Showing papers on "MIMO published in 2005"
Book•
01 Jan 2005TL;DR: In this paper, the authors propose a multiuser communication architecture for point-to-point wireless networks with additive Gaussian noise detection and estimation in the context of MIMO networks.
Abstract: 1. Introduction 2. The wireless channel 3. Point-to-point communication: detection, diversity and channel uncertainty 4. Cellular systems: multiple access and interference management 5. Capacity of wireless channels 6. Multiuser capacity and opportunistic communication 7. MIMO I: spatial multiplexing and channel modeling 8. MIMO II: capacity and multiplexing architectures 9. MIMO III: diversity-multiplexing tradeoff and universal space-time codes 10. MIMO IV: multiuser communication A. Detection and estimation in additive Gaussian noise B. Information theory background.
8,084 citations
Book•
01 Jan 2005
TL;DR: The Wireless Communications, Second Edition as mentioned in this paper provides an authoritative overview of the principles and applications of mobile communication technology, including wireless propagation channels, transceivers and signal processing, multiple access and advanced transceiver schemes, and standardised wireless systems.
Abstract: "Professor Andreas F. Molisch, renowned researcher and educator, has put together the comprehensive book, Wireless Communications. The second edition, which includes a wealth of new material on important topics, ensures the role of the text as the key resource for every student, researcher, and practitioner in the field."Professor Moe Win, MIT, USAWireless communications has grown rapidly over the past decade from a niche market into one of the most important, fast moving industries. Fully updated to incorporate the latest research and developments, Wireless Communications, Second Edition provides an authoritative overview of the principles and applications of mobile communication technology.The author provides an in-depth analysis of current treatment of the area, addressing both the traditional elements, such as Rayleigh fading, BER in flat fading channels, and equalisation, and more recently emerging topics such as multi-user detection in CDMA systems, MIMO systems, and cognitive radio. The dominant wireless standards; including cellular, cordless and wireless LANs; are discussed.Topics featured include: wireless propagation channels, transceivers and signal processing, multiple access and advanced transceiver schemes, and standardised wireless systems.Combines mathematical descriptions with intuitive explanations of the physical facts, enabling readers to acquire a deep understanding of the subject.Includes new chapters on cognitive radio, cooperative communications and relaying, video coding, 3GPP Long Term Evolution, and WiMax; plus significant new sections on multi-user MIMO, 802.11n, and information theory.Companion website featuring: supplementary material on 'DECT', solutions manual and presentation slides for instructors, appendices, list of abbreviations and other useful resources.
1,579 citations
TL;DR: This correspondence proposes a quantized precoding system where the optimal precoder is chosen from a finite codebook known to both receiver and transmitter and performs close to optimal unitary precoding with a minimal amount of feedback.
Abstract: Multiple-input multiple-output (MIMO) wireless systems use antenna arrays at both the transmitter and receiver to provide communication links with substantial diversity and capacity. Spatial multiplexing is a common space-time modulation technique for MIMO communication systems where independent information streams are sent over different transmit antennas. Unfortunately, spatial multiplexing is sensitive to ill-conditioning of the channel matrix. Precoding can improve the resilience of spatial multiplexing at the expense of full channel knowledge at the transmitter-which is often not realistic. This correspondence proposes a quantized precoding system where the optimal precoder is chosen from a finite codebook known to both receiver and transmitter. The index of the optimal precoder is conveyed from the receiver to the transmitter over a low-delay feedback link. Criteria are presented for selecting the optimal precoding matrix based on the error rate and mutual information for different receiver designs. Codebook design criteria are proposed for each selection criterion by minimizing a bound on the average distortion assuming a Rayleigh-fading matrix channel. The design criteria are shown to be equivalent to packing subspaces in the Grassmann manifold using the projection two-norm and Fubini-Study distances. Simulation results show that the proposed system outperforms antenna subset selection and performs close to optimal unitary precoding with a minimal amount of feedback.
943 citations
TL;DR: It is somewhat surprising that the upper bound can meet the lower bound under certain regularity conditions (not necessarily degradedness), and therefore the capacity can be characterized exactly; previously this has been proven only for the degraded Gaussian relay channel.
Abstract: We study the capacity of multiple-input multiple- output (MIMO) relay channels. We first consider the Gaussian MIMO relay channel with fixed channel conditions, and derive upper bounds and lower bounds that can be obtained numerically by convex programming. We present algorithms to compute the bounds. Next, we generalize the study to the Rayleigh fading case. We find an upper bound and a lower bound on the ergodic capacity. It is somewhat surprising that the upper bound can meet the lower bound under certain regularity conditions (not necessarily degradedness), and therefore the capacity can be characterized exactly; previously this has been proven only for the degraded Gaussian relay channel. We investigate sufficient conditions for achieving the ergodic capacity; and in particular, for the case where all nodes have the same number of antennas, the capacity can be achieved under certain signal-to-noise ratio (SNR) conditions. Numerical results are also provided to illustrate the bounds on the ergodic capacity of the MIMO relay channel over Rayleigh fading. Finally, we present a potential application of the MIMO relay channel for cooperative communications in ad hoc networks.
878 citations
01 Jan 2005
TL;DR: A system where the receiver has perfect channel knowledge, but the transmitter only receives quantized information regarding the channel instantiation is analyzed and simple expressions for the capacity degradation due to finite rate feedback as well as the required increases in feedback load per mobile as a function of the number of access point antennas and the system SNR are provided.
Abstract: Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e. multiplexing) gains, even when receivers have only single antennas. However, receiver and transmitter channel state information is generally required. In this paper, a system where the receiver has perfect channel knowledge, but the transmitter only receives quantized information regarding the channel instantiation is analyzed. Simple expressions for the capacity degradation due to finite rate feedback as well as the required increases in feedback load per mobile as a function of the number of access point antennas and the system SNR are provided.
744 citations
27 Jun 2005
TL;DR: Two ASIC implementations of MIMO sphere decoders with efficient implementation of the enumeration approach recently proposed in .
Abstract: Multiple-input multiple-output (MIMO) techniques are a key enabling technology for high-rate wireless communications. This paper discusses two ASIC implementations of MIMO sphere decoders. The first ASIC attains maximum-likelihood performance with an average throughput of 73 Mb/s at a signal-to-noise ratio (SNR) of 20 dB; the second ASIC shows only a negligible bit-error-rate degradation and achieves a throughput of 170 Mb/s at the same SNR. The three key contributing factors to high throughput and low complexity are: depth-first tree traversal with radius reduction, implemented in a one-node-per-cycle architecture, the use of the /spl lscr//sup /spl infin//-instead of /spl lscr//sup 2/-norm, and, finally, the efficient implementation of the enumeration approach recently proposed in . The resulting ASICs currently rank among the fastest reported MIMO detector implementations.
666 citations
TL;DR: It is established that the set of plants which can be stabilized by linear controllers over fading channels is fundamentally limited by the channel generated uncertainty, and the notion of mean square capacity, defined for a single channel in the loop, captures this limitation precisely.
567 citations
05 Dec 2005
TL;DR: This paper reports on the interim beyond-3G (B3G) channel model developed by and used within the European WINNER project, which is a comprehensive spatial channel model for 2 and 5 GHz frequency bands and supports bandwidths up to 100 MHz in three different outdoor environments.
Abstract: This paper reports on the interim beyond-3G (B3G) channel model developed by and used within the European WINNER project. The model is a comprehensive spatial channel model for 2 and 5 GHz frequency bands and supports bandwidths up to 100 MHz in three different outdoor environments. It further features time-evolution of system-level parameters for challenging advanced communication algorithms, as well as a reduced-variability tapped delay-line model for improved usability in calibration and comparison simulations.
561 citations
TL;DR: In this paper, the authors considered the problem of maximizing sum rate of a multiple-antenna Gaussian broadcast channel (BC) with dirty-paper coding and derived simple and fast iterative algorithms that provide the optimum transmission policies for the MAC, which can easily be mapped to the optimal BC policies.
Abstract: In this correspondence, we consider the problem of maximizing sum rate of a multiple-antenna Gaussian broadcast channel (BC). It was recently found that dirty-paper coding is capacity achieving for this channel. In order to achieve capacity, the optimal transmission policy (i.e., the optimal transmit covariance structure) given the channel conditions and power constraint must be found. However, obtaining the optimal transmission policy when employing dirty-paper coding is a computationally complex nonconvex problem. We use duality to transform this problem into a well-structured convex multiple-access channel (MAC) problem. We exploit the structure of this problem and derive simple and fast iterative algorithms that provide the optimum transmission policies for the MAC, which can easily be mapped to the optimal BC policies.
556 citations
TL;DR: Switch between spatial multiplexing and transmit diversity is proposed as a simple way to improve the diversity performance of spatialmultiplexing.
Abstract: Multiple-input multiple-output (MIMO) wireless communication systems can offer high data rates through spatial multiplexing or substantial diversity using transmit diversity. In this letter, switching between spatial multiplexing and transmit diversity is proposed as a simple way to improve the diversity performance of spatial multiplexing. In the proposed approach, for a fixed rate, either multiplexing or diversity is chosen based on the instantaneous channel state and the decision is conveyed to the transmitter via a low-rate feedback channel. The minimum Euclidean distance at the receiver is computed for spatial multiplexing and transmit diversity and is used to derive the selection criterion. Additionally, the Demmel condition number of the matrix channel is shown to provide a sufficient condition for multiplexing to outperform diversity. Monte Carlo simulations demonstrate improvement over either multiplexing or diversity individually in terms of bit error rate.
447 citations
TL;DR: This paper studies randomly spread code-division multiple access (CDMA) and multiuser detection in the large-system limit using the replica method developed in statistical physics and based on a general linear vector channel model.
Abstract: This paper studies randomly spread code-division multiple access (CDMA) and multiuser detection in the large-system limit using the replica method developed in statistical physics. Arbitrary input distributions and flat fading are considered. A generic multiuser detector in the form of the posterior mean estimator is applied before single-user decoding. The generic detector can be particularized to the matched filter, decorrelator, linear minimum mean-square error (MMSE) detector, the jointly or the individually optimal detector, and others. It is found that the detection output for each user, although in general asymptotically non-Gaussian conditioned on the transmitted symbol, converges as the number of users go to infinity to a deterministic function of a "hidden" Gaussian statistic independent of the interferers. Thus, the multiuser channel can be decoupled: Each user experiences an equivalent single-user Gaussian channel, whose signal-to-noise ratio (SNR) suffers a degradation due to the multiple-access interference (MAI). The uncoded error performance (e.g., symbol error rate) and the mutual information can then be fully characterized using the degradation factor, also known as the multiuser efficiency, which can be obtained by solving a pair of coupled fixed-point equations identified in this paper. Based on a general linear vector channel model, the results are also applicable to multiple-input multiple-output (MIMO) channels such as in multiantenna systems.
TL;DR: It is shown that the TAS/MRC scheme outperforms some more complex space-time codes of the same spectral efficiency and channel estimation errors based on pilot symbols have no impact on the diversity order over quasi-static fading channels.
Abstract: In this paper, we investigate a multiple-input-multiple-output (MIMO) scheme combining transmit antenna selection and receiver maximal-ratio combining (the TAS/MRC scheme). In this scheme, a single transmit antenna, which maximizes the total received signal power at the receiver, is selected for uncoded transmission. The closed-form outage probability of the system with transmit antenna selection is presented. The bit error rate (BER) of the TAS/MRC scheme is derived for binary phase-shift keying (BPSK) in flat Rayleigh fading channels. The BER analysis demonstrates that the TAS/MRC scheme can achieve a full diversity order at high signal-to-noise ratios (SNRs), as if all the transmit antennas were used. The average SNR gain of the TAS/MRC is quantified and compared with those of uncoded receiver MRC and space-time block codes (STBCs). The analytical results are verified by simulation. It is shown that the TAS/MRC scheme outperforms some more complex space-time codes of the same spectral efficiency. The cost of the improved performance is a low-rate feedback channel. We also show that channel estimation errors based on pilot symbols have no impact on the diversity order over quasi-static fading channels.
TL;DR: The commonly used statistical multiple-input multiple-output (MIMO) model is inadequate and antenna theory is applied to take into account the area and geometry constraints, and to define the spatial signal space so to interpret experimental channel measurements in an array-independent but manageable description of the physical environment.
Abstract: Multiple-antenna systems that are limited by the area and geometry of antenna arrays, are considered. Given these physical constraints, the limit on the available number of spatial degrees of freedom is derived. The commonly used statistical multiple-input multiple-output (MIMO) model is inadequate. Antenna theory is applied to take into account the area and geometry constraints, and to define the spatial signal space so as to interpret experimental channel measurements in an array-independent but manageable description of the physical environment. Based on these modeling strategies, for a spherical array of effective aperture A in a physical environment of angular spread |/spl Omega/| in solid angle, the number of spatial degrees of freedom is shown to be A|/spl Omega/| for uni-polarized antennas and 2A|/spl Omega/| for tri-polarized antennas. Together with the 2WT degrees of freedom for a system of bandwidth W transmitting in an interval T, the total degrees of freedom of a multiple-antenna channel is therefore 4WTA|/spl Omega/|.
TL;DR: The tightness of this bound in a time-varying channel where the channel experiences uncorrelated Rayleigh fading and in some situations the dirty paper gain is upper-bounded by the ratio of transmit-to-receive antennas is found.
Abstract: We compare the capacity of dirty-paper coding (DPC) to that of time-division multiple access (TDMA) for a multiple-antenna (multiple-input multiple-output (MIMO)) Gaussian broadcast channel (BC) We find that the sum-rate capacity (achievable using DPC) of the multiple-antenna BC is at most min(M,K) times the largest single-user capacity (ie, the TDMA sum-rate) in the system, where M is the number of transmit antennas and K is the number of receivers This result is independent of the number of receive antennas and the channel gain matrix, and is valid at all signal-to-noise ratios (SNRs) We investigate the tightness of this bound in a time-varying channel (assuming perfect channel knowledge at receivers and transmitters) where the channel experiences uncorrelated Rayleigh fading and in some situations we find that the dirty paper gain is upper-bounded by the ratio of transmit-to-receive antennas We also show that min(M,K) upper-bounds the sum-rate gain of successive decoding over TDMA for the uplink channel, where M is the number of receive antennas at the base station and K is the number of transmitters
04 Apr 2005
TL;DR: In this article, a six-monopole circular antenna array for use in a MIMO system is considered and the authors show how to calculate the embedded element patterns, both by classical analytical modeling and by the method of moments.
Abstract: A six-monopole circular antenna array for use in a MIMO system is considered. The authors show how to calculate the embedded element patterns, both by classical analytical modeling and by the method of moments. Thereafter, these are used to calculate the radiation efficiency of each embedded element, correlation and diversity gain, as well as the maximum average capacity of the MIMO system when the array is located in a rich scattering environment. The theoretical value for the capacity is obtained by numerically distributing many plane wave sources statistically uniformly over 4/spl pi/, letting them illuminate the calculated embedded element pattern and using Shannon's capacity formula on the received wave amplitudes. The calculated results are compared with measurement in a reverberation chamber, representing a similar scattering environment. The agreement is good.
TL;DR: This paper proposes a joint transceiver design that combines the geometric mean decomposition (GMD) with either the conventional zero-forcing VBLAST decoder or the more recent dirty paper precoder, and proves that the scheme is asymptotically optimal for (moderately) high SNR in terms of both channel throughput and bit error rate (BER) performance.
Abstract: In recent years, considerable attention has been paid to the joint optimal transceiver design for multi-input multi-output (MIMO) communication systems. In this paper, we propose a joint transceiver design that combines the geometric mean decomposition (GMD) with either the conventional zero-forcing VBLAST decoder or the more recent zero-forcing dirty paper precoder (ZFDP). Our scheme decomposes a MIMO channel into multiple identical parallel subchannels, which can make it rather convenient to design modulation/demodulation and coding/decoding schemes. Moreover, we prove that our scheme is asymptotically optimal for (moderately) high SNR in terms of both channel throughput and bit error rate (BER) performance. This desirable property is not shared by any other conventional schemes. We also consider the subchannel selection issues when some of the subchannels are too poor to be useful. Our scheme can also be combined with orthogonal frequency division multiplexing (OFDM) for intersymbol interference (ISI) suppression. The effectiveness of our approaches has been validated by both theoretical analyses and numerical simulations.
TL;DR: A low-complexity blind carrier frequency offset estimator for orthogonal frequency-division multiplexing (OFDM) systems is developed using a kurtosis-type criterion, and it is shown that this approach can be applied to blind CFO estimation in multi-input multi-output and multiuser OFDM systems.
Abstract: Relying on a kurtosis-type criterion, we develop a low-complexity blind carrier frequency offset (CFO) estimator for orthogonal frequency-division multiplexing (OFDM) systems. We demonstrate analytically how identifiability and performance of this blind CFO estimator depend on the channel's frequency selectivity and the input distribution. We show that this approach can be applied to blind CFO estimation in multi-input multi-output and multiuser OFDM systems. The issues of channel nulls, multiuser interference, and effects of multiple antennas are addressed analytically, and tested via simulations.
TL;DR: A V-BLAST-type combination of orthogonal frequency-division multiplexing with MIMO (MIMO-OFDM) for enhanced spectral efficiency and multiuser downlink throughput and a new joint data detection and channel estimation algorithm is proposed which combines the QRD-M algorithm and Kalman filter.
Abstract: The use of multiple transmit/receive antennas forming a multiple-input multiple-output (MIMO) system can significantly enhance channel capacity. This paper considers a V-BLAST-type combination of orthogonal frequency-division multiplexing (OFDM) with MIMO (MIMO-OFDM) for enhanced spectral efficiency and multiuser downlink throughput. A new joint data detection and channel estimation algorithm for MIMO-OFDM is proposed which combines the QRD-M algorithm and Kalman filter. The individual channels between antenna elements are tracked using a Kalman filter, and the QRD-M algorithm uses a limited tree search to approximate the maximum-likelihood detector. A closed-form symbol-error rate, conditioned on a static channel realization, is presented for the M=1 case with QPSK modulation. An adaptive complexity QRD-M algorithm (AC-QRD-M) is also considered which assigns different values of M to each subcarrier according to its estimated received power. A rule for choosing M using subcarrier powers is obtained using a kernel density estimate combined with the Lloyd-Max algorithm.
30 Jun 2005
TL;DR: This work focuses on the development of a single model for the MIMO Wireless Channel Modeling and Experimental Characterization of OSTBCs, and some of the techniques used in this study were adapted from this model.
Abstract: List of Contributors. Preface. Acknowledgements. 1 MIMO Wireless Channel Modeling and Experimental Characterization (Michael A. Jensen and Jon W. Wallace). 1.1 Introduction. 1.2 MIMO Channel Measurement. 1.3 MIMO Channel Models. 1.4 The Impact of Antennas on MIMO Performance. References. 2 Multidimensional Harmonic Retrieval with Applications in MIMO Wireless Channel Sounding (Xiangqian Liu, Nikos D. Sidiropoulos, and Tao Jiang). 2.1 Introduction. 2.2 Harmonic Retrieval Data Model. 2.3 Identifiability of Multidimensional Harmonic Retrieval. 2.4 Multidimensional Harmonic Retrieval Algorithms. 2.5 Numerical Examples. 2.6 Multidimensional Harmonic Retrieval for MIMO Channel Estimation. 2.7 Concluding Remarks. References. 3 Certain Computations Involving Complex Gaussian Matrices with Applications to the Performance Analysis of MIMO Systems (Ming Kang, Lin Yang, and Mohamed-Slim Alouini). 3.1 Introduction. 3.2 Performance Measures of Multiple Antenna Systems. 3.3 SomeMathematical Preliminaries. 3.4 General Calculations with MIMO Applications. 3.5 Summary. References. 4 Recent Advances in Orthogonal Space-Time Block Coding (Mohammad Gharavi-Alkhansari, Alex B. Gershman, and Shahram Shahbazpanahi). 4.1 Introduction. 4.2 Notations and Acronyms. 4.3 Mathematical Preliminaries. 4.4 MIMO System Model and OSTBC Background.8 4.5 Constellation Space Invariance and Equivalent Array-Processing-Type MIMO Model. 4.6 Coherent ML Decoding. 4.7 Exact Symbol Error Probability Analysis of Coherent ML Decoder. 4.8 Optimality Properties of OSTBCs. 4.9 Blind Decoding of OSTBCs. 4.10 Multiaccess MIMO Receivers for OSTBCs. 4.11 Conclusions. References. 5 Trace-Orthogonal Full Diversity Cyclotomic Space-Time Codes (Jian-Kang Zhang, Jing Liu, and Kon Max Wong). 5.1 Introduction. 5.2 Channel Model with Linear Dispersion Codes. 5.3 Good Structures for LD Codes: Trace Orthogonality. 5.4 Trace-orthogonal LD Codes. 5.5 Construction of Trace Orthogonal LD Codes. 5.6 Design of Full Diversity LD Codes. 5.7 Design of Full Diversity Linear Space-time Block Codes for N
TL;DR: A new real-time fault estimation module that estimates the actuator effectiveness is developed and simulation results of a helicopter in vertical plane is presented to demonstrate the performance of the proposed fault-tolerant control scheme.
Abstract: In this brief, a methodology for detection and accommodation of actuator faults for a class of multi-input-multi-output (MIMO) stochastic systems is presented. First, a new real-time fault estimation module that estimates the actuator effectiveness is developed. The actuator fault diagnosis is based on the estimation of the state vector. Under some conditions, the stochastic system is transformed into two separate subsystems. One of them is not affected by actuator faults, so a reduced order Kalman filter can be used to estimate its states. The other, whose states are measurable, is affected by the faults. Then, the output of the nominal controller is reconfigured to compensate for the loss of actuator effectiveness in the system. Simulation results of a helicopter in vertical plane is presented to demonstrate the performance of the proposed fault-tolerant control scheme.
TL;DR: A transceiver design which contains a linear precoder and a MMSE-VBLAST detector is proposed which can decompose, in a capacity lossless manner, a MIMO channel into multiple subchannels with identical capacities.
Abstract: Assuming the availability of the channel state information at the transmitter (CSIT) and receiver (CSIR), we consider the joint optimal transceiver design for multi-input multi-output (MIMO) communication systems. Using the geometric mean decomposition (GMD), we propose a transceiver design that can decompose, in a strictly capacity lossless manner, a MIMO channel into multiple subchannels with identical capacities. This uniform channel decomposition (UCD) scheme has two implementation forms. One is the combination of a linear precoder and a minimum mean-squared-error VBLAST (MMSE-VBLAST) detector, which is referred to as UCD-VBLAST, and the other includes a dirty paper (DP) precoder and a linear equalizer followed by a DP decoder, which we refer to as UCD-DP. The UCD scheme can provide much convenience for the modulation/demodulation and coding/decoding procedures due to obviating the need for bit allocation. We also show that UCD can achieve the maximal diversity gain. The simulation results show that the UCD scheme exhibits excellent performance, even without the use of any error correcting codes.
31 Oct 2005
TL;DR: It is proved that for N = 2 the multiplexing gain is 1, and generalizations to larger networks are considered.
Abstract: At high SNR the capacity of a point-to point MIMO system with N T transmit antenna and NR receive antenna is min{N T, NR} log(SNR) + O(1). The factor in front of the log is called the multiplexing gain. In this paper we consider a network with 2N nodes (N source destination pairs) that each have only a single antenna. These single antenna nodes could cooperate to form larger virtual arrays, usually called cooperative diversity, user cooperation, or coded cooperation. The question we ask is: how large a multiplexing gain is possible. We prove that for N = 2 the multiplexing gain is 1, and consider generalizations to larger networks
TL;DR: This paper proposes multimode antenna selection, which uses a low-rate feedback channel to improve the error rate performance of spatial multiplexing systems with linear receivers.
Abstract: Spatial multiplexing is a simple transmission technique for multiple-input multiple-output (MIMO) wireless communication links in which data is multiplexed across the transmit antennas. In Rayleigh fading matrix channels, however, spatial multiplexing with low-complexity linear receivers suffers due to a lack of diversity advantage. This paper proposes multimode antenna selection, which uses a low-rate feedback channel to improve the error rate performance of spatial multiplexing systems with linear receivers. In the proposed technique, both the number of substreams and the mapping of substreams to antennas are dynamically adjusted, for a fixed total data rate, to the channel based on limited feedback from the receiver. Dual-mode selection, where spatial multiplexing or selection diversity is adaptively chosen, dramatically improves the diversity gain achieved. Multimode selection (i.e., allowing any number of substreams to be dynamically selected) provides additional array gain. Various criteria for selecting the number of substreams and the optimal mapping of substreams to transmit antennas are derived. Relationships are made between the selection criteria and the eigenmodes of the channel. A probabilistic analysis of the selection criteria are provided for Rayleigh fading channels. Applications to nonlinear receivers are mentioned. Monte Carlo simulations demonstrate significant performance improvements in independent and identically distributed (i.i.d.) flat-fading Rayleigh matrix channels with minimal feedback.
Book•
01 Oct 2005
TL;DR: This book discusses the construction of Ultra Wideband Receiver Architectures, the potential benefits of MIMO and UWB, and the effect of NBI in UWB Systems.
Abstract: Preface. Contributors. Chapter 1 Introduction to Ultra Wideband (Huseyin Arslan and Maria-Gabriella Di Benedetto) 1.1 Introduction. 1.2 Scope of the Book. Chapter 2 UWB Channel Estimation and Synchronization (Irena Maravic and Martin Vetterli). 2.1 Introduction. 2.2 Channel Estimation at SubNyquist Sampling Rate. 2.3 Performance Evaluation. 2.4 Estimating UWB Channels with Frequency-Dependent Distortion. 2.5 Channel Estimation from Multiple Bands. 2.6 Low-Complexity Rapid Acquisition in UWB Localizers. 2.7 Conclusions. Chapter 3 Ultra Wideband Geolocation (Sinan Gezici, Zafer Sahinoglu, Hisashi Kobayashi, and H. Vincent Poor). 3.1 Introduction. 3.2 Signal Model. 3.3 Positioning Techniques. 3.4 Main Sources of Error in Time-Based Positioning. 3.5 Ranging and Positioning. 3.6 Location-Aware Applications. 3.7 Conclusions. Chapter 4 UWB Modulation Options (H&uUML seyin Arslan, Ismail Guenç, and Sadia Ahmed). 4.1 Introduction. 4.2 UWB Signaling Techniques. 4.3 Data Mapping. 4.4 Spectral Characteristics. 4.5 Data Mapping and Transceiver Complexity. 4.6 Modulation Performances in Practical Conditions. 4.7 Conclusion. Chapter 5 Ultra Wideband Pulse Shaper Design (Zhi Tian, Timothy N. Davidson, Xiliang Luo, Xianren Wu, and Georgios B. Giannakis). 5.1 Introduction. 5.2 Transmit Spectrum and Pulse Shaper. 5.3 FIR Digital Pulse Design. 5.4 Optimal UWB Single Pulse Design. 5.5 Optimal UWB Orthogonal Pulse Design. 5.6 Design Examples and Comparisons. 5.7 Conclusions. Chapter 6 Antenna Issues (Zhi Ning Chen). 6.1 Introduction. 6.2 Design Considerations. 6.3 Antenna and Pulse versus BER Performance. Chapter 7 Ultra Wideband Receiver Architectures (Huseyin Arslan). 7.1 Introduction. 7.2 System Model. 7.3 UWB Receiver Related Issues. 7.4 TH-IR-UWB Receiver Options. 7.5 Conclusion. Chapter 8 Ultra Wideband Channel Modeling and Its Impact on System Design (Chia-Chin Chong). 8.1 Introduction. 8.2 Principles and Background of UWB Multipath Propagation Channel Modeling. 8.3 Channel Sounding Techniques. 8.4 UWB Statistical-Based Channel Modeling. 8.5 Impact of UWB Channel on System Design. 8.6 Conclusion. Chapter 9 MIMO and UWB (Thomas Kaiser). 9.1 Introduction. 9.2 Potential Benefits of MIMO and UWB. 9.3 Literature Review of UWB Multiantenna Techniques. 9.4 Spatial Channel Measurements and Modeling. 9.5 Spatial Multiplexing. 9.6 Spatial Diversity. 9.7 Beamforming. 9.8 Conclusion and Outlook. Chapter 10 Multiple-Access Interference Mitigation in Ultra Wideband Systems (Sinan Gezici, Hisashi Kobayashi, and H. Vincent Poor). 10.1 Introduction. 10.2 Signal Model. 10.3 Multiple-Access Interference Mitigation at the Receiver Side. 10.4 Multiple-Access Interference Mitigation at the Transmitter Side. 10.5 Concluding Remarks. Chapter 11 Narrowband Interference Issues in Ultra Wideband Systems (Huseyin Arslan and Mustafa E. Sahin). 11.1 Introduction. 11.2 Effect of NBI in UWB Systems. 11.3 Avoiding NBI. 11.4 Canceling NBI. 11.5 Conclusion and Future Research. Chapter 12 Orthogonal Frequency Division Multiplexing for Ultra Wideband Communications (Ebrahim Saberina and Ahmed H. Tewfik). 12.1 Introduction. 12.2 Multiband OFDM System. 12.3 Multiband Pulsed-OFDM UWB system. 12.4 Comparing MB-OFDM and MB-Pulsed-OFDM systems. 12.5 Conclusion. Chapter 13 UWB Networks and Applications (Krishna M. Sivalingam and Aniruddha Rangnekar). 13.1 Introduction. 13.2 Background. 13.3 Medium Access Protocols. 13.4 Network Applications. 13.5 Summary and Discussion. Acknowledgments. Chapter 14 Low-Bit-Rate UWB Networks (Luca DeNardis and Gian Mario Maggio). 14.1 Low Data-Rate UWB Network Applications. 14.2 The 802.15.4 MAC Standard. 14.3 Advanced MAC Design for Low-Bit-Rate UWB Networks. Chapter 15 An Overview of Routing Protocols for Mobile Ad Hoc Networks (David A. Sumy, Branimir Vojcic, and Jinghao Xu). 15.1 Introduction. 15.2 Ad Hoc Networks. 15.3 Routing in MANETs. 15.4 Proactive Routing. 15.5 Reactive Routing. 15.6 Power-Aware Routing. 15.7 Hybrid Routing. 15.8 Other. 15.9 Conclusion. Appendix. Chapter 16 Adaptive UWB Systems (Francesca Cuomo and Crishna Martello). 16.1 Introduction. 16.2 A Distributed Power-Regulated Admission Control Scheme for UWB. 16.3 Performance Analysis. 16.4 Summary. Chapter 17 UWB Location and Tracking-A Practical Example of an UWB-Based Sensor Network (Ian Oppermann, Kegen Yu, Alberto Rabbachin, Lucian Stoica, Paul Cheong, Jean-Philippe Montillet, and Sakari Tiuraniemi). 17.1 Introduction. 17.2 Multiple Access in UWB Sensor Systems. 17.3 UWB Sensor Network Case Study. 17.4 System Description-UWEN. 17.5 System Implementation. 17.6 Location System. 17.7 Position Calculation Methods. 17.8 Tracking Moving Objects. 17.9 Conclusion. Acknowledgments. Index.
Patent•
27 May 2005TL;DR: A modified preamble is used by extended devices that operate at higher rates, such as MIMO or other extensions relative to strict 802.11a-compliant devices.
Abstract: A modified preamble is used by extended devices that operate at higher rates, MIMO or other extensions relative to strict 802.11a-compliant devices. The extended devices might use multiple antenna techniques (MIMO), where multiple data streams are multiplexed spatially and/or multi-channel techniques, where an extended transmitter transmits using more than one 802.11a channel at a time. Such extensions to IEEE 802.11a can exist in extended devices. The modified preamble is usable for signaling, to legacy devices as well as extended devices, to indicate capabilities and to cause legacy devices or extended devices to defer to other devices such that the common communication channel is not subject to unnecessary interference. The modified preamble is also usable for obtaining MIMO channel estimates and/or multi-channel estimates. The modified preamble preferably includes properties that facilitate detection of conventional and/or extended modes (“mode detection”) and provides some level of coexistence with legacy IEEE 802.11a devices.
Book•
04 Nov 2005
TL;DR: In this article, the authors present an OFDM-based wireless network architecture and propose a cross-layer MAC protocol for wireless networks, based on the idea of phase noise compensation.
Abstract: Preface. 1. Introduction. 1.1 OFDM-based wireless network overview. 1.1.1 Digital broadcasting and DVB-T. 1.1.2 Wireless LAN and IEEE 802.11. 1.1.3 WiMAX and IEEE 802.16. 1.2 The need for "cross-layer" design. 1.3 Organization of this text. 2. OFDM Fundamentals. 2.1 Broadband radio channel characteristics. 2.1.1 Envelope fading. 2.1.2 Time dispersive channel. 2.1.3 Frequency dispersive channel. 2.1.4 Statistical characteristics of broadband channels. 2.2 Canonical form of broadband transmission. 2.3 OFDM realization. 2.4 Summary. 3. PHY Layer Issues - System Imperfections. 3.1 Frequency synchronization. 3.1.1 OFDM carrier offset data mode. 3.1.2 Pilot-based estimation. 3.1.3 Non-pilot based estimation. 3.2 Channel estimation. 3.2.1 Pilots for 2D OFDM channel estimation . 3.2.2 2DMMSE channel estimation. 3.2.3 Reduced complexity channel estimation. 3.3 I/Q imbalance compensation. 3.3.1 I/Q Imbalance Model. 3.3.2 Digital compensation receiver. 3.3.3 Frequency offset estimation with I/Q imbalance. 3.4 Phase noise compensation. 3.4.1 Mathematical models for phase noise. 3.4.2 CPE estimation with channel state information. 3.4.3 Time domain channel estimation in the presence of CPE. 3.4.4 CPE estimation without explicit CSI. 3.5 Summary. 4. PHY Layer Issues - Spatial Processing. 4.1 Antenna array fundamentals. 4.2 Beam forming. 4.2.1 Coherent combining. 4.2.2 Zero-forcing. 4.2.3 MMSE reception (optimum linear receiver). 4.2.4 SDMA. 4.2.5 Broadband beam forming. 4.3 MIMO channels and capacity. 4.4 Space-time coding. 4.4.1 Spatial multiplexing. 4.4.2 Orthogonal space-time block coding. 4.4.3 Concatenated ST transmitter. 4.4.4 Beam forming with ST coding. 4.4.5 ST beam forming in OFDM. 4.5 Wide-area MIMO beam forming. 4.5.1 Data model. 4.5.2 Uncoded OFDM design criterion. 4.5.3 Coded OFDM design criterion. 4.6 Summary. 4.7 Appendix I: Derivation of Pe. 4.8 Appendix II: Proof of Proposition 5. 4.9 Appendix III: Proof of Proposition 6. 5. Multiple Access Control Protocols. 5.1 Introduction. 5.2 Basic MAC protocols. 5.2.1 Contention based protocols. 5.2.2 Non-contention based MAC protocols. 5.3 OFDMA advantages. 5.4 Multiuser diversity. 5.5 OFDMA optimality. 5.5.1 Multiuser multicarrier SISO systems. 5.5.2 Multiuser multicarrierMIMO systems. 5.6 Summary. 5.7 Appendix I: Cn(p) is a convex function in OFDMA/SISO case. 5.8 Appendix II: C(p) is a convex function in OFDMA/MIMO case. 6. OFDMA Design Considerations. 6.1 Cross layer design introduction. 6.2 Mobility-dependent OFDMA traffic channels. 6.2.1 OFDMA traffic channel. 6.2.2 System model. 6.2.3 Channel configuration for fixed/portable applications. 6.2.4 Channel configuration for mobile application. 6.3 IEEE 802.16e traffic channels. 6.4 Summary. 7. Frequency Planning in Multi-cell Networks. 7.1 Introduction. 7.1.1 Fixed channel allocation. 7.1.2 Dynamic channel allocation. 7.2 OFDMA DCA. 7.2.1 Protocol design. 7.2.2 Problem formulation for the RNC. 7.2.3 Problem formulation for BSs. 7.2.4 Fast algorithm for the RNC. 7.2.5 Fast algorithm for BSs. 7.3 Spectrum efficiency under different cell/sector configurations. 7.3.1 System configuration and signaling overhead. 7.3.2 Channel loading gains. 7.4 Summary. 8. Appendix. 8.1 IEEE 802.11 and WiFi. 8.1.1 802.11 overview. 8.1.2 802.11 network architecture. 8.1.3 The MAC layer technologies. 8.1.4 The physical layer technologies. 8.2 IEEE 802.16e and Mobile WiMAX. 8.2.1 Overview. 8.2.2 The physical layer technologies. 8.2.3 The MAC layer technologies. 8.3 Performance analysis of WiMAX systems. 8.3.1 WiMAX OFDMA-TDD. 8.3.2 Comparison Method. Notations and Acronym. About the Authors. Index.
01 Dec 2005
TL;DR: This work proposes two low-complexity suboptimal user selection algorithms for multiuser MIMO systems with block diagonalization that aim to select a subset of users such that the total throughput is nearly maximized.
Abstract: Block diagonalization (BD) is a preceding technique that eliminates inter-user interference in downlink multiuser multiple-input multiple-output (MIMO) systems. With the assumptions that all users have the same number of receive antennas and utilize all receive antennas when scheduled for transmission, the number of simultaneously supportable users with BD is limited by the ratio of the number of basestation transmit antennas to the number of user receive antennas. In a downlink MIMO system with a large number of users, the basestation may select a subset of users to serve in order to maximize the total throughput The brute-force search for the optimal user set, however, is computationally prohibitive. We propose two low-complexity suboptimal user selection algorithms for multiuser MIMO systems with BD. Both algorithms aim to select a subset of users such that the total throughput is nearly maximized. The first user selection algorithm greedily maximizes the total throughput, whereas the criterion of the second algorithm is based on the channel energy. We show that both algorithms have linear complexity in the total number of users and achieve around 95% of the total throughput of the complete search method in simulations
TL;DR: An adaptive resource-allocation approach, which jointly adapts subcarrier allocation, power distribution, and bit distribution according to instantaneous channel conditions, is proposed for multiuser multiple-input multiple-output (MIMO)/orthogonal frequency-division multiplexing systems.
Abstract: Fast adaptive transmission has been recently identified as a key technology for exploiting potential system diversity and improving power-spectral efficiency in wireless communication systems. An adaptive resource-allocation approach, which jointly adapts subcarrier allocation, power distribution, and bit distribution according to instantaneous channel conditions, is proposed for multiuser multiple-input multiple-output (MIMO)/orthogonal frequency-division multiplexing systems. The resultant scheme is able to: 1) optimize the power efficiency; 2) guarantee each user's quality of service requirements, including bit-error rate and data rate; 3) ensure fairness to all the active users; and 4) be applied to systems with various types of multiuser-detection schemes at the receiver. For practical implementation, a reduced-complexity allocation algorithm is developed. This algorithm decouples the complex multiuser joint resource-allocation problem into simple single-user optimization problems by controlling the subcarrier sharing according to the users' spatial separability. Numerical results show that significant power and diversity gains are achievable, compared with nonadaptive systems. It is also demonstrated that the MIMO system is able to multiplex several users without sacrificing antenna diversity by using the proposed algorithm.
TL;DR: A general space-frequency block code structure that can guarantee full-rate and full-diversity transmission in multiple-input multiple-output-orthogonal frequency-division multiplexing (MIMO-OFDM) systems is proposed and an interleaving strategy to maximize the "extrinsic" diversity product is proposed.
Abstract: A general space-frequency (SF) block code structure is proposed that can guarantee full-rate (one channel symbol per subcarrier) and full-diversity transmission in multiple-input multiple-output-orthogonal frequency-division multiplexing (MIMO-OFDM) systems. The proposed method can be used to construct SF codes for an arbitrary number of transmit antennas, any memoryless modulation and arbitrary power-delay profiles. Moreover, assuming that the power-delay profile is known at the transmitter, we devise an interleaving method to maximize the overall performance of the code. We show that the diversity product can be decomposed as the product of the "intrinsic" diversity product, which depends only on the used signal constellation and the code design, and the "extrinsic" diversity product, which depends only on the applied interleaving method and the power delay profile of the channel. Based on this decomposition, we propose an interleaving strategy to maximize the "extrinsic" diversity product. Extensive simulation results show that the proposed SF codes outperform the previously existing codes by about 3-5 dB, and that the proposed interleaving method results in about 1-3-dB performance improvement compared to random interleaving.
Patent•
07 Dec 2005
TL;DR: In this article, a cooperative multiple-input multiple-output (MIMO) transmission scheme for multicell wireless networks is proposed. But the cooperative MIMO transmission scheme supports higher dimension space-time-frequency processing for increased capacity and system performance.
Abstract: A method and system for cooperative multiple-input multiple output (MIMO) transmission operations in a multicell wireless network. Under the method, antenna elements from two or more base stations are used to from an augmented MIMO antenna array that is used to transmit and receive MIMO transmissions to and from one or more terminals. The cooperative MIMO transmission scheme supports higher dimension space-time-frequency processing for increased capacity and system performance.