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A Survey of Downlink Non-orthogonal Multiple Access for 5G Wireless Communication Networks
Zhiqiang Wei, Jinhong Yuan, Derrick Wing Kwan Ng, Maged Elkashlan, Zhiguo Ding
TL;DR
5G wireless communication networks face challenging requirements, while orthogonal multiple access schemes are not sufficient to support the needed transmission. This survey examines NOMA through single-carrier and multicarrier models, reviews performance analysis, resource allocation, and MIMO-NOMA, and discusses its features and research challenges; it presents NOMA as promising because of high overloading transmission, spectral efficiency, massive connectivity, and low latency.
Problem
5G wireless communication networks pose challenging requirements, and OMA schemes are not sufficient to support the needed transmission.
Method
The survey explains two-user single-carrier and general multicarrier NOMA models, then reviews performance analysis, resource allocation, and MIMO-NOMA.
Results
NOMA is presented as a promising multiple access technique for 5G because it supports high overloading transmission and offers high spectral efficiency, massive connectivity, and low latency.
Takeaways & Limitations
The survey identifies NOMA's key features and potential research challenges while positioning it as a promising technique for 5G wireless networks.
Abstract
from arXiv · showhide
Non-orthogonal multiple access (NOMA) has been recognized as a promising multiple access technique for the next generation cellular communication networks. In this paper, we first discuss a simple NOMA model with two users served by a single-carrier simultaneously to illustrate its basic principles. Then, a more general model with multicarrier serving an arbitrary number of users on each subcarrier is also discussed. An overview of existing works on performance analysis, resource allocation, and multiple-input multiple-output NOMA are summarized and discussed. Furthermore, we discuss the key features of NOMA and its potential research challenges.
I. INTRODUCTION AND BACKGROUND
The paper motivates power-domain NOMA as a response to OMA's limits for massive connectivity and diverse QoS, then surveys its principles, benefits, related schemes, and research scope.
- Motivation: OMA assigns users orthogonal time, frequency, or code resources, but limited degrees of freedom constrain massive connectivity and diverse QoS support.Users with better channels may receive priority, while poorer-channel users face waiting, unfairness, and latency.
- NOMA principle: NOMA shares degrees of freedom through superposition, requiring multiple-user detection to separate users that share the same resource.This introduces controllable symbol collision while retaining simultaneous transmission.
- Benefits: NOMA can support high-overloading transmission and improve system capacity under limited spectrum or antenna resources.Multiple users with different traffic requirements can be multiplexed concurrently on the same degree of freedom.
- Benefits: NOMA is associated with improved latency, fairness, spectral efficiency, and massive connectivity in 5G wireless networks.The paper also cites high user fairness and low latency among NOMA's recognized benefits.
- Survey scope: The survey focuses on power-domain NOMA, covering its basic concepts, key features, existing performance and resource-allocation work, MIMO-NOMA, and research challenges.It also situates NOMA alongside related non-orthogonal transmission work such as MUST and CDM-NOMA.
- NOMA variants: NOMA schemes are categorized as code-domain or power-domain multiplexing, with power-domain NOMA serving multiple users on the same degree of freedom using SIC.Power-domain NOMA is described as simpler to implement because transmitter physical-layer procedures change little relative to 4G.
II. FUNDAMENTALS OF NOMA
This section introduces downlink single-antenna NOMA through a two-user single-carrier model and then generalizes it to multicarrier operation with arbitrary users per subcarrier.
- II. FUNDAMENTALS OF NOMA: The fundamentals section presents a basic model and concepts for single-antenna downlink NOMA.It begins with a simple single-carrier case before moving to a more general model.
- II. FUNDAMENTALS OF NOMA: The single-carrier model serves two users simultaneously, illustrating the basic operation of downlink NOMA.The section frames this as the first subsection's simple system model.
- II. FUNDAMENTALS OF NOMA: The multicarrier model supports an arbitrary number of users on each subcarrier.This generalizes the two-user single-carrier setup to multicarrier transmission.
1) Two-user SC-NOMA:
Two-user SC-NOMA superimposes both users’ messages on one subcarrier, allocating more power to the weak user and using receiver-side SIC according to channel strength. Its rate region is reported as likely outperforming OMA, while covering only part of the broadcast-channel capacity region.
- Two-user SC-NOMA: The BS superimposes two users’ messages on the same subcarrier with powers p1 and p2 subject to p1 + p2 = 1.The transmitted signal is x = √p1s1 + √p2s2.
- Power allocation: More power is allocated to the weak, cell-edge user to provide fairness and facilitate SIC, so p1 ≤ p2.User 1 is the strong cell-center user, while user 2 is the weak cell-edge user.
- Successive interference cancellation: The strong user decodes the weak user’s message first and subtracts it before decoding its own, whereas the weak user decodes directly with interference.The SIC decoding order descends by channel gain normalized by noise.
- Performance: NOMA schemes are very likely to outperform OMA in the two-user rate-region comparison.The comparison is illustrated in Fig. 4 for a strong user 1 and weak user 2.
- Performance: The NOMA rate region covers only part of the broadcast-channel capacity region with a SIC receiver because of power allocation.This is a stated scope boundary of the considered NOMA rate region.
2) Multiuser MC-NOMA:
Multiuser MC-NOMA schedules arbitrary user sets across subcarriers, superimposes their messages with channel-dependent powers, and applies SIC in descending channel-gain order. Sharing subcarriers can accommodate diverse QoS requirements and support massive connectivity and IoT, including overloading scenarios unavailable to OFDMA.
- Multiuser MC-NOMA: MC-NOMA allows an arbitrary number of users to be multiplexed simultaneously on each subcarrier.The system schedules users across subcarriers using user sets ξk and occupied-subcarrier sets ζn.
- Benefits: MC-NOMA supports overloading with N < K subcarriers, a scenario that OFDMA cannot afford.The channel gains are assumed perfectly known at both transmitter and receiver sides.
- Scheduling and power allocation: Users sharing subcarrier k receive channel-dependent powers ordered as pk,b(1) ≤ pk,b(2) ≤ · · · ≤ pk,b(|ξk|).The mapping between ordered channel gains and users can differ across subcarriers because of frequency-selective fading.
- Benefits: Subcarrier sharing saves resources that might otherwise be wasted by weak-user-only transmission and accommodates more users with diverse QoS requirements.The paper connects this property to massive connectivity and IoT in 5G networks.
- Signal model and SIC: On each subcarrier, all scheduled users’ messages are superimposed, and receivers use SIC to eliminate inter-user interference.The received signal includes the superimposed messages and AWGN.
- Signal model and SIC: SIC follows descending channel-gain order: each user decodes and subtracts later-ordered messages, then treats earlier-ordered messages as interference.For three users with |hk,2|2 ≥ |hk,3|2 ≥ |hk,1|2, the mapping is b(1) = 2, b(2) = 3, and b(3) = 1.
III. PERFORMANCE AND KEY FEATURES OF NOMA
The paper surveys NOMA performance characteristics and discusses the advantages and disadvantages of NOMA schemes.
- Performance and key features of NOMA: This section presents performance characteristics of NOMA reported in existing works.It then discusses the pros and cons of NOMA schemes.
- Performance and key features of NOMA: The section frames NOMA through both reported performance and its associated advantages and disadvantages.The supplied passage provides the section’s scope rather than specific performance values.
A. Performance of NOMA
The surveyed performance studies report gains for NOMA over OMA across spectral efficiency, outage probability, throughput, fairness, and sum rate. Analyses also examine CSI quality, user pairing, and channel heterogeneity, while multicarrier performance remains less explored.
- NOMA offers considerable performance gains over OMA in spectral efficiency and outage probability.
- Under perfect CSI simulations, NOMA achieves higher overall cell throughput, cell-edge throughput, and proportional fairness than OMA.
- NOMA outperforms native TDMA with high probability in both sum rate and individual rates.
- User n achieves diversity gain n, and NOMA is asymptotically equivalent to opportunistic multiple access.
- Performance analysis has focused mainly on single-carrier systems because multicarrier user scheduling has combinatorial complexity.
- The NOMA advantage over OMA increases with larger channel-gain differences between paired users.
C. Cons
NOMA introduces CSI, signaling, computational, interference, and optimization challenges. Existing resource-allocation studies address sum rate and fairness, but imperfect-CSI scheduling, power allocation, and decoding-order selection remain difficult.
- Challenges: Perfect CSI is needed to arrange SIC decoding order, increasing CSI feedback overhead.
- Challenges: SIC increases receiver computational complexity and delay, especially in multicarrier and multiuser systems.
- Challenges: Strong users require weaker-user power-allocation information for SIC, increasing system signaling overhead.
- Challenges: Allocating more power to cell-edge weak users introduces more inter-cell interference.
- Resource allocation: Joint power and channel allocation in multicarrier NOMA is NP-hard, motivating competitive suboptimal algorithms.
- Resource allocation: Resource allocation studies optimize both sum rate and fairness using proportional-fairness, max-min, outage-balancing, and low-complexity approaches.
- Open problems: Few works jointly address user scheduling and power allocation for multicarrier NOMA under imperfect CSI, including decoding-order selection.
B. MIMO-NOMA
MIMO-NOMA extends NOMA into the spatial domain through beamforming, precoding, detection, clustering, and power allocation. Existing studies report advantages over conventional MIMO-OMA and identify spatial scheduling and allocation as underexplored.
- MIMO-NOMA multiplexes users sharing a beam in the power domain and uses spatial transmission with one base station serving multiple users.
- Initial studies demonstrated that MIMO-NOMA outperforms conventional MIMO-OMA.
- Two-stage multicast beamforming outperforms zero-forcing beamforming in two-user NOMA broadcasting.
- Precoding and detection designs target inter-cluster interference, while studies optimize covariance matrices, power allocation, and signal alignment.
- MISO-NOMA outperforms conventional OMA particularly at low transmit SNR when users outnumber base-station antennas.
- User scheduling and power allocation in the spatial domain remain rarely discussed despite their role in improving MIMO-NOMA spatial efficiency.
C. Other Works on NOMA
Related work extends NOMA beyond downlink studies to uplink, asynchronous, cooperative, energy-harvesting, cognitive-radio, visible-light, and physical-layer-security settings. The survey does not discuss these areas in detail because of limited space.
- These additional NOMA directions are not discussed in detail because of limited space.
- Uplink and asynchronous NOMA have been studied in several works.
- Cooperative NOMA uses strong users as relays for weak users.
- NOMA has also been combined with energy harvesting, cognitive radio, visible light communication, and physical layer security.
V. RESEARCH CHALLENGES
NOMA can improve spectral efficiency, user fairness, and support for diverse QoS requirements, but practical deployment raises challenges involving imperfect CSI, decoding order, and joint resource design.
- NOMA is positioned to improve spectral efficiency, user fairness, and support for massive connections with diverse QoS requirements.
- Most resource-allocation studies assume perfect transmitter-side CSI, although estimation error and feedback delay make this assumption difficult in practice.
- Future research should examine how CSI errors affect NOMA performance and how resource allocation can remain robust under such uncertainty.
- Robust resource allocation under imperfect CSI is needed, including outage-based approaches for delay-sensitive 5G applications.
- SIC decoding order under imperfect CSI remains open, alongside joint optimization of power allocation, user scheduling, and decoding-order selection.
B. Cooperative NOMA
Cooperative NOMA uses strong users as relays for weak users, potentially achieving maximum diversity gain while introducing complexity and delay. The paper also highlights QoS-aware grouping as a way to broaden NOMA applicability beyond channel-based pairing.
- B. Cooperative NOMA: Strong users can relay weak users in cooperative NOMA, potentially utilizing spatial DoF even when users have single antennas.
- B. Cooperative NOMA: Preliminary studies show that cooperative NOMA can achieve the maximum diversity gain for all users.
- B. Cooperative NOMA: Cooperative NOMA motivates research on optimal resource allocation and distributed beamforming to harvest spatial DoF with limited signaling overhead.
- B. Cooperative NOMA: Cooperative NOMA introduces additional complexity and delay, creating a tradeoff with system performance.
- QoS-aware NOMA: QoS heterogeneity can inform NOMA power allocation and user scheduling, rather than treating channel conditions as the sole grouping criterion.
- QoS-aware NOMA: QoS-based grouping can improve the design of SIC order, power allocation, and scheduling, while enabling scenarios where users have similar channel conditions.