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A Survey on Non-Orthogonal Multiple Access for 5G Networks: Research Challenges and Future Trends

Zhiguo Ding, Xianfu Lei, George K. Karagiannidis, Robert Schober, Jihong Yuan, Vijay Bhargava

arXiv:1706.05347v1cs.IT

TL;DR

5G requires multiple access that can accommodate low latency, high reliability, massive connectivity, fairness, and high throughput. This survey reviews NOMA research, innovations, applications, and future challenges, concluding that simultaneous service can improve throughput, connectivity, and delay while NOMA is being integrated into relevant standards.

  • Problem

    5G needs a multiple-access approach capable of meeting heterogeneous demands including low latency, high reliability, massive connectivity, improved fairness, and high throughput.

  • Method

    The survey synthesizes NOMA research and innovations, including special-issue papers, applications, implementation issues, and future research challenges.

  • Results

    NOMA serves multiple users simultaneously, improving system throughput, supporting massive connectivity, and reducing delay because users need not wait for an orthogonal resource block.

  • Takeaways & Limitations

    NOMA’s inclusion in 5G, LTE-A, and digital-TV standards demonstrates its integration into wireless systems.

Abstract

from arXiv · show

Non-orthogonal multiple access (NOMA) is an essential enabling technology for the fifth generation (5G) wireless networks to meet the heterogeneous demands on low latency, high reliability, massive connectivity, improved fairness, and high throughput. The key idea behind NOMA is to serve multiple users in the same resource block, such as a time slot, subcarrier, or spreading code. The NOMA principle is a general framework, and several recently proposed 5G multiple access schemes can be viewed as special cases. This survey provides an overview of the latest NOMA research and innovations as well as their applications. Thereby, the papers published in this special issue are put into the content of the existing literature. Future research challenges regarding NOMA in 5G and beyond are also discussed.

I. INTRODUCTION

NOMA serves multiple users on the same orthogonal resource block, addressing OMA’s inefficiency while supporting fairness, throughput, and massive connectivity. The survey synthesizes NOMA research, applications, implementation issues, and future directions across several 5G access schemes.

  • Related schemes: Power-domain NOMA, SCMA, LDS, PDMA, and LPMA are presented as examples of 5G multiple-access techniques based on the same non-orthogonal principle.
  • Core principle: NOMA serves more than one user in each orthogonal resource block, including a time slot, frequency channel, spreading code, or spatial degree of freedom.
  • Motivation: OMA dedicates each resource block to one user, so serving a poorly conditioned user can reduce overall spectrum efficiency and throughput.
  • Benefits: NOMA concurrently serves the weak user and users with better channel conditions on the same bandwidth, allowing higher system throughput when fairness is required.
  • Benefits: Research has demonstrated that NOMA can support massive connectivity, an important capability for 5G Internet of Things functionality.
  • Survey scope: The survey reviews single- and multi-carrier NOMA, MIMO and cooperative NOMA, mmWave integration, implementation issues, and relevant standards and applications.

II. SINGLE-CARRIER NOMA

Single-carrier NOMA uses the power domain to serve multiple users on one resource block, assigning different power levels and using SIC at the stronger user. The survey discusses its spectral-efficiency, connectivity, QoS, and resource-allocation implications.

  • Power-domain NOMA: Power-domain NOMA serves multiple users in the same time slot, OFDMA subcarrier, or spreading code by allocating different power levels.
  • Power-domain NOMA: In a two-user downlink, the base station superimposes messages and allocates more power to the user with poorer channel conditions.
  • Successive interference cancellation: The stronger user performs SIC by decoding the weaker user’s message first, removing it, and then decoding its own message.
  • Spectral efficiency and connectivity: NOMA improves spectral efficiency over OMA by sharing a subcarrier between an IoT device needing low rate and a broadband user needing higher rate.
  • Spectral efficiency and connectivity: NOMA efficiently supports massive connectivity and diverse QoS requirements through spectrum sharing.
  • Resource-allocation considerations: Adaptive OMA resource allocation may require dynamically changing orthogonal resources and very short time slots that might not be realistic in practice.

B. CR-NOMA

CR-NOMA allocates power to satisfy predefined user QoS requirements before serving additional users with remaining power. In multi-carrier and hybrid NOMA, grouping and resource allocation can improve throughput while limiting decoding complexity.

  • CR-NOMA: CR-NOMA designs power allocation to strictly ensure some or all users’ predefined QoS requirements.The scheme treats NOMA as a cognitive-radio-inspired policy with QoS constraints.
  • CR-NOMA: All power is allocated to user 1 when its target data rate R1 is sufficiently large.User 2 is served only with power remaining after user 1’s QoS requirement is satisfied.
  • CR-NOMA: CR-NOMA can satisfy an IoT device’s targeted QoS while admitting an additional user on the same subcarrier, increasing overall throughput.This contrasts with OMA, where one subcarrier is solely occupied by the low-rate IoT device.
  • Multi-carrier NOMA: Multi-carrier NOMA assigns users within each group to the same subcarrier and uses NOMA for intra-group interference, while different groups use different subcarriers.This structure avoids inter-group interference and realizes overloading with a limited number of users per subcarrier.
  • User grouping: Pairing users with the most different channel conditions yields the highest performance gain over OMA, although poorer-channel users can experience lower individual rates.The sum rate is typically much larger than OMA’s, particularly for pairs with very different channel conditions.
  • Resource allocation: Joint user grouping, subcarrier allocation, and power allocation form a coupled non-convex problem, motivating optimal and low-complexity successive-convex solutions.The optimal solution provides an algorithmic upper bound, while the suboptimal algorithm achieves a performance gain close to that bound.

B. LDS, SCMA, and PDMA

LDS, SCMA, and PDMA realize multi-carrier NOMA by spreading or assigning user information across subcarriers according to structured allocation patterns. Their design balances overloading and receiver complexity through sparse or configurable subcarrier use.

  • LDS and SCMA: LDS and SCMA spread one user’s information over multiple subcarriers, while assigning fewer subcarriers per user keeps the number of shared users manageable.The sparse spreading feature limits system complexity.
  • SCMA: SCMA uses a factor graph matrix to specify which users can use each subcarrier.For the six-user, four-subcarrier example, each user employs two subcarriers because each matrix column has two non-zero entries.
  • SCMA: SCMA uses multidimensional coding to spread information across subcarriers and requires joint decoding with MPA at the receiver.Joint decoding distinguishes SCMA from power-domain NOMA, which employs SIC instead.
  • PDMA: PDMA performance is largely determined by its subcarrier allocation pattern matrix.Unlike LDS and SCMA, PDMA does not strictly require low-density spreading, and some users may use all subcarriers.

IV. MIMO-NOMA

MIMO-NOMA exploits spatial degrees of freedom but introduces difficult channel ordering and performance-evaluation issues. Channel decomposition, alignment, and grouping methods can convert the system into SISO-NOMA subchannels, reducing design complexity while preserving useful spatial gains.

  • IV. MIMO-NOMA: MIMO-NOMA is harder to design than SISO-NOMA because multi-antenna channels are vectors or matrices rather than scalar channel gains.This makes user ordering according to channel conditions difficult.
  • Performance comparison: MIMO-NOMA outperforms MIMO-OMA, and NOMA can achieve larger individual and sum rates than OMA in the considered comparisons.The cited results establish performance comparisons for MIMO and downlink NOMA settings.
  • General principles: When users’ channels are quasi-degraded, MIMO-NOMA can achieve the same performance as DPC and optimal performance in the MIMO context.Extending quasi-degradation to general multi-user MIMO remains an open problem.
  • Decomposing MIMO-NOMA to SISO-NOMA: Decomposition-based designs exploit spatial degrees of freedom to form separate SISO-NOMA subchannels and significantly reduce system complexity.These approaches can apply to both uplink and downlink transmission.
  • Decomposing MIMO-NOMA to SISO-NOMA: Signal alignment enables zero-forcing precoder design when the original linear-equation system has no solution, relaxing the condition N ≥ M.The resulting model is similar to a SISO-NOMA system.
  • Decomposing MIMO-NOMA to SISO-NOMA: GSVD simultaneously diagonalizes two users’ channel matrices and pairs effective channel gains of different strengths on each separated SISO channel.This pairing is described as ideal for NOMA design.
  • Decomposing MIMO-NOMA to SISO-NOMA: Spatial-degree-of-freedom methods can produce Angle Division Multiple Access, while antenna selection preserves maximum diversity at the cost of reduced multiplexing gain.The decomposition approaches are general and applicable to uplink and downlink transmission.

C. When users have similar channel conditions

When users have similar channel conditions, NOMA’s advantages can disappear because many designs rely on channel differences; QoS-aware ordering and beamforming can restore useful separation.

  • NOMA’s sum rate becomes exactly identical to OMA when users have the same channels.
  • Many MIMO-NOMA designs rely on different path losses and cannot work properly when users have similar channel conditions.
  • QoS-based schemes order users by their requirements rather than channel gains.
  • NOMA can avoid OMA’s mismatch in which a low-rate IoT user receives excess bandwidth while a broadband user receives too little.
  • Beamforming toward the broadband user improves its effective gain and enlarges the channel-condition difference, while power allocation can meet the IoT user’s QoS.

V. COOPERATIVE NOMA

Cooperative NOMA uses cooperation among users or relays to assist weaker users and improve spectral efficiency. Its benefits arise from relay reuse and reduced transmission slots, with full duplexing offering further gains.

  • Motivation: Cooperative NOMA is motivated by redundant information created when the strong user decodes the weak user’s signal before its own.
  • Cooperation among NOMA users: In user-cooperative NOMA, the strong user decodes the weak user’s message and can relay it to assist the weak user.
  • Cooperation among NOMA users: Cooperative NOMA broadcasts the superimposed signals first, then uses the strong user as a relay in a second phase.
  • Cooperation among NOMA users: Cooperative NOMA can outperform cooperative OMA because it requires two time slots, whereas cooperative OMA requires three.
  • Full-duplex relaying: Full-duplex relaying improves spectral efficiency by allowing the strong user to receive and relay simultaneously, avoiding a dedicated relay slot.

B. Employing dedicated relays

Dedicated-relay NOMA reduces transmission-slot requirements and supports relay-assisted coverage, including in mmWave networks. Relay selection should prioritize strict QoS users rather than simply maximizing the minimum rate.

  • Dedicated relays: With a dedicated relay, cooperative NOMA can reduce transmission from four time slots to two.
  • Dedicated relays: Idle users in densely populated venues can serve as dedicated relays to help other users and improve system coverage.
  • Relay selection: The max-min criterion is not optimal for cooperative NOMA, motivating relay selection based on QoS requirements.
  • Relay selection: A two-stage relay strategy first identifies relays satisfying strict QoS, then selects one maximizing the other user’s rate.
  • Relay selection: The proposed relay selection strategy outperforms max-min selection and minimizes overall outage probability.
  • mmWave NOMA: In mmWave networks, NOMA can simultaneously serve many users with different QoS requirements and substantially improve throughput under strongly correlated channels.
  • mmWave NOMA: Random beamforming reduces feedback overhead by requiring users to report only scalar effective channel gains instead of full channel vectors.

VII. PRACTICAL IMPLEMENTATION OF NOMA

Practical NOMA implementation requires coding and modulation schemes that can realize theoretical rates. Lattice-based and coded designs provide alternatives to conventional power-domain NOMA and SIC.

  • Coding and modulation: Effective channel coding and modulation are crucial for realizing the achievable rates predicted by NOMA theory.
  • Coded NOMA: PAM with gray labeling and turbo codes yields a NOMA scheme that does not rely on SIC and is superior to conventional OMA and NOMA schemes.
  • New NOMA forms: Sophisticated coding and modulation have led to new NOMA forms, including Network-Coded Multiple Access.
  • LPMA: LPMA uses lattice coding and prime-number multiplication to form a linear combination of two users’ encoded messages.
  • LPMA: LPMA removes multiple-access interference through modulo operations at the receivers.
  • LPMA: LPMA can outperform conventional power-domain NOMA, particularly when users have similar channel conditions.

B. Imperfect CSI

Imperfect channel state information is a central obstacle for NOMA because estimation errors can create user-ordering ambiguities, while partial CSI and limited feedback reduce overhead. Existing studies nevertheless report that NOMA can retain performance advantages over OMA under several imperfect-CSI settings.

  • Channel estimation errors: Channel estimation errors damage NOMA by causing ambiguities in user ordering.The cited studies examine their effects on power-domain NOMA and SCMA.
  • Channel estimation errors: NOMA is reported to be more resilient to channel estimation errors than OMA.This conclusion is based on studies of power-domain NOMA and SCMA.
  • Statistical CSI: With only statistical CSI, QoS heterogeneity can determine SIC decoding order, and suboptimal power allocation with user scheduling achieves close-to-optimal performance while significantly outperforming OMA.The design is studied for multicarrier NOMA.
  • Partial CSI: Partial CSI can be sufficient to realize NOMA’s performance gains while requiring less transmitter overhead than estimating full channel information.Large-scale path-loss information changes more slowly than small-scale multipath fading, reducing estimation burden.
  • Limited feedback: One-bit feedback reduces overhead by having users report whether received signal strength exceeds a broadcast threshold.The threshold choice is crucial, and optimal thresholds are developed for different power constraints.
  • Resource allocation: NOMA resource allocation is complex because clustering, power allocation, beamforming, and subcarrier allocation are coupled; centralized optimization can impose prohibitive signaling overhead and complexity.These constraints motivate distributed resource-allocation methods and alternating or matching-based designs.

VIII. FUTURE RESEARCH CHALLENGES

Future NOMA research extends across wireless power transfer, cognitive radio, security, and practical implementation constraints. The survey highlights promising combinations while emphasizing that idealized assumptions, topology dependence, and security complexity remain unresolved boundaries.

  • Wireless power transfer: SWIPT can power a strong-user relay, increasing its incentive to assist a weak user without consuming its own battery energy.The strong user harvests energy from base-station signals and uses it for relay transmission.
  • Wireless power transfer: SWIPT-NOMA jointly considers wireless information and power transfer, including resource allocation for uplink transmission and cooperative relaying.Power allocation and transfer durations can be jointly designed to address the doubly near-far effect.
  • Wireless power transfer: Most existing SWIPT-NOMA schemes rely on idealizing assumptions, leaving hardware impairments, nonlinear harvesting, and circuit energy consumption insufficiently addressed.These practical constraints are identified as affecting performance.
  • Cognitive radio: Cognitive-radio NOMA must constrain superimposed-signal power to avoid excessive interference to primary receivers, while current results remain dependent on network topology.The survey calls for a more fundamental and general understanding of the synergy between the two techniques.
  • Security: NOMA can support physical-layer security because eavesdroppers may be unable to perform SIC when power allocation follows legitimate users’ channel conditions.NOMA is also studied for multicast and unicast transmission, where beamforming can increase secrecy data rate.
  • Security: Security in NOMA remains a promising area requiring practical, low-complexity schemes, partly because SIC requires one user to decode another user’s message.The survey notes that this risk also exists in other multiple-access techniques.

D. Applications of NOMA to other 5G scenarios

NOMA applications extend beyond conventional cellular access to heterogeneous, machine-type, visible-light, satellite, random-access, vehicular, broadcasting, and caching scenarios. The survey reports broader connectivity and throughput benefits, while identifying limited MIMO-VLC work as an open boundary.

  • 5G scenarios: In heterogeneous networks, NOMA allows more users to be served in a small cell through spectrum sharing between macro and small-cell base stations.Related studies also examine NOMA for M2M, ultra-dense, and massive machine-type communications.
  • 5G scenarios: NOMA can effectively support massive connectivity and IoT functionality in M2M, ultra-dense, and massive machine-type communications.These applications are studied as extensions of NOMA to additional 5G scenarios.
  • Beyond cellular networks: NOMA is applied to content caching and is compatible with non-cellular applications including terrestrial-satellite, ALOHA random-access, and vehicular ad-hoc networks.Content caching is described as a spectrally efficient way to deliver content to users.
  • Visible light communications: In VLC, NOMA supports more users, benefits from high SNRs, and has a large performance gap over OMA in the high-SNR regime.Existing studies report that NOMA-VLC outperforms OMA-VLC.
  • Visible light communications: For strongly correlated VLC channels, NOMA can achieve performance close to optimal dirty-paper coding, motivating NOMA-based VLC precoding and beamforming.The survey identifies this as a promising future direction.
  • Conclusions: The survey concludes that simultaneous service improves throughput, enables massive connectivity, and reduces delay by avoiding waits for orthogonal resource blocks.It also notes industrial efforts to include NOMA in 5G, LTE-A, and digital-TV standards.
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