Non Orthogonal Multiple Acess with SIC

Non-orthogonal Multiple Access with SIC
Short Discourse
Jiljo K Moncy
jkmoncy@caltech.edu
http://www.linkedin.com/in/jiljokmoncy
California Institute of Technology
Pasadena, CA
March 9, 2018
Jiljo K Moncy March 9, 2018 NOMA with SIC 1 / 23

Agenda
• Introduction
• Concept of NOMA
• System Model
• Downlink
• Uplink
• Component Technologies
• Power allocation
• Scheduling
• Receiver
• Multiple antennas
• Performance Evaluation
• Receiver test-bed

Introduction
• Orthogonal Random
Access
• RA procedure
• Preamble collision
• Excessive Overhead
RA Procedure

Concept of NOMA
Concept of NOMA [3]
• Signals superimposed in power domain
• Superimposed signal considered as interference
• SIC employed at user with higher SINR

Concept of NOMA

NOMA Vs OMA
• OMA splits available BW
• NOMA uses different power for transmissions
Spectrum Usage [2]

System Model
Downlink
Transmission
• Each sub-band caters multiple users
• Scheduling to determine multiplicity
• Transmission power assigned to users
• Cell centered users have smaller power allocation than cell edge
users
x =
m
k=1
Pk sk (1)
Pk = βk
PBS
NSB
(2)
Downlink: Resource allocation [3]

System Model
Downlink
Reception
• Assumption: Users in descending SINR order
• SIC performed at usern to remove intra cell interference
• Cell centered users have more SIC stages than cell edge users
• SIC performed in ascending SINR order
yn = hn
N
k=1
sk Pk + In + nn (3)
yn =
Desired signal
hn sn Pn + hn
N
k=n+1
sk Pk
Removed by SIC
+
Intra cell interfernce
hn
n−1
k=1
sk Pk +In + nn (4)

System Model
Uplink
Signals from cell centered users are decoded prior to other users
Uplink SIC [2]

Component Technologies
• Multi-user transmission power allocation
• Scheduling algorithm
• Receiver design
• Combination with multiple antennas

Multi-user transmission power
Post-processing SINR of usern after SIC,
SINRPost
n =
βn
n−1
k=1 βk + 1
SINRn
(5)
SINRn = |hn|2 PBS
NSBPI+N
(6)
• Power assignment ratios control the user throughput
• [3] proposes allocation by maximizing geometric mean user
throughput

Power allocation
Maximizing geometrical mean user throughput
Choose {β∗
1 , β∗
2 , ...} such that geometric mean of user throughput is
maximized
{β∗
1 , β∗
2 , ...β∗
N} = argmax
{β∗
1
,β∗
2
,...β∗
N
}
N
N
n=1
SEn (7)
Spectral efﬁciency of usern,
SEn = SEMCS∗
n 1 − BLERMCS∗
n
SINRPost
n
(8)
where,
MCS∗
= arg max
{MCS}
SEMCS
n 1 − BLERMCS
n
SINRPost
n
(9)

Power allocation
Algorithms
Computational complexity increases exponentially with users
Other algorithms proposed:
• Allocation proportional to pathloss
• Allocation with limited power assignment ratio sets
• Tree-search based low complexity algorithm

Scheduling algorithm
Determine the best user set
User set cardinality
M
1
+
M
2
+ .... +
M
Nmax
(10)
Example:
• Maximize proportional fairness
• Trade off between total throughput and minimal level of user
service.
• SINRPost
calculated for all sets
• Metric:
Λψj
=
n ψj
1 +
rk,n(t)
(tc − 1)Rn(t)
(11)
ψoptimal
= arg max
ψj
(Λψj
) (12)

Receiver design
Two kinds of receiver
1 Symbol-level SIC receiver
2 Codeword-level SIC
Receiver Design [3]

Multiple Antennas
• MIMO exploits spatial domain
• NOMA exploits power domain
Two modes:
SU-MIMO

Multiple Antennas
• MIMO exploits spatial domain
• NOMA exploits power domain
Two modes:
MU-MIMO

Performance Evaluation
Link-level
Performance comparison of different receivers [3]

System-level
Cummulative throughput of downlink
user throughput [2] Downlink total user throughput [2]

System-level
Performance comparison: NOMA Vs OFDMA [3]

Experimental Trial
NOMA test bed [1]

Conclusion

Conclusion
1 Efﬁcient spectrum usage & higher throughput

Conclusion
2 Robust performance gain in high-mobility scenarios

Conclusion
3 Compatible with OFDMA (thus LTE)

Conclusion
4 Compatible with beamforming and multi-antenna

Conclusion
5 Avoids collision and overload

Conclusion
5 Avoids collision and overload
6 Random access procedures eliminated

References
A. Benjebbour, K. Saito, A. Li, Y. Kishiyama, and T. Nakamura, “Non-orthogonal multiple
access (noma): Concept, performance evaluation and experimental trials,” in Wireless
Networks and Mobile Communications (WINCOM), 2015 International Conference on,
pp. 1–6, IEEE, 2015.
K. Higuchi and A. Benjebbour, “Non-orthogonal multiple access (noma) with successive
interference cancellation for future radio access,” IEICE Transactions on Communications,
vol. 98, no. 3, pp. 403–414, 2015.
L. Anxin, L. Yang, C. Xiaohang, and J. Huiling, “Non-orthogonal multiple access (noma) for
future downlink radio access of 5g,” China Communications, vol. 12, no. Supplement,
pp. 28–37, 2015.
M. Shirvanimoghaddam, M. Dohler, and S. J. Johnson, “Massive non-orthogonal multiple
access for cellular iot: Potentials and limitations,” IEEE Communications Magazine, vol. 55,
no. 9, pp. 55–61, 2017.

Non Orthogonal Multiple Acess with SIC

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