Optimal Resource Allocation for Multi-User OFDMA-URLLC MEC Systems

Abstract

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 employ joint uplink-downlink resource allocation and finite blocklength transmission. Furthermore, we propose 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 of each user. Despite the non-convexity and the complicated structure 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. For the first suboptimal scheme, the optimization problem is reformulated in the canonical form of difference of convex programming. Then, successive convex approximation (SCA) is used to determine a locally optimal solution. For the second suboptimal scheme, we use a high signal-to-noise ratio approximation for the channel dispersion. Then, via novel transformations, we convert the nonconvex quality-of-service constraints of the original problem into equivalent second-order-cone constraints. 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 an excellent performance at comparatively low complexity.

Rate Splitting in MIMO RIS-Assisted Systems With Hardware Impairments and Improper Signaling

“…, which proves the equality in (17). Note that if X is a positive semi-definite matrix with non-zero eigenvalues λ i , then its pseudo-inverse, denoted as X −1 , is also a positive semidefinite matrix, and its non-zero eigenvalues are λ −1 i .…”

“…All the aforementioned papers [11]- [16] studied multiuser RIS-assisted URLLC systems, but only supported singlestream data transmission per user. However, RIS can also improve the system performance when parallel frequency-domain channels are employed along with FBL coding [17]. Nevertheless, resource allocation has not been designed for multi-user MIMO URLLC systems supporting multiple streams per user in the open literature.…”

Hypergraph-Based Resource-Efficient Collaborative Reinforcement Learning for B5G Massive IoT

“…However, multi-user NRM is usually modeled theoretically as a problem with NP-hard characteristics, which are challenging to solve by typical optimization methods [13]- [15]. Ghanem et al [16] use a branch-and-bound approach based on discrete monotonic optimization theory to develop a globally optimal solution for the NRM problem and reformulate the optimization problem using the canonical form of difference of convex programming. Although adopting convex-optimization-based approaches can solve NRM problems, primal problems must be converted into solvable problems.…”