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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} HSDPA systems Downlink scheduling

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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}


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)


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)


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


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)


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)


Some tests for M/G/2 (relative to PS)

Erlang flow sizes

Pareto flow sizes








Exponential flow sizes











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