867 research outputs found
Delay Performance of MISO Wireless Communications
Ultra-reliable, low latency communications (URLLC) are currently attracting
significant attention due to the emergence of mission-critical applications and
device-centric communication. URLLC will entail a fundamental paradigm shift
from throughput-oriented system design towards holistic designs for guaranteed
and reliable end-to-end latency. A deep understanding of the delay performance
of wireless networks is essential for efficient URLLC systems. In this paper,
we investigate the network layer performance of multiple-input, single-output
(MISO) systems under statistical delay constraints. We provide closed-form
expressions for MISO diversity-oriented service process and derive
probabilistic delay bounds using tools from stochastic network calculus. In
particular, we analyze transmit beamforming with perfect and imperfect channel
knowledge and compare it with orthogonal space-time codes and antenna
selection. The effect of transmit power, number of antennas, and finite
blocklength channel coding on the delay distribution is also investigated. Our
higher layer performance results reveal key insights of MISO channels and
provide useful guidelines for the design of ultra-reliable communication
systems that can guarantee the stringent URLLC latency requirements.Comment: This work has been submitted to the IEEE for possible publication.
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Energy-delay tradeoff in wireless network coding
A queueing model for wireless communication network in which network coding is employed is introduced. It is shown that networks with coding are closely related to queueing networks with positive and negative customers. Analytical upper and lower bounds on the energy consumption and the delay are obtained using a Markov reward approach. The tradeoff between minimizing energy consumption and minimizing delay is investigated. Exact expressions are given for the minimum energy consumption and the minimum delay attainable in a network
Sharp Bounds in Stochastic Network Calculus
The practicality of the stochastic network calculus (SNC) is often questioned
on grounds of potential looseness of its performance bounds. In this paper it
is uncovered that for bursty arrival processes (specifically Markov-Modulated
On-Off (MMOO)), whose amenability to \textit{per-flow} analysis is typically
proclaimed as a highlight of SNC, the bounds can unfortunately indeed be very
loose (e.g., by several orders of magnitude off). In response to this uncovered
weakness of SNC, the (Standard) per-flow bounds are herein improved by deriving
a general sample-path bound, using martingale based techniques, which
accommodates FIFO, SP, EDF, and GPS scheduling. The obtained (Martingale)
bounds gain an exponential decay factor of in
the number of flows . Moreover, numerical comparisons against simulations
show that the Martingale bounds are remarkably accurate for FIFO, SP, and EDF
scheduling; for GPS scheduling, although the Martingale bounds substantially
improve the Standard bounds, they are numerically loose, demanding for
improvements in the core SNC analysis of GPS
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