Receiver and resource allocation optimization for uplink NOMA in 5G wireless networks

Hassan

IEEE Commun. Surv. Tutorials

et al. 2020

291

159

Internet-of-Things (IoT) refers to a massively heterogeneous network formed through smart devices connected to the Internet. In the wake of disruptive IoT with huge amount and variety of data, Machine Learning (ML) and Deep Learning (DL) mechanisms will play pivotal role to bring intelligence to the IoT networks. Among other aspects, ML and DL can play an essential role in addressing the challenges of resource management in large-scale IoT networks. In this article, we conduct a systematic and in-depth survey of the ML-and DL-based resource management mechanisms in cellular wireless and IoT networks. We start with the challenges of resource management in cellular IoT and low-power IoT networks, review the traditional resource management mechanisms for IoT networks, and motivate the use of ML and DL techniques for resource management in these networks. Then, we provide a comprehensive survey of the existing ML-and DL-based resource allocation techniques in wireless IoT networks and also techniques specifically designed for HetNets, MIMO and D2D communications, and NOMA networks. To this end, we also identify the future research directions in using ML and DL for resource allocation and management in IoT networks.

Section: Topic(s) Of the Survey Related Contentmentioning

confidence: 99%

Machine Learning for Resource Management in Cellular and IoT Networks: Potentials, Current Solutions, and Open Challenges

Hassan

IEEE Commun. Surv. Tutorials

et al. 2020

291

159

Computer Communications

“…The Rayleigh fading for all users over all subchannels follows the Rayleigh distribution with 0 mean and 4.3 variance. Using all these parameters, the gain of each subchannel for a user towards the BS is computed following (22) in [49]. We employ the SIC technique in [50,51] for a user in order to decode the SC-coded signal of each subchannel transmitted by the BS.…”

Section: Simulation Setupmentioning

confidence: 99%

“…[18] uses the water filling-based approach for the power allocation, and in [19], the authors use difference of convex (DC) programming-based approach [20] for the power allocation at both the user and subchannel levels. For uplink systems, there are some works as well, such as [21,22,23]. In [22], the authors used the iterative water filling idea [24] for both the subchannel-user mapping problem and their granular power allocation.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhanced energy-efficient downlink resource allocation in green non-orthogonal multiple access systems

Ruby

Zhong

et al. 2019

Non-orthogonal multiple access (NOMA) technique has the ability to support multiple transmissions simultaneously using the same physical resource, the idea of which achieves much enhanced performance for a system compared to the conventional orthogonal multiple access (OMA) techniques. Despite numerous advantages, this phenomenon of NOMA technique can bring additional interference for the neighboring ultra-dense networks if the power consumption of the system is not properly optimized. While targeting on the green communication concept, in this paper, we propose an energy-efficient downlink resource allocation scheme for a NOMA-equipped cellular network. The objective of this work is to allocate subchannels and power of the base station among the users so that the overall energy efficiency is maximized. Since this problem is NP-hard, we attempt to find an elegant solution with reasonable complexity that provides good performance for some realistic applications. To this end, we decompose the problem into a subchannel allocation subproblem followed by a power loading subproblem that allocates power to each user's data stream on each of its allocated subchannels. We first employ a many-to-many matching model under the assumption of uniform power loading in order to obtain the solution of the first subproblem with reasonable performance. Once the the subchanneluser mapping information is known from the first solution, we propose a geometric programming (GP)-based power loading scheme upon approximating the energy efficiency of the system by a ratio of two posynomials. The techniques adopted for these subproblems better exploit the available multi-user diversity compared to the techniques used in an earlier work. Having observed the computational overhead of the GP-based power loading scheme, we also propose a suboptimal computationally-efficient algorithm for the power loading subproblem with a polynomial time complexity that provides reasonably good performance. Extensive simulation has been conducted to verify that our proposed solution schemes always outperform the existing work while consuming much less power at the base station.

“…This happens however at the cost/trade-off of achieving lower data rates for the users, depriving them from fully exploiting system's capabilities. Non-orthogonal multiple access (NOMA) technique has been acknowledged as an effective technique for providing high data rates in 5G systems, especially in the uplink communication [1]. Nevertheless, in order to further improve user satisfaction from the provided services, while getting aligned with the vision of subscriber self-optimization and self-adaptation in future wireless networks, sophisticated user-centric resource management approaches are required.…”

Section: Introductionmentioning

confidence: 99%

A user-centric economic-driven paradigm for rate allocation in non-orthogonal multiple access wireless systems

Vamvakas

Tsiropoulou

Papavassiliou

2018

J Wireless Com Network

In this paper, a novel approach towards optimizing users' rate allocation and price customization in a nonorthogonal multiple access (NOMA) wireless network under quality of service (QoS)-differentiated requested services is proposed and studied. A multi-service wireless system is considered, where each user's QoS requirements are reflected through a utility function, alongside his willingness to pay for the corresponding service. Within this setting, in order to jointly allocate the customized price and rate, a two-variable optimization problem arises. Based on the principles of S-modular theory, the above two-variable (rate and price) optimization problem is modeled and solved as a distributed non-cooperative game. The existence and convergence to the Nash equilibrium point with reference to both user's uplink transmission rate and price is proven. The proposed approach, allowing for decision-making at the user side, is well aligned with the self-optimization and self-adaptation objectives of future emerging 5G wireless networks. The performance evaluation of the devised framework is conducted via modeling and simulation under various scenarios, and the numerical results clearly demonstrate its superiority against other existing approaches.