Optimal Packet Scheduling and Radio Resource Allocation. By Subhendu Batabyal Basabdatta Palit Prabhu chandar Dr. Suvra Sekhar Das

Transcription

1 Optimal Packet Scheduling and Radio Resource Allocation By Subhendu Batabyal Basabdatta Palit Prabhu chandar Dr. Suvra Sekhar Das

2 Background - System Flow for Packet Scheduling Cellular Layout Tx Modulator Turbo Encoder Frequency Selective Channel Model (ITU-R M.2135) User Distribution & Mobility Throughput Traffic Generation SINR Results Capacity SINR per UE 1.Path Loss 2.Shadowing 3.Small Scale Fading (ITU-R M.2135) CQI Feedback (Sub Band) Rx Demodulator Turbo Decoder Link Level Simulator EESM (Link to System Mapping) Packet Scheduling and Radio Resource Allocation SINR vs. BLER HARQ QoS Power Control Link Adaptation

3 Physical Resource Block in OFDMA UE4 UE3 UE1 UE2 B.S OFDM(A) UE5 Physical (Radio) Resource Block Allocation 4

4 Generalized System Model enode-b Direction of antenna main-lobe Admission Control Link Adaptation RRM Equivalent BW Estimator QoS Packet Scheduler Transport Block Size CB CB CB CB CB CB Time Domain Frequency Domain HARQ TBS Form Call Request CB Segmentation & CRC Data Stream 2 MIMO MIMO Diversity SU-MIMO MU-MIMO Sector/cell CQI Reference Signal Beam forming ACK/NACK CQI Data Stream 1 ACK/NACK Data BE kbps, No Delay Constraint Data Video - 64 kbps, Delay Constraint VoIP - 16 kbps, Delay Constraint Sub Band Sub Frame Duration Small Scale Fading Multi path Velocity Reference Signal Target cells CQI Estimation HARQ Path gain Shadowing

5 Background Packet Scheduling Packet Scheduling Time Domain Packet Scheduling (TDPS) to arrange the packets according to their Time to Live to give priority to packets which have smaller time to expire Frequency Domain Packet Scheduling (FDPS) to allocate the packets to the Physical Resource Blocks minimize the usage of the number of available resources and exploit multiuser frequency diversity

6 Background Packet Scheduling in Time and Frequency Pending ReTx Buffer User PRB ReTx Packets New Packets Buffer Management Packets with Lifetime>Current Time Drop packets with Lifetime<Current Time H V10 V9 H5 V6 V12 H3 H11 V20 Packets with Time Domain Packet Scheduling Lifetime Validity H5 V10 H3 H11 V9 V6 V12 V20 Packets arranged as per TDPS Algorithm Radio Resource Allocation and Frequency Domain Packet Scheduling (FDPS) H3 R max CQIs Reported for each packet

7 Motivation Efficiency of Resource Allocation plays a crucial role in determining network efficiency. If the no. of users exceeds a certain limit, there is a exponential fall in system performance. It is crucial to quantify the above limit accurately, while also maximizing it in order to dimension the network properly with maximum network efficiency for the operator. Computational resources are limited, therefore exhaustive search is impossible.

8 State of the Art Existing Time Domain Scheduling Algorithms Based on Time to live (delay criterion) considers only packet expiry criterion Round Robin (Allocation Fairness criterion) considers users in their arrival sequence only. Based on best channel condition considers only present throughput Blind equal throughput considers only past throughput Existing Frequency Domain Scheduling Algorithms Round Robin (RR) Max C/I greedy approach that ignores fairness Existing Joint Time/Frequency Domain Scheduling Algorithms Proportional Fair (PF) considers past and present throughput but ignores factors such as time to live of the packets, no. of packets per user etc.

9 Gaps in Literature We need an algorithm that combines fairness with optimal capacity in terms of no. of supported real time (VoIP) users. We need an algorithm that reduces the exponential complexity of exhaustive search without compromising too much on performance. We need an algorithm that considers packet life-time, no. of packets per user as well as user s past throughput and error rates. We need to find the allocation order at a given snapshot of time that results in the best possible usage of transmission resources

10 Exponential Complexity of Exhaustive Search RB User RB 1 RB 2 RB 3 RB 4 RB K-1 RB K User 1 User 2 User 3 User U-1 User U U Users assigned to K Resource Blocks U K combinations! Allocation order -> U! possible sequences Need for Heuristic Search schemes

11 Problem Description: Metric based prioritization for TDPS m u u A B n m ( ) C m p um, C u u D S A, B, C, D 0 n m p C S u um, u u Time interval Arrival time of u th user p th packet of u th user arrived at m th time instant Expected future throughput of u th user Average past throughput of u th user

12 Problem Description: Improved FDPS User1: User2: Each rectangular box represents a Physical Resource Block (PRB) and the number inside indicates the Channel Quality Indicator (CQI)

13 Problem Description: Improved FDPS User1: User2: Each rectangular box represents a Physical Resource Block (PRB) and the number inside indicates the Channel Quality Indicator (CQI)

14 Problem Definition Optimal and Fair PRB Allocation for VOIP Packets in OFDMA Downlink for LTE to Maximize Spectral Efficiency

15 Work Plan A. Optimal PS and RRA for a single enb Best Order of Allocation Approach 1. 2 users 2. 4 users 3. Any no. of users B. Joint PS and RRA with enb coordination Design of Joint PS and RRA with enb coordination 1. 2 enbs 2. 3 enbs Extensions of Simulator for Joint PS and RRA with enb coordination 3. 2 enbs 4. 3 enbs

16 Pert Chart for Time Plan Activity Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 A(1,2,3) B1 B2 B3 B4

17 THANK YOU Finally..

18 Approach to Problem - Design Objectives Design of Improved TDPS considering Time to live of the packet User s previous throughput User s predicted future throughput No. of Packets to be Scheduled Design of Improved FDPS considering All possible allocation orders for a set of users Maximizing number of users scheduled per TTI Maximizing number of PRBs utilized per TTI Integrating improved TDPS and FDPS Make use of TDPS priorities in FDPS

19 Approach to Problem Overall Objectives Design of Optimal Packet Scheduling and Radio Resource Allocation for OFDMA Cellular Networks carrying Multimedia and Data traffic System Goals Maximize number of supported real time users and bandwidth utilization Fairness among the users Inter Cell Interference Mitigation Cell Edge User Throughput Maximization

20 Approach to Problem - Design Constraints Computational Complexity Allocation of resources to users while satisfying User s bandwidth requirement Delay/Latency constraints Control Channel Limitations Maintaining a minimum FER for a minimum percentage of users

21 State of the Art Sl. No. Paper Details Contents 1 Kyuho 2009 Authors have formulated utility maximization problem with network wide Proportional Fair (PF) as an objective in a multi-cell network with Partial Frequency Reuse (PFR) Proposed online algorithms are based on the inter/intra-handover and cell-site selection in which a metric is changed from the signal strength to the average throughput. Centralized approach which needs fast information exchange and computational complexity 2 Xu Kai 2007 Proposed optimal and sub-optimal inter-cell scheduling strategies based onthe coordinated transmission from the interfering cells This paper extends the utility function based multi-user packet scheduling strategies derived for single cell scenario to multi-cell scenario Centralized approach 3 Yavuz 2009 Proposed a scheduler that allows for opportunistic behavior from a single sessions point of view but keeps the overall caused interference variation at a low level in order to improve the performance oflink Adaptation Ignores fairness among the users 4 Falconetti 2010 A codebook based inter-cell interference coordination scheme has been proposed where, the supporting cells try to use a precoding matrix for which the interference received by selected cell-edge UEs remains acceptable. Methods to solve the conflicts of interest are also discussed 1/23/

22 State of the Art Sl. No. Paper Details Contents 5 Xinying 2009 A signal leakage information based scheme has been proposed for designing the precoding vector to reduce the Inter Carrier Interference (ICI). In the proposed method coordinating enb uses PMIs that would result in minimum interference 6 Jianchi 2010 A large-scale Channel State Information (CSI) for precoding has been proposed to reduce the over-head involved in the coordination process A coordination scheme has been proposed to determine best groups of transmission points and coordinated UEs 7 Jing 2010 A joint PF scheduling algorithm has been for CoMP-SU-MIMO The proposed algorithm treats the cell-edge UEs and cell-center UEs equally in every TTI without partitioning the frequency band for CoMP operation 1/23/