Fundamentals of Power Allocation Strategies for Downlink Multi-User NOMA With Target Rates

Oviedo, Jose Armando; Sadjadpour, Hamid R.

doi:10.1109/twc.2019.2959763

Cited by 12 publications

(6 citation statements)

References 22 publications

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“…In the same channel, when a user with a smaller channel gain decodes the signal, the signal of a user whose channel gain is larger than itself is regarded as interference [39], so the signal-to-interference ratio (SINR) of U E n in the sub-channel s can be expressed as (3).…”

Section: Wireless Channel Modelmentioning

confidence: 99%

Wireless Network Resource Allocation for Video Transmission Based on Non-Orthogonal Multiple Access

Li,

Xu,

Song

et al. 2024

Preprint

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In wireless networks, we study the downlink scenario of a non-orthogonal multiple access (NOMA) network based on scalable video coding techniques. The resource allocation problem based on wireless NOMA network is modeled as a mixed-integer nonlinear programming problem, which is subject to video coding characteristics, sub-channel capacity, system available power, and real-time requirements of wireless network. In order to solve the resource allocation problem of wireless NOMA network, three sub-problems are proposed, e.g., user grouping sub-problem, power allocation sub-problem, and video layer allocation sub-problem. Firstly, we propose an auction-based solution for the user grouping sub-problem. Then, for the power allocation sub-problem, we choose successive convex approximation and Lagrangian dual decomposition method. Finally, for the video layer allocation sub-problem, we propose a greedy strategy. Simulation experiments show that our proposed algorithms ensure the real-time and reliability requirements of the wireless network and also improve the quality of the video experience.

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Section: Wireless Channel Modelmentioning

confidence: 99%

Wireless Network Resource Allocation for Video Transmission Based on Non-Orthogonal Multiple Access

Li,

Xu,

Song

et al. 2024

Preprint

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“…Indeed, this scattering causes a nondetectable constellation and a conceptual flaw in downlink NOMA schemes. Therefore, the power allocation (PA) constraint becomes too strict in practical scenarios and, contrary to PA optimization studies in terms of various constraints [40]- [43], no device can detect symbols if this strict PA constraint is not satisfied. In [31], it was shown that the PA of one of the users should be higher than 0.9 even in a case involving two users with 16-QAM.…”

Section: A Related Work and Motivationmentioning

confidence: 99%

“…2) Power Allocation Constraint: In the literature, great efforts have been devoted to optimizing the PA coefficient under different constraints, such as sum-rate maximization, outage probability, energy efficiency, etc., [40]- [43]. To the best of the authors' knowledge, all of the aforementioned studies optimize PA by considering the theoretical Shannon limit, and no transceiver (i.e., baseband signal processing) is considered.…”

Section: Multiaccessmentioning

confidence: 99%

Bit-Interleaved Multiple Access: Improved Fairness, Reliability, and Latency for Massive IoT Networks

Kara

Kaya

Yanıkömeroğlu

et al. 2023

IEEE Internet Things J.

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Internet-of-Things (IoT) networks require massive connections in dense areas. Therefore, a resource efficient multiple access scheme seems inevitable to enable immense connectivity where multiple devices have to share the same resource block. Non-orthogonal multiple access (NOMA) has been considered as the strongest candidate in recent years. However, in this paper, by considering the practical implementation, we first provide a true power allocation (PA) constraint with finite alphabet inputs for conventional downlink NOMA and demonstrate that it cannot support massive connections in practical systems. To this end, we propose bit-interleaved multiple access (BIMA) scheme in downlink IoT networks. The proposed BIMA scheme implements bitwise multiaccess interleaving and deinterleaving at the transceiver ends and there are no strict PA constraints, unlike conventional NOMA, thus allowing a high number of devices in the same resource block. We provide a comprehensive analytical framework for BIMA by investigating all key performance indicators (KPIs) to present both information-theoretic (i.e., ergodic capacity [EC] and outage probability [OP]) and finite alphabet inputs (i.e., bit error rate [BER]) performance metrics with both instantaneous and statistical channel ordering. In addition, we define Jain's fairness index and proportional fairness index in terms of all KPIs. Based on the extensive computer simulations, we reveal that BIMA outperforms conventional NOMA significantly, with a performance gain of up to 20-30 dB in terms of KPIs in some scenarios. In other words, compared to conventional NOMA schemes, the same KPIs are met in BIMA with 20-30 dB less transmit power, which is quite promising for energy-limited use cases. Moreover, this performance gain becomes greater when more IoT devices are supported. BIMA provides a full diversity order for all IoT devices and enables the implementation of an arbitrary number of devices and modulation orders, which is crucial for IoT networks where a huge number of devices should be supported in a single resource block in dense areas. In addition to the overall performance gain, BIMA guarantees a fairness system where none of the devices gets a severely degraded performance and the sum-rate is shared in a fair manner among devices. It guarantees QoS satisfaction for all devices. Finally, we provide an intense complexity and latency analysis for BIMA and demonstrate that it provides lower latency compared to conventional NOMA receivers, since it allows parallel computation at the receivers and no iterative operations are required. We show that compared to conventional NOMA receivers, BIMA reduces latency by up to 350% for specific IoT devices and 170% on average.

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“…Energy efficient based SIC decoding order with improved performance of outage probability in [2]. Thereafter, a new scheme for user admission through threshold difference based is explained in [3], and in resource block [4] contains all the related parameters like subcarriers, time slots and spreading codes with 5G requirements.…”

Section: Related Work On Nomamentioning

confidence: 99%

A Comprehensive Realization of Resource Spread Multiple Access / NOMA performances in 5G

Elayaraja*,

Amali,

Sridevi

et al. 2020

IJITEE

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Since 4G has limited in capacity, restricted in resource utilization, reasonable latency and so on. In order to yield high performance factors of the above-said, a Non Orthogonal Multiple Access (NOMA) plays an important role in 5G communication networks. The NOMA is otherwise called as Resource Spread Multiple Access (RSMA), whose performance is quite fit while envisioned for the forthcoming communication (i.e., 5G and above), which fulfil the requirements of the current 5th Generation, in view of capacity improvement, power allocation, lower latency, outage probability for both uplink and downlink configurations. This paper gets into the analytical and simulation analysis of the power domain NOMA technique to two users case on sum capacity and outage power probability for conventional un-ordered (not fixed) and ordered (fixed). Based on the position of the users as near base stations and cell edges are analyzed, subjected not to compromise the quality of service (QoS), maximize the spectrum utilization, out-and-out resource allocation, good energy efficiency and so on. Here an interesting view on both the channel gain and SNR are possessed higher degree of consent on outage power and successive interference cancellation. When the channel gains are equal on both the users, correspondingly SNR and outage probability varies. On the other hand, when the gain of the weaker user channel is higher than the gain of the stronger user channel, then a cross over point arises significantly. Moreover, the Successive Interferences Cancellation (SIC) has increased by reducing the Signal to Noise ratio (SNR) and vice versa. This ideology is suited for the applications of 5G requiring asynchronous and grant-free access like IOT and mission critical control.

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Fundamentals of Power Allocation Strategies for Downlink Multi-User NOMA With Target Rates

Cited by 12 publications

References 22 publications

Wireless Network Resource Allocation for Video Transmission Based on Non-Orthogonal Multiple Access

Wireless Network Resource Allocation for Video Transmission Based on Non-Orthogonal Multiple Access

Bit-Interleaved Multiple Access: Improved Fairness, Reliability, and Latency for Massive IoT Networks

A Comprehensive Realization of Resource Spread Multiple Access / NOMA performances in 5G

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