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Multi-user scheduling in HSDPA systems

Multi-user scheduling in HSDPA systems. Samuli Aalto and Pasi Lassila Department of Communications and Networking TKK Helsinki University of Technology Email: {Samuli.Aalto, Pasi.Lassila}@tkk.fi. HSDPA systems Downlink scheduling

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Multi-user scheduling in HSDPA systems

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  1. Multi-user scheduling in HSDPA systems Samuli Aalto and Pasi Lassila Department of Communications and Networking TKK Helsinki University of Technology Email: {Samuli.Aalto, Pasi.Lassila}@tkk.fi 1(9)

  2. HSDPA systems • Downlink scheduling • BS decides allocation of radio resources for different users’ traffic • Radio resource management • In HSDPA, resources = orthogonal codes • Each user terminal has a ”category” • Category defines the processing power limitation of the terminal Scheduling: • Based on user’s channel quality and terminal category a coding scheme and number of codes is used which determines the ”bit rate” • Scheduler should use all resources (i.e., schedule multiple users) 2(9)

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  4. Flow-level model (1) • Elastic flows with random sizes with a general distribution, Poisson arrivals with rate l • At time t there are N(t) flows, each flow is indexed by n • We do not consider fast fading • Flows only see average channel behavior • Flows have different channel’s due to, e.g., distance to the base station • Codes correspond to servers • Number of servers denoted by K and servers indexed by k • The service rate of each server k is user dependent, denoted by rnk • e.g., rate attenuates with distance dn, rnk ~ Min{1,(d0/dn)a} • Aggregate rate is linear in number of codes (orthogonality) 4(9)

  5. Flow-level model (2) • Terminal category • Associated with each flow n is terminal category cn telling the number of codes • Due to terminal category constraints multiple flows need to be scheduled simultaneously (HSDPA systems) • Earlier we assumed all codes are given to exactly one user (old CDMA 1xEV-DO systems) • Multiple servers can serve one flow • classical multiserver problem assumes one server per queue (flow) • Servers are heterogeneous (service rate depends on flow) • Again, size-based information is used to select flows intelligently • Same problem formulation applies to OFDMA systems • Carriers correspond to codes 5(9)

  6. Problem reduction • Idea: First try to simplify the problem to the simplest possible system amenable to analysis • Gives insight for analyzing more complex scenarios • Assumptions • All flows have identical channels (symmetric situation) • All flows have the same terminal category so that K/2 codes can be allocated per user • Corresponds to an M/G/2 system (with homogenous servers) • Even for this system the optimal scheduling rule is not known (for minimizing mean flow delay) 6(9)

  7. Collection of useful results (found so far…) • In a static setting with a fixed number of flows SRPT is optimal1 • Applies even with heterogeneous servers • Assumes one server / flow • In the dynamic setting • “long jobs are stuck at the end of the busy period”2 • Gain from (size-based) scheduling • Impact greatest for M/G/1 queue • For M/G/n, as n increases, scheduling has less and less impact • In an M/G/∞ queue scheduling does not affect performance 1 Pinedo (1995) 2 Wierman (2007) 7(9)

  8. Some tests for M/G/2 (relative to PS) Erlang flow sizes Pareto flow sizes SRPT/LRPT SRPT/LRPT FCFS SRPT SRPT SRPT* SRPT* Exponential flow sizes SRPT FCFS SRPT/LRPT Det SRPT SRPT* Erl Exp Pareto 8(9)

  9. Two forthcoming ACM MSWiM 2008 papers • Pasi Lassila and Samuli Aalto”Combining opportunistic and size-based scheduling in wireless systems” • studying how to optimally combine channel-aware and size-based scheduling of elastic flows in HSPDA/HDR type systems • channel-awareness exploits variations in the quality of the radio channel • size-based schduling gets rid of flows as soon as possible • Jarno Nousiainen, Jorma Virtamo and Pasi Lassila”Forwarding capacity of an infinite wireless network” • studying the maximal forwarding capacity of a massively dense wireless multihop network • separation of micro (single hop) and macroscopic (end-to-end path) levels • formulation of the forwarding problem and development of simulation algorithms for obtaing upper bounds 9(9)

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