></ul><ul><ul><li>100 Mbps DL/ 50 Mbps UL </li></ul></ul><ul><li>Mobility </li></ul><ul><ul><li>Optimized for 0 ~ 15 km/h. </li></ul></ul><ul><ul><li>15 ~ 120 km/h supported with high performance </li></ul></ul><ul><ul><li>Supported up to 350 km/h or even up to 500 km/h. </li></ul></ul><ul><li>Coverage </li></ul><ul><ul><li>Performance should be met for 5 km cells with slight degradation for 30 km cells. </li></ul></ul><ul><li>Spectrum flexibility </li></ul><ul><ul><li>1.25 ~ 20 MHz </li></ul></ul><ul><li>2X2 MIMO </li></ul>
  5. 5. IEEE 802.16 QoS
  6. 6. Challenges <ul><li>Limited Resources : Capacity-limited medium </li></ul><ul><li>Traffic patterns, user locations, constantly changing network conditions </li></ul><ul><li>Heterogeneous traffic </li></ul><ul><li>Hard QoS constraints </li></ul><ul><li>Maximize number of users </li></ul><ul><li>Maximize network coverage </li></ul><ul><li>Minimize outage probability </li></ul><ul><li>Guaranteed user satisfaction </li></ul>
  7. 7. Channel Dynamics Wireless Channel is time-varying and frequency-selective Multipath fading provides high peaks to exploit Channel capacity is achieved by such an opportunistic strategy Channel varies faster and has more dynamic range in mobile environments More appropriate for data with soft latency requirements
  8. 8. Multi User Diversity In a large system with users fading independently, there is likely to be a user with a good channel condition Multi user diversity takes advantage of rather than compensate for the channel fading Fast frequency selective fading now becomes beneficial Instead of pumping more energy to compensate the multipath loss, exploit the peaks in a channel-aware way
  9. 9. Why another degree of freedom? By principal, a well designed communication system should take the available degrees of freedom of the channel as much as possible Multiuser diversity provides a system-wide benefit Challenge is to share the benefit among the users in a fair and optimum way Frequency diversity gain is achieved by allocating OFDMA sub-carrier to the user with relatively better channel, ideally the channel with the maximum gain Time diversity is traditionally obtained by interleaving and coding over symbols across different coherent time periods Time diversity is achieved by averaging the fading of the channel over time Time Diversity technique is restricted to delay-tolerant applications, such as video-on-demand or multimedia and data transfer
  10. 10. How time-diversity gain can be achieved Time Diversity technique fundamentally consists of retransmitting the corrupted information at times when the channel is expected to be more favourable, that is at time spacing exceeding the channel coherence time of the channel In the context of OFDMA subcarrier allocation, time diversity gain is achieved by computing the resource allocation metric over time duration more than the coherence time of the channel, which should be typically few numbers of frames
  11. 11. QoS awa re OFDMA Sub -carrier allocation <ul><li>The purpose of Resource Allocation is to intelligently allocate the limited resources (sub-carriers) among users to meet users’ service requirements (QoS) and to enhance system capacity </li></ul><ul><li>It’s a system optimization problem </li></ul><ul><li>Available Resources </li></ul><ul><ul><li>Transmit power </li></ul></ul><ul><ul><li>Frequency bandwidth </li></ul></ul><ul><ul><li>Transmission time </li></ul></ul><ul><ul><li>Code resource </li></ul></ul><ul><ul><li>Spatial antennas </li></ul></ul><ul><li>Resource allocation impacts </li></ul><ul><ul><li>Less Power consumption </li></ul></ul><ul><ul><li>More User throughput </li></ul></ul><ul><ul><li>Enhanced System Capacity </li></ul></ul><ul><ul><li>User QoS Guarantee </li></ul></ul>
  12. 12. Problem <ul><li>Optimum instantaneous sub-carrier allocation is not possible in real time because of high computational cost </li></ul><ul><li>QoS parameters for different applications are different </li></ul><ul><li>Same resource allocation algorithm does not fetch optimum performance gain for all the QoS classes </li></ul><ul><li>Optimum capacity from hard QoS (UGS,RTPS) based applications can only take advantage of frequency diversity </li></ul><ul><li>Time as well as frequency diversity gains can be jointly exploited in delay tolerant applications like file transfer, multimedia messaging, web browsing, in NRTPS, BE class of QoS </li></ul><ul><li>Find an optimum resource allocation scheme/algorithm for non premium (NRTPS, BE) classes </li></ul>
  13. 13. Long Term Proportional Fair Resource Allocation
  14. 14. System Model <ul><li>Single cell multi-user OFDMA system with FRF=1 </li></ul><ul><li>Conditions: </li></ul><ul><li>Sub-carrier bandwidth < coherence bandwidth of the channel </li></ul><ul><li>Sub-carrier allocation period < coherence time </li></ul><ul><li>Resource allocation metric computation time > coherence time </li></ul><ul><li>Assumptions: </li></ul><ul><li>Perfect channel state information </li></ul>Sub-carrier allocation IFFT fN P/S CSI Sub-carrier to user mapping FFT S/P Remove Cyclic prefix Sub-carrier Allocation Module User1 … QoS1 QoSK Transmitter Wireless Channel Add Cyclic prefi x User1 UserK UserK
  15. 15. Proportional Fair Optimization <ul><li>Objective is to maximize the sum rate over time with a feedback on users’ already achieved data rate, i.e., users’ past performance is also taken into account along with instantaneous channel condition to allocate sub-carrier </li></ul><ul><li>PF scheduler or resource allocation heuristically tries to balance the fairness among the users in terms of outcome or throughput, while implicitly maximizing the system throughput in a greedy manner. </li></ul><ul><li>Let be the achievable rate for kth user at tth instant . </li></ul><ul><li>In PF optimization subcarrier n is allocated to k* user when the following condition is satisfied: </li></ul>
  16. 16. Traditional PF is instantaneous decision maker <ul><li>Traditional Proportional Fair Optimization is based on instantaneous computation of proportional fair metric </li></ul><ul><li>The resultant optimization does not take advantage of the time diversity gain </li></ul><ul><li>Traditional PF suitable for real time applications with hard delay requirement </li></ul>
  17. 17. Long Term Proportional Fair <ul><li>Objective is to </li></ul>

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