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Partial Non-Orthogonal Multiple Access (NOMA) in Downlink Poisson Networks
Information Theory · Submitted 30 Jan 2021
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim We also show that with appropriate parameter selection and resource allocation, partial-NOMA can outperform NOMA.
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The abstract, sentence by sentence
Non-orthogonal multiple access (NOMA) allows users sharing a resource-block to efficiently reuse spectrum and improve cell sum rate $\mathcal{R}_{\rm tot}$ at the expense of increased interference. Orthogonal multiple access (OMA), on the other hand, guarantees higher coverage. We introduce partial-NOMA in a large two-user downlink network to provide both throughput and reliability. The associated partial overlap controls interference while still offering spectrum reuse. The nature of the partial overlap also allows us to employ receive-filtering to further suppress interference. For signal decoding in our partial-NOMA setup, we propose a new technique called flexible successive interference cancellation (FSIC) decoding. We plot the rate region abstraction and compare with OMA and NOMA. We formulate a problem to maximize $\mathcal{R}_{\rm tot}$ constrained to a minimum throughput requirement for each user and propose an algorithm to find a feasible resource allocation efficiently. Our results show that partial-NOMA allows greater flexibility in terms of performance. Partial-NOMA can also serve users that NOMA cannot. We also show that with appropriate parameter selection and resource allocation, partial-NOMA can outperform NOMA.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | Non-orthogonal multiple access (NOMA) allows users sharing a resource-block to efficiently reuse spectrum and improve cell sum rate $\mathcal{R}_{\rm tot}$ at the expense of increased interference. | background 1.00 | 0.06 | 0.03 | asserted 0.99 |
| s1 | Orthogonal multiple access (OMA), on the other hand, guarantees higher coverage. | background 1.00 | 0.06 | 0.02 | asserted 1.00 |
| s2 | We introduce partial-NOMA in a large two-user downlink network to provide both throughput and reliability. | contribution 1.00 | 0.89 | 0.12 | asserted 0.98 |
| s3 | The associated partial overlap controls interference while still offering spectrum reuse. | method 0.70 | 0.43 | 0.07 | asserted 0.99 |
| s4 | The nature of the partial overlap also allows us to employ receive-filtering to further suppress interference. | method 0.80 | 0.45 | 0.05 | asserted 0.84 |
| s5 | For signal decoding in our partial-NOMA setup, we propose a new technique called flexible successive interference cancellation (FSIC) decoding. | method 0.70 | 0.91 | 0.04 | asserted 0.99 |
| s6 | We plot the rate region abstraction and compare with OMA and NOMA. | method 0.49 | 0.93 | 0.04 | asserted 0.82 |
| s7 | We formulate a problem to maximize $\mathcal{R}_{\rm tot}$ constrained to a minimum throughput requirement for each user and propose an algorithm to find a feasible resource allocation efficiently. | contribution 0.57 | 0.94 | 0.08 | asserted 0.99 |
| s8 | Our results show that partial-NOMA allows greater flexibility in terms of performance. | result 0.68 | 0.91 | 0.28 | asserted 0.97 |
| s9 | Partial-NOMA can also serve users that NOMA cannot. | result 0.95 | 0.79 | 0.14 | hedged possibility 0.63 |
| s10 | We also show that with appropriate parameter selection and resource allocation, partial-NOMA can outperform NOMA. | result 0.98 | 0.94 | 0.45 | asserted 0.72 |
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Original abstract
Non-orthogonal multiple access (NOMA) allows users sharing a resource-block to efficiently reuse spectrum and improve cell sum rate $\mathcal{R}_{\rm tot}$ at the expense of increased interference. Orthogonal multiple access (OMA), on the other hand, guarantees higher coverage. We introduce partial-NOMA in a large two-user downlink network to provide both throughput and reliability. The associated partial overlap controls interference while still offering spectrum reuse. The nature of the partial overlap also allows us to employ receive-filtering to further suppress interference. For signal decoding in our partial-NOMA setup, we propose a new technique called flexible successive interference cancellation (FSIC) decoding. We plot the rate region abstraction and compare with OMA and NOMA. We formulate a problem to maximize $\mathcal{R}_{\rm tot}$ constrained to a minimum throughput requirement for each user and propose an algorithm to find a feasible resource allocation efficiently. Our results show that partial-NOMA allows greater flexibility in terms of performance. Partial-NOMA can also serve users that NOMA cannot. We also show that with appropriate parameter selection and resource allocation, partial-NOMA can outperform NOMA.
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However, works such as [ref] have shown that this not necessarily the case and in [ref] it is explicitly shown that in fact clustering UEs with lower channel disparity but overall better channel conditions is superior.
On Clustering and Channel Disparity in Non-Orthogonal Multiple Access (NOMA) · Network Model for Partial-NOMA · 0.96
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