Invited talk in KTH EECS school: Grant free access for IoT over 5G cellular networks

1. KTH ROYAL INSTITUTE
OF TECHNOLOGY
Grant-Free Radio Access for IoT
Communications
Amin Azari
RS Lab, EECS School, KTH

2.  Part I: Introduction
 Part II) Coexistence analysis
 Analytical modeling of Interference and reliability
 Part III) Resource provisioning and operation control
 Analytical modeling of tradeoff between cost/reliability/durability
 Part IV) Advanced receiver design for performance
improvement
 Part V: Distributed learning for performance improvement
 Part VI: Conclusions, future works
Outline
2 / 51

3.  Amin Azari, born 1988, Iran
 Education:
 BS (2010,TU), MS (2013,TU/RU), Tehran & Rostock University
 Lic. (2016) from ICT school, KTH (Evolutionary IoT)
 Battery lifetime-aware cellular network design
 Results: 1 thesis, 1 patent, 3 journals, 7 conf. publications
 Towards PhD (from 2017): (Revolutionary IoT)
 Grant-free access for IoT communications
 Distributed optimization, machine learning, stochastic geometry
 Visit Aalborg University (3 months in 2017, Petar Popovski’s grant)
I.1) About me
3 / 51Part I) Introduction

4. I.2) IoT in 5G Era (1)
IoT involves in 2 out of 3 major derivers of 5G,
– Massive IoT
– Ultra-reliable IoT
Realization of networked society requires enabling:
• large-scale
• low-cost
• durable
connectivity for smart devices with limited
• battery capacity
• processing/memory capability
• radio frontend.
4 / 51Part I) Introduction

5. I.2) IoT in 5G Era (2)
Characteristics of IoT traffic
 massive in number of connections
 small payload size
 heterogeneous (characteristics & QoS req.)
Legacy cellular networks: grant-based radio access
 extensive signaling
 sending several bits requires several bytes overhead
– inefficiency for battery-limited devices
– causes congestion in network for control
– for URLLC: reliability depends on 3 transactions that cannot be
coded jointly, i.e. access reservation, response, data transfer.
5 / 51Part I) Introduction

6. I.3) Motivation fro grant-free access
 no need for synchronization
– reduce delay & overhead & energy consumption
– reduce cost of device, i.e. low-cost oscillator
 no need for access reservation
– reduce delay & overhead & energy consumption
 but the above benefits are not coming for free…
– When/how much can we gain from grant-free access?
 Recent interests:
– [3GPP, “Overall solutions for UL grant free transmission”, 2017.]
– SigFox, LoRaWAN, etc.
6 / 51Part I) Introduction
Grant-free access

7. I.4) Prior Works
 Performance Evaluation:
– 2D time-frequency interference modelling using stochastic
geometry for performance evaluation LPWANs,
[Z. Li et al., 2016]
– Stochastic geometry for analysis of non-IoT traffic
 Protocol design:
– Enhanced contention resolution ALOHA
[Clazzer et al, 2013]
– Asynchronous contention resolution diversity
[De Gaudenzi et al, 2014]
– LoRa and SigFox technologies
7 / 51Part I) Introduction

8.  Part I: Introduction
 Part II) Coexistence analysis
 Analytical modeling of Interference and reliability
 Part III) Resource provisioning and operation control
 Analytical modeling of tradeoff between cost/reliability/durability
 Part IV) Advanced receiver design for performance
improvement
 Part V: Distributed learning for performance improvement
 Part VI: Conclusions, future works
Outline
8 / 51

9. II.1) system model (1)
 Devices: 𝐾 types:
– Heterogeneous
• Different technologies and services
– Different in:
• Pattern of time/freq. usage,
• Transmit power,
• Rates of packet arrival at nodes, etc.
 Dense IoT device deployment, PCP.
– (𝜆 𝑘, 𝜐 𝑘, 𝑓(𝑥)) density of PP, avg. DP per PP, dist. of DP
 Fading: Nakagami-m. Pathloss:1/(𝑎 + 𝑏 𝑧
𝛿
)
 Application: ISM-band solutions, Cellular-band solutions
9 / 51Part II) Coexistence analysis

10. II.1) system model (2)
10 / 51Part II) Coexistence analysis
Heterogeneity in communication characteristics
and distribution processes of locations of devices

11. II.2) Research questions
 Probability of failure in uplink communication at
distance 𝑑 from the serving AP?
 Reliability of communications at a random point of
network for a given deployment of APs
– Joint/independent reception
11 / 51Part II) Coexistence analysis

12. II.3) Interference analysis
 Finding Laplace functional (LF) of the interference,
because:
 LF of interference in cellular networks mature,
while for short packet communications there is no
prior work.
12 / 51Part II) Coexistence analysis
LF interference LF noise

13. II.3) Interference analysis
Problems in using stochastic geometry:
 Partial overlapping in time and frequency
 Different
– transmit powers,
– packet lengths and data rates
– packet generation rates,
– BW of signals,
– total BW for communications
– semi-orthogonal codes shared among subset of devices,
– Etc.
 Furthermore, in stochastic geometry with PCP, probability of
success for homogeneous case has no closed-form [MH].
13 / 51Part II) Coexistence analysis

14. II.3) Interference analysis
 By defining time and frequency activity factors, we
have derived LF of interference:
14 / 51Part II) Coexistence analysis
×
Own cluster
All other clusters

15. II.3) Reliability analysis (1)
15 / 51Part II) Coexistence analysis

16. II.3) Reliability analysis (2)
16 / 51Part II) Coexistence analysis

17. II.3) Reliability analysis (3)
17 / 51Part II) Coexistence analysis

18. II.4) Validation of analytical expressions
18 / 51Part II) Coexistence analysis
Device distribution: K = 2, l1=0.19, l2=3.8, v1=1200, v2=30,P1=21 dBm, and
P2=25 dBm.

19.  Part I: Introduction
 Part II) Coexistence analysis
 Analytical modeling of Interference and reliability
 Part III) Resource provisioning and operation control
 Analytical modeling of tradeoff between cost/reliability/durability
 Part IV) Advanced receiver design for performance
improvement
 Part V: Distributed learning for performance improvement
 Part VI: Conclusions, future works
Outline
19 / 51

20. III.2) Research questions
 Resource provisioning/deployment phase:
– Regarding impact of network resources on costs and
reliability:
• optimized amount of investment in densification and
spectrum leasing for a given reliability for IoT.
 Operation phase:
– Regarding impact of transmit power and number of
replica transmission on battery lifetime and reliability:
• optimized operation policy for for IoT devices for a given
reliability and AP deployment density.
20 / 51Part III) Deployment and operation optimization

21. III.2) Research questions
*Resource provisioning *Operation control
21 / 51Part III) Deployment and operation optimization

22. III.3) KPI modeling
 Cost of the access network:
 Reliability of communications:
– Investigated in part II, Ps(i) for type i.
 Expected battery lifetime of devices:
– 𝐿 𝑖 =
[Energy Storage: 𝐸0]
[Energy Consumed Per Reporting]
[Reporting Period: 𝑇𝑖]
22 / 51Part III) Deployment and operation optimization
where

23. III.4) Analysis (0)
 Performance tradeoffs
23 / 51Part III) Deployment and operation optimization

24. III.4) Analysis (1)
 Optimize resource provisioning
24 / 51Part III) Deployment and operation optimization
minimize

25. III.4) Analysis (2)
 Optimize resource provisioning
25 / 51Part III) Deployment and operation optimization

26. III.4) Analysis (3)
 Optimize operation control
26 / 51Part III) Deployment and operation optimization
minimize

27. III.4) Analysis (4)
 Optimize operation control
27 / 51Part III) Deployment and operation optimization

28. III.4) Analysis (5)
 Scalability analysis
28 / 51Part III) Deployment and operation optimization
Scaling number of devices
Scaling reliability requirement

29.  Part I: Introduction
 Part II) Coexistence analysis
 Analytical modeling of Interference and reliability
 Part III) Resource provisioning and operation control
 Analytical modeling of tradeoff between cost/reliability/durability
 Part IV) Advanced receiver design for performance
improvement
 Part V: Distributed learning for performance improvement
 Part VI: Conclusions, future works
Outline
29 / 51

30. IV.1) system model
 Dense IoT device deployment
 Packet arrival at nodes: Poisson

signal BW
Available spectrum
≪ 1
– Utra-NarrowBand (UNB) system
30 / 51Part IV) Receiver design

31. IV.3) Key idea
In UNB the signal bandwidth is smaller than the
precision of the carrier frequency
 random frequency deviation has been proposed for
identification of RFIDs tags:
[Fyhn, Jacobsen, Popovski, Scaglione, Larsen, 2011]
 It can also be used in contention resolution for us!
frequency
Intended carrier frequency
Max
drift
Max
drift
𝑉𝐹𝑖
Δ𝑓𝑖
𝜏𝑖
time
31 / 51Part IV) Receiver design

32. IV.4) Transmission structure
 virtual frame
– sends one replica of packet immediately;
– forms a virtual frame, selects N slots to send replicas.
– N: design parameter
– use of SIC
𝑀 slots =𝑀𝑇𝑝 seconds
Reference time
carrier frequency:
𝑓𝑖 = 𝑓 + Δ𝑓𝑖
selected N slots for
main packet/replicas
transmissions
32 / 51Part IV) Receiver design

33. IV.5) Receiver design
 Problem of partial interference due to
asynchronicity
Frequency
Intended carrier frequency
Max
drift
Max
drift
𝑉𝐹𝑖
Δ𝑓𝑖
𝜏𝑖
Time
33 / 51Part IV) Receiver design

34. IV.5) Receiver design
i. Use of Zadoff-Chu as preamble in transmitters:
i. length of preamble: design parameter.
ii. tradeoff: overhead vs. decodeability
ii. window the received signal
i. time length: design parameter.
ii. offers tradeoff: delay and decodeability
iii. decode with intended frequency
iv. use periodogram and search for peaks
i. peaks drift from carrier frequencies in the receive
signal, i.e. represent a signature of some devices.
34 / 51Part IV) Receiver design

35. IV.5) Proposed receiver design
 Potential solution: Graphical overview
35 / 51Part IV) Receiver design

36. IV.6) Analysis: battery lifetime/delay
36 / 51Part IV) Receiver design
Lifetime
Delay

37. IV.6) Analysis: reliability
37 / 51Part IV) Receiver design

38.  Part I: Introduction
 Part II) Coexistence analysis
 Analytical modeling of Interference and reliability
 Part III) Resource provisioning and operation control
 Analytical modeling of tradeoff between cost/reliability/durability
 Part IV) Advanced receiver design for performance
improvement
 Part V: Distributed learning for performance improvement
 Part VI: Conclusions, future works
Outline
38 / 51

39. V.1) Research problem: description
In grant-based cellular networks
• BS is responsible for access management,
• Neighbor-cell interference management,
Need for management in grant-free access networks
• inter-network interference:
• Tackle situations at which some resources are blocked
occasionally, semi-persistent, or…
• choice of transmission code, power, and etc., for reliable
communication,
39 / 51Part V) Distributed learning

40. V.1) Research problem: motivation
40 / 51Part V) Distributed learning
Source: Interference Impact on
coverage and capacity for LPWA
IoT networks
Aalborg University,
IEEE WCNC 2017

41. V.1) Research problem: continued
 Let focus on LoRa.
 Each LoRa device has 3 choices:
• Choice of subchannel: 𝐶𝑖 ∈ 𝑪 = {1,2,3},
• Choice of transmission power: 𝑃𝑖 ∈ 𝑷 = {2,5,8,11,14},
• Choice of code (spreading factor): 𝑆𝑖 ∈ 𝑺 = {7,8,9,10,11,12}
• Remark:
– 𝑆𝑖 ↑−→ 𝑆𝑁𝑅𝑡ℎ ↓−→more coverage (due to noise),
– 𝑆𝑖 ↑−→ 𝑇𝑇 ↑−→ more collision, less coverage,
– 𝑃𝑖 ↑−→ more coverage for device 𝑖,
– 𝑃𝑖 ↑−→more energy cons. for 𝑖, more collision for others,
– 𝐶𝑖s experience different levels of IN interference.
41 / 51Part V) Distributed learning
How to select 𝑆𝑖, 𝑃𝑖, 𝐶𝑖?

42. V.2) State of the art
1. In LoRa:
PL-based approach, select SF based on RSSI.
e.g.: -100 dBm<RSSI<-50 dBm SF=8, distributed.
2. IEEE WiMob 2017, CNIT/ University of Rome, Italy
EXPLoRa: EXtending the Performance of LoRa by suitable
spreading factor allocations
- Load balancing:
𝑛 𝑖
𝑛 𝑗
=
𝑇 𝑗
𝑇 𝑖
, ∑𝑛𝑖 = 𝑁, centralized
3. IEEE ICC 2017, KU Leuven, Belgium,
Power and Spreading Factor Control in LPWA Networks
-Complex waterfilling algorithem, centralized.
42 / 51Part V) Distributed learning

43. V.3) Open problem
• The distributed solution performs much worse than the
centralized approaches. (simulation results).
• The centralized solutions provides more throughput for
networks, but
– making nodes dependent on choices of the AP.
– It also results in extra energy consumptions for nodes.
• --Need for more efficient distributed solution--
43 / 51Part V) Distributed learning

44. V.4) Proposed solution
• Each node independently learns from its transmissions.
• Based on received ACKs, device learns which {channel,
code, transmit power} set suits well.
• Action set ∪ {𝐶𝑖, 𝑆𝑖, 𝑃𝑖}, where 𝐶𝑖 ∈ 𝑪, 𝑃𝑖 ∈ 𝑷, 𝑆𝑖 ∈ 𝑺.
• Problem is non-stochastic MAB.
– Rewards are not stationary regarding multi-agent learning at the
same time.
44 / 51Part V) Distributed learning

45. V.4) Proposed solution
• MAB has been investigated for decades,
• Mature theoretical and simulation analyses for stochastic
MAB
• Non-stationary and non-stochastic MAB are under heavy
investigation, especially in ComSoc.
45 / 51Part V) Distributed learning

46. V.4) Proposed solution: continued
• We develop a solution based on UCB1, optimized for
stationary stochastic MAB.
• In UCB1, we have an external award for action 𝑖:
• UCB1’s operation in each phase:
• We add the internal reward to UCB1:
– 𝐴𝑗 𝑡 + 1 = 𝐴𝑗 𝑡 + ACK(1 + 𝛽
min
𝑖
𝐸 𝑖
𝐸 𝑗
),
46 / 51Part V) Distributed learning
• 𝑖∗ = arg max
𝑗
𝑔𝑗 𝑡
• 𝑔𝑗 𝑡 =
𝐴 𝑗(𝑡)
𝑛 𝑗(𝑡)
+
𝛼 log 𝑡
𝑛 𝑗(𝑡)
• 𝐴𝑗 𝑡 + 1 = 𝐴𝑗 𝑡 + ACK, ACK ∈ {0,1}
Sum awards for j
Number of trials of j
This lets you to select the most reliable yet EE set!

47. V.5) Preliminary results
• 5000 nodes,
• 1 packet per 600 sec, 100 bytes.
• 𝑪 = {1}, 𝑷 = {2,5,8,11,14}, 𝑺 = {7,8,9,10,11,12}
• The respective threshold SNR of spreading factors:
– [-6,-9,-12,-15,-17.5,-20] dB,
• Threshold SIR = 6 dB
• Signal BW = 125 KHz
• Service area: Circle, radius of 1 Km
47 / 51Part V) Distributed learning

48. V.5) Preliminary results: success rate
48 / 51Part V) Distributed learning
No external interference With external interference
Set of actions to select: Sc1: {Transmission codes (6 codes)}
Sc2: {Transmission codes (6 codes),
Transmit power (2 level)}

49. V.5) Preliminary results: energy cons.
49 / 51Part V) Distributed learning
No external interference With external interference
Energy consumption per reporting period

50. V.5) Preliminary results: partial learning
50 / 51Part V) Distributed learning
When X% of devices learn and other perform randomly:
Energy consumption per reporting period Rate of success

51.  Part I: Introduction
 Part II) Coexistence analysis
 Analytical modeling of Interference and reliability
 Part III) Resource provisioning and operation control
 Analytical modeling of tradeoff between cost/reliability/durability
 Part IV) Advanced receiver design for performance
improvement
 Part V: Distributed learning for performance improvement
 Part VI: Conclusions, future works
Outline
51 / 51

52. VI.1: Concluding remarks
 In low to medium traffic load regimes,
grant-free access can:
– achieve low energy consumption,
– decrease the experienced delay.
 There is a switchover traffic load beyond which
grant-based access outperform grant-free access.
 Under very low load, ultra low-delay reliable communication
can be achieved.
52 / 51Part IV) Receiver design

53. VI.2: future works
 Use of machine learning for
– physical layer authentication!
– better contention resolution!
– Interference management!
– Edge-computing assisted IoT connectivity
53 / 51Part IV) Receiver design

54. Some references
– Grant-Free Radio Access for Short-Packet Communications over 5G
Networks, A Azari, P Popovski, G Miao, C Stefanovic, IEEE GC 2017
– Optimized Resource Provisioning and Operation Control for Low-Power
Wide-Area IoT Networks, Amin Azari, C Cavdar, IEEE TWC (to be
submitted, Q1 2018)
– Grant-free, Grant-based, or Hybrid? Mode Switching MAC for Cellular IoT
Networks, Amin Azari, IEEE TCom (to be submitted, 2018)
– Grant-free Radio Access IoT Networks: Scalability Analysis in Coexistence
Scenarios, M Masoudi, A Azari, EA Yavuz, C Cavdar, IEEE ICC 2018
54 / 51Part V) Future works

55. Questions
 Thanks for your kind attention,
 Questions and answers.

56. The End
The End!

57. IV.6) Analysis
57 / 51Part IV) Receiver design

58. IV.6) Analysis
 TiSy: devices are slot synchronized.
 FrSy: the CFOs of the devices can take equally-spaced
discrete values, i.e. the devices are sub-channel
synchronized, where the channels are spaced each 200 Hz.
 reference: granted radio access with
10 random access resources each 2 seconds.
 offered load per channel
58 / 51Part IV) Receiver design

59. IV.6) Analysis
 TiSy: devices are slot synchronized.
 FrSy: the CFOs of the devices can take equally-spaced
discrete values, i.e. the devices are sub-channel
synchronized, where the channels are spaced each 200 Hz.
 Reference: granted radio access with
10 random access resources each 2 seconds.
59 / 51Part IV) Receiver design

60. IV.6) Analysis: network EE
60 / 51Part IV) Receiver design