The SON (Self-Organizing Networks) Ecosystem: 2015 – 2030 – Opportunities, Challenges, Strategies & Forecast -

Published: Aug, 2015 | Pages: 180 | Publisher: SNS Research
Industry: Telecommunications | Report Format: Electronic (PDF)

SON (Self-organizing networks) ecosystem technology eradicates the manual configuration of equipment in the period of the deployment and minimizes the lifecycle cost of running the mobile network.  Furthermore, SON market revenue is expected to grow over $4 billion by the year 2014 despite, crossing over conventional mobile network optimization revenue nearly over 60%. This is during the time of deployment right through troubleshooting and dynamically optimizing the performance. Furthermore, this improves the Opex to revenue ratio and reduces the cost of operator’s services. 

On addition, Mobile operators are eager to capitalize on SON amid expanding demand for   connectivity for mobile broadband .This is mainly to minimize operations expenditure and rollout delays associated with their current HetNet and LTE deployments. .  This is expected to impact the overall Self-organizing networks ecosystem market
.SON technology is now utilized in mobile core as it is originally targeted (Radio Access network) RAN segment.  Furthermore, it also being used in transport network and mobile core segments. SON ecosystem is undergoing convergence with other technological innovations like DPI, Big Data and predictive analysis. 

SON basically replaces the manual steps of equipment by automated technologies. On addition, It helps solidify networks and cut down the cost of deployment by eliminating human error. Firm utilizing self-organizing have reported impressive level of progress in connecting. Self-organizing networks are sharpened as the demand for mobile broadband services has increased. 

Topics Covered
The report covers the following topics: 
 - Conventional mobile network planning & optimization
 - SON technology and architecture
 - Key benefits and market drivers of SON
 - Challenges to SON adoption
 - SON use cases
 - SON deployment case studies
 - Future roadmap of the SON ecosystem
 - Company profiles and strategies of over 70 SON ecosystem players
 - OpEx and CapEx saving analysis of SON
 - Wireless network infrastructure spending, traffic projections and value chain
 - Convergence of SON with Big Data, predictive analytics and DPI
 - Strategic recommendations for SON solution providers, wireless infrastructure OEMs and mobile operators
 - Market analysis and forecasts from 2015 till 2030

Historical Revenue & Forecast Segmentation
Market forecasts and historical figures are provided for each of the following submarkets and their subcategories:

Mobile Network Optimization
 - SON
 - Conventional Mobile Network Planning & Optimization

SON Network Segment Submarkets
 - RAN
 - Mobile Core
 - Mobile Backhaul & Transport

SON Architecture Submarkets
 - C-SON (Centralized SON)
 - D-SON (Distributed SON)

SON Wireless Network Generation Submarkets
 - 2G & 3G
 - 4G & Beyond

Regional Markets
 - Asia Pacific
 - Eastern Europe
 - Latin & Central America
 - Middle East & Africa
 - North America
 - Western Europe

Country Markets
 - Australia
 - Brazil
 - Canada
 - China
 - France
 - Germany
 - India
 - Italy
 - Japan
 - Russia
 - South Korea
 - Spain
 - Taiwan
 - UK
 - USA

Key Questions Answered
The report provides answers to the following key questions:
 - How big is the SON and mobile network optimization ecosystem?
 - How is the ecosystem evolving by segment and region?
 - What will the market size be in 2020 and at what rate will it grow?
 - What trends, challenges and barriers are influencing its growth?
 - Who are the key SON vendors and what are their strategies?
 - What is the outlook for QoE based SON solutions?
 - What is the outlook for C-SON and D-SON adoption?
 - What is the outlook for SON associated OpEx savings by region?
 - How will SON investments compare with those on traditional mobile network optimization?
 - What opportunities exist for SON in mobile core and transport networks? 
 - How will SON use cases evolve overtime in 3GPP releases?
 - Which regions will see the highest number of SON investments?
 - How much will mobile operators invest in SON solutions?

Key Findings 
The report has the following key findings: 
 - Despite challenges relating to implementation complexities and multi-vendor interoperability, SON revenue is expected to grow to more than $4 Billion by the end of 2017, exceeding conventional mobile network optimization revenue by nearly 60%
 - SNS Research estimates that SON can enable mobile operators to save nearly 40% of their electrical power consumption by dynamically activating and deactivating RAN nodes in line with the changing traffic and user distribution profile
 - SNS Research estimates that a Tier 1 mobile operator can save more than 30% of its overall OpEx by employing SON across the RAN, mobile core and transport segments of the network
 - Mobile operators have reported up to a 50% reduction in dropped calls and over 20% higher data rates with SON implementation
 - SON platforms are moving from reactive systems to more advanced implementations that incorporate predictive analytics technology to make necessary changes to a network before any degradation occurs
 - Infrastructure and software incumbents are aggressively eyeing acquisitions of smaller established C-SON players to accelerate their entry path into the C-SON market

 Table of Contents
1	Chapter 1: Introduction	13
1.1	Executive Summary	13
1.2	Topics Covered	15
1.3	Historical Revenue & Forecast Segmentation	16
1.4	Key Questions Answered	18
1.5	Key Findings	19
1.6	Methodology	20
1.7	Target Audience	21
1.8	Companies & Organizations Mentioned	22
		
2	Chapter 2: SON & Mobile Network Optimization Ecosystem	25
2.1	Conventional Mobile Network Optimization	25
2.1.1	Network Planning	25
2.1.2	Measurement Collection: Drive Tests, Probes and End User Data	26
2.1.3	Post-Processing, Optimization & Policy Enforcement	26
2.2	The SON (Self-Organizing Network) Concept	27
2.2.1	What is SON?	27
2.2.2	The Need for SON	27
2.3	Functional Areas of SON	28
2.3.1	Self-Configuration	29
2.3.2	Self-Optimization	29
2.3.3	Self-Healing	29
2.4	Market Drivers for SON Adoption	30
2.4.1	Continued Wireless Network Infrastructure Investments	30
2.4.2	Optimization in Multi-RAN & HetNet Environments	32
2.4.3	OpEx & CapEx Reduction: The Cost Saving Potential	33
2.4.4	Improving Subscriber Experience and Churn Reduction	33
2.4.5	Power Savings	34
2.4.6	Enabling Small Cell Deployments	34
2.4.7	Traffic Management	34
2.5	Market Barriers for SON Adoption	34
2.5.1	Complexity of Implementation	35
2.5.2	Reorganization & Changes to Standard Engineering Procedures	35
2.5.3	Lack of Trust in Automation	35
2.5.4	Lack of Operator Control: Proprietary SON Algorithms	35
2.5.5	Coordination between Distributed and Centralized SON	36
2.5.6	Network Security Concerns: New Interfaces and Lack of Monitoring	36
		
3	Chapter 3: SON Technology, Use Cases & Implementation Architectures	37
3.1	Where Does SON Sit Within a Mobile Network?	37
3.1.1	RAN	38
3.1.2	Mobile Core	38
3.1.3	Mobile Backhaul & Transport	39
3.1.4	Device-Assisted SON	40
3.2	SON Architecture	41
3.2.1	C-SON (Centralized SON)	41
3.2.2	D-SON (Distributed SON)	42
3.2.3	H-SON (Hybrid SON)	43
3.3	SON Use-Cases	44
3.3.1	Self-Configuration of Network Elements	44
3.3.2	Automatic Connectivity Management	44
3.3.3	Self-Testing of Network Elements	44
3.3.4	Self-Recovery of Network Elements/Software	45
3.3.5	Self-Healing of Board Faults	45
3.3.6	Automatic Inventory	45
3.3.7	ANR (Automatic Neighbor Relations)	45
3.3.8	PCI (Physical Cell ID) Configuration	46
3.3.9	CCO (Coverage & Capacity Optimization)	46
3.3.10	MRO (Mobility Robustness Optimization)	46
3.3.11	MLB (Mobile Load Balancing)	47
3.3.12	RACH (Random Access Channel) Optimization	47
3.3.13	ICIC (Inter-Cell Interference Coordination)	47
3.3.14	eICIC (Enhanced ICIC)	48
3.3.15	Energy Savings	48
3.3.16	Cell Outage Detection & Compensation	48
3.3.17	Self-Configuration & Optimization of Small Cells	49
3.3.18	Optimization of DAS (Distributed Antenna Systems)	49
3.3.19	RAN Aware Traffic Shaping	49
3.3.20	Traffic Steering in HetNets	50
3.3.21	Optimization of Virtualized Network Resources	50
3.3.22	Auto-Provisioning of Transport Links	50
3.3.23	Transport Network Bandwidth Optimization	50
3.3.24	Transport Network Interference Management	50
3.3.25	SON Coordination Management	51
3.3.26	Seamless Vendor Infrastructure Swap	51
		
4	Chapter 4: SON Standardization	52
4.1	NGNM (Next Generation Mobile Networks) Alliance	52
4.1.1	Conception of the SON Initiative	52
4.1.2	Functional Areas and Requirements	53
4.1.3	Implementation Approach	54
4.1.4	P-SmallCell (Project Small Cell)	54
4.1.5	Recommendations for Multi-Vendor SON Deployment	55
4.2	3GPP (Third Generation Partnership Project)	56
4.2.1	Release 8	56
4.2.2	Release 9	57
4.2.3	Release 10	57
4.2.4	Release 11	57
4.2.5	Release 12, 13 & Beyond	58
4.2.6	Implementation Approach	59
		
5	Chapter 5: SON Deployment Case Studies	60
5.1	AT&T Mobility	60
5.1.1	Vendor Selection & Contract Value	60
5.1.2	Implemented Use Cases	60
5.1.3	Results	60
5.2	Singtel	61
5.2.1	Vendor Selection & Contract Value	61
5.2.2	Implemented Use Cases	61
5.2.3	Results	61
5.3	TIM Brasil	62
5.3.1	Vendor Selection & Contract Value	62
5.3.2	Implemented Use Cases	62
5.3.3	Results	62
5.4	KDDI	63
5.4.1	Vendor Selection & Contract Value	63
5.4.2	Implemented Use Cases	63
5.4.3	Results	63
5.5	SK Telecom	64
5.5.1	Vendor Selection & Contract Value	64
5.5.2	Implemented Use Cases	64
5.5.3	Results	64
5.6	Globe Telecom	65
5.6.1	Vendor Selection & Contract Value	65
5.6.2	Implemented Use Cases	65
5.6.3	Results	65
		
6	Chapter 6: Industry Roadmap & Value Chain	66
6.1	Industry Roadmap	66
6.1.1	Large Scale Adoption of SON Technology: 2015 - 2020	66
6.1.2	Towards QoE/QoS Based End-to-End SON: 2020 - 2025	67
6.1.3	Continued Investments to Support 5G Rollouts: 2025 - 2030	67
6.2	Value Chain	68
6.3	Embedded Technology Ecosystem	68
6.3.1	Chipset Developers	68
6.3.2	Embedded Component/Software Providers	68
6.4	RAN Ecosystem	70
6.4.1	Macrocell RAN OEMs	70
6.4.2	Pure-Play and Specialist Small Cell OEMs	70
6.4.3	WiFi Access Point OEMs	70
6.4.4	DAS & Repeater Solution Providers	70
6.4.5	C-RAN Solution Providers	71
6.4.6	Other Technology & Network Component Providers/Enablers	71
6.5	Mobile Backhaul & Fronthaul Ecosystem	71
6.5.1	Backhaul & Fronthaul Solution Providers	71
6.6	Mobile Core Ecosystem	71
6.6.1	Core Network Infrastructure & Software Providers	71
6.7	Connectivity Ecosystem	72
6.7.1	2G, 3G & 4G Wireless Carriers	72
6.7.2	WiFi Connectivity Providers	74
6.7.3	SCaaS (Small Cells as a Service) Providers	74
6.8	SON & Mobile Network Optimization Ecosystem	74
6.8.1	SON Solution Providers	74
6.8.2	Mobile Network Optimization Solution Providers	74
6.9	SDN & NFV Ecosystem	75
6.9.1	SDN & NFV Providers	75
		
7	Chapter 7: Vendor Landscape	76
7.1	Accedian Networks	76
7.2	Accuver	77
7.3	AirHop Communications	78
7.4	Airspan Networks	79
7.5	Alcatel-Lucent	80
7.6	Amdocs	81
7.7	Anite	82
7.8	Arcadyan	83
7.9	Argela	84
7.10	Aricent	85
7.11	ARItel	86
7.12	Ascom	87
7.13	Astellia	88
7.14	ATDI	89
7.15	Avago Technologies	90
7.16	Avvasi	91
7.17	BLiNQ Networks	92
7.18	Cavium	93
7.19	CBNL (Cambridge Broadband Networks Limited)	94
7.20	CellMining	95
7.21	Cellwize	96
7.22	Celtro Communications	97
7.23	CENTRI	98
7.24	Cisco Systems	99
7.25	Citrix Systems	100
7.26	Comarch	101
7.27	CommAgility	102
7.28	Commsquare	103
7.29	Coriant	104
7.30	Datang Mobile	105
7.31	ECE (European Communications Engineering)	106
7.32	Ericsson	107
7.33	Flash Networks	108
7.34	Forsk	109
7.35	Fujitsu	110
7.36	Guavus	111
7.37	Hitachi	112
7.38	Huawei	113
7.39	InfoVista	114
7.40	Intel Corporation	115
7.41	InterDigital	116
7.42	ip.access	117
7.43	Lavastorm	118
7.44	Lemko Corporation	119
7.45	NEC Corporation	120
7.46	Nokia Networks	121
7.47	NXP Semiconductors	122
7.48	Optulink	123
7.49	P.I.Works	124
7.50	Plano Engineering	125
7.51	Qualcomm	126
7.52	RADCOM	127
7.53	Radisys Corporation	128
7.54	Reverb Networks	129
7.55	Rohde & Schwarz	130
7.56	Rorotika	131
7.57	Samsung Electronics	132
7.58	SEDICOM	133
7.59	Siklu Communication	134
7.60	SpiderCloud Wireless	135
7.61	Tarana Wireless	136
7.62	Tektronix Communications	137
7.63	TEOCO	138
7.64	Theta Networks	139
7.65	TI (Texas Instruments)	140
7.66	TTG International	141
7.67	Tulinx	142
7.68	Vasona Networks	143
7.69	Viavi Solutions	144
7.70	WebRadar	145
7.71	XCellAir	146
7.72	ZTE	147
		
8	Chapter 8: Market Analysis & Forecasts	148
8.1	SON & Mobile Network Optimization Revenue	148
8.2	SON Revenue	149
8.3	SON Revenue by Network Segment	149
8.3.1	SON in RAN	150
8.3.2	SON in Mobile Core	150
8.3.3	SON in Mobile Backhaul	151
8.4	SON Revenue by Architecture: Centralized vs. Distributed	151
8.4.1	C-SON	152
8.4.2	D-SON	152
8.5	SON Revenue by Wireless Network Generation: 2G/3G vs. 4G & Beyond	153
8.5.1	2G & 3G SON	153
8.5.2	4G & Beyond SON	154
8.6	SON Revenue by Region	154
8.7	Conventional Mobile Network Planning & Optimization Revenue	155
8.8	Conventional Mobile Network Planning & Optimization Revenue by Region	155
8.9	Asia Pacific	156
8.9.1	SON	156
8.9.2	Conventional Mobile Network Planning & Optimization	156
8.10	Eastern Europe	157
8.10.1	SON	157
8.10.2	Conventional Mobile Network Planning & Optimization	157
8.11	Latin & Central America	158
8.11.1	SON	158
8.11.2	Conventional Mobile Network Planning & Optimization	158
8.12	Middle East & Africa	159
8.12.1	SON	159
8.12.2	Conventional Mobile Network Planning & Optimization	159
8.13	North America	160
8.13.1	SON	160
8.13.2	Conventional Mobile Network Planning & Optimization	160
8.14	Western Europe	161
8.14.1	SON	161
8.14.2	Conventional Mobile Network Planning & Optimization	161
8.15	Top Country Markets	162
8.15.1	Australia	162
8.15.2	Brazil	162
8.15.3	Canada	163
8.15.4	China	163
8.15.5	France	164
8.15.6	Germany	164
8.15.7	India	165
8.15.8	Italy	165
8.15.9	Japan	166
8.15.10	Russia	166
8.15.11	South Korea	167
8.15.12	Spain	167
8.15.13	Taiwan	168
8.15.14	UK	168
8.15.15	USA	169
		
9	Chapter 9: Conclusion & Strategic Recommendations	170
9.1	Moving Towards QoE Based SON Platforms	170
9.2	Capitalizing on DPI (Deep Packet Inspection)	170
9.3	The Convergence of Big Data, Predictive Analytics & SON	171
9.4	Optimizing M2M & IoT Services	172
9.5	SON for NFV & SDN: The Push from Mobile Operators	172
9.6	Moving Towards Mobile Core and Transport Networks	173
9.7	Assessing the Impact of SON on Optimization & Field Engineers	173
9.8	SON Associated OpEx Savings: The Numbers	175
9.9	What SON Capabilities Will 5G Networks Entail?	176
9.10	The C-SON Versus D-SON Debate	176
9.11	Strategic Recommendations	178
9.11.1	SON & Conventional Mobile Network Optimization Solution Providers	178
9.11.2	Wireless Infrastructure OEMs	179
9.11.3	Mobile Operators	180

List of Figures	
	Figure 1: Functional Areas of SON with the Mobile Network Lifecycle	28
	Figure 2: Annual Global Throughput of Mobile Network Data Traffic by Region: 2015 – 2030 (Exabytes)	30
	Figure 3: Global Wireless Network Infrastructure Revenue Share by Submarket (%)	31
	Figure 4: Global Mobile Network Data Traffic Distribution by Access Network Form Factor: 2015 – 2030 (%)	32
	Figure 5: SON Associated OpEx & CapEx Savings by Network Segment	33
	Figure 6: Potential Areas of SON Implementation	37
	Figure 7: Mobile Backhaul & Transport Segmentation by Technology	39
	Figure 8: C-SON (Centralized SON) in a Wireless Carrier Network	41
	Figure 9: D-SON (Distributed SON) in a Wireless Carrier Network	42
	Figure 10: H-SON (Hybrid SON) in a Wireless Carrier Network	43
	Figure 11: NGNM SON Use Cases	53
	Figure 12: SON Industry Roadmap: 2015 - 2030	66
	Figure 13: The Wireless Network Infrastructure Value Chain	69
	Figure 14: List of LTE Trials & Deployments	73
	Figure 15: Global SON & Mobile Network Optimization Revenue: 2015 - 2030 ($ Million)	148
	Figure 16: Global SON Revenue: 2015 - 2030 ($ Million)	149
	Figure 17: Global SON Revenue by Network Segment: 2015 - 2030 ($ Million)	149
	Figure 18: Global RAN SON Revenue: 2015 - 2030 ($ Million)	150
	Figure 19: Global Mobile Core SON Revenue: 2015 - 2030 ($ Million)	150
	Figure 20: Global Mobile Backhaul & Transport SON Revenue: 2015 - 2030 ($ Million)	151
	Figure 21: Global SON Revenue by Architecture: 2015 - 2030 ($ Million)	151
	Figure 22: Global C-SON Revenue: 2015 - 2030 ($ Million)	152
	Figure 23: Global D-SON Revenue: 2015 - 2030 ($ Million)	152
	Figure 24: Global SON Revenue by Wireless Network Generation: 2015 - 2030 ($ Million)	153
	Figure 25: Global 2G & 3G SON Revenue: 2015 - 2030 ($ Million)	153
	Figure 26: Global 4G & Beyond SON Revenue: 2015 - 2030 ($ Million)	154
	Figure 27: SON Revenue by Region: 2015 - 2030 ($ Million)	154
	Figure 28: Global Conventional Mobile Network Planning & Optimization Revenue: 2015 - 2030 ($ Million)	155
	Figure 29: Conventional Mobile Network Planning & Optimization Revenue by Region: 2015 - 2030 ($ Million)	155
	Figure 30: Asia Pacific SON Revenue: 2015 - 2030 ($ Million)	156
	Figure 31: Asia Pacific Conventional Mobile Network Planning & Optimization Revenue: 2015 - 2030 ($ Million)	156
	Figure 32: Eastern Europe SON Revenue: 2015 - 2030 ($ Million)	157
	Figure 33: Eastern Europe Conventional Mobile Network Planning & Optimization Revenue: 2015 - 2030 ($ Million)	157
	Figure 34: Latin & Central America SON Revenue: 2015 - 2030 ($ Million)	158
	Figure 35: Latin & Central America Conventional Mobile Network Planning & Optimization Revenue: 2015 - 2030 ($ Million)	158
	Figure 36: Middle East & Africa SON Revenue: 2015 - 2030 ($ Million)	159
	Figure 37: Middle East & Africa Conventional Mobile Network Planning & Optimization Revenue: 2015 - 2030 ($ Million)	159
	Figure 38: North America SON Revenue: 2015 - 2030 ($ Million)	160
	Figure 39: North America Conventional Mobile Network Planning & Optimization Revenue: 2015 - 2030 ($ Million)	160
	Figure 40: Western Europe SON Revenue: 2015 - 2030 ($ Million)	161
	Figure 41: Western Europe Conventional Mobile Network Planning & Optimization Revenue: 2015 - 2030 ($ Million)	161
	Figure 42: Australia SON Revenue: 2015 - 2030 ($ Million)	162
	Figure 43: Brazil SON Revenue: 2015 - 2030 ($ Million)	162
	Figure 44: Canada SON Revenue: 2015 - 2030 ($ Million)	163
	Figure 45: China SON Revenue: 2015 - 2030 ($ Million)	163
	Figure 46: France SON Revenue: 2015 - 2030 ($ Million)	164
	Figure 47: Germany SON Revenue: 2015 - 2030 ($ Million)	164
	Figure 48: India SON Revenue: 2015 - 2030 ($ Million)	165
	Figure 49: Italy SON Revenue: 2015 - 2030 ($ Million)	165
	Figure 50: Japan SON Revenue: 2015 - 2030 ($ Million)	166
	Figure 51: Russia SON Revenue: 2015 - 2030 ($ Million)	166
	Figure 52: South Korea SON Revenue: 2015 - 2030 ($ Million)	167
	Figure 53: Spain SON Revenue: 2015 - 2030 ($ Million)	167
	Figure 54: Taiwan SON Revenue: 2015 - 2030 ($ Million)	168
	Figure 55: UK SON Revenue: 2015 - 2030 ($ Million)	168
	Figure 56: USA SON Revenue: 2015 - 2030 ($ Million)	169
	Figure 57: SON Associated OpEx Savings by Region: 2015 - 2030 ($ Million)	175