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On uplink non-orthogonal multiple access for 5g: Opportunities and

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Article  in  China Communications · December 2017

DOI: 10.1109/CC.2017.8246331

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China Communications • December 2017 143

quency division multiple access (FDMA) and time division multiple access (TDMA) are two typical examples of orthogonal multiple access schemes.

In 3G cellular systems, code division mul-tiple access (CDMA) is applied to facilitate non-orthogonal multiple access (NOMA) in uplink transmission [1]. Uplink signals from different users are spread using user-specific scrambling codes and superpose with each other in shared physical resource. Though multiple user interference is introduced by non-orthogonal transmission, the quality of service can be guaranteed the spreading fac-tor is large. However, long spreading factor implies low data rate, which is only accept-able for voice/text services in 3rd generation (3G) wireless systems but not so suitable for wideband services in Long Term Evolution (LTE)/5th generation (5G) wireless systems. Further- more, intricate power control is need-ed to combat the near-far issue and maintain a same level of signal to interference and noise ratio (SINR) across users to guarantee the per-formance of cell edge users [2].

Another example of NOMA is multi-user superposition transmission (MUST) adopted in Long Term Evolution-Advanced (LTE-A) as a complementary multiple access technique [3][4][5]. Multiple users share the same physical

Abstract: In this paper, the concept of grant-free non-orthogonal multiple access (NOMA) for uplink data transmission is elaborated. NOMA in combination with grant-free can be applied to ultra reliability low latency com-munication (URLLC), massive machine type communication (mMTC), enhanced mobile broadband (eMBB) small packet and two-step random-access channel (RACH) scenarios. The advantages of grant-free NOMA are low latency and signaling overhead, high access capability and efficient resource utilization. Candidate uplink NOMA schemes are sum-marized and preliminary comparison among a subset of schemes are presented. Furthermore, design aspects for grant-free NOMA are dis-cussed, with special notes on particular issues such as blind UE identification and transmit-ter/receiver (Tx/Rx) impairments in realistic deployment.Keywords: 5G New Radio; non-orthogonal multiple access; grant-free transmission

I. INTRODUCTION

Orthogonal multiple access schemes have been used in cellular systems since the first generation, where physical resources are di-vided into time-frequency units and allocated to different users without overlapping. Fre-

On Uplink Non-orthogonal Multiple Access for 5G: Opportunities and ChallengesLi Tian, Chunlin Yan, Weimin Li, Zhifeng Yuan, Wei Cao, Yifei Yuan*

ZTE Corporation, South Keji Road, 55, Shenzhen, China, 508118* The corresponding author, email: yifei.yuan@ztetx.com

Editor: Zhongshan ZhangReceived: Jun. 30, 2017Revised: Sep. 13, 2017

EMERGING TECHNOLOGIES & APPLICATIONS

China Communications • December 2017144

marized and preliminary comparison among a subset of schemes are presented in Section III. In Section IV, design aspects for grant-free NOMA are discussed, especially for those realistic issues which may be different from the traditional grant-based transmissions. The conclusions are provided in Section V.

II. SCENARIOS AND DESIGN TARGETS

In grant-free, as the name implies, UE can au-tonomously transmit packets without the need to send the scheduling request and waiting for the dynamic grant. The benefits of such sched-uling request-free and grant-free are reduced signaling overhead, reduced UE power con-sumption, reduced latency, etc. Grant-free can be either orthogonal resource based, or non-or-thogonal multiple access based. In the former, even though the resources themselves are or-thogonal, different users may select the same resource, thus causing collision occasionally. Whenever such collision occurs, the link performance would be significantly degrad-ed. Hence, the resource utilization of grant-free orthogonal based is not high. Grant-free NOMA is capable of handling more number of overlapped or collided users, without severe loss of performance, due to the transmitter side processing and the advanced receiver. Grant-free NOMA is a generic technology that can bring benefits to mMTC, URLLC, eMBB small data, 2-step RACH, etc. This grant-free transmission can be realized at different lev-els: 1) UE’s resources are pre-configured and periodically allocated, and each time when a packet arrives, the UE would choose the near-est allowable time-frequency resource for the uplink transmission, which is called semi-per-sistent-scheduling (SPS) based grant-free; 2) UE can randomly select a resource at any time for uplink transmission, leading to conten-tion-based transmission. At both levels, base station (gNB) should perform blind detection either for UE identification or activation. SPS-based grant-free transmission fits for periodic traffic such as VoIP, or when the traffic load is not high such as URLLC. However, due to

resource in downlink to increase the system capacity. Diversity in power domain due to near-far effect is exploited in MUST, where 2 users with an appropriate power gap are paired for scheduling. Advanced receiver at the near user can cancel the interference of its paired far user. The following general principles are applied in MUST: 1) Far users should be as-signed with low code rate and low modulation order, 2) Gray- mapping property of composite constellation should be ensured at transmitter via certain processing. By following the above principles, interference cancellation of far us-ers can be achieved even without code-word level interference cancellation at user equip-ment (UE). Nevertheless, MUST is only appli-cable for grant-based systems where dynamic users pairing and scheduling is one of the key designs in base station (BS). Moreover, grant signaling related to MUST has to be sent to the paired users, so that each user gets neces-sary information to correctly decode its data from received downlink data packet. The men-tioned necessary information includes: near/far user role, resource allocation, modulation order and code rate and so on. Since user pair-ing and corresponding grant signaling changes per transmission time interval (TTI) (1ms), this grant-based NOMA is more suitable for enhanced mobile broadband (eMBB) with large amount of downlink data to be sent. The signal overhead is negligible compared with data packet size. On the contrary, signaling overhead in grant-based NOMA becomes a major concern in massive machine type com-munication (mMTC) scenarios [6][7]. A large number of users with sporadic small data need to be served in uplink simultaneously, where grant-based NOMA is not the best choice. To solve this problem, grant-free NOMA has been studied as a promising solution, which is more suitable and efficient for systems with massive connections but infrequent small data packets.

In this paper, grant-free NOMA for uplink transmission is discussed. The paper is orga-nized as follows. In Section II, scenarios and design targets of NOMA are elaborated. Can-didates of NOMA schemes are generally sum-

China Communications • December 2017 145

extended coverage.The uplink traffic of mMTC tends to be

sporadic with small packets. If traditional grant-based orthogonal transmission is applied for mMTC, contention-based random access has to be carried out for each uplink small data packet before the packet is to be transmitted. The number of simultaneously connected UEs is generally limited by the capacity of 5G Node B (gNB), base station. To support massive connections with limited resources, UE has to release its connection session and transfer back to inactive or idle state after the transmission is completed, without having no new packet to be sent. Apparently, signaling overhead sometimes may require even more time-frequency resources than the data pack-et, which leads to highly inefficient resource utilization. To solve this problem, grant-free transmission is proposed due to following advantages: 1) signifi cantly reduced signaling overhead and transmission latency with min-imum scheduling procedures, 2) low power consumption at UE side with reduced signal-ing processing, 3) low cost/complexity UE transceiver design relaxed requirements for time/frequency synchronization.

Based on the above analysis, it is benefi cial to operate mMTC UL transmission in RRC inactive or RRC idle state. In this case, the resources and MA signatures of different UEs will not be uniquely allocated, and UE can randomly pick resources from the confi gured resource pool (fully grant-free). Collision of both physical resource and MA signature are inevitable, and the collision probability is non-negligible for mMTC scenario since the

low resource utilization, SPS based grant free cannot achieve high spectral effi ciency or high connection density for sporadic traffi c, such as mMTC, eMBB small data, and 2-step RACH.

2.1 Ultra reliable low latency communication (URLLC)

The design targets of URLLC are low latency, high reliability and efficient resource utiliza-tion. Periodic or infrequent event-triggered traffic is typical in URLLC use cases. And due to the requirement of high reliability, data transmission is mainly operated in “RRC-con-nected” mode, which means that received sig-nals from different UEs are time synchronized within cyclic prefix and the frequency offset is small. Here the resource includes time-fre-quency resources and multiple access (MA) signatures (i.e., spreading code, interleaver/ scrambling pattern, demodulation reference signals (DMRS), preamble). When NOMA is applied for URLLC, the time- frequency resources can be shared by different user equipments (UEs), but the MA signatures (at least DMRS) should be uniquely pre-allocated without collision, as shown in fi gure 1, in or-der to achieve reliable user detection and accu-rate channel estimation. To support low laten-cy, grant-free transmission with autonomous repetition can be adopted for URLLC where the resources are semi-statically confi gured by radio resource control (RRC) signaling (SPS-based grant-free). When aforementioned RRC configuration is completed, there is no need for dynamic grant or layer 1 (L1) activation/ deactivation signaling in following transmis-sion.

From the receiver side, on the one hand, advanced interference cancellation schemes may be needed to ensure high reliability in the presence of physical resource collision. On the other hand, the complexity should be moder-ate due to the low latency requirement.

2.2 mMTC

Different from URLLC, the design targets of mMTC are mainly massive connections with very low cost, low power consumption, and F ig. 1 Channel structure of NOMA for URLLC

T

F

Superposed data of multiple users

DMRS with orthogonal sequences

China Communications • December 2017146

Open-loop power control is another challenge for multi-user decoding and interference man-agement. Compared with URLLC, latency requirement for mMTC is not so strict. Thus receiver complexity issue can be relatively less critical as long as multiple users can be decoded within the time slot(s) for one-shot transmission. Advanced receiver that is robust to channel uncertainties and physical resource or MA signature collision, e.g. successive interference cancellation (SIC) or maximum likelihood (ML)-type receiver, should be at high priority.

2.3 eMBB small packet

Grant-free NOMA can also benefit eMBB scenario with small packet transmission. The design targets would be low latency, low pow-er consumption and signaling overhead reduc-tion. The traffi c of eMBB can be either period-ic (e.g. voice services) or event-triggered in-frequent traffi c (e.g. irregular data uploading). Therefore, grant-free NOMA in eMBB can be designed either similar to URLLC, i.e., operat-ed in RRC connected transmission, or similar to mMTC, i.e., RRC inactive transmission. For eMBB use cases, cell-edge user through-put is mainly the bottleneck of overall network performance. By enabling spreading-based NOMA and contention-based resource shar-ing, the operating point of cell-edge users can be lower, leading to less inter-site interferenc-es and the improved spectral effi ciency

2.4 Two-step RACH

RACH process, as an intrinsic conten-tion-based transmission, can also be enhanced by grant-free NOMA. The design target is to improve the capacity of random access (i.e. similar to those for mMTC) while achieving accurate timing-advance (TA) estimation. Tra-ditional four steps in RACH can be simplifi ed to two steps shown in fi gure 3, where a one-shot transmission with preamble and data is transmitted together.

With NOMA, spreading/interleaving/scrambling is applied at transmitter side. With advanced receiver, superposed two-step

number of potential UEs that are simultane-ously accessing the system would be quite large. For example, as shown in fi gure 2, the number of arrival packet per TTI is shown with different assumptions of inter-arrival time, considering the requirement of 1 mil-lion/km2 connection density. Assuming that each UE’s packet occupies 1 physical resource block (PRB) and 1 ms resource, there will be more than 15% percentage that more than 6 packets are multiplexed on the same physi-cal resource. The number of packets per TTI would be even larger considering retransmis-sion and low code rate, which implies high overloading requirement in NOMA schemes.

From the viewpoint of receiver, UE detec-tion and channel estimation has to be carried out in a “blind” manner. Moreover, with lim-ited control signaling, synchronization within cyclic prefix cannot easily be maintained.

Fig. 2 Probability for the number of arrival packets per TTI

Fig. 3 Illustration of (a) traditional four-step RACH and (b) two-step RACH

0 5 10 150

0.1

0.2

0.3

0.4

0.5

0.6

0.7

Number of Arrival Packets Per TTI

Pro

babi

lity

InterArrivalTime=2hoursInterArrivalTime=1hoursInterArrivalTime=30minutesInterArrivalTime=30minutesInterArrivalTime=30minutesInterArrivalTime=30minutesInterArrivalTime=20minutesInterArrivalTime=20minutesInterArrivalTime=20minutesInterArrivalTime=20minutesInterArrivalTime=15minutesInterArrivalTime=15minutesInterArrivalTime=15minutesInterArrivalTime=15minutes

Random Access Preamble

RAR

Message 4

1

2

3

4

UE gNB

L2/L3 message

DL Synch.UE gNB

Random Access PreambleL2/L3 message

DL Synch.

Message 4

1

2

(a) (b)

China Communications • December 2017 147

Table 1, a few schemes are listed for exam-ple [9]-[18]. These schemes can be roughly categorized into three types: interleaver/scrambling based schemes, low code rate based schemes and spreading based schemes. Interleaver based schemes are usually operat-ed at bit level, where inter-user interference is alleviated via the bit-level repetition and ran-dom permutation. Spreading based schemes are normally operated at symbol level, where the low inter-user interference is achieved by using low cross- correlation sequences which is called full-length spreading, or using low density codes as named sparse-sequence based spreading.

Receiver algorithms are usually standard transparent and up to manufacturers’ imple-mentation. However in the case of non-or-thogonal transmission, the receiver bears much higher burden than orthogonal trans-mission, in terms of inter-user interference cancellation. Therefore, accurate modelling of advanced receiver is crucial for the per-formance evaluation and the assessment of implementation complexity. The choice of receiver algorithms is highly coupled with the transmission schemes. For instance, minimum mean squared error and successive interfer-ence cancellation (MMSE-SIC) is often used for short sequence type of signatures with low cross-correlation where matrix inversion op-eration is more feasible, as shown in [12][19]. In fi gure 5 the MMSE-SIC receiver is shown. After channel decoding, only signals passing cyclic redundancy check are applied for inter-ference cancellation. For the module of trans-mitted signal regeneration, the successfully decoded bits are re-encoded, re-modulated and re-spread. Matched filter (MF) operation is more suitable for long sequence type of signa-tures and its computational complexity is very

Fig. 4 General structure of transmitter side processing for non-orthogonal multi-ple access schemes

RACH signal (including preamble and data) from multiple UEs can be decoded, even in the presence of asynchronization and collision. And this can significantly increase the trans-mission efficiency of two-step RACH. Two-step RACH procedure starts from RRC idle and the UE identifi cation is carried in the data part. Once this data is successfully decoded, the gNB would send a response to the UE.

III. CANDIDATE SCHEMES FOR UL NOMA

3.1 Candidate schemes

In grant-free NOMA, the code rate and modu-lation order of each user is not high. The target is to multiplex more number of users and to achieve higher sum spectral efficiency than grant-free orthogonal resource based transmis-sion. A good system design of non-orthogonal multiple access needs to consider at least the following three aspects: 1) transmission scheme; 2) receiver implementation; 3) re-source confi guration and scheduling if applied.

Transmission scheme is important to make non-orthogonal transmission more feasible. Transmitter side processing of grant-free NOMA is mainly to keep the per-UE spec-tral efficiency low, while introducing good characteristics of transmit signals to facilitate multi-user interference cancellation at the re-ceiver side. There are different ways to keep the bit rate low and to distinguish different UEs, e.g., multiple access (MA) signatures. MA signatures can be spreading sequence/code, interleaver/scrambler pattern, or even preamble, demodulation reference signal. They may be operated at modulation symbol level, or at coded bit level or at both. In fi gure 4 we show a general structure of transmitter side for non-orthogonal multiple access schemes. Non-orthogonal multiple access schemes may involve channel coding, interleaver/scram-bling, bit-to-symbol mapping, or spreading.

Quite a number of schemes were proposed in 3GPP TR38.802 [8], targeting various aspects of the aforementioned scenarios. In

Channelcoding

Bit-levelinterleaver/scrambling

Bit to symbol

mapping

Spreading or symbol to

resource mappingresource mapping

Data

China Communications • December 2017148

detection complexity of ESE or MAP detector for iterative MF-SIC is relatively small, and its complexity only linearly increases with the number of users. While several iterative detec-tions are needed to achieve good performance, decoding for each user is needed in each iterative detection. Therefore, the total com-putational complexity and signal processing latency may be high. MPA type of receivers can achieve the best performance in theory, however, the detection complexity exponen-tially increases with the modulation order and the number of users. Although some methods were proposed to reduce the detection com-plexity [22][23], those algorithms have not be well verifi ed yet.

Needless to say, receiver design constitutes a big portion of hardware implementation of a non-orthogonal system. Realistic receiver needs to take into account of practical issues such as active user detection for grant-free transmission，non-ideal channel estimation, time and frequency offset handling, and re-ceiver complexity.

Within the spreading sequence/code fam-ily, the sequence/code can be relatively long or short. For the long spreading type such as pseudo-random noise (PN) sequence, it is easy to maintain low cross-correlation be-tween different users, and the size of sequence pool (2^N where N is the spreading length) can be large enough to achieve low collision probability in the case of random selection of the spreading sequences. For short sequence type, different properties can be imposed, for example, full-length spreading with low cross-correlation, or sparse spreading with low density. High overloading factor can be achieved, e.g. through the optimization of complex-valued codebook. As the number of user increases, the complexity of receiver is increased, the processing delay gets longer, and the performance is degraded due to the error propagation in successive interference cancellation. Long spreading in time domain can be useful for extreme coverage case, while short spreading code can be readily combined with multiple antennas to further decrease the

low [20]. To improve the performance of MF type receiver, MF-SIC receiver can be applied. Because MF cannot suppress the multiple user interference effi ciently, there is certain perfor-mance loss when the number of users is large or near-far effect is significant [9]. Iterative MF-SIC is a different type receiver which contains elementary signal estimator (ESE) and maximum a posteriori probability (MAP) detector [9][10][21]. Because joint detection is decoupled into several single user detections with the information updating of the expec-tation and variance of the interferences, the

Table I Categorization of the non-orthogonal multiple access schemesCategory Examples Receiver

Interleaver/scrambling based schemes

Interleaver Division Multiple Access (IDMA) [9];Interleaver Grid Multiple Ac-cess (IGMA) [10]

Chip-by-chip ESE or chip-by-chip MAP

Low code rate based schemes

Low Code Rate Spreading (LCRS) [11]

Minimum Mean Square Error or Matched Filter (MMSE or MF) together with Successive or Para- llel Interference Can-cel- lation (SIC or PIC)

S p r e a d i n g b a s e d schemes

1. Full-length spreading:Multi-User Shared Access (MUSA) [12];Non-orthogonal Coded Multi-ple Access (NCMA) [13];Non-Orthogonal Coded Ac-cess (NOCA) [14];Group Orthogonal Coded Ac-cess (GOCA) [15];Resource Spread Multiple Ac-cess (RSMA) [16].

MMSE/MF – SIC/PIC

2. Sparse-based spreading:Sparse Code Multiple Access (SCMA) [17]; Pattern-Division Multiple Ac-cess (PDMA) [18]

Maximum Likelihood (ML) or Message Pass Algorithm (MPA)

Fig. 5 MMSE-SIC receiver

MMSEequalization Demodulation Channel

decoder CRC check

Received signal Transmitted

bitsTransmitted

Signalre-construction

Interference cacellation

Y

China Communications • December 2017 149

DMRS resources are preconfigured without DMRS collision. So from the simulation re-sults, it seems that without DMRS collision, different spreading sequences behave more or less the same in terms of BLER performance. This implies that how to avoid/solve RS colli-sion and perform blind detection is more cru-cial to the design of grant-free NOMA.

IV. DESIGN ASPECTS

4.1 Grant-free

As mentioned in Section III, how to support

cross-correlation and thus enhance the over-loading capability.

3.2 Preliminary results

Preliminary performance comparisons are shown in this sub- section with several exam-ples of short full-length spreading with low cross-correlation are listed in Table 2.

Given the suggested values of spreading length L and size of sequence pool K for each scheme, the cumulative distribution func-tion (CDF) of cross-correlation is depicted in figure 6. It can be found that, the longer the spreading length, the smaller the overall cross-correlation can be achieved.

Figure 7 illustrate the performance compari-son among MUSA, NCMA and NOCA at 200% overloading, with the ideal channel estimation. It is observed that NOCA with longer sequence and lower cross-correlation has the lowest block error rate (BLER), due to the higher frequency diversity gain. However, NOCA per-forms the worst for the realistic channel estima-tion, as shown in fi gure 8. It means that longer sequence suffers more severe error propagation of channel estimates since more UEs are mul-tiplexed on the same resources given the same overloading factor. For MUSA and NCMA with the same spreading length and different pool sizes, there is no significant difference on the BLER performances.

Different from MUSA, NCMA and NOCA, additional random scrambling is performed in GOCA after symbol-level full- length spreading, aiming to lower the inter-user in-terferences. The performance between MUSA and GOCA are compared as in fi gure 9. Given the same spreading length and overloading factor, it is seen that scrambling provides lit-tle improvement in the BLER performance, assuming the ideal UE identifi cation, e.g., the scrambling sequence of each UE is known to gNB. Note that in reality, blind detection of scrambling sequences would be quite diffi cult and thus lead to signifi cant performance deg-radation.

It is noticed that, even for the simulation of realistic channel estimation, different UEs’

F ig. 6 Cross-correlation properties of different full-length spreading schemes

Table II Examples of NOMA schemes with short full-length spreadingScheme Characteristics Design targets

MUSA

Random spreading that the elements are taken from {1,-1,i,-i}, large pool size of sequences with short spreading factor, low cross-correlation

Target to loosely meet Welch-bound, minimize inter-user interference, and easy receiver processing

NOCA

Zadoff–Chu (ZC)-like sequences, large pool size of sequences with short/medi-um spreading factor, low cross-correla-tion

Minimize inter-user interfer-ence, joint design with pream-ble sequence

NCMA

Grassmannian codes , ident ica l cross-correlation value for a given spreading factor and number of se-quences

Target to strictly meet Welch-bound, minimize inter-user interference

GOCATwo-stage spreading sequences (short spreading + scrambling), low cross-cor-relation and interference randomization

Minimize inter-user and in-ter-cell interference

0 0.2 0.4 0.6 0.8 10

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Cross−correlation

CD

F

MUSA, L=4, K=64NOCA, L=12, K=360NCMA, L=4, K=8GOCA, L=3, K=24

China Communications • December 2017150

ly, preamble (e.g. ZC- sequence) can be used for channel estimation and user detection for UL transmission of UE in RRC-inactive state such as mMTC or two-step RACH scenarios. However, in the latter case, UE is required to perform random selection within a sequence pool of preamble, and therefore collision be-tween different UEs may happen. Preamble collision would lead to the following outcome:

1) if the SNRs of multiple users are similar, it is very likely that none of the users can be correctly decoded due to the strong cross-in-terference;

2) if one of the users has much higher SNR than others’, it is possible that only this user’s signal can be successfully decoded if the sum-mation of the interference from the other users is negligible.

The collision probability, which is the probability that multiple users select the same preamble sequence, determines the number of superposed users. For example, assuming that the pool size is N and M UEs each randomly choose a preamble sequence from the pool, the collision probability can be calculated as:

Pc c, = −1NAN

M

M (1)

where, ANM =

( )!N M−N !

.

In light of this, the size of the sequence pool N should be large enough to support mul-tiple UEs to share the same resources. How-ever, note that the size of the pool is also con-strained by the length of preamble sequence which should not be too long in order to keep the overhead acceptable. Besides, the com-plexity of blind multi-user detection linearly increases as the pool size grows.

Another way to support grant-free and blind multi-user detection (MUD) is to use data symbol itself, where full use of the avail-able time/frequency resources is possible [24][25]. Here blind MUD means that when two UEs select the same physical resource, it is still possible to decode the UE with higher SINR based on roughly channel estimation. UE ID can be explicitly included in the data

grant-free is quite important for NOMA eval-uations. The key issue for grant-free is that how to perform UE identifi cations. For uplink transmission in RRC-connected state, orthog-onal DMRS can be preconfi gured by gNB and used for both UE identification and channel estimation, if the traffic is not quite heavy such as URLLC or eMBB scenarios. Similar-

F ig. 7 Performance comparison between MUSA, NCMA and NOCA with ideal channel estimation

2 3 4 5 6 7 810−3

10−2

10−1

10010010

SNR

BLE

R

Ideal CE, Rx2, equal SNR, w/o. collision

MUSA, 200% OverloadingNCMA, 200% OverloadingNOCA, 200% Overloading

5 6 7 8 9 10 11 1210−2

10−1

10010010

SNR

BLE

R

Realistic CE, Rx2, unequal SNR, wt. collision

MUSA, 200%, L=4, K=64MUSA, 300%, L=4, K=64NCMA, 200%, L=4, K=64NCMA, 300%, L=4, K=64NOCA, 200%, L=12, K=360NOCA, 300%, L=12, K=360

Fi g. 8 Performance comparison between MUSA, NCMA and NOCA with realistic channel estimation

China Communications • December 2017 151

extremely light traffic, NOMA may not be reliable or effi cient, fallback mechanism from grant-free NOMA to grant-based orthogonal multiple access (OMA) should be allowed.

Another open issue is how to conduct ap-propriate performance evaluation and analysis for grant-free NOMA through link and system level simulation. At least the following aspect should be taken into account in the simula-tions:

● Traffi c model and deployment scenarios of eMBB (small packet), URLLC and mMTC.

● Device power consumption.● Coverage (link budget).● Latency and signalling overhead.● BLER reliability, capacity and system

load.Realistic modeling of transmitter (Tx)/re-

ceiver (Rx) impairments, for instance, poten-tial peak-to-average-power ratio (PAPR) issue, channel estimation error, power control accu-racy, resource collision, etc. should be con-sidered. For system-level simulation, proper physical layer abstraction models for link-to- system (L2S) mapping are still under develop-ing, in order to take into account the blind UE detection and realistic Tx/Rx impairments.

so that UE identifi cation can be achieved once the data is successfully decoded. The decoded data can be further utilized to refi ne the chan-nel estimation. The error propagation is mini-mized by code-word level interference cancel-lation and it is then possible to also decode the UE with lower SINR. The pros of data-only solution are: 1) the overhead for preamble or DMRS can be saved; 2) high overloading can be achieved since there is no preamble/DMRS collision. The cons of data only are 1) com-plexity is significant since all possible trans-mission hypotheses should be tested; 2) it is non-trivial to perform hybrid automatic repeat request (HARQ) retransmission and combing.

4.2 Open issues

There are still many open issues for grant-free NOMA, regardless of specifi c transmitter or receiver schemes, especially on the related procedures, such as

● UL transmission detection● HARQ related procedures● RRC and L1 signalling ■ Resource confi guration ■ Link adaptation ■ Power control● Switching between orthogonal and

non-orthogonal multiple accessGrant-free NOMA is feasible only when

the UL transmission of different UEs sharing the same resources can be correctly detected and identified. For UL grant-free transmis-sion, L1 signalling may not always be needed for activation, and in this case, some of the parameters such as time/frequency resourc-es, RS parameter, modulation and coding scheme (MCS)/transport block size (TBS) value, and power control related parameters should be configure via RRC signalling. In addition, HARQ related procedures including how many HARQ processes are supported, acknowledgement (ACK)/ Negative Acknowl-edgement (NACK) feedback scheme, and combining scheme of retransmissions should have proper design to further enhance the sys-tem performance. Considering the possibility that for extremely high overloaded traffi c and

Fig. 9 Performance evaluation between short full-length spreading with (GOCA) and without (MUSA) scrambling

0 5 10 15 2010−4

10−3

10−2

10−1

10010010

Sum SNR (dB)

BLE

R

Spreading wt. or w/o. scrambling, MMSE−SIC, no DMRS collision

GOCA, L=3, 8UEGOCA, L=3, 12UEGOCA, L=3, 16UEMUSA, L=3, 8UEMUSA, L=3, 12UEMUSA, L=3, 16UE

China Communications • December 2017152

[12] 3GPP, R1-1608953, “Link-level performance evaluation for MUSA,” ZTE, ZTE Microelectron-ics.

[13] 3GPP, R1-1609223, “Further evaluation results of NCMA in UL LLS,” LG Electronics.

[14] 3GPP, R1-167249, “Non-orthogonal coded ac-cess (NOCA),” Nokia, Alcatel-Lucent Shanghai Bell.

[15] 3GPP, R1-1609332, “LLS results for GOCA scheme,” MediaTek Inc.

[16] 3GPP, R1-163510, “Candidate NR Multiple Ac-cess Schemes,” Qualcomm Incorporated.

[17] 3GPP, R1-164037, “LLS results for uplink multi-ple access,” Huawei, HiSilicon.

[18] 3GPP, R1-1608755, “LLS results of PDMA with realistic channel estimation,” CATT.

[19] Z. Yuan, G. Yu, W. Li, Y. Yuan, and X. Wang, “Multi-user shared access for internet of things,” in IEEE 83rd Vehicular Technology Conference, VTC Spring., vol. 1, 2016, pp. 1–5.

[20] 3GPP, R1-164689, “RSMA and SCMA compari-son,” Qualcomm Incorporated.

[21] L. Ping, “Interleave-division multiple access and chip-by-chip iterative multi-user detection,” IEEE Communications Magazine, vol. 43, no. 6, 2005, pp. S19-S23.

[22] Y-S. Cheng, M. J. Neely, and K. M. Chugg, “Iter-ative Message Passing Algorithm for Bipartite Maximum Weighted Matching,” IEEE Interna-tional Symposium on Information Theory, July 2006, pp.1934 - 1938.

[23] Y. Wu, S. Zhang, Y. Chen, “Iterative multiuser re-ceiver in sparse code multiple access systems,” IEEE International Conference on Communica-tions, 2015, pp.2918-2923.

[24] 3GPP, R1-166404, “Receiver Details and Link Performance for MUSA,” ZTE, ZTE Microelec-tronics.

[25] Z. Yuan, C. Yan, Y. Yuan, W. Li, “Blind Multiple User Detection for Grant-free MUSA without Reference Signal”, IEEE 86th Vehicular Technolo-gy Conference, 2017, pp 1-5.

BiographiesLi Tian, was born in Xiantao, China, in 1988. He received the bachelor degree in commu-nication engineering and the Ph.D degree in Control Science and Control Engineering from Tongji University, Shanghai, China, in July 2009 and January

2015, respectively. From 2013 to 2014, he was a vis-iting Ph.D student at the Department of Electronics and Information Systems (DEIS), University of Bo-logna, working with Prof. Vittorio Degli-Esposti. He participated in the 5G project sponsored by National Natural Science Foundation of China. He is now a Senior Engineer at the Department of Algorithms,

V. CONCLUSIONS

In this paper, we discuss non-orthogonal mul-tiple access (NOMA) technique for uplink data transmission. The use cases and design targets of NOMA for different scenarios are analyzed. Preliminary comparisons between several NOMA schemes are presented. From the per-formance evaluation, it seems that reference signal/data collision is crucial to the design of grant-free NOMA. Further study is needed for design aspects of grant-free NOMA, especial-ly for the realistic issues such as blind detec-tion, realistic channel estimation with possible RS collision and Tx/Rx impairments.

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[5] 3GPP TR 36.859, Study on downlink multiuser superposition transmission (MUST).

[6] C. Bockelmann, N. Pratas, H. Nikopour, K. Au, T. Svensson, Č. Stefanović, P. Popovski, A. De-korsy, “Massive Machine-type Communications in 5G: Physical and MAC-layer solutions,” IEEE Communications Magazine, vol.54, no.9, 2016, pp.59-65.

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[8] 3GPP, TR38.802, “Study on New Radio Access Technology: Physical Layer Aspects (Release 14)”, 2017.

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China Communications • December 2017 153

Wei Cao, received her PhD degree from the Department of Electrical and Computer En-gineering, National University of Singapore in 2008. Currently, she is working for ZTE as a senior algorithm expert. Her research interests include wire-

less communications and signal processing for com-munications.

Yifei Yuan, (yuan.yifei@zte.com.cn) received Bachelor and Master degrees from Tsinghua University of China, and a Ph.D. from Carnegie Mellon Univer-sity, USA. He was with Alca-tel-Lucent from 2000 to 2008, working on 3G/4G key tech-nologies. Since 2008, he has

been with ZTE, responsible for standards research on LTE-Advanced physical layer, and 5G technologies. His research interests include MIMO, iterative codes, resource scheduling, non-orthogonal multiple access, internet-of-things (IoT). He was admitted to Thou-sand Talent Plan Program of China in 2010. He has extensive publications, including a book on LTE-A relay, a book on LTE-Advanced key technologies and system performance, and a book on narrow-band IoT. He has over 50 granted patents.

ZTE Corporation. His current research interests are in the field of 5G channel modeling and new air-in-terface. Dr. Tian serves as reviewer for a number of international journals including IEEE Transactions on Vehicular Technology, IEEE Access, IEEE Antennas and Wireless Propagation Letters, and International Jour-nal of Antennas and Propagation.

Chunlin Yan, (yan.chunlin@zte.com.cn) received Ph.D degree from university of elec-tronic science and technology of China (UESTC) on Dec. 2004. He works at ZTE Corporation as senior algorithm expert. His main research interests fall in synchronization, channel esti-

mation, channel coding, MIMO detection, multi-user precoding, and non-orthogonal multiple access technique for 5G.

Weimin Li, (li.weimin6@zte.com.cn) received Master de-gree in communication and information system from Nan-jing University of Posts and Telecommunications in 2010. Since then, he has been with ZTE, responsible for technology

research on power control and interference control in wireless communication systems. Recently his re-search focus is on multiple access technology for 5G.

Zhifeng Yuan, (yuan.zhifeng@zte.com.cn) received MS de-gree in signal and information processing from Nanjing Uni-versity of Post and Telecommu-nications in 2005. He has been as a member of the wireless technology advance research

department at ZTE since 2006 and leader of the re-search team of new multi-access (NMA) for 5G wire-less system since 2012. His research interests include wireless communication, MIMO systems, information theory, multiple access, error control coding, adap-tive algorithm, and high-speed VLSI design.

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