3 research outputs found
Energy-Efficient Resource Allocation for NOMA enabled MEC Networks with Imperfect CSI
The combination of non-orthogonal multiple access (NOMA) and mobile edge
computing (MEC) can significantly improve the spectrum efficiency beyond the
fifth-generation network. In this paper, we mainly focus on energy-efficient
resource allocation for a multi-user, multi-BS NOMA assisted MEC network with
imperfect channel state information (CSI), in which each user can upload its
tasks to multiple base stations (BSs) for remote executions. To minimize the
energy consumption, we consider jointly optimizing the task assignment, power
allocation and user association. As the main contribution, with imperfect CSI,
the optimal closed-form expressions of task assignment and power allocation are
analytically derived for the two-BS case. Specifically, the original formulated
problem is nonconvex. We first transform the probabilistic problem into a
non-probabilistic one. Subsequently, a bilevel programming method is proposed
to derive the optimal solution. In addition, by incorporating the matching
algorithm with the optimal task and power allocation, we propose a low
complexity algorithm to efficiently optimize user association for the
multi-user and multi-BS case. Simulations demonstrate that the proposed
algorithm can yield much better performance than the conventional OMA scheme
but also the identical results with lower complexity from the exhaustive search
with the small number of BSs
Cache-Aided NOMA Mobile Edge Computing: A Reinforcement Learning Approach
A novel non-orthogonal multiple access (NOMA) based cache-aided mobile edge
computing (MEC) framework is proposed. For the purpose of efficiently
allocating communication and computation resources to users' computation tasks
requests, we propose a long-short-term memory (LSTM) network to predict the
task popularity. Based on the predicted task popularity, a long-term reward
maximization problem is formulated that involves a joint optimization of the
task offloading decisions, computation resource allocation, and caching
decisions. To tackle this challenging problem, a single-agent Q-learning
(SAQ-learning) algorithm is invoked to learn a long-term resource allocation
strategy. Furthermore, a Bayesian learning automata (BLA) based multi-agent
Q-learning (MAQ-learning) algorithm is proposed for task offloading decisions.
More specifically, a BLA based action select scheme is proposed for the agents
in MAQ-learning to select the optimal action in every state. We prove that the
BLA based action selection scheme is instantaneously self-correcting and the
selected action is an optimal solution for each state. Extensive simulation
results demonstrate that: 1) The prediction error of the proposed LSTMs based
task popularity prediction decreases with increasing learning rate. 2) The
proposed framework significantly outperforms the benchmarks like all local
computing, all offloading computing, and non-cache computing. 3) The proposed
BLA based MAQ-learning achieves an improved performance compared to
conventional reinforcement learning algorithms.Comment: 30 pages, 10 figure
Optimal Resource Allocation for Delay Minimization in NOMA-MEC Networks
Multi-access edge computing (MEC) can enhance the computing capability of
mobile devices, while non-orthogonal multiple access (NOMA) can provide high
data rates. Combining these two strategies can effectively benefit the network
with spectrum and energy efficiency. In this paper, we investigate the task
delay minimization in multi-user NOMA-MEC networks, where multiple users can
offload their tasks simultaneously through the same frequency band. We adopt
the partial offloading policy, in which each user can partition its computation
task into offloading and locally computing parts. We aim to minimize the task
delay among users by optimizing their tasks partition ratios and offloading
transmit power. The delay minimization problem is first formulated, and it is
shown that it is a nonconvex one. By carefully investigating its structure, we
transform the original problem into an equivalent quasi-convex. In this way, a
bisection search iterative algorithm is proposed in order to achieve the
minimum task delay. To reduce the complexity of the proposed algorithm and
evaluate its optimality, we further derive closed-form expressions for the
optimal task partition ratio and offloading power for the case of two-user
NOMA-MEC networks. Simulations demonstrate the convergence and optimality of
the proposed algorithm and the effectiveness of the closed-form analysis.Comment: Accepted by IEEE Transactions on Communications 2020. arXiv admin
note: substantial text overlap with arXiv:1904.1238