Orthogonal frequency-division multiplexing (OFDM) effectively mitigates intersymbol interference (ISI) caused by the delay spread of wireless channels. Therefore, it has been used in many wireless systems and adopted by various standards. In this paper, we present a comprehensive survey on OFDM for wireless communications. We address basic OFDM and related modulations, as well as techniques to improve the performance of OFDM for wireless communications, including channel estimation and signal detection, time- and frequency-offset estimation and correction, peak-to-average power ratio reduction, and multiple-input-multiple-output (MIMO) techniques. We also describe the applications of OFDM in current systems and standards.

OFDM and Its Wireless Applications: A Survey

At some point in the future, how far out we do not exactly know, wireless access to the Internet will outstrip all other forms of access bringing the freedom of mobility to the way we access the we...

ACM SIGCOMM computer communication review

We address the problem of interference aware routing in multi-radio infrastructure mesh networks wherein each mesh node is equipped with multiple radio interfaces and a subset of nodes serve as Internet gateways. We present a new interference aware routing metric - iAWARE that aids in finding paths that are better in terms of reduced interflow and intra-flow interference. We incorporate this metric and new support for multi-radio networks in the well known AODV routing protocol to design an enhanced AODV-MR routing protocol. We study the performance of our new routing metric by implementing it in our wireless testbed consisting of 12 mesh nodes. We show that iAWARE tracks changes in interfering traffic far better than existing well known link metrics such as ETT and IRU. We also demonstrate that our AODV-MR protocol delivers increased throughput in single radio and two radio mesh networks compared to similar protocol with WCETT and MIC routing metrics. We also show that in the case of two radio mesh networks, our metric achieves good intra-path channel diversity.

/pdf/interference-aware-routing-in-multi-radio-wireless-mesh-3gcw7xdmih.pdf

Interference aware routing in multi-radio wireless mesh networks

Cognitive radio allows for usage of licensed frequency bands by unlicensed users. However, these unlicensed (cognitive) users need to monitor the spectrum continuously to avoid possible interference with the licensed (primary) users. Apart from this, cognitive radio is expected to learn from its surroundings and perform functions that best serve its users. Such an adaptive technology naturally presents unique signal-processing challenges. In this paper, we describe the fundamental signal-processing aspects involved in developing a fully functional cognitive radio network, including spectrum sensing and spectrum sculpting.

Signal Processing in Cognitive Radio

1. Introduction 1.1 An Overview 1.2 Some Examples 1.3 A Brief History 1.4 A Chapter Summary 2. Probability 2.1 Introduction 2.2 Sample Spaces and the Algebra of Sets 2.3 The Probability Function 2.4 Conditional Probability 2.5 Independence 2.6 Combinatorics 2.7 Combinatorial Probability 2.8 Taking a Second Look at Statistics (Monte Carlo Techniques) 3. Random Variables 3.1 Introduction 3.2 Binomial and Hypergeometric Probabilities 3.3 Discrete Random Variables 3.4 Continuous Random Variables 3.5 Expected Values 3.6 The Variance 3.7 Joint Densities 3.8 Transforming and Combining Random Variables 3.9 Further Properties of the Mean and Variance 3.10 Order Statistics 3.11 Conditional Densities 3.12 Moment-Generating Functions 3.13 Taking a Second Look at Statistics (Interpreting Means) Appendix 3.A.1 MINITAB Applications 4. Special Distributions 4.1 Introduction 4.2 The Poisson Distribution 4.3 The Normal Distribution 4.4 The Geometric Distribution 4.5 The Negative Binomial Distribution 4.6 The Gamma Distribution 4.7 Taking a Second Look at Statistics (Monte Carlo Simulations) Appendix 4.A.1 MINITAB Applications Appendix 4.A.2 A Proof of the Central Limit Theorem 5. Estimation 5.1 Introduction 5.2 Estimating Parameters: The Method of Maximum Likelihood and the Method of Moments 5.3 Interval Estimation 5.4 Properties of Estimators 5.5 Minimum-Variance Estimators: The Crami?½r-Rao Lower Bound 5.6 Sufficient Estimators 5.7 Consistency 5.8 Bayesian Estimation 5.9 Taking A Second Look at Statistics (Beyond Classical Estimation) Appendix 5.A.1 MINITAB Applications 6. Hypothesis Testing 6.1 Introduction 6.2 The Decision Rule 6.3 Testing Binomial Dataâ H0: p = po 6.4 Type I and Type II Errors 6.5 A Notion of Optimality: The Generalized Likelihood Ratio 6.6 Taking a Second Look at Statistics (Statistical Significance versus â Practicalâ Significance) 7. Inferences Based on the Normal Distribution 7.1 Introduction 7.2 Comparing Y-i?½ s/ vn and Y-i?½ S/ vn 7.3 Deriving the Distribution of Y-i?½ S/ vn 7.4 Drawing Inferences About i?½ 7.5 Drawing Inferences About s2 7.6 Taking a Second Look at Statistics (Type II Error) Appendix 7.A.1 MINITAB Applications Appendix 7.A.2 Some Distribution Results for Y and S2 Appendix 7.A.3 A Proof that the One-Sample t Test is a GLRT Appendix 7.A.4 A Proof of Theorem 7.5.2 8. Types of Data: A Brief Overview 8.1 Introduction 8.2 Classifying Data 8.3 Taking a Second Look at Statistics (Samples Are Not â Validâ !) 9. Two-Sample Inferences 9.1 Introduction 9.2 Testing H0: i?½X =i?½Y 9.3 Testing H0: s2X=s2Yâ The F Test 9.4 Binomial Data: Testing H0: pX = pY 9.5 Confidence Intervals for the Two-Sample Problem 9.6 Taking a Second Look at Statistics (Choosing Samples) Appendix 9.A.1 A Derivation of the Two-Sample t Test (A Proof of Theorem 9.2.2) Appendix 9.A.2 MINITAB Applications 10. Goodness-of-Fit Tests 10.1 Introduction 10.2 The Multinomial Distribution 10.3 Goodness-of-Fit Tests: All Parameters Known 10.4 Goodness-of-Fit Tests: Parameters Unknown 10.5 Contingency Tables 10.6 Taking a Second Look at Statistics (Outliers) Appendix 10.A.1 MINITAB Applications 11. Regression 11.1 Introduction 11.2 The Method of Least Squares 11.3 The Linear Model 11.4 Covariance and Correlation 11.5 The Bivariate Normal Distribution 11.6 Taking a Second Look at Statistics (How Not to Interpret the Sample Correlation Coefficient) Appendix 11.A.1 MINITAB Applications Appendix 11.A.2 A Proof of Theorem 11.3.3 12. The Analysis of Variance 12.1 Introduction 12.2 The F Test 12.3 Multiple Comparisons: Tukeyâ s Method 12.4 Testing Subhypotheses with Contrasts 12.5 Data Transformations 12.6 Taking a Second Look at Statistics (Putting the Subject of Statistics togetherâ the Contributions of Ronald A. Fisher) Appendix 12.A.1 MINITAB Applications Appendix 12.A.2 A Proof of Theorem 12.2.2 Appendix 12.A.3 The Distribution of SSTR/(kâ 1) SSE/(nâ k)When H1 is True 13. Randomized Block Designs 13.1 Introduction 13.2 The F Test for a Randomized Block Design 13.3 The Paired t Test 13.4 Taking a Second Look at Statistics (Choosing between a Two-Sample t Test and a Paired t Test) Appendix 13.A.1 MINITAB Applications 14. Nonparametric Statistics 14.1 Introduction 14.2 The Sign Test 14.3 Wilcoxon Tests 14.4 The Kruskal-Wallis Test 14.5 The Friedman Test 14.6 Testing for Randomness 14.7 Taking a Second Look at Statistics (Comparing Parametric and Nonparametric Procedures) Appendix 14.A.1 MINITAB Applications Appendix: Statistical Tables Answers to Selected Odd-Numbered Questions Bibliography Index

http://library.um.ac.id/free-contents/downloadpdf.php/buku/an-introduction-to-mathematical-statistics-and-its-applications-richard-j-larsen-morris-l-marx-12385.pdf

An introduction to mathematical statistical and its applications / Richard J. Larsen, Morris L. Marx

Designing routing metrics is critical for performance in wireless mesh networks. The unique characteristics of mesh networks, such as static nodes and the shared nature of the wireless medium, invalidate existing solutions from both wired and wireless networks and impose unique requirements on designing routing metrics for mesh networks. In this paper, we focus on identifying these requirements. We first analyze the possible types of routing protocols that can be used and show that proactive hop-by-hop routing protocols are the most appropriate for mesh networks. Then, we examine the requirements for designing routing metrics according to the characteristics of mesh networks and the type of routing protocols used. Finally, we study several existing routing metrics, including hop count, ETX, ETT, WCETT and MIC in terms of their ability to satisfy these requirements. Our simulation results of the performance of these metrics confirm our analysis of these metrics.

/pdf/designing-routing-metrics-for-mesh-networks-49pmmbzjpj.pdf

Designing routing metrics for mesh networks

Load balancing is critical for improving performance in wireless mesh networks. The unique characteristics of mesh networks, such as static nodes and the shared nature of the wireless medium, invalidate existing solutions from both wired and wireless networks and introduce new challenges for providing load balancing. In this paper, we focus on addressing these challenges. We first formulate the objective of load balancing in mesh networks and provide a theoretical solution to optimally achieve this objective. Then, we investigate some existing practical approaches to load balancing in mesh networks and show that none of them sufficiently address these challenges and some may even cause non-optimal paths and forwarding loops. In response, we propose a new path weight function, called MIC, and a novel routing scheme, called LIBRA, to provide interferenceaware and multi-channel/multi-radio aware load balancing for mesh networks, while still ensuring routing optimality and loopfreedom. We use extensive simulations to evaluate our scheme by comparing it with both the theoretical optimal solution and existing practical solutions. The results show close-to-optimum performance and indicate that LIBRA is a good candidate for load balancing and routing in wireless mesh networks.

Interference-aware Load Balancing for Multihop Wireless Networks

The design of a routing protocol must be based on the characteristics of its target networks. The diversity of wireless networks motivates the design of different routing metrics, capturing different aspects of wireless communications. The design of routing metrics, however, is not arbitrary since it has a great impact on the proper operation of routing protocols. Combining a wrong type of routing metrics with a routing protocol may result in routing loops and suboptimal paths. In this paper, we thoroughly study the relationship between routing metrics and routing protocols. Our work provides important guidelines for designing routing metrics and identifies the specific properties that a routing metric must have in order to be combined with certain type of routing protocols.

http://www.ece.vt.edu/yyang8/papers/infocom08.pdf

Design Guidelines for Routing Metrics in Multihop Wireless Networks

For the provision of quality of service (QoS) in the Internet, differentiated service (DiffServ) has been proposed as a cost-effective solution. Traffic is classified into several service classes with different priorities, premium class traffic being the highest. The routing algorithm used by the premium class service has significant effects on the traffic of all other classes as well as its own. Shortest hop-count routing used in the current Internet is no longer sufficient in DiffServ networks. Based on hop-by-hop routing, an optimal routing algorithm must be found for premium class traffic such that (1) it works correctly and efficiently for premium traffic; (2) it reduces negative influences (such as bandwidth starvation, excessive delay jitter, etc.) to other traffic classes. This problem, the optimal premium-class routing (OPR) problem, is NP-complete. To handle the OPR problem, first, we analyze the strength and weaknesses of two existing algorithms (widest-shortest-path algorithm and bandwidth-inversion shortest-path algorithm). Second, we apply to the OPR problem a novel heuristic algorithm, called the enhanced bandwidth-inversion shortest-path (EBSP) algorithm. We prove theoretically the correctness of the EBSP algorithm, i.e., it is a consistent and loop-free hop-by-hop routing algorithm. Our extensive simulations in different network environments show clearly that the EBSP algorithm performs better for premium class traffic in complex, heterogeneous networks than the other two hop-by-hop routing algorithms.

Hop-by-hop routing algorithms for premium-class traffic in DiffServ networks

To enable the end-to-end quality of service (QoS) guarantees in the Internet, based on the border gateway protocol (BGP), inter-domain QoS advertising and routing are important. However, little research has been done in this area so far. Two major challenges, scalability and heterogeneity, make the QoS extension to BGP difficult. Two existing approaches, link capacity routing (LCR) and available bandwidth routing (ABR), address QoS advertising and routing in BGP with respect to the bandwidth metric, but neither of them can solve the two challenges well. We extend BGP to advertise bandwidth information, but, instead of using link capacities or instantaneous available bandwidth values, a novel QoS metric, available bandwidth index (ABI), is defined and used to perform bandwidth advertising and routing. The two major contributions of ABI are: (1) ABI dynamically abstracts available bandwidth into a probability interval, therefore, it is very flexible to represent heterogenous and dynamic bandwidth values; (2) by capturing the statistical property of the detailed available bandwidth distribution, ABI is so efficient that it can highly decrease the message overhead in routing, thereby making the QoS advertising and routing very scalable. Our extensive simulations confirm both contributions of the ABI extension to BGP very well.

Jun Wang

Papers

Designing routing metrics for mesh networks

Interference-aware Load Balancing for Multihop Wireless Networks

Design Guidelines for Routing Metrics in Multihop Wireless Networks

Hop-by-hop routing algorithms for premium-class traffic in DiffServ networks

QoS extension to BGP