942 research outputs found
A Greedy Link Scheduler for Wireless Networks with Fading Channels
We consider the problem of link scheduling for wireless networks with fading
channels, where the link rates are varying with time. Due to the high
computational complexity of the throughput optimal scheduler, we provide a low
complexity greedy link scheduler GFS, with provable performance guarantees. We
show that the performance of our greedy scheduler can be analyzed using the
Local Pooling Factor (LPF) of a network graph, which has been previously used
to characterize the stability of the Greedy Maximal Scheduling (GMS) policy for
networks with static channels. We conjecture that the performance of GFS is a
lower bound on the performance of GMS for wireless networks with fading
channel
Multiuser Scheduling in a Markov-modeled Downlink using Randomly Delayed ARQ Feedback
We focus on the downlink of a cellular system, which corresponds to the bulk
of the data transfer in such wireless systems. We address the problem of
opportunistic multiuser scheduling under imperfect channel state information,
by exploiting the memory inherent in the channel. In our setting, the channel
between the base station and each user is modeled by a two-state Markov chain
and the scheduled user sends back an ARQ feedback signal that arrives at the
scheduler with a random delay that is i.i.d across users and time. The
scheduler indirectly estimates the channel via accumulated delayed-ARQ feedback
and uses this information to make scheduling decisions. We formulate a
throughput maximization problem as a partially observable Markov decision
process (POMDP). For the case of two users in the system, we show that a greedy
policy is sum throughput optimal for any distribution on the ARQ feedback
delay. For the case of more than two users, we prove that the greedy policy is
suboptimal and demonstrate, via numerical studies, that it has near optimal
performance. We show that the greedy policy can be implemented by a simple
algorithm that does not require the statistics of the underlying Markov channel
or the ARQ feedback delay, thus making it robust against errors in system
parameter estimation. Establishing an equivalence between the two-user system
and a genie-aided system, we obtain a simple closed form expression for the sum
capacity of the Markov-modeled downlink. We further derive inner and outer
bounds on the capacity region of the Markov-modeled downlink and tighten these
bounds for special cases of the system parameters.Comment: Contains 22 pages, 6 figures and 8 tables; revised version including
additional analytical and numerical results; work submitted, Feb 2010, to
IEEE Transactions on Information Theory, revised April 2011; authors can be
reached at [email protected]/[email protected]/[email protected]
Feedback Allocation For OFDMA Systems With Slow Frequency-domain Scheduling
We study the problem of allocating limited feedback resources across multiple
users in an orthogonal-frequency-division-multiple-access downlink system with
slow frequency-domain scheduling. Many flavors of slow frequency-domain
scheduling (e.g., persistent scheduling, semi-persistent scheduling), that
adapt user-sub-band assignments on a slower time-scale, are being considered in
standards such as 3GPP Long-Term Evolution. In this paper, we develop a
feedback allocation algorithm that operates in conjunction with any arbitrary
slow frequency-domain scheduler with the goal of improving the throughput of
the system. Given a user-sub-band assignment chosen by the scheduler, the
feedback allocation algorithm involves solving a weighted sum-rate maximization
at each (slow) scheduling instant. We first develop an optimal
dynamic-programming-based algorithm to solve the feedback allocation problem
with pseudo-polynomial complexity in the number of users and in the total
feedback bit budget. We then propose two approximation algorithms with
complexity further reduced, for scenarios where the problem exhibits additional
structure.Comment: Accepted to IEEE Transactions on Signal Processin
Scheduling for next generation WLANs: filling the gap between offered and observed data rates
In wireless networks, opportunistic scheduling is used to increase system throughput by exploiting multi-user diversity. Although recent advances have increased physical layer data rates supported in wireless local area networks (WLANs), actual throughput realized are significantly lower due to overhead. Accordingly, the frame aggregation concept is used in next generation WLANs to improve efficiency. However, with frame aggregation, traditional opportunistic schemes are no longer optimal. In this paper, we propose schedulers that take queue and channel conditions into account jointly, to maximize throughput observed at the users for next generation WLANs. We also extend this work to design two schedulers that perform block scheduling for maximizing network throughput over multiple transmission sequences. For these schedulers, which make decisions over long time durations, we model the system using queueing theory and determine users' temporal access proportions according to this model. Through detailed simulations, we show that all our proposed algorithms offer significant throughput improvement, better fairness, and much lower delay compared with traditional opportunistic schedulers, facilitating the practical use of the evolving standard for next generation wireless networks
Spatial CSMA: A Distributed Scheduling Algorithm for the SIR Model with Time-varying Channels
Recent work has shown that adaptive CSMA algorithms can achieve throughput
optimality. However, these adaptive CSMA algorithms assume a rather simplistic
model for the wireless medium. Specifically, the interference is typically
modelled by a conflict graph, and the channels are assumed to be static. In
this work, we propose a distributed and adaptive CSMA algorithm under a more
realistic signal-to-interference ratio (SIR) based interference model, with
time-varying channels. We prove that our algorithm is throughput optimal under
this generalized model. Further, we augment our proposed algorithm by using a
parallel update technique. Numerical results show that our algorithm
outperforms the conflict graph based algorithms, in terms of supportable
throughput and the rate of convergence to steady-state.Comment: This work has been presented at National Conference on Communication,
2015, held at IIT Bombay, Mumbai, Indi
Autonomous Algorithms for Centralized and Distributed Interference Coordination: A Virtual Layer Based Approach
Interference mitigation techniques are essential for improving the
performance of interference limited wireless networks. In this paper, we
introduce novel interference mitigation schemes for wireless cellular networks
with space division multiple access (SDMA). The schemes are based on a virtual
layer that captures and simplifies the complicated interference situation in
the network and that is used for power control. We show how optimization in
this virtual layer generates gradually adapting power control settings that
lead to autonomous interference minimization. Thereby, the granularity of
control ranges from controlling frequency sub-band power via controlling the
power on a per-beam basis, to a granularity of only enforcing average power
constraints per beam. In conjunction with suitable short-term scheduling, our
algorithms gradually steer the network towards a higher utility. We use
extensive system-level simulations to compare three distributed algorithms and
evaluate their applicability for different user mobility assumptions. In
particular, it turns out that larger gains can be achieved by imposing average
power constraints and allowing opportunistic scheduling instantaneously, rather
than controlling the power in a strict way. Furthermore, we introduce a
centralized algorithm, which directly solves the underlying optimization and
shows fast convergence, as a performance benchmark for the distributed
solutions. Moreover, we investigate the deviation from global optimality by
comparing to a branch-and-bound-based solution.Comment: revised versio
Performance analysis of SIMO space-time scheduling with convex utility function: Zero-forcing linear processing
In a multiple-antenna system, an optimized design across the link and scheduling layers is crucial toward fully exploiting the temporal and spatial dimensions of the communication channel. In this paper, based on discrete optimization techniques, we derive a novel analytical framework for designing optimal space-time scheduling algorithms with respect to general convex utility functions. We focus on the reverse link (i.e., client to base station) and assume that the mobile terminal has a single transmit antenna while the base station has nR receive antennas. In order that our proposed framework is practicable and can be implemented with a reasonable cost in a real environment, we further assume that the physical layer involves only linear-processing complexity in separating signals from different users. As an illustration of the efficacy of our proposed analytical design framework, we apply the framework to two commonly used system utility functions, namely maximal throughput and proportional fair. We then devise an optimal scheduling algorithm based on our design framework. However, in view of the formidable time complexity of the optimal algorithm, we propose two fast practical scheduling techniques, namely the greedy algorithm and the genetic algorithm (GA). The greedy algorithm, which is similar to the one widely used in 3G1X and Qualcomm high-data-rate (HDR) systems (optimal when nR = 1), exhibits significantly inferior performance when nR > 1 as compared with the optimal approach. On the other hand, the GA is quite promising in terms of performance complexity tradeoff, especially for a system with a large number of users with even a moderately large nR. As a case in point, for a system with 20 users and nR = 4, the GA is more than 36 times faster than the optimal while the performance degradation is less than 10%, making it an attractive choice in the practical implementation for real-time link scheduling.published_or_final_versio
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