Cyclic division algebras: a tool for space-time coding

Multiple antennas at both the transmitter and receiver ends of a wireless digital transmission channel may increase both data rate and reliability. Reliable high rate transmission over such channels can only be achieved through Space–Time coding. Rank and determinant code design criteria have been proposed to enhance diversity and coding gain. The special case of full-diversity criterion requires that the difference of any two distinct codewords has full rank. Extensive work has been done on Space–Time coding, aiming at finding fully diverse codes with high rate. Division algebras have been proposed as a new tool for constructing Space–Time codes, since they are non-commutative algebras that naturally yield linear fully diverse codes. Their algebraic properties can thus be further exploited to improve the design of good codes. The aim of this work is to provide a tutorial introduction to the algebraic tools involved in the design of codes based on cyclic division algebras. The different design criteria involved will be illustrated, including the constellation shaping, the information lossless property, the non-vanishing determinant property, and the diversity multiplexing trade-off. The final target is to give the complete mathematical background underlying the construction of the Golden code and the other Perfect Space–Time block codes

Distributed Quasi-Orthogonal Space-Time coding in wireless cooperative relay networks

Cooperative diversity provides a new paradigm in robust wireless re- lay networks that leverages Space-Time (ST) processing techniques to combat the effects of fading. Distributing the encoding over multiple relays that potentially observe uncorrelated channels to a destination terminal has demonstrated promising results in extending range, data- rates and transmit power utilization. Specifically, Space Time Block Codes (STBCs) based on orthogonal designs have proven extremely popular at exploiting spatial diversity through simple distributed pro- cessing without channel knowledge at the relaying terminals. This thesis aims at extending further the extensive design and analysis in relay networks based on orthogonal designs in the context of Quasi- Orthogonal Space Time Block Codes (QOSTBCs). The characterization of Quasi-Orthogonal MIMO channels for cooper- ative networks is performed under Ergodic and Non-Ergodic channel conditions. Specific to cooperative diversity, the sub-channels are as- sumed to observe different shadowing conditions as opposed to the traditional co-located communication system. Under Ergodic chan- nel assumptions novel closed-form solutions for cooperative channel capacity under the constraint of distributed-QOSTBC processing are presented. This analysis is extended to yield closed-form approx- imate expressions and their utility is verified through simulations. The effective use of partial feedback to orthogonalize the QOSTBC is examined and significant gains under specific channel conditions are demonstrated. Distributed systems cooperating over the network introduce chal- lenges in synchronization. Without extensive network management it is difficult to synchronize all the nodes participating in the relaying between source and destination terminals. Based on QOSTBC tech- niques simple encoding strategies are introduced that provide compa- rable throughput to schemes under synchronous conditions with neg- ligible overhead in processing throughout the protocol. Both mutli- carrier and single-carrier schemes are developed to enable the flexi- bility to limit Peak-to-Average-Power-Ratio (PAPR) and reduce the Radio Frequency (RF) requirements of the relaying terminals. The insights gained in asynchronous design in flat-fading cooperative channels are then extended to broadband networks over frequency- selective channels where the novel application of QOSTBCs are used in distributed-Space-Time-Frequency (STF) coding. Specifically, cod- ing schemes are presented that extract both spatial and mutli-path diversity offered by the cooperative Multiple-Input Multiple-Output (MIMO) channel. To provide maximum flexibility the proposed schemes are adapted to facilitate both Decode-and-Forward (DF) and Amplify- and-Forward (AF) relaying. In-depth Pairwise-Error-Probability (PEP) analysis provides distinct design specifications which tailor the distributed- STF code to maximize the diversity and coding gain offered under the DF and AF protocols. Numerical simulation are used extensively to confirm the validity of the proposed cooperative schemes. The analytical and numerical re- sults demonstrate the effective use of QOSTBC over orthogonal tech- niques in a wide range of channel conditions

Generalized distributive law for ML decoding of STBCs

The Generalized Distributive Law (GDL) is a message passing algorithm which can efficiently solve a certain class of computational problems, and includes as special cases the Viterbi's algorithm, the BCJR algorithm, the Fast-Fourier Transform, Turbo and LDPC decoding algorithms. In this paper GDL based maximum-likelihood (ML) decoding of Space-Time Block Codes (STBCs) is introduced and a sufficient condition for an STBC to admit low GDL decoding complexity is given. Fast-decoding and multigroup decoding are the two algorithms used in the literature to ML decode STBCs with low complexity. An algorithm which exploits the advantages of both these two is called Conditional ML (CML) decoding. It is shown in this paper that the GDL decoding complexity of any STBC is upper bounded by its CML decoding complexity, and that there exist codes for which the GDL complexity is strictly less than the CML complexity. Explicit examples of two such families of STBCs is given in this paper. Thus the CML is in general suboptimal in reducing the ML decoding complexity of a code, and one should design codes with low GDL complexity rather than low CML complexity

Generalized distributive law for ML decoding of STBCs: further results

The problem of designing good Space-Time Block Codes (STBCs) with low maximum-likelihood (ML) decoding complexity has gathered much attention in the literature. All the known low ML decoding complexity techniques utilize the same approach of exploiting either the multigroup decodable or the fast-decodable (conditionally multigroup decodable) structure of a code. We refer to this well known technique of decoding STBCs as Conditional ML (CML) decoding. In [1], we introduced a framework to construct ML decoders for STBCs based on the Generalized Distributive Law (GDL) and the Factor-graph based Sum-Product Algorithm, and showed that for two specific families of STBCs, the Toepltiz codes and the Overlapped Alamouti Codes (OACs), the GDL based ML decoders have strictly less complexity than the CML decoders. In this paper, we introduce a `traceback' step to the GDL decoding algorithm of STBCs, which enables roughly 4 times reduction in the complexity of the GDL decoders proposed in [1]. Utilizing this complexity reduction from `traceback', we then show that for any STBC (not just the Toeplitz and Overlapped Alamouti Codes), the GDL decoding complexity is strictly less than the CML decoding complexity. For instance, for any STBC obtained from Cyclic Division Algebras that is not multigroup or conditionally multigroup decodable, the GDL decoder provides approximately 12 times reduction in complexity compared to the CML decoder. Similarly, for the Golden code, which is conditionally multigroup decodable, the GDL decoder is only about half as complex as the CML decoder

Generalized Distributive Law for ML Decoding of STBCs: Further Results

Abstract—The problem of designing good Space-Time Block Codes (STBCs) with low maximum-likelihood (ML) decoding complexity has gathered much attention in the literature. All the known low ML decoding complexity techniques utilize the same approach of exploiting either the multigroup decodable or the fast-decodable (conditionally multigroup decodable) structure of a code. We refer to this well known technique of decoding STBCs as Conditional ML (CML) decoding. In [1], we introduced a framework to construct ML decoders for STBCs based on the Generalized Distributive Law (GDL) and the Factor-graph based Sum-Product Algorithm, and showed that for two specific families of STBCs, the Toepltiz codes and the Overlapped Alamouti Codes (OACs), the GDL based ML decoders have strictly less complexity than the CML decoders. In this paper, we introduce a ‘traceback ’ step to the GDL decoding algorithm of STBCs, which enables roughly 4 times reduction in the complexity of the GDL decoders proposed in [1]. Utilizing this complexity reduction from ‘traceback’, we then show that for any STBC (not just the Toeplitz and Overlapped Alamouti Codes), the GDL decoding complexity is strictly less than the CML decoding complexity. For instance, for any STBC obtained from Cyclic Division Algebras that is not multigroup or conditionally multigroup decodable, the GDL decoder provides approximately 12 times reduction in complexity compared to the CML decoder. Similarly, for the Golden code, which is conditionally multigroup decodable, the GDL decoder is only about half as complex as the CML decoder. I

Generalized distributive law for ML decoding of space-time block codes

The problem of designing good Space-Time Block Codes (STBCs) with low maximum-likelihood (ML) decoding complexity has gathered much attention in the literature. All the known low ML decoding complexity techniques utilize the same approach of exploiting either the multigroup decodable or the fast-decodable (conditionally multigroup decodable) structure of a code. We refer to this well known technique of decoding STBCs as Conditional ML (CML) decoding. In this paper we introduce a new framework to construct ML decoders for STBCs based on the Generalized Distributive Law (GDL) and the Factor-graph based Sum-Product Algorithm. We say that an STBC is fast GDL decodable if the order of GDL decoding complexity of the code is strictly less than Mλ, where λ is the number of independent symbols in the STBC, and M is the constellation size. We give sufficient conditions for an STBC to admit fast GDL decoding, and show that both multigroup and conditionally multigroup decodable codes are fast GDL decodable. For any STBC, whether fast GDL decodable or not, we show that the GDL decoding complexity is strictly less than the CML decoding complexity. For instance, for any STBC obtained from Cyclic Division Algebras which is not multigroup or conditionally multigroup decodable, the GDL decoder provides about 12 times reduction in complexity compared to the CML decoder. Similarly, for the Golden code, which is conditionally multigroup decodable, the GDL decoder is only half as complex as the CML decoder

Generalized distributive law for ML decoding of space-time block codes

The problem of designing good space-time block codes (STBCs) with low maximum-likelihood (ML) decoding complexity has gathered much attention in the literature. All the known low ML decoding complexity techniques utilize the same approach of exploiting either the multigroup decodable or the fast-decodable (conditionally multigroup decodable) structure of a code. We refer to this well-known technique of decoding STBCs as conditional ML (CML) decoding. In this paper, we introduce a new framework to construct ML decoders for STBCs based on the generalized distributive law (GDL) and the factor-graph-based sum-product algorithm. We say that an STBC is fast GDL decodable if the order of GDL decoding complexity of the code, with respect to the constellation size, is strictly less than M-lambda, where lambda is the number of independent symbols in the STBC. We give sufficient conditions for an STBC to admit fast GDL decoding, and show that both multigroup and conditionally multigroup decodable codes are fast GDL decodable. For any STBC, whether fast GDL decodable or not, we show that the GDL decoding complexity is strictly less than the CML decoding complexity. For instance, for any STBC obtained from cyclic division algebras which is not multigroup or conditionally multigroup decodable, the GDL decoder provides about 12 times reduction in complexity compared to the CML decoder. Similarly, for the Golden code, which is conditionally multigroup decodable, the GDL decoder is only half as complex as the CML decoder