5,321 research outputs found
Selective Fair Scheduling over Fading Channels
Imposing fairness in resource allocation incurs a loss of system throughput,
known as the Price of Fairness (). In wireless scheduling, increases
when serving users with very poor channel quality because the scheduler wastes
resources trying to be fair. This paper proposes a novel resource allocation
framework to rigorously address this issue. We introduce selective fairness:
being fair only to selected users, and improving by momentarily blocking
the rest. We study the associated admission control problem of finding the user
selection that minimizes subject to selective fairness, and show that
this combinatorial problem can be solved efficiently if the feasibility set
satisfies a condition; in our model it suffices that the wireless channels are
stochastically dominated. Exploiting selective fairness, we design a stochastic
framework where we minimize subject to an SLA, which ensures that an
ergodic subscriber is served frequently enough. In this context, we propose an
online policy that combines the drift-plus-penalty technique with
Gradient-Based Scheduling experts, and we prove it achieves the optimal .
Simulations show that our intelligent blocking outperforms by 40 in
throughput previous approaches which satisfy the SLA by blocking low-SNR users
On Asymptotic Optimality of Dual Scheduling Algorithm In A Generalized Switch
Generalized switch is a model of a queueing system where parallel servers are interdependent and have time-varying service capabilities. This paper considers the dual scheduling algorithm that uses rate control and queue-length based scheduling to allocate resources for a generalized switch. We consider a saturated system in which each user has infinite amount of data to be served. We prove the asymptotic optimality of the dual scheduling algorithm for such a system, which says that the vector of average service rates of the scheduling algorithm maximizes some aggregate concave utility functions. As the fairness objectives can be achieved by appropriately choosing utility functions, the asymptotic optimality establishes the fairness properties of the dual scheduling algorithm.
The dual scheduling algorithm motivates a new architecture for scheduling, in which an additional queue is introduced to interface the user data queue and the time-varying server and to modulate the scheduling process, so as to achieve different performance objectives. Further research would include scheduling with Quality of Service guarantees with the dual scheduler, and its application and implementation in various versions of the generalized switch model
Counter-intuitive throughput behaviors in networks under end-to-end control
It has been shown that as long as traffic sources adapt their rates to aggregate congestion measure in their paths, they implicitly maximize certain utility. In this paper we study some counter-intuitive throughput behaviors in such networks, pertaining to whether a fair allocation is always inefficient and whether increasing capacity always raises aggregate throughput. A bandwidth allocation policy can be defined in terms of a class of utility functions parameterized by a scalar a that can be interpreted as a quantitative measure of fairness. An allocation is fair if alpha is large and efficient if aggregate throughput is large. All examples in the literature suggest that a fair allocation is necessarily inefficient. We characterize exactly the tradeoff between fairness and throughput in general networks. The characterization allows us both to produce the first counter-example and trivially explain all the previous supporting examples. Surprisingly, our counter-example has the property that a fairer allocation is always more efficient. In particular it implies that maxmin fairness can achieve a higher throughput than proportional fairness. Intuitively, we might expect that increasing link capacities always raises aggregate throughput. We show that not only can throughput be reduced when some link increases its capacity, more strikingly, it can also be reduced when all links increase their capacities by the same amount. If all links increase their capacities proportionally, however, throughput will indeed increase. These examples demonstrate the intricate interactions among sources in a network setting that are missing in a single-link topology
Delay Minimizing User Association in Cellular Networks via Hierarchically Well-Separated Trees
We study downlink delay minimization within the context of cellular user
association policies that map mobile users to base stations. We note the delay
minimum user association problem fits within a broader class of network utility
maximization and can be posed as a non-convex quadratic program. This
non-convexity motivates a split quadratic objective function that captures the
original problem's inherent tradeoff: association with a station that provides
the highest signal-to-interference-plus-noise ratio (SINR) vs. a station that
is least congested. We find the split-term formulation is amenable to
linearization by embedding the base stations in a hierarchically well-separated
tree (HST), which offers a linear approximation with constant distortion. We
provide a numerical comparison of several problem formulations and find that
with appropriate optimization parameter selection, the quadratic reformulation
produces association policies with sum delays that are close to that of the
original network utility maximization. We also comment on the more difficult
problem when idle base stations (those without associated users) are
deactivated.Comment: 6 pages, 5 figures. Submitted on 2013-10-03 to the 2015 IEEE
International Conference on Communications (ICC). Accepted on 2015-01-09 to
the 2015 IEEE International Conference on Communications (ICC
Decentralized Fair Scheduling in Two-Hop Relay-Assisted Cognitive OFDMA Systems
In this paper, we consider a two-hop relay-assisted cognitive downlink OFDMA
system (named as secondary system) dynamically accessing a spectrum licensed to
a primary network, thereby improving the efficiency of spectrum usage. A
cluster-based relay-assisted architecture is proposed for the secondary system,
where relay stations are employed for minimizing the interference to the users
in the primary network and achieving fairness for cell-edge users. Based on
this architecture, an asymptotically optimal solution is derived for jointly
controlling data rates, transmission power, and subchannel allocation to
optimize the average weighted sum goodput where the proportional fair
scheduling (PFS) is included as a special case. This solution supports
decentralized implementation, requires small communication overhead, and is
robust against imperfect channel state information at the transmitter (CSIT)
and sensing measurement. The proposed solution achieves significant throughput
gains and better user-fairness compared with the existing designs. Finally, we
derived a simple and asymptotically optimal scheduling solution as well as the
associated closed-form performance under the proportional fair scheduling for a
large number of users. The system throughput is shown to be
, where is the
number of users in one cluster, is the number of subchannels and is
the active probability of primary users.Comment: 29 pages, 9 figures, IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL
PROCESSIN
Joint Scheduling and ARQ for MU-MIMO Downlink in the Presence of Inter-Cell Interference
User scheduling and multiuser multi-antenna (MU-MIMO) transmission are at the
core of high rate data-oriented downlink schemes of the next-generation of
cellular systems (e.g., LTE-Advanced). Scheduling selects groups of users
according to their channels vector directions and SINR levels. However, when
scheduling is applied independently in each cell, the inter-cell interference
(ICI) power at each user receiver is not known in advance since it changes at
each new scheduling slot depending on the scheduling decisions of all
interfering base stations. In order to cope with this uncertainty, we consider
the joint operation of scheduling, MU-MIMO beamforming and Automatic Repeat
reQuest (ARQ). We develop a game-theoretic framework for this problem and build
on stochastic optimization techniques in order to find optimal scheduling and
ARQ schemes. Particularizing our framework to the case of "outage service
rates", we obtain a scheme based on adaptive variable-rate coding at the
physical layer, combined with ARQ at the Logical Link Control (ARQ-LLC). Then,
we present a novel scheme based on incremental redundancy Hybrid ARQ (HARQ)
that is able to achieve a throughput performance arbitrarily close to the
"genie-aided service rates", with no need for a genie that provides
non-causally the ICI power levels. The novel HARQ scheme is both easier to
implement and superior in performance with respect to the conventional
combination of adaptive variable-rate coding and ARQ-LLC.Comment: Submitted to IEEE Transactions on Communications, v2: small
correction
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