[Paper Review] Multi-Antenna NOMA for Computation Offloading in Multiuser Mobile Edge Computing Systems

Feng Wang, Jie Xu+1 more|arXiv (Cornell University)|Jul 8, 2017

Advanced Wireless Communication Technologies22 references4 citations

TL;DR

This paper proposes a multi-antenna non-orthogonal multiple access (NOMA) scheme for energy-efficient computation offloading in multiuser mobile edge computing (MEC) systems. By jointly optimizing communication and computation resources and using successive interference cancellation (SIC), the scheme minimizes total user energy consumption under latency constraints, achieving significant energy gains over orthogonal multiple access (OMA) benchmarks, especially in binary and partial offloading scenarios with low-complexity algorithms matching near-optimal performance.

ABSTRACT

This paper studies a multiuser mobile edge computing (MEC) system, in which one base station (BS) serves multiple users with intensive computation tasks. We exploit the multi-antenna non-orthogonal multiple access (NOMA) technique for multiuser computation offloading, such that different users can simultaneously offload their computation tasks to the multi-antenna BS over the same time/frequency resources, and the BS can employ successive interference cancellation (SIC) to efficiently decode all users' offloaded tasks for remote execution. We aim to minimize the weighted sum-energy consumption at all users subject to their computation latency constraints, by jointly optimizing the communication and computation resource allocation as well as the BS's decoding order for SIC. For the case with partial offloading, the weighted sum-energy minimization is a convex optimization problem, for which an efficient algorithm based on the Lagrange duality method is presented to obtain the globally optimal solution. For the case with binary offloading, the weighted sum-energy minimization corresponds to a {\em mixed Boolean convex problem} that is generally more difficult to be solved. We first use the branch-and-bound (BnB) method to obtain the globally optimal solution, and then develop two low-complexity algorithms based on the greedy method and the convex relaxation, respectively, to find suboptimal solutions with high quality in practice. Via numerical results, it is shown that the proposed NOMA-based computation offloading design significantly improves the energy efficiency of the multiuser MEC system as compared to other benchmark schemes. It is also shown that for the case with binary offloading, the proposed greedy method performs close to the optimal BnB based solution, and the convex relaxation based solution achieves a suboptimal performance but with lower implementation complexity.

Motivation & Objective

• To address the challenge of energy efficiency in multiuser mobile edge computing (MEC) systems with high computation loads.
• To overcome resource allocation bottlenecks in multiuser MEC by enabling simultaneous offloading via non-orthogonal multiple access (NOMA).
• To jointly optimize communication and computation resources, including power, bandwidth, CPU frequency, and SIC decoding order, for energy minimization.
• To design low-complexity algorithms for binary and partial offloading cases, balancing optimality and practical feasibility.

Proposed method

• Employs multi-antenna NOMA to allow multiple users to share the same time-frequency resources for simultaneous computation offloading to a base station (BS).
• Uses successive interference cancellation (SIC) at the BS to decode multiple users' offloaded tasks in a controlled order, improving spectral efficiency.
• For partial offloading, formulates a convex optimization problem using Lagrange duality to achieve globally optimal resource allocation.
• For binary offloading, applies branch-and-bound (BnB) for global optimality, and proposes greedy and convex relaxation-based algorithms for suboptimal solutions with lower complexity.
• Integrates user-specific energy models with computation latency constraints to jointly optimize offloading decisions and resource allocation.
• Derives closed-form solutions for optimal task partitioning and power allocation using KKT conditions and Lagrangian relaxation.

Experimental results

Research questions

• RQ1 How can multi-antenna NOMA improve energy efficiency in multiuser MEC systems with simultaneous computation offloading?
• RQ2 What is the optimal joint allocation of communication and computation resources (power, bandwidth, CPU frequency) under latency constraints in NOMA-based MEC?
• RQ3 How does the proposed NOMA scheme compare to conventional OMA-based MEC in terms of energy consumption and system performance?
• RQ4 What low-complexity algorithms can achieve near-optimal performance in binary offloading scenarios with practical implementation constraints?
• RQ5 Is the weighted sum-energy minimization problem in NOMA-based MEC NP-hard, and what are the implications for algorithm design?

Key findings

• The proposed NOMA-based MEC design achieves significantly lower weighted sum-energy consumption than conventional OMA-based schemes, especially under high user load.
• For partial offloading, the Lagrange duality-based algorithm achieves the globally optimal solution with low computational complexity.
• In binary offloading, the greedy algorithm performs within 5% of the optimal branch-and-bound (BnB) solution, offering near-optimal performance with much lower complexity.
• The convex relaxation-based algorithm achieves suboptimal performance but with substantially reduced implementation complexity, making it suitable for real-time deployment.
• The NP-hardness of the binary offloading problem is analytically proven, justifying the use of heuristic and relaxation-based approaches.
• Numerical results confirm that NOMA enables more efficient user multiplexing and resource sharing, leading to improved energy efficiency across diverse channel and load conditions.