2,512 research outputs found
Optimal Resource Allocation for Multi-user OFDMA-URLLC MEC Systems
In this paper, we study resource allocation algorithm design for multi-user
orthogonal frequency division multiple access (OFDMA) ultra-reliable low
latency communication (URLLC) in mobile edge computing (MEC) systems. To meet
the stringent end-to-end delay and reliability requirements of URLLC MEC
systems, we propose joint uplink-downlink resource allocation and finite
blocklength transmission. Furthermore, we employ a partial time overlap between
the uplink and downlink frames to minimize the end-to-end delay, which
introduces a new time causality constraint. The proposed resource allocation
algorithm is formulated as an optimization problem for minimization of the
total weighted power consumption of the network under a constraint on the
number of URLLC user bits computed within the maximum allowable computation
time, i.e., the end-to-end delay of a computation task. Despite the
non-convexity of the formulated optimization problem, we develop a globally
optimal solution using a branch-and-bound approach based on discrete monotonic
optimization theory. The branch-and-bound algorithm minimizes an upper bound on
the total power consumption until convergence to the globally optimal value.
Furthermore, to strike a balance between computational complexity and
performance, we propose two efficient suboptimal algorithms based on successive
convex approximation and second-order cone techniques. Our simulation results
reveal that the proposed resource allocation algorithm design facilitates URLLC
in MEC systems, and yields significant power savings compared to three baseline
schemes. Moreover, our simulation results show that the proposed suboptimal
algorithms offer different trade-offs between performance and complexity and
attain a close-to-optimal performance at comparatively low complexity.Comment: 32 pages, 9 figures, submitted for an IEEE journal. arXiv admin note:
substantial text overlap with arXiv:2005.0470
A Survey on Delay-Aware Resource Control for Wireless Systems --- Large Deviation Theory, Stochastic Lyapunov Drift and Distributed Stochastic Learning
In this tutorial paper, a comprehensive survey is given on several major
systematic approaches in dealing with delay-aware control problems, namely the
equivalent rate constraint approach, the Lyapunov stability drift approach and
the approximate Markov Decision Process (MDP) approach using stochastic
learning. These approaches essentially embrace most of the existing literature
regarding delay-aware resource control in wireless systems. They have their
relative pros and cons in terms of performance, complexity and implementation
issues. For each of the approaches, the problem setup, the general solution and
the design methodology are discussed. Applications of these approaches to
delay-aware resource allocation are illustrated with examples in single-hop
wireless networks. Furthermore, recent results regarding delay-aware multi-hop
routing designs in general multi-hop networks are elaborated. Finally, the
delay performance of the various approaches are compared through simulations
using an example of the uplink OFDMA systems.Comment: 58 pages, 8 figures; IEEE Transactions on Information Theory, 201
Practical Resource Allocation Algorithms for QoS in OFDMA-based Wireless Systems
In this work we propose an efficient resource allocation algorithm for OFDMA
based wireless systems supporting heterogeneous traffic. The proposed algorithm
provides proportionally fairness to data users and short term rate guarantees
to real-time users. Based on the QoS requirements, buffer occupancy and channel
conditions, we propose a scheme for rate requirement determination for delay
constrained sessions. Then we formulate and solve the proportional fair rate
allocation problem subject to those rate requirements and power/bandwidth
constraints. Simulations results show that the proposed algorithm provides
significant improvement with respect to the benchmark algorithm.Comment: To be presented at 2nd IEEE International Broadband Wireless Access
Workshop. Las Vegas, Nevada USA Jan 12 200
Cross-layer design for single-cell OFDMA systems with heterogeneous QoS and partial CSIT
Abstract— This paper proposes a novel cross-layer scheduling scheme for a single-cell orthogonal frequency division multiple access (OFDMA) wireless system with partial channel state information (CSI) at transmitter (CSIT) and heterogeneous user delay requirements. Previous research efforts on OFDMA resource allocation are typically based on the availability of perfect CSI or imperfect CSI but with small error variance. Either case consists to typify a non tangible system as the potential facts of channel feedback delay or large channel estimation errors have not been considered. Thus, to attain a more realistic resolution our cross-layer design determines optimal subcarrier and power allocation policies based on partial CSIT and individual user’s quality of service (QoS) requirements. The simulation results show that the proposed cross-layer scheduler can maximize the system’s throughput and at the same time satisfy heterogeneous delay requirements of various users with significant low power consumption
Spectral Efficiency of Multi-User Adaptive Cognitive Radio Networks
In this correspondence, the comprehensive problem of joint power, rate, and
subcarrier allocation have been investigated for enhancing the spectral
efficiency of multi-user orthogonal frequency-division multiple access (OFDMA)
cognitive radio (CR) networks subject to satisfying total average transmission
power and aggregate interference constraints. We propose novel optimal radio
resource allocation (RRA) algorithms under different scenarios with
deterministic and probabilistic interference violation limits based on a
perfect and imperfect availability of cross-link channel state information
(CSI). In particular, we propose a probabilistic approach to mitigate the total
imposed interference on the primary service under imperfect cross-link CSI. A
closed-form mathematical formulation of the cumulative density function (cdf)
for the received signal-to-interference-plus-noise ratio (SINR) is formulated
to evaluate the resultant average spectral efficiency (ASE). Dual decomposition
is utilized to obtain sub-optimal solutions for the non-convex optimization
problems. Through simulation results, we investigate the achievable performance
and the impact of parameters uncertainty on the overall system performance.
Furthermore, we present that the developed RRA algorithms can considerably
improve the cognitive performance whilst abide the imposed power constraints.
In particular, the performance under imperfect cross-link CSI knowledge for the
proposed `probabilistic case' is compared to the conventional scenarios to show
the potential gain in employing this scheme
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