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A Tutorial on Nonorthogonal Multiple Access for 5G and Beyond
Mahmoud Aldababsa, Mesut Toka, Selahattin Gokceli, Gunes Karabulut Kurt, Oguz Kucur
TL;DR
Rising device diversity and traffic demands expose limits of orthogonal multiple access for 5G requirements. This tutorial unifies NOMA across uplink, downlink, MIMO, and cooperative scenarios, reporting 30% higher downlink and 100% higher uplink spectral efficiency than OMA in eMBB.
Problem
Increasing demands for spectral efficiency, low latency, and massive connectivity expose limits in OMA-based wireless networks.
Method
The paper develops a unified NOMA model covering uplink, downlink, MIMO, and cooperative communication, alongside implementation aspects and open issues.
Results
NOMA shows 30% higher downlink and 100% higher uplink spectral efficiency than OMA for eMBB.
Takeaways & Limitations
NOMA can simultaneously serve users with different channel conditions, supporting improved fairness, lower latency, and higher massive connectivity.
Takeaways & Limitations
NOMA requires careful pilot allocation because overlapping signals create interference and degrade error performance relative to OMA.
Abstract
from arXiv · showhide
Today's wireless networks allocate radio resources to users based on the orthogonal multiple access (OMA) principle. However, as the number of users increases, OMA based approaches may not meet the stringent emerging requirements including very high spectral efficiency, very low latency, and massive device connectivity. Nonorthogonal multiple access (NOMA) principle emerges as a solution to improve the spectral efficiency while allowing some degree of multiple access interference at receivers. In this tutorial style paper, we target providing a unified model for NOMA, including uplink and downlink transmissions, along with the extensions tomultiple inputmultiple output and cooperative communication scenarios. Through numerical examples, we compare the performances of OMA and NOMA networks. Implementation aspects and open issues are also detailed.
1. Introduction
The introduction motivates NOMA as a response to growing device diversity, spectrum reuse, and 5G requirements that expose OMA’s limitations. It presents NOMA’s efficiency, fairness, latency, connectivity, compatibility, and tutorial scope, while noting its dependence on channel-gain differences.
- Motivation: Growing device diversity, spectrum reuse, and IoT connectivity demands create challenges that current wireless systems and OMA cannot adequately address.The introduction frames future requirements around very high capacity, broad connectivity, and evolving 5G scenarios.
- NOMA principle: NOMA allows multiple users to share nonorthogonal resources concurrently, using power-domain superposition and successive interference cancellation at receivers.NOMA is classified into power-domain and code-domain multiplexing; power-domain schemes assign coefficients according to channel conditions.
- Advantages: 30% downlink and 100% uplink spectral-efficiency gains are reported for NOMA in eMBB compared with OMA.The cited comparison identifies NOMA as more spectral-efficient than OMA in both transmission directions.
- Advantages: NOMA can improve spectral efficiency, throughput, user fairness, latency, and massive connectivity by simultaneously serving users with different channel conditions.Unlike OMA scheduling, NOMA reuses the same frequency resource for multiple users, including those with good and bad channels.
- Paper scope: The tutorial covers NOMA in uplink, downlink, MIMO, and cooperative scenarios, alongside implementation aspects, 3GPP standardization, and 5G applications.It also analyzes MIMO-NOMA and cooperative NOMA performance to make the concepts more understandable.
- Limitations: NOMA’s performance depends on considerable channel-gain differences among users, which can restrict the number of users supported.The introduction identifies this requirement as a limitation despite NOMA’s potential for future generations.
2. Basic Concepts of NOMA
This section develops basic downlink and uplink NOMA models through SINR and sum-rate analyses, then compares NOMA with OMA at high SNR. Downlink NOMA uses superposition transmission and successive interference cancellation, while uplink NOMA uses SIC at the base station.
- Downlink NOMA: Downlink NOMA superposes multiple users’ signals with channel-dependent power coefficients, assigning more power to users with worse channels.The coefficients are inversely proportional to channel conditions, with channel gains ordered from weakest to strongest.
- Downlink NOMA: Receivers successively detect and subtract stronger signals, then decode their own signal while treating lower-power signals as noise.A user with the weakest channel may decode without SIC, whereas other users perform successive cancellation.
- Downlink NOMA: The downlink model derives each user’s SINR and achievable data rate, whose sum gives the downlink NOMA sum rate.The SINR formulation uses γ = P_s/σ^2 as the signal-to-noise ratio.
- Uplink NOMA: In uplink NOMA, mobile users transmit to the base station, which performs SIC iterations and decodes users orderly according to their power coefficients.The section also derives uplink SINRs and the corresponding sum rate under a common maximum transmission power.
- NOMA versus OMA: NOMA outperforms OMA in sum rate for both downlink and uplink in two-user networks at high SNR.The comparison uses the derived downlink and uplink rate expressions and the OMA formulation with equally shared resources.
3. MIMO-NOMA
MIMO-NOMA research studies capacity, rate, outage, resource allocation, antenna selection, beamforming, massive-MIMO, mmWave, and IoT applications. Reported analyses show NOMA outperforming OMA in several MIMO settings, including outage probability and ergodic sum rate.
- MIMO-NOMA performance: MIMO-NOMA is widely studied for improving sum rate and ergodic sum rate relative to MIMO-OMA under varied system conditions.Research includes ergodic-sum-rate maximization for two-user systems over Rayleigh fading with partial CSI and power and minimum-rate constraints.
- MIMO-NOMA performance: For multiple-antenna user clusters, NOMA outperforms OMA in sum channel capacity and ergodic capacity.The achievable sum rate decreases as the number of users per cluster increases, creating a trade-off involving admitted users.
- Antenna selection: Antenna selection reduces hardware complexity, redundant power consumption, and cost while retaining MIMO diversity, but interuser interference complicates MIMO-NOMA gains.Two joint algorithms, max-min-max and max-max-max, address average sum-rate maximization in two-user multiantenna NOMA systems.
- Massive MIMO and mmWave: Massive MIMO-NOMA studies address antenna diversity, imperfect-CSI reliability, achievable rate, and pilot design using orthogonal or superimposed pilots.Orthogonal pilots occupy time/frequency slots, whereas superimposed pilots share them with information.
- Performance results: Higher antenna numbers improve outage performance, while NOMA outperforms conventional OMA in outage probability and ergodic sum rate.At high SNR, the first user's ergodic rate is approximately constant, whereas the second user's rate increases proportionally with SNR because it experiences no interference from the first user.
4. Cooperative NOMA
Cooperative NOMA uses dedicated relays to extend coverage and exploit cooperation beyond short-range user cooperation. Studies analyze relay protocols, power and resource allocation, antenna and relay-selection strategies, energy harvesting, and full-duplex operation, with results showing performance gains over conventional schemes and TDMA.
- Cooperative relay architectures: Dedicated relays extend cooperative NOMA coverage beyond short-range user-cooperation scenarios such as ultrawideband and Bluetooth.Cooperative communication can extend coverage, increase capacity, and reduce multipath-fading degradation.
- Cooperative relay architectures: Cooperative NOMA research covers coordinated direct-and-relay transmission, relay protocols, Nakagami-m fading, buffer-aided relaying, and power-allocation strategies.Analyses include exact and asymptotic outage-probability expressions, while optimization approaches target reliability and average sum-rate.
- Performance results: Cooperative relaying outperforms conventional relaying, while cooperative transmission outperforms TDMA in outage probability.The reported results include exact and asymptotic achievable-average-rate expressions and outage-probability analyses.
- Example cooperative NOMA system: The example system is a dual-hop downlink NOMA network with one single-antenna BS, one AF half-duplex relay, and L single-antenna users without direct BS-user links.All links are modeled with flat Nakagami-m fading, with users outside BS range or experiencing poor direct channels.
- Performance results: Outage performances of the second and third users are better than the first user’s and become equal at high SNR, while increasing channel parameters increases all users’ outage probabilities.Theoretical outage-probability results closely match simulations.
- Performance results: Optimal relay placement depends on channel strength: it is near the BS for the strongest-channel user and far from the BS for other users with higher power-allocation coefficients.These placement conclusions are obtained from outage probability versus normalized BS-relay distance.
5. Practical Implementation Aspects
NOMA implementation primarily involves power allocation and user clustering, while real-time studies remain limited. Key challenges include hardware complexity, SIC error propagation, CSI and pilot requirements, and synchronization impairments.
- Implementation studies: Power allocation and user clustering are the main NOMA design problems, commonly formulated as optimization tasks, with some approaches targeting real-time applications.Imperfect CSI is also assumed in some studies.
- Implementation studies: 61% data-rate improvement is reported for NOMA in a testbed experiment using wider bandwidth.The experiment integrated SU-MIMO with downlink and uplink NOMA and compared rates with OMA.
- Implementation studies: Real-time NOMA implementation research is very limited, with only three main studies identified by the authors.Available work includes testbed experiments and network-coded multiple access using physical-layer network coding to address improper power allocation.
- Hardware complexity: SIC increases NOMA hardware complexity and detection delay, potentially reducing battery-limited device efficiency, especially with many users or fast transmission.Effective user clustering and power allocation can help alleviate this limitation.
- SIC reliability: SIC error propagation makes successful reception depend on estimating high-power signals correctly, while channel and hardware impairments can degrade detection.The better-channel-condition user is estimated first through SIC, so estimation errors can affect subsequent reception.
- CSI and synchronization: NOMA requires careful pilot allocation and near-perfect CSI, while instantaneous CSI estimation can block the main user’s transmission.Overlapped signals create interference, and orthogonal transmissions may be needed to estimate a secondary user’s CSI.
- CSI and synchronization: CFO and TO can significantly degrade transmission quality, making robust joint estimation and correction necessary for practical NOMA systems.OFDM-based multicarrier waveforms can support robust CFO and TO estimation and correction.
6. Conclusion
The paper concludes that NOMA improves multiple-access efficiency by separating users in the power domain and better exploiting differing channel conditions than OMA. It presents a unified NOMA model covering MIMO and cooperative communication, alongside implementation aspects, open issues, and a literature survey.
- Conclusion: NOMA improves radio-resource usage by separating users in the power domain, addressing OMA’s limited simultaneous-user capacity.OMA allocates time, frequency, or code resources orthogonally, which limits multiple-access efficiency.
- Conclusion: NOMA can exploit users’ differing channel conditions by allowing users with better channels to use bands assigned inefficiently under OMA.Under OMA, users experiencing deteriorating channels may receive large frequency bands, limiting effectiveness.
- Conclusion: NOMA performance can be significantly improved through MIMO and cooperative communication techniques.The conclusion identifies both techniques as performance-enhancing extensions.
- Conclusion: The paper provides a unified system model for NOMA covering MIMO and cooperative communication scenarios, and details implementation aspects and related open issues.It also includes a comprehensive literature survey on the state of the art.
The Scientific World Journal
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- The passages repeatedly identify Hindawi and Volume 2018.
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- Additional publication titles include Modelling & Simulation in Engineering, Navigation and Observation, Active and Passive Electronic Components, Acoustics and Vibration Advances, and Shock and Vibration.