Efficient algorithms for broadband space-time-coded wireless communication
02 Nov 2001-Vol. 4529, pp 104-117
TL;DR: A simple signature-time transmit diversity paradigm, requiring just a single-sensor transmit-antenna at the mobiles, is shown to counter degradations, in conjunction with iterative minimum mean square error (MMSE) multiuser decoding, at the base stations, to deliver significant fractions of the predicted MIMO throughputs.
Abstract: Multivariate signaling using multiple-in multiple-out (MIMO) configurations, increases the Shannon capacity of communication links, several fold. Among existing schemes that aim at realizing these predicted gains in practical scenarios, e.g., at high transmission rates or when the transmitter lacks channel state information, are orthogonal space-time block codes (STBCs), which are now known to display good performance characteristics, when concatenated with conventional trellis codes. It is shown that the only full-rate orthogonal STBC (for frequency-flat fading channels) achieves the (2,1)- channel capacity for single-sensor reception, and a full-rate block-orthogonal STBC is developed for broadband channels. While this STBC is easily adapted for use in the code division multiaccess down-link, a similar technique is necessary for the uplink, where performance degrades in fading media at high system loads. A simple signature-time transmit diversity paradigm, requiring just a single-sensor transmit-antenna at the mobiles, is shown to counter these degradations, in conjunction with iterative minimum mean square error (MMSE) multiuser decoding, at the base stations. Finally, an efficient layered space-time signaling technique for trellis coded packets, in systems with multisensor transmit- and receive-antennas, as in peer-to-peer communication environments, is proposed. With iterative MMSE decision feedback equalization and multistream decoding, it is observed to deliver significant fractions of the predicted MIMO throughputs.
Citations
More filters
TL;DR: Simulation results in a multicell scenario show that the CRA scheduler is robust regarding channel quality knowledge and that it provides significant gains in joint system capacity in single and mixed service scenarios.
Abstract: We study the impact of scheduling algorithms on the provision of multiple services in the long term evolution (LTE) system. In order to measure how well the services are provided by the system, we use the definition of joint system capacity. In this context, we claim that scheduling strategies should consider the current satisfaction level of each service and the offered load to the system by each service. We propose a downlink-scheduling strategy according to these ideas named capacity-driven resource allocation (CRA). The CRA scheduler dynamically controls the resource sharing among flows of different services such as delay-sensitive and rate demanding ones. Moreover, CRA scheduler exploits the channel-quality knowledge to utilize the system resources efficiently. Simulation results in a multicell scenario show that the CRA scheduler is robust regarding channel quality knowledge and that it provides significant gains in joint system capacity in single and mixed service scenarios.
31 citations
TL;DR: A permutation transmit diversity technique that exploits channel time-selectivity is proposed in order to enhance the interuser separation afforded by multisensor reception.
Abstract: The iterative MMSE multiuser detection paradigm is applied to the suppression of cochannel interference in the coded narrowband (multicell) uplink. The equivalent of multiple chips per bit (necessary for MMSE multiuser demodulation) is generated via multisensor reception, the array responses serving as user signatures. This receiver's robustness to overloading allows its sensor count to be much lower than the typical number of other-cell cochannel interferers. A permutation transmit diversity technique that exploits channel time-selectivity is proposed in order to enhance the interuser separation afforded by multisensor reception.
7 citations
TL;DR: This paper presents a meta-modelling architecture suitable for wireless mesh networks and describes the design and construction of mesh networks in the real-time environment.
Abstract: 1Department of Electrical and Computer Engineering, The University of British Columbia, Vancouver, BC, Canada V6T 1Z4 2Centre for Wireless Network Design, University of Bedfordshire, Bedfordshire LU1 3JU, UK 3NSN Research, Nokia Siemens Networks, 02610 Espoo, Finland 4Key Laboratory of Universal Wireless Communication, Beijing University of Posts and Telecommunications (BUPT), Beijing 100876, China
2 citations
Cites background from "Efficient algorithms for broadband ..."
...…Electrical and Computer Engineering, University of British Columbia, 2332 Main Mall, Vancouver, BC, Canada V6T 1Z4 Correspondence should be addressed to Raymond Kwan, raymond.y.c.kwan@gmail.com Received 10 April 2010; Accepted 30 May 2010 Academic Editor: Wang Wenbo Copyright © 2010 R. Kwan and…...
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TL;DR: The impact of iterative minimum mean square error detection on multisensor signaling schemes in frequency-selective media is considered and a combination of the LMMSE and DF-MMSE front ends seems appropriate in channels with large delay spreads.
Abstract: The impact of iterative minimum mean square error (MMSE) detection on multisensor signaling schemes in frequency-selective media is considered. The suitability of a decision-feedback MMSE (DF-MMSE) front end relative to a linear MMSE (LMMSE) front end is examined using two case studies, namely, layered spatial-multiplexing and block-orthogonal space-time signaling. Iterative MMSE detection is observed to afford near-optimal performance. Further, in channels with large delay spreads, a combination of the LMMSE and DF-MMSE front ends seems appropriate: the former in earlier iterations and the latter in concluding iterations.
1 citations
Cites background or methods from "Efficient algorithms for broadband ..."
...This motivates the construction of block-orthogonal codes by effectively replacing symbol pairs by mutually isolated symbol-vector pairs of adequate length [21], [23]–[25]....
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...(This essential technique was first discovered and reported in [21], [23], and [26], and independently, also in [25]....
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...The known (mutually orthogonal) symbol vectors u and u((1)) are used to estimate channel responses [21], [23] in practice, and the isolation between the received vector mixtures is effected by subtracting the intersymbol interference contributions of these training symbol vectors from the received signal x after the channel impulse responses have been estimated; 0 is a zero vector of the same length as u and u((1)); and 0l is a zero vector of the same length as each symbol vector (which is much longer than a training vector, as expected)....
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...When the delay spread extends over a large number of symbols, however, a decision feedback (DF) front end such as the DF-MMSE equalizer [16], [21], [22] presents itself as a useful alternative....
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...An efficient way of redeeming this bandwidth for a more useful function is to replace these 0 vectors by known data for the purpose of channel response estimation [21], [23], as noted in Section III....
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References
More filters
01 Nov 1999
TL;DR: In this paper, the authors investigate the use of multiple transmitting and/or receiving antennas for single user communications over the additive Gaussian channel with and without fading, and derive formulas for the capacities and error exponents of such channels, and describe computational procedures to evaluate such formulas.
Abstract: We investigate the use of multiple transmitting and/or receiving antennas for single user communications over the additive Gaussian channel with and without fading. We derive formulas for the capacities and error exponents of such channels, and describe computational procedures to evaluate such formulas. We show that the potential gains of such multi-antenna systems over single-antenna systems is rather large under independenceassumptions for the fades and noises at different receiving antennas.
12,542 citations
AT&T1
TL;DR: A generalization of orthogonal designs is shown to provide space-time block codes for both real and complex constellations for any number of transmit antennas and it is shown that many of the codes presented here are optimal in this sense.
Abstract: We introduce space-time block coding, a new paradigm for communication over Rayleigh fading channels using multiple transmit antennas. Data is encoded using a space-time block code and the encoded data is split into n streams which are simultaneously transmitted using n transmit antennas. The received signal at each receive antenna is a linear superposition of the n transmitted signals perturbed by noise. Maximum-likelihood decoding is achieved in a simple way through decoupling of the signals transmitted from different antennas rather than joint detection. This uses the orthogonal structure of the space-time block code and gives a maximum-likelihood decoding algorithm which is based only on linear processing at the receiver. Space-time block codes are designed to achieve the maximum diversity order for a given number of transmit and receive antennas subject to the constraint of having a simple decoding algorithm. The classical mathematical framework of orthogonal designs is applied to construct space-time block codes. It is shown that space-time block codes constructed in this way only exist for few sporadic values of n. Subsequently, a generalization of orthogonal designs is shown to provide space-time block codes for both real and complex constellations for any number of transmit antennas. These codes achieve the maximum possible transmission rate for any number of transmit antennas using any arbitrary real constellation such as PAM. For an arbitrary complex constellation such as PSK and QAM, space-time block codes are designed that achieve 1/2 of the maximum possible transmission rate for any number of transmit antennas. For the specific cases of two, three, and four transmit antennas, space-time block codes are designed that achieve, respectively, all, 3/4, and 3/4 of maximum possible transmission rate using arbitrary complex constellations. The best tradeoff between the decoding delay and the number of transmit antennas is also computed and it is shown that many of the codes presented here are optimal in this sense as well.
7,348 citations
TL;DR: This paper addresses digital communication in a Rayleigh fading environment when the channel characteristic is unknown at the transmitter but is known (tracked) at the receiver with the aim of leveraging the already highly developed 1-D codec technology.
Abstract: This paper addresses digital communication in a Rayleigh fading environment when the channel characteristic is unknown at the transmitter but is known (tracked) at the receiver. Inventing a codec architecture that can realize a significant portion of the great capacity promised by information theory is essential to a standout long-term position in highly competitive arenas like fixed and indoor wireless. Use (n T , n R ) to express the number of antenna elements at the transmitter and receiver. An (n, n) analysis shows that despite the n received waves interfering randomly, capacity grows linearly with n and is enormous. With n = 8 at 1% outage and 21-dB average SNR at each receiving element, 42 b/s/Hz is achieved. The capacity is more than 40 times that of a (1, 1) system at the same total radiated transmitter power and bandwidth. Moreover, in some applications, n could be much larger than 8. In striving for significant fractions of such huge capacities, the question arises: Can one construct an (n, n) system whose capacity scales linearly with n, using as building blocks n separately coded one-dimensional (1-D) subsystems of equal capacity? With the aim of leveraging the already highly developed 1-D codec technology, this paper reports just such an invention. In this new architecture, signals are layered in space and time as suggested by a tight capacity bound.
6,812 citations
"Efficient algorithms for broadband ..." refers background or result in this paper
...q = 0 W0 w1 w2 w3 w4 w0 w1 (1) (0) (3) (5) (4) (1) (0) (3) (2) (1) (5) (4) 1 w0 W1 W2 W0 W1 1 W0 W1 W2 W3 W4 W0 W1 (2) (1) (0) (6) (5) (2) (1) (0) (3) (2) (6) (5) 2 W0 W1 W2 W0 W1 2 W0 W1 W2 W3 W4 W0 W1 (3) (2) (1) (7) (6) (3) (2) (1) (0) (3) (7) (6) 3 W0 W1 W2 W0 W1 3 W0 W1 W2 W3 W4 W0 W1...
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...The (4, 4)— configuration quadruples this average throughput, with just an additional 0....
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...Further, it loses less than a dB if the number of receive-sensors is reduced to 3, relative to the (4, 4)—case, demonstrating, its robustness to overloading, in contrast with most other suboptimal MIMO receivers,4'21'27 which require R Q....
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...Figure 3(b) indicates that iterative MMSE multistream decoding, gains almost 4 dB after 4 iterations, over the subtract-and-null approach (with hard decoding) of the BLAST family in a (4, 4)—configuration....
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...(0) (3) (2) (4) (7) (0) (3) (2) (1) (0) (4) (7) q = 0 w0 w1 W2 W0 W1 ....
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Book•
01 Jan 1995TL;DR: Generating Pseudorandom Signals (Pseudonoise) from PseudOrandom Sequences by Modulation and Demodulation of Spread Spectrum Signals in Multipath and Multiple Access Interference.
Abstract: 1. Introduction. Definition and Purpose. Basic Limitations of the Conventional Approach. Spread Spectrum Principles. Organization of the Book. 2. Random and Pseudorandom Signal Generation. Purpose. Pseudorandom Sequences. Maximal Length Linear Shift Register Sequences. Randomness Properties of MLSR Sequences. Conclusion. Generating Pseudorandom Signals (Pseudonoise) from Pseudorandom Sequences. First- and Second-Order Statistics of Demodulator Output in Multiple Access Interference. Statistics for QPSK Modulation by Pseudorandom Sequences. Examples. Bound for Bandlimited Spectrum. Error Probability for BPSK or QPSK with Constant Signals in Additive Gaussian Noise and Interference. Appendix 2A: Optimum Receiver Filter for Bandlimited Spectrum. 3. Synchronization of Pseudorandom Signals. Purpose. Acquisition of Pseudorandom Signal Timing. Hypothesis Testing for BPSK Spreading. Hypothesis Testing for QPSK Spreading. Effect of Frequency Error. Additional Degradation When N is Much Less Than One Period. Detection and False Alarm Probabilities. Fixed Signals in Gaussian Noise (L=1). Fixed Signals in Gaussian Noise with Postdetection Integration (L>1). Rayleigh Fading Signals (L>/=1). The Search Procedure and Acquisition Time. Single-Pass Serial Search (Simplified). Single-Pass Serial Search (Complete). Multiple Dwell Serial Search. Time Tracking of Pseudorandom Signals. Early-Late Gate Measurement Statistics. Time Tracking Loop. Carrier Synchronization. Appendix 3A: Likelihood Functions and Probability Expressions. Bayes and Neyman-Pearson Hypothesis Testing. Coherent Reception in Additive White Gaussian Noise. Noncoherent Reception in AWGN for Unfaded Signals. Noncoherent Reception of Multiple Independent Observations of Unfaded Signals in AWGN. Noncoherent Reception of Rayleigh-Faded Signals in AWGN. 4. Modulation and Demodulation of Spread Spectrum Signals in Multipath and Multiple Access Interference. Purpose. Chernoff and Battacharyya Bounds. Bounds for Gaussian Noise Channel. Chernoff Bound for Time-Synchronous Multiple Access Interference with BPSK Spreading. Chernoff Bound for Time-Synchronous Multiple Access Interference with QPSK Spreading. Improving the Chernoff Bound by a Factor of 2. Multipath Propagation: Signal Structure and Exploitation. Pilot-Aided Coherent Multipath Demodulation. Chernoff Bounds on Error Probability for Coherent Demodulation with Known Path Parameters. Rayleigh and Rician Fading Multipath Components. Noncoherent Reception. Quasi-optimum Noncoherent Multipath Reception for M-ary Orthogonal Modulation. Performance Bounds. Search Performance for Noncoherent Orthogonal M-ary Demodulators. Power Measurement and Control for Noncoherent Orthogonal M-ary Demodulators. Power Control Loop Performance. Power Control Implications. Appendix 4A: Chernoff Bound with Imperfect Parameter Estimates. 5. Coding and Interleaving. Purpose. Interleaving to Achieve Diversity. Forward Error Control Coding - Another Means to Exploit Redundancy. Convolutional Code Structure. Maximum Likelihood Decoder - Viterbi Algorithm. Generalization of the Preceding Example. Convolutional Code Performance Evaluation. Error Probability for Tailed-off Block. Bit Error Probability. Generalizations of Error Probability Computation. Catastrophic Codes. Generalization to Arbitrary Memoryless Channels - Coherent and Noncoherent. Error Bounds for Binary-Input, Output-Symmetric Channels with Integer Metrics. A Near-Optimal Class of Codes for Coherent Spread Spectrum Multiple Access. Implementation. Decoder Implementation. Generating Function and Performance. Performance Comparison and Applicability. Orthogonal Convolutional Codes for Noncoherent Demodulation of Rayleigh Fading Signals. Implementation. Performance for L-Path Rayleigh Fading. Conclusions and Caveats. Appendix 5A: Improved Bounds for Symmetric Memoryless Channels and the AWGN Channel. Appendix 5B: Upper Bound on Free Distance of Rate 1/n Convolutional Codes. 6. Capacity, Coverage, and Control of Spread Spectrum Multiple Access Networks. General. Reverse Link Power Control. Multiple Cell Pilot Tracking and Soft Handoff. Other-Cell Interference. Propagation Model. Single-Cell Reception - Hard Handoff. Soft Handoff Reception by the Better of the Two Nearest Cells. Soft Handoff Reception by the Best of Multiple Cells. Cell Coverage Issues with Hard and Soft Handoff. Hard Handoff. Soft Handoff. Erlang Capacity of Reverse Links. Erlang Capacity for Conventional Assigned-Slot Multiple Access. Spread Spectrum Multiple Access Outage - Single Cell and Perfect Power Control. Outage with Multiple-Cell Interference. Outage with Imperfect Power Control. An Approximate Explicit Formula for Capacity with Imperfect Power Control. Designing for Minimum Transmitted Power. Capacity Requirements for Initial Accesses. Erlang Capacity of Forward Links. Forward Link Power Allocation. Soft Handoff Impact on Forward Link. Orthogonal Signals for Same-Cell Users. Interference Reduction with Multisectored and Distributed Antennas. Interference Cancellation. Epilogue. References and Bibliography. Index.
2,795 citations
"Efficient algorithms for broadband ..." refers background in this paper
...Denoting signaling interval j of burst interval 3, in (20) above, by n, thence sensor q's transmission, 0 <q < Q— 1, in signaling interval n, by s, and the channel gain at sensor r, 0 r R — 1, by a'' , the corresponding pulse-matched filtered output is ;') = + vT) (22)...
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TL;DR: Using log-likelihood algebra, it is shown that any decoder can be used which accepts soft inputs-including a priori values-and delivers soft outputs that can be split into three terms: the soft channel and aPriori inputs, and the extrinsic value.
Abstract: Iterative decoding of two-dimensional systematic convolutional codes has been termed "turbo" (de)coding. Using log-likelihood algebra, we show that any decoder can be used which accepts soft inputs-including a priori values-and delivers soft outputs that can be split into three terms: the soft channel and a priori inputs, and the extrinsic value. The extrinsic value is used as an a priori value for the next iteration. Decoding algorithms in the log-likelihood domain are given not only for convolutional codes but also for any linear binary systematic block code. The iteration is controlled by a stop criterion derived from cross entropy, which results in a minimal number of iterations. Optimal and suboptimal decoders with reduced complexity are presented. Simulation results show that very simple component codes are sufficient, block codes are appropriate for high rates and convolutional codes for lower rates less than 2/3. Any combination of block and convolutional component codes is possible. Several interleaving techniques are described. At a bit error rate (BER) of 10/sup -4/ the performance is slightly above or around the bounds given by the cutoff rate for reasonably simple block/convolutional component codes, interleaver sizes less than 1000 and for three to six iterations.
2,632 citations
"Efficient algorithms for broadband ..." refers background in this paper
...Observing that (23) is similar in form to (11), a correspondence may be established between them, so that the transmitted streams q, 0 q Q — 1, relate to the users k, 0 k < K —1, the channel gain vector a, to the scaled spreading vector and the multiple receive-sensor outputs to the chip-rate samples constituting the respective received vectors....
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