481 research outputs found

    Technical Rate of Substitution of Spectrum in Future Mobile Broadband Provisioning

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    Dense deployment of base stations (BSs) and multi-antenna techniques are considered key enablers for future mobile networks. Meanwhile, spectrum sharing techniques and utilization of higher frequency bands make more bandwidth available. An important question for future system design is which element is more effective than others. In this paper, we introduce the concept of technical rate of substitution (TRS) from microeconomics and study the TRS of spectrum in terms of BS density and antenna number per BS. Numerical results show that TRS becomes higher with increasing user data rate requirement, suggesting that spectrum is the most effective means of provisioning extremely fast mobile broadband.Comment: 5 pages, 5 figures, conferenc

    Analysis of Radio Spectrum Market Evolution Possibilities

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    A tremendous growth in wireless traffic volumes and a shortage of feasible radio spectrum has led to a situation where the old and rigid spectrum regime is not a viable option for spectrum management and a shift towards a more market driven approach has begun. Great uncertainty still exists over how such a radio spectrum market will come about and what kind of shape it would take. This paper studies some long term macro level evolution possibilities for how this radio spectrum market could emerge and what would be the corresponding value chain configurations. The scenario planning and system dynamics methods are utilized to build four alternative future spectrum market scenarios.Spectrum Markets, Spectrum Policy, Flexible Spectrum Usage, Cognitive Radio, Value Networks, Scenario Planning, System Dynamics.

    Citizens Adoption and Intellectual Capital Approach

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    ν•™μœ„λ…Όλ¬Έ (박사)-- μ„œμšΈλŒ€ν•™κ΅ λŒ€ν•™μ› : κ³΅κ³ΌλŒ€ν•™ ν˜‘λ™κ³Όμ • κΈ°μˆ κ²½μ˜Β·κ²½μ œΒ·μ •μ±…μ „κ³΅, 2019. 2. Hwang, Junseok .The emergence of knowledge intensive industries gave rise to the issue of intellectual capital management which is used as an instrument to identify and measure the hidden sources of value creation at the firm, regional and national level. Knowledge-intensive companies are rated much higher than their book value suggests, and thus need to identify the intangible valuables of the company for the improvement and sustainability of their learning and capitalization system. Intellectual capital components are the key resources that can be leveraged for smart city development which intends to use information and communication technologies in order to bring efficiency and sustainability to the urban functions. The role of intellectual capital components in smart city implementation needs to be studied due to the fact that attributes of intellectual capital components would have a distinguished impact on value creation and the increase in productivity and performance. Despite the existence of a significant number of literatures on intellectual capital, the role of its components in the success of smart city implementation has not been examined. This research aims to investigate the role of intellectual capital components towards smart city success using an analysis of experts preferences for human capital and structural capital. The research also includes the demand-side perspective towards smart city information services characteristics that influences the adoption decision. The analysis is performed using two methodologies: Analytics Hierarchy Process (AHP) for human capital and structural capital and discrete choice analysis using a mixed logit model for the adoption of smart city information services. The first study employs a multidimensional approach to the development of a model for human capital using individual-level characteristics and the collective behavior. The identification of the sources of value in human capital is critical to the success of smart city implementations as these capabilities can be leveraged and upgraded to improve productivity and performance. Human capital components have been categorized into personal qualifications, personal traits, culture and social factors. The findings reveal that the most important category is personal qualifications followed by culture. Moreover, the overall priority weights estimation shows that the existence of domain-specific tacit knowledge gained through experience, the multi-disciplinary scope of education and the density of R&D personnel are the top-three ranked attributes of human capital towards smart city success. The study on the structural capital examined 24 smart city cases across the globe to identify the structural capital elements valuable in the smart city development process. The different orchestration of these structural capital elements can influence the outcome of the development process and its impact on the efficiency of the urban systems. The identified structural capital elements have been categorized into process, relational and infrastructural dimensions. The findings reveal that the infrastructural dimension comprising communication and information system is most critical towards the smart city success, followed by the process category with the most dominant component of policy. The overall ranking of these elements suggest that the decision makers need to focus on city-level policies and the development and enforcement of procedures for innovation generation. Finally, the citizens preferences analysis was performed for the case of Islamabad city in Pakistan which is at the early stage of smart city development and can benefit from a better understanding of the demand-side perspective. The characteristics of smart city information services considered in the study comprise language, access mode, service ownership, interoperability and security. Willingness-to-pay was used to observe the price sensitivity of the end users choices. The findings reveal that citizens in Islamabad have a higher utility towards the use of the English language, a mobile access mode and a high level of security. In conclusion, the study provides guidelines for policy makers who are concerned with the early stage of smart city development. The demand-side study of Islamabad city provides valuable insights in to existing trends that affect the rapid adoption of smart city services.ꡭ문초둝 지식집약적 μ‚°μ—…μ˜ μΆœν˜„μœΌλ‘œ κΈ°μ—…, 지역 및 κ΅­κ°€ μ°¨μ›μ—μ„œ κ°€μΉ˜ 창좜의 μˆ¨κ²¨μ§„ 좜처λ₯Ό νŒŒμ•…ν•˜κ³  μΈ‘μ •ν•˜λŠ” λ„κ΅¬λ‘œ μ‚¬μš©λ˜λŠ” 지적 자본 관리가 쟁점으둜 λ– μ˜¬λžλ‹€. 지식집약적 기업은 μˆœμžμ‚°λ³΄λ‹€ 훨씬 높은 평가λ₯Ό λ°›κ³  있기 λ•Œλ¬Έμ— κ·Έλ“€μ˜ ν•™μŠ΅κ³Ό μžλ³Έν™” μ‹œμŠ€ν…œμ˜ κ°œμ„ κ³Ό 지속 κ°€λŠ₯성을 μœ„ν•΄ νšŒμ‚¬μ˜ λ¬΄ν˜• κ°€μΉ˜λ₯Ό 확인할 ν•„μš”κ°€ μžˆλ‹€. 지적 μžλ³Έμš”μ†ŒλŠ” 정보톡신 κΈ°μˆ μ„ μ΄μš©ν•΄ λ„μ‹œ κΈ°λŠ₯에 νš¨μœ¨μ„±κ³Ό 지속성을 λ†’μ΄λŠ” 슀마트 μ‹œν‹° κ°œλ°œμ— ν™œμš©λ  수 μžˆλŠ” 핡심 μžμ›μ΄λ‹€. 지적 자본 μš”μ†Œμ˜ 속성은 κ°€μΉ˜ 창좜과 생산성 및 μ„±λŠ₯ ν–₯상에 가변적인 영ν–₯을 λ―ΈμΉ  수 있기 λ•Œλ¬Έμ— 슀마트 μ‹œν‹° κ΅¬ν˜„μ—μ„œμ˜ 지적 자본 μš”μ†Œμ˜ 역할을 연ꡬ할 ν•„μš”κ°€ μžˆλ‹€. 지적 μžλ³Έμ— κ΄€ν•œ μ€‘μš”ν•œ 연ꡬ λ¬Έν—Œλ“€μ΄ μžˆμ§€λ§Œ 슀마트 μ‹œν‹°μ˜ 성곡적인 κ΅¬ν˜„μ„ μœ„ν•œκ° μš”μ†Œλ“€μ˜ 역할은 κ²€ν† λ˜μ§€ μ•Šμ•˜λ‹€. 이 μ—°κ΅¬λŠ” 인적자본과 κ΅¬μ‘°μžλ³Έμ— λŒ€ν•œ μ „λ¬Έκ°€μ˜ μ„ ν˜Έλ„ 뢄석을 μ‚¬μš©ν•˜μ—¬ 슀마트 μ‹œν‹°μ˜ 성곡을 μœ„ν•œ 지적 자본 μš”μ†Œμ˜ μ—­ν•  쑰사λ₯Ό λͺ©μ μœΌλ‘œ ν•œλ‹€. λ˜ν•œ 수용 μ˜μ‚¬ 결정에 영ν–₯을 λ―ΈμΉ˜λŠ” 슀마트 μ‹œν‹° 정보 μ„œλΉ„μŠ€ νŠΉμ„±μ— λŒ€ν•œ μˆ˜μš” 츑면의 관점도 μ‘°μ‚¬ν•œλ‹€. 뢄석은 인적 자본 및 ꡬ쑰적 μžλ³Έμ„ μœ„ν•œ 뢄석 계측 ν”„λ‘œμ„ΈμŠ€(AHP)와 슀마트 μ‹œν‹° 정보 μ„œλΉ„μŠ€ 채택을 μœ„ν•œ ν˜Όν•© λ‘œμ§“ λͺ¨λΈμ„ μ΄μš©ν•œ 이산 선택 λΆ„μ„μ΄λΌλŠ” 두 가지 방법을 μ‚¬μš©ν•œλ‹€. 첫 번째 μ—°κ΅¬λŠ” 닀차원적 접근법을 μ‚¬μš©ν•΄ 개인 μˆ˜μ€€μ˜ νŠΉμ„±κ³Ό 집단 행동을 μ΄μš©ν•œ 인적 μžλ³Έμ— λŒ€ν•œ λͺ¨λΈμ„ κ°œλ°œν•œλ‹€. 인적 자본의 κ°€μΉ˜μ˜ 근원을 μ‹λ³„ν•˜λŠ” 것은 슀마트 μ‹œν‹° κ΅¬ν˜„ 성곡에 맀우 μ€‘μš”ν•˜λ‹€. μ΄λŸ¬ν•œ λŠ₯λ ₯듀이 ν™œμš©λ˜κ³  κ°œμ„ λ˜μ–΄ 생산성과 μ„±λŠ₯을 ν–₯μƒμ‹œν‚¬ 수 있기 λ•Œλ¬Έμ΄λ‹€. 인적 자본 μš”μ†ŒλŠ” 개인의 자격, 성격, λ¬Έν™”, μ‚¬νšŒμ  μš”μΈμœΌλ‘œ λΆ„λ₯˜λ˜μ—ˆλ‹€. κ·Έ κ²°κ³Ό, 첫번째둜 μ€‘μš”ν•œ 것은 개인의 μžκ²©μš”κ±΄μ΄λ©° λ‘λ²ˆμ§ΈλŠ” λ¬Έν™”μž„μ„ λ°ν˜€λƒˆλ‹€. λ˜ν•œ, 전체적인 μš°μ„ μˆœμœ„ κ°€μ€‘μΉ˜ 좔정은 κ²½ν—˜μ„ 톡해 얻은 도메인 고유의 암묡적 μ§€μ‹μ˜ 쑴재, λ‹€λΆ„μ•Όμ˜ ꡐ윑 λ²”μœ„ 및 R&D 인λ ₯의 λ°€λ„λŠ” 슀마트 μ‹œν‹° 성곡을 μœ„ν•œ 인적 자본의 μƒμœ„ 3λŒ€ μ†μ„±μž„μ„ 보여쀀닀. ꡬ쑰적 μžλ³Έμ— κ΄€ν•œ μ—°κ΅¬λŠ” μ „ 세계 24개 슀마트 μ‹œν‹° 사둀λ₯Ό 쑰사해 슀마트 μ‹œν‹° 개발 κ³Όμ •μ—μ„œ κ°€μΉ˜ μžˆλŠ” ꡬ쑰적 자본의 μš”μ†Œλ₯Ό ν™•μΈν–ˆλ‹€. μ„œλ‘œ λ‹€λ₯Έ ꡬ쑰적 자본 μš”μ†Œμ˜ 쑰정은 개발 ν”„λ‘œμ„ΈμŠ€μ˜ 결과와 λ„μ‹œ μ‹œμŠ€ν…œμ˜ νš¨μœ¨μ„±μ— 영ν–₯을 λ―ΈμΉ  수 μžˆλ‹€. ν™•μΈλœ ꡬ쑰적 자본 μš”μ†ŒλŠ” ν”„λ‘œμ„ΈμŠ€, 관계 및 기반 ꡬ쑰 μ°¨μ›μœΌλ‘œ λΆ„λ₯˜λ˜μ—ˆλ‹€. μ΄λŠ” 톡신과 정보 μ‹œμŠ€ν…œμ„ κ΅¬μ„±ν•˜λŠ” 기반 ꡬ쑰의 차원이 슀마트 μ‹œν‹°μ˜ 성곡에 κ°€μž₯ μ€‘μš”ν•˜λ©° κ·Έ λ‹€μŒμœΌλ‘œ μ •μ±…μ˜ κ°€μž₯ μš°μ„Έν•œ ꡬ성 μš”μ†Œλ₯Ό 가진 ν”„λ‘œμ„ΈμŠ€ λ²”μ£Όκ°€ μ€‘μš”ν•˜λ‹€λŠ” 것을 보여쀀닀. 이듀 μš”μ†Œμ˜ 전체 μˆœμœ„λŠ” μ˜μ‚¬κ²°μ •μžλ“€μ΄ ν˜μ‹  생성을 μœ„ν•œ λ„μ‹œ μˆ˜μ€€μ˜ μ •μ±…κ³Ό 절차 개발과 집행에 μ΄ˆμ μ„ 맞좜 ν•„μš”κ°€ μžˆμŒμ„ μ‹œμ‚¬ν•œλ‹€. λ§ˆμ§€λ§‰μœΌλ‘œ, 슀마트 μ‹œν‹° 개발의 초기 단계에 있으며 μˆ˜μš” μΈ‘λ©΄ κ΄€μ μ—μ„œ μœ μš©ν•  수 μžˆλŠ” νŒŒν‚€μŠ€νƒ„μ˜ μ΄μŠ¬λΌλ§ˆλ°”λ“œ λ„μ‹œμ— λŒ€ν•œ μ‹œλ―Όμ˜ μ„ ν˜Έ 뢄석이 μ΄λ£¨μ–΄μ‘Œλ‹€. λ³Έ μ—°κ΅¬μ—μ„œ κ³ λ €ν•œ 슀마트 μ‹œν‹° 정보 μ„œλΉ„μŠ€μ˜ νŠΉμ„±μ€ μ–Έμ–΄, μ ‘κ·Ό λͺ¨λ“œ, μ„œλΉ„μŠ€ μ†Œμœ κΆŒ, μƒν˜Έμš΄μš©μ„± 및 λ³΄μ•ˆμœΌλ‘œ κ΅¬μ„±λœλ‹€. μ§€λΆˆ μ˜μ§€λŠ” μ΅œμ’… μ‚¬μš©μžμ˜ 선택에 λ”°λ₯Έ 가격 민감도λ₯Ό κ΄€μ°°ν•˜κΈ° μœ„ν•΄ μ‚¬μš©λ˜μ—ˆλ‹€. 연ꡬ κ²°κ³ΌλŠ” μ΄μŠ¬λΌλ§ˆλ°”λ“œ μ‹œλ―Όλ“€μ΄ 높은 μˆ˜μ€€μ˜ λ³΄μ•ˆκ³Ό ν•¨κ»˜ μ˜μ–΄ μ‚¬μš©μ— 더 높은 νš¨μš©μ„ 가지고 μžˆλ‹€λŠ” 것을 보여쀀닀. 결둠적으둜, 이 μ—°κ΅¬λŠ” νŠΉλ³„νžˆ 슀마트 μ‹œν‹° 개발의 초기 단계에 μžˆλŠ” μ •μ±… μž…μ•ˆμžλ“€μ„ μœ„ν•œ 지침을 μ œκ³΅ν•œλ‹€. μ΄μŠ¬λΌλ§ˆλ°”λ“œμ‹œμ— λŒ€ν•œ μˆ˜μš” μΈ‘λ©΄ μ—°κ΅¬λŠ” 슀마트 μ‹œν‹° μ„œλΉ„μŠ€μ˜ μ‹ μ†ν•œ 채택을 μ§€μ›ν•˜λŠ” κΈ°μ‘΄ 좔세에 λŒ€ν•œ κ·€μ€‘ν•œ 톡찰λ ₯을 μ œκ³΅ν•œλ‹€. μ£Όμš” 단어: 슀마트 μ‹œν‹°, 지적 자본, 인적 자본, ꡬ쑰적 자본, 정보 μ„œλΉ„μŠ€Chapter 1 Introduction 1 1.1 Overview 1 1.2 Purpose of the Research 9 1.3 Contribution of the Research 12 1.4 Research Outline 15 Chapter 2 Literature Review 18 2.1 Smart Cities 18 2.1.1 Smart City Definitions 19 2.1.2 Smart City Components 22 2.1.3 Smart City Systems Architecture 28 2.2 Intellectual Capital 30 2.2.1 Existing Studies on Intellectual Capital 32 2.2.2 Intellectual Capital and Smart Cities 37 2.2.3 Intellectual Capital Components 39 Chapter 3 Study on the Role of Human Capital for Smart City Success 50 3.1 Model 52 3.1.1 Personal Qualifications 54 3.1.2 Personal Traits 57 3.1.3 Culture 58 3.1.4 Social Factors 59 3.2 Methodology 60 3.2.1 Survey for Analytic Hierarchy Process 63 3.3 Estimation of Results 66 Chapter 4 Study on Structural Capital Role for Smart City Success 74 4.1 Model 77 4.1.1 Process Elements 77 4.1.2 Relational Elements 81 4.1.3 Infrastructural Elements 82 4.2 Methodology 85 4.2.1 Survey for Analytic Hierarchy Process 85 4.3 Estimation of Results 87 Chapter 5 Adoption of Smart City Information Services 95 5.1 Citizens Preferences Analysis towards the Adoption of Smart City Information Services 95 5.2 Model 97 5.3 Methodology 101 5.3.1 Random Utility Model 101 5.3.2 Willingness to Pay 104 5.4 Survey Design and Data 105 5.4.1 Survey for Discrete Choice Analysis 105 5.5 Estimation of Results 109 Chapter 6 Discussion and Conclusion 115 6.1 Discussion and Implications 115 6.2 Conclusion 128 6.3 Limitations and Future Work 131 References 134 Appendix A: Description of Attributes for AHP Survey 152 Appendix B: Survey Questionnaire for AHP 155 Appendix C: Conjoint Survey for Citizens Preference Analysis 163 ꡭ문초둝 166 Acknowledgments 169Docto

    Converged wireline and wireless signal distribution in optical fiber access networks

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    Investment in broadband and the emerging market structure in South Africa

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    This research study investigates the investments that have been made in broadband infrastructure in South Africa, with special focus on the impact on the broadband market made by ECA 2005 and subsequently the Altech judgement of 2008 (which unblocked infrastructure investment bottlenecks by ruling that Value Added Network Services (VANS) licensees were entitled to ECNS licences to build their own build infrastructure). The Altech judgment was a defining development of the South African telecoms sector, as it sounded the death knell to the dependence by VANS and other firms on wholesale infrastructure from the incumbent Telkom. The judgement, based on provisions of the ECA, was followed by heightened investment as firms, both MNOs and VANS, stepped up investment in self-provisioning infrastructure, thereby creating a period of intense facilities-based competition. The post-ECA and Altech judgement period coincided with the significant global market shift of fixed-to-mobile substitution, thereby dictating that the market structure that emerged in South Africa would be tilted towards the growth of mobile telephony, the latter becoming the foundation of mobile broadband through the emergence of next generation technologies of the smartphones and 3G and LTE. Through application of interpretive methods and qualitative analysis of published data and interviews with sector experts, research observations confirm that firms have lapped up the self-provision benefits of the post-2005 licencing regime and developed significant supply-side vertical capacities that have led to infrastructure duplications and competing network externalities. The resultant market structure appeared inefficient, with a high degree of concentration and equally high barriers to entry. This research used the investment calculus by Bauer (2010) as the applied method of analysis in order to develop a systematic analysis of investment decisions and firm behaviour. Due to significant capital outlays and expectations of return on investment (ROI) by firms competing in the broadband market, it follows that they have entrenched a rigid, costly wholesale interconnection market that has been immensely profitable for the firms, but has not passed benefits to new entrants and consumers. Whilst supply-side firms have refined capital investment strategies through application of real options, the subsequent market structure has been made less competitive due to inefficient regulatory interventions by ICASA, and the slow implementation of recommendations of SA Connect, the national broadband policy, leading to market inefficiencies and a widening digital divide
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