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Application of Non-orthogonal Multiple Access in LTE and 5G Networks
Zhiguo Ding, Yuanwei Liu, Jinho Choi, Qi Sun, Maged Elkashlan, Chih-Lin I, H. Vincent Poor
TL;DR
NOMA addresses the low spectral efficiency of orthogonal access by serving multiple users through power-domain access. The article systematically reviews its fundamentals, MIMO and cooperative extensions, cognitive-radio interplay, and LTE/5G standardization, reporting almost 20% cell-average and cell-edge throughput gains.
Problem
Orthogonal access can have low spectral efficiency when bandwidth resources are assigned to users with poor channel conditions.
Method
The article systematically examines NOMA fundamentals, MIMO-NOMA, cooperative NOMA, cognitive-radio interplay, and LTE/5G standardization.
Results
Almost 20% cell-average and cell-edge throughput gains can be obtained.
Takeaways & Limitations
NOMA is presented as a multiple-access technology for LTE and envisioned as an essential component of 5G networks.
Abstract
from arXiv · showhide
As the latest member of the multiple access family, non-orthogonal multiple access (NOMA) has been recently proposed for 3GPP Long Term Evolution (LTE) and envisioned to be an essential component of 5th generation (5G) mobile networks. The key feature of NOMA is to serve multiple users at the same time/frequency/code, but with different power levels, which yields a significant spectral efficiency gain over conventional orthogonal MA. This article provides a systematic treatment of this newly emerging technology, from its combination with multiple-input multiple-output (MIMO) technologies, to cooperative NOMA, as well as the interplay between NOMA and cognitive radio. This article also reviews the state of the art in the standardization activities concerning the implementation of NOMA in LTE and 5G networks.
I. INTRODUCTION
NOMA uses the power domain to serve users simultaneously, addressing low spectral efficiency in orthogonal access. The article introduces NOMA fundamentals and surveys its integration with MIMO, cooperation, cognitive radio, and LTE/5G standardization.
- NOMA uses power-domain multiple access rather than the time, frequency, or code domains used in earlier mobile networks.
- NOMA lets users access all subcarrier channels, allowing strong-channel users to access resources allocated to weak-channel users.
- NOMA balances system throughput and user fairness by serving users with different channel conditions in a timely manner.
- The article covers NOMA power allocation, successive interference cancellation, and information-theoretic relationships, illustrated with a two-user example.
- It surveys MIMO-NOMA designs, cooperative NOMA, cognitive-radio interplay, LTE/5G standardization, and research challenges for spectrally and energy-efficient systems.
II. NOMA BASICS
NOMA serves users with different channel conditions simultaneously using superimposed signals and unequal power allocation, with SIC enabling message separation. Compared with OMA, it can improve throughput and fairness, but strict power allocation may reduce low-SNR performance.
- Users with weaker channels receive more transmission power and decode their messages by treating stronger users’ information as noise.
- Stronger-channel users first decode and subtract their partners’ messages before decoding their own information through SIC.
- NOMA’s sum-rate advantage over OMA is particularly large when the stronger user’s channel gain greatly exceeds the weaker user’s gain.
- NOMA suffers some performance loss relative to OMA at low SNR when strict NOMA power allocation is used.
- NOMA superimposes users’ signals in the same time, frequency, and code resources while assigning different power levels.
- NOMA balances throughput and fairness by serving users with poor channel conditions, unlike throughput-only allocation that may leave them unserved.
- NOMA can serve many IoT devices in a single channel use, although poor-channel users may experience low rates because of residual interference.
III. MIMO NOMA TRANSMISSION
MIMO NOMA extends NOMA to multi-antenna base stations and users. Downlink antennas can support beamforming for improved SINR or spatial multiplexing for increased throughput.
- MIMO NOMA equips the base station and users with multiple antennas, extending the basic NOMA concept.
- For downlink transmission, the base station can use multiple antennas for beamforming to improve SINR or spatial multiplexing to increase throughput.
A. NOMA with Beamforming
NOMA with beamforming combines power-domain multiplexing within user clusters with spatial separation between clusters. Beamforming suppresses inter-cluster interference while SIC separates users sharing a beam.
- A. NOMA with Beamforming: NOMA with beamforming exploits both power and spatial domains to increase spectral efficiency.
- A. NOMA with Beamforming: Users within each cluster should have highly correlated spatial channels so one beam can transmit to both users.
- A. NOMA with Beamforming: Inter-cluster interference is suppressed when each cluster beam is orthogonal to the other cluster’s users’ channel vectors.
- A. NOMA with Beamforming: The stronger user in a cluster decodes and subtracts the weaker user’s signal before decoding its own using SIC, while the weaker user decodes under interference.
- A. NOMA with Beamforming: Zero-forcing beamforming enables 2N users to share the same frequency and time slot using N beams.
- A. NOMA with Beamforming: Receive beamforming can suppress inter-cluster interference and allow the base station to use a less restrictive beamforming approach than zero forcing.
B. NOMA with Spatial Multiplexing
NOMA with spatial multiplexing combines NOMA and MIMO to increase achievable rates through multiple antennas, while cooperative NOMA improves weak-user reliability by using stronger users as relays.
- NOMA with Spatial Multiplexing: NOMA-SM combines MIMO and NOMA, with each transmit antenna sending an independent data stream.Under rich scattering, the achievable rate scales with the number of transmit antennas, requiring multiple antennas at the users as well.
- NOMA with Spatial Multiplexing: NOMA users can achieve rates up to two-times higher than OMA because each user uses a whole time slot instead of sharing one.The comparison considers OMA with equal TDMA slot allocation.
- Cooperative NOMA Transmission: Cooperative NOMA recruits stronger-channel users as relays after they decode weaker users’ messages through SIC.The relay forwards the decoded information during the cooperative transmission phase, providing the weak user with another message copy.
- Cooperative NOMA Transmission: Cooperative NOMA outperforms non-cooperative NOMA in user-pair and poor-user outage probability and achieves a larger outage-probability slope.The larger slope is attributed to achieving maximum diversity gain for all users.
- Cooperative NOMA Transmission: User pairing, with OMA among groups and cooperative NOMA within groups, can reduce complexity while distinctive channel conditions yield significant sum-rate gains.Cooperative NOMA otherwise faces coordination overhead and extra time-slot consumption.
- Cooperative NOMA Transmission: Power-allocation optimization remains an important research direction because allocation coefficients strongly affect cooperative NOMA performance.Cooperative SWIPT-NOMA protocols also use stronger users as energy-harvesting relays and are evaluated through stochastic geometry.
V. INTERPLAY BETWEEN COGNITIVE RADIO AND NOMA
The cognitive-radio perspective explains NOMA power allocation as meeting a weaker user’s QoS while serving a stronger user opportunistically. The relationship is bidirectional: NOMA can also improve secondary-user connectivity.
- Interplay Between Cognitive Radio and NOMA: NOMA can be viewed as a special case of cognitive radio in which the weaker-channel user acts as the primary user.The stronger user is introduced into the primary user’s occupied channel.
- Interplay Between Cognitive Radio and NOMA: Although the stronger user adds interference and reduces the weaker user’s rate, overall system throughput increases significantly because of the stronger user’s BS connection.The tradeoff balances throughput against the weaker user’s performance.
- Interplay Between Cognitive Radio and NOMA: CR-inspired power allocation strictly meets the weaker user’s targeted QoS while exploiting power-domain degrees of freedom to serve the stronger user.This approach is particularly useful in MIMO, where ordering users by channel conditions is difficult.
- Interplay Between Cognitive Radio and NOMA: Applying NOMA in cognitive-radio networks can serve multiple secondary users simultaneously, increasing their chance of connectivity and avoiding separated bandwidth resources.Without NOMA, separate bandwidth resources can introduce long service delays for secondary users.
- Interplay Between Cognitive Radio and NOMA: Power control in CR-NOMA must limit degradation at the primary receiver by controlling total primary-receiver interference and interference among secondary users.The total interference observed at the primary receiver is identified as an important criterion.
VI. STATE OF THE ART FOR NOMA IN 3GPP LTE AND 5G
The paper reviews non-orthogonal transmission candidates and their LTE/5G standardization context, including MUST, SCMA, MUSA, and PDMA. Reported evaluations indicate substantial throughput or spectral-efficiency gains, while standardization remains under discussion.
- State of the Art for NOMA in 3GPP LTE and 5G: 3GPP initiated a study on downlink MUST for LTE Release 13, alongside broader standardization activity for non-orthogonal transmission.The standardization status is still under active discussion.
- State of the Art for NOMA in 3GPP LTE and 5G: MUST schemes are grouped into three categories based on transmitter processing, including adaptive power ratios, composite constellations, and label-bit assignments.Examples of all three categories are shown in Fig. 6.
- State of the Art for NOMA in 3GPP LTE and 5G: Almost 20% cell-average and cell-edge throughput gains are envisioned for the evaluated non-orthogonal transmission schemes.The gains are based on initial link-level and system-level evaluations provided by various companies.
- State of the Art for NOMA in 3GPP LTE and 5G: SCMA combines sparse codebook-based non-orthogonal spreading with subcarrier allocation, coding, modulation, and low-complexity message-passing detection.At each subcarrier, multiple users share the same bandwidth resource.
- State of the Art for NOMA in 3GPP LTE and 5G: MUSA uses low-correlation spreading sequences with linear processing and SIC, achieving remarkable gains especially when user overloading exceeds 300%.The scheme is described as an uplink approach based on enhanced MC-CDMA.
- State of the Art for NOMA in 3GPP LTE and 5G: PDMA uses multi-domain non-orthogonal patterns to maximize diversity and minimize user overlap, with reported 1-2 times system spectral-efficiency increases.Its domains can include code, power, spatial, or combined dimensions.
VII. RESEARCH CHALLENGES
The paper notes that most examples consider two-user scenarios, delimiting the scope of the presented examples.
- Research Challenges: Most examples in the article consider two-user scenarios.
1) User Pairing/Clustering:
NOMA can serve users in general uplink and downlink scenarios, but multi-user superposition coding and SIC increase complexity. User pairing or clustering reduces coordination demands, while optimal clustering is generally NP-hard and requires low-complexity algorithms.
- NOMA can be applied to general uplink and downlink scenarios.
- Superposition coding and SIC can cause extra system complexity when serving more than two users.
- User pairing or clustering reduces complexity by coordinating fewer users in NOMA implementation.
- Dynamic allocation of users to clusters with different sizes is challenging, and the resulting combinatorial optimization problem is generally NP-hard.
- Exhaustive search is computationally prohibitive, motivating new low-complexity algorithms for optimal user clustering.
2) Hybrid Multiple Access:
NOMA is being combined with other multiple-access schemes and 5G technologies, while its MIMO extensions introduce substantial allocation, beamforming, receiver, and multidimensional resource-management challenges.
- Hybrid Multiple Access: NOMA has been applied with other multiple-access techniques, including the LTE hybrid scheme MUST between NOMA and OFDMA.
- Hybrid Multiple Access: NOMA is particularly considered for users with very different CSI, such as one near the base station and another at the cell edge.
- Hybrid Multiple Access: Combining NOMA with conventional OMA and newly developed 5G multiple-access schemes remains an important research topic.
- MIMO-NOMA: Joint user allocation and beamforming schemes have not been considered because their computational complexity would be prohibitively high.
- MIMO-NOMA: MIMO-NOMA faces receiver complexity because a strong user may need to jointly detect multiple signals twice.
- MIMO-NOMA: Extending spatial multiplexing to more than two users with multiple carriers requires clustering and allocation across frequency, time, spatial, and power domains.This creates an analytical and computational challenge.
4) Imperfect CSI:
Imperfect CSI is a practical concern because improving channel-estimation accuracy reduces spectral efficiency and transmitter-side CSI can create substantial overhead. The article also emphasizes cross-layer optimization and surveys NOMA’s broader transmission, cooperation, cognitive-radio, and standardization developments.
- Imperfect CSI: Sending more pilot signals to improve channel-estimation accuracy reduces spectral efficiency.
- Imperfect CSI: The impact of imperfect CSI on reception reliability remains important to study because many NOMA protocols rely on strong CSI assumptions.
- Imperfect CSI: Requiring CSI at the transmitter can cause significant system overhead, while a few feedback bits may suffice to obtain users’ channel ordering.
- Cross-Layer Optimization: Cross-layer optimization is important for meeting 5G demands including spectral efficiency, energy efficiency, massive connectivity, and low latency.
- Cross-Layer Optimization: NOMA’s physical-layer gains must be connected to upper-layer protocol design, with scheduling, pairing, power allocation, pilot, and retransmission schemes jointly optimized.
- Article Scope: The article covers two-user NOMA, MIMO-NOMA protocols, cooperative NOMA, cognitive-radio interplay, LTE and 5G standardization, and research challenges.