Single carrier digital terrestrial television broadcasting

\u3cp\u3eA transmission system for digital terrestrial television broadcasting has been designed. This system is based on the European cable system but uses stronger error correction and better equalization. The stronger error correction is a concatenation of Reed Solomon coding RS[204,188,17] and convolutional coding with R \u3csub\u3econv\u3c/sub\u3e =1/2, 2/3, 3/4, 5/6 and 7/8. The algorithm which is used for convolutional decoding is the Viterbi algorithm. To provide the Viterbi decoder with soft decision information, every symbol bit will be expanded with two soft decision (reliability) bits. The modulation scheme of the terrestrial transmission system is 64-QAM square root raised cosine filtered with a roll off factor α = 0.15. The mapping of the symbols into the 64-QAM constellation is a Gray-mapping over the complete I,Q-plane. In this paper the performances of the terrestrial transmission system are simulated and analyzed.\u3c/p\u3

Quasi-synchronous code-division multiple access with high-order modulation

\u3cp\u3eCode-division multiple access (CDMA) is a multiplexing technique where a number of users simultaneously access a transmission channel by modulating and spreading their signals with preassigned codewords. This paper studies the performance of CDMA signals with orthogonal (Walsh-Hadamard) codewords and synchronization errors smaller than the chip time. Two high-order modulation techniques, M-level quadrature amplitude modulation (M-QAM) and M-level phase-shift keying (M-PSK) are compared with respect to bit-error rate (BER). The results are especially important for the return channel of cable TV networks and summarized as follows. Synchronization errors between transmitters lead to interference noise, whereas synchronization errors between the transmitter and the receiver lead to a decreased amplitude of the received user signal. Both effects have significant impact on the system performance. Closed expressions are obtained for the BER of a CDMA signal with M-PSK and M-QAM with a given maximum synchronization error. The higher the modulation order, the more sensitive the system gets for synchronization errors. The BER is highly dependent on the assigned codewords out of the Walsh-Hadamard code set. The BER performance of M-QAM outperforms that of M-PSK.\u3c/p\u3

Two-dimensional block-based reception for differentially encoded OFDM systems : a study on improved reception techniques for digital audio broadcasting systems

Digital audio broadcast (DAB), DAB+ and Terrestrial-Digital Multimedia Broadcasting (T-DMB) systems use multi-carrier modulation (MCM). The principle of MCM in the DAB-family is based on orthogonal frequency division multiplexing (OFDM), for which every subcarrier is modulated by p 4 differentially encoded quaternary phase shift keying (DE-QPSK). In DAB systems convolutional codes and interleaving are used to enable DAB receivers to perform error correction. The objective of the work, described in the thesis, is to improve reception techniques for DAB, DAB+, and T-DMB systems. In the thesis, two-dimensional (2D) block-based reception for differentially encoded OFDM systems is investigated. The blocks are based on the time and frequency dimension. Commonly used DAB receivers perform non-coherent two-symbol differential detection (2SDD) with soft-decision Viterbi decoding. It is well-known that 2SDD can be improved if the detection is based on more than two received symbols as, e.g., in noncoherent multi-symbol differential detection (MSDD). For improving the performance of the demodulation procedures of DAB-like streams, demodulation based on 2D blocks of received symbols with a decomposed demodulation trellis is proposed in the thesis. Peleg and Shamai [58] demonstrated that iterative techniques could increase the performance of the demodulation procedures of DE-QPSK streams even further. In the thesis, their approach is generalized to the 2D setting where again the decomposed demodulation trellis is used. In this way a problem connected to the small lengths of the trellises for each subcarrier is solved. The application of these iterative decoding techniques in DAB receivers is only feasible if their complexity can be drastically reduced. A significant complexity reduction is obtained by iterating only in a dominant sub-trellis of the decomposed demodulation trellis. In this way, a real-time and bit-true DAB receiver based on iterative decoding techniques is realized In Chapter 2, simulationmodels are introduced. These models are later applied to evaluate the proposed reception methods. The Additive White Gaussian Noise (AWGN) channel model with an input power constraint and the channel model for M-level PSK are first discussed. In addition, the TU-6 (Typical Urban 6 taps) channel model defined in COST-207 [1] is introduced. This channelmodel is commonly used to assess the performance of DAB, DAB+, or T-DMB transmission. Finally, the basic elements of a DAB transmitter and a standard receiver are described. In Chapter 3 of the thesis, the state of the art in non-iterative detection and decoding techniques for DE-QPSK streams with convolutional encoding is described. First, as a reference, coherent detection of DE-QPSK with soft-decision Viterbi decoding is studied. Then it is demonstrated that 2SDD of DE-QPSK with soft-decision Viterbi decoding degrades the performance. This non-coherent differential detection scheme can be improved by, for example, MSDD, which is a maximum likelihood procedure for finding a block of information symbols after having observed a block of received symbols. For large numbers of observations, the performance of MSDD approaches the performance of coherent detection of DE-QPSK. Since reference symbols (pilots) are lacking for DAB systems, detection based on observing multiple received symbols is a technique that could lead to reception improvement for DAB receivers. By applying this technique, as will be shown later, a DAB receiver approaches the performance of a receiver that performs coherent detection of p 4 -DE-QPSK with soft-decision Viterbi decoding. In Chapter 4, a-posteriori symbol probabilities and log-likelihood ratios (LLRs) for coherently detected p 4 -DE-QPSK are studied. It is demonstrated, as an extension to the results known in the literature, that an approximation of maximum a-posteriori (MAP) symbol detection, based on selecting dominant exponentials, leads to MAP sequence detection. To improve the performance towards MAP symbol detection, a better approximation is proposed. This approximation relies on piecewise-linear fitting of the logarithm of the hyperbolic cosine and results in a performance quite close to that of MAP symbol detection. For the coded case, where the symbols are produced by convolutional encoding and Gray mapping, the LLRs are investigated. Again a simple approximation based on selecting dominant exponentials and an improved approximation relying on piecewise-linear fits, is proposed. As in the uncoded case, the improved approximation gives a performance quite close to ideal. These improved approximations are also of interest for DAB systems, as will be shown later, if 2D and trellis-based detection is considered as a reception technique. Peleg et al. [56][57][58] and Chen et al. [18] demonstrated that iterative decoding techniques developed by Benedetto et al. [9] for serially concatenated convolutional codes lead to good results for the concatenation of convolutional and differential encoding, also referred to as Turbo-DPSK. In Chapter 5 the iterative decoding procedures corresponding to these serially concatenated codes are explained. In this chapter also parallel concatenated systems, turbo-codes, first described by Berrou et al. [11] are considered. The iterative decoding procedures for the serially concatenated codes as well as for the turbo-codes are based on modified versions of the BCJR algorithm [4]. The approach taken in Chapter 5 to explain these iterative decoding procedures, is similar to the approach Gallager [32] followed to investigate iterative procedures for decoding low-density parity-check (LDPC) codes. This way of explaining iterative decoding procedures for the serially concatenated codes as well as for the turbo-codes does not appear in the literature. It is well-known that iterative (turbo) decoding procedures approach channel capacity, e.g., in the AWGN setting. For that reason, in Chapter 6 and Chapter 7, iterative decoding techniques for DAB-like streams are studied. At the time that the DAB standard was proposed, the results of Berrou et al. [11] on turbocodes were not available. As a consequence, it is not a common practice to use iterations in DAB receivers. In Chapter 6, motivated by encouraging results on Turbo-DPSK, trellis decoding and iterative techniques for DAB receivers are investigated. Specifically, the usage of 2D-blocks and trellis decomposition in decoding is considered. Each 2D-block consists of a number of adjacent subcarriers of a number of subsequent OFDM symbols. Focussing on 2D-blocks was motivated by the fact that the channel coherence-time is typically limited to a small number of OFDM symbols, and that DAB-transmissions use time-multiplexing of services, which limits the number of OFDM symbols in a codeword. Extension in the subcarrier direction is required then to get reliable phase estimates. The trellis-decomposition method allows for an estimation of the unknown channel phase, since this phase relates to sub-trellises. A-posteriori sub-trellis probabilities are determined, and these probabilities are used for weighting the a-posteriori symbol probabilities resulting from all the sub-trellises. Alternatively, a dominant sub-trellis can be determined from the a-posteriori sub-trellis probabilities and the a-posteriori symbol probabilities corresponding to this dominant sub-trellis can be used. This dominant sub-trellis approach results in a significant complexity reduction, which is the subject of Chapter 7. In the first part of Chapter 7, complexity reduction of the inner decoder is investigated. This complexity reduction is realized by choosing, based on a-posteriori sub-trellis probabilities, in two different ways a dominant sub-trellis. In the first approach, a method is investigated that is based on finding, at the start of a new iteration, the dominant subtrellis first and then do the forward-backward processing for demodulation only in this dominant sub-trellis. The second approach involves choosing the dominant sub-trellis only once, before starting with the iterations. In the second part of Chapter 7, an implementation of a MAP channel-phase estimator based on the second dominant sub-trellis approach is described. In addition, an implementation of a channel-gain estimator based on the received symbols within a 2D-block is discussed. Finally, a real-time and bit-true DAB-receiver is sketched. This DAB receiver operates according to the proposed 2Dblock- based iterative decoding procedure within a dominant sub-trellis obtained by the second method. The performance improvements of this DAB receiver are evaluated for various numbers of iterations, block-sizes, and Doppler-frequencies. The main conclusions can be found in Chapter 8. For the non-iterative 2D-case, investigations show that the performance of non-coherent detection based on trellis-decomposition is very close to the performance of coherent detection of DE-QPSK. The gain of 2D trellis-decomposition is modest compared to the standard 2SDD technique. Iterative 2D procedures result in a significantly larger gain. In this context, it needs to be emphasized that part of this gain comes from the 2D-processing. The dominant sub-trellis approach appears to be crucial for achieving an acceptable complexity reduction

Two-dimensional iterative processing for dab receivers based on trellis-decomposition

\u3cp\u3eWe investigate iterative trellis decoding techniques for DAB, with the objective of gaining from processing 2D-blocks in an OFDM scheme, that is, blocks based on the time and frequency dimension, and from trellis decomposition. Trellis-decomposition methods allow us to estimate the unknown channel phase since this phase relates to the sub-trellises. We will determine a-posteriori sub-trellis probabilities, and use these probabilities for weighting the a-posteriori symbol probabilities resulting from all the sub-trellises. Alternatively we can determine a dominant sub-trellis and use the a-posteriori symbol probabilities corresponding to this dominant sub-trellis. This dominant sub-trellis approach results in a significant complexity reduction. We will investigate both iterative and non-iterative methods. The advantage of non-iterative methods is that their forwardbackward procedures are extremely simple; however, also their gain of 0.7dB, relative to two-symbol differential detection (2SDD) at a BER of 10-4, is modest. Iterative procedures lead to the significantly larger gain of 3.7dB at a BER of 10-4 for five iterations, where a part of this gain comes from 2D processing. Simulations of our iterative approach applied to the TU-6 (COST207) channel show that we get an improvement of 2.4dB at a Doppler frequency of 10Hz.\u3c/p\u3

On constellation shaping for short block lengths

Gaussian channel inputs are required to achieve the capacity of additive white Gaussian noise (AWGN) channels. Equivalently, the n-dimensional constellation boundary must be an n-sphere. In this work, constellation shaping is discussed for short block lengths. Two different approaches are considered: Sphere shaping and constant composition distribution matching (CCDM). It is shown that both achieve the maximum rate and generate Maxwell-Boltzmann (MB) distributed inputs. However sphere shaping achieves this maximum faster than CCDM and performs more efficiently in the short block length regime. This is shown by computing the finite-length rate losses. Then the analysis is justified by numerical simulations employing low-density parity-check (LDPC) codes of the IEEE 802.11 standard

Constellation shaping for IEEE 802.11

A constellation shaping scheme is proposed. The motivation is to decrease the required transmit power for a specified spectral efficiency. Instead of imposing a non-uniform distribution on the constellation or using nonuniformly spaced symbols, a sphere constraint is employed on the n-dimensional signal space. An efficient algorithm called enumerative amplitude shaping is given to find and index all signal points in the sphere. A comparison with a prominent probabilistic shaping algorithm is provided. The enumerative approach achieves the target rate more efficiently for small block lengths. To introduce error correction, the convolutional encoder used in IEEE Std 802.11 is combined with the shaper in a novel way. Instead of utilizing a larger constellation in combination with shaping, a higher code rate is used by puncturing. Gains up to 1.61 dB are observed for the rates 3 to 6 bits/2-D with 64- and 256-QAM schemes in AWGN channels. The contribution of puncturing in these gains is discussed

Partial enumerative sphere shaping

\u3cp\u3eThe dependency between the Gaussianity of the input distribution for the additive white Gaussian noise (AWGN) channel and the gap-to-capacity is discussed. We show that a set of particular approximations to the Maxwell- Boltzmann (MB) distribution virtually closes most of the shaping gap. We relate these symbol-level distributions to bit-level distributions, and demonstrate that they correspond to keeping some of the amplitude bit-levels uniform and independent of the others. Then we propose partial enumerative sphere shaping (P-ESS) to realize such distributions in the probabilistic amplitude shaping (PAS) framework. Simulations over the AWGN channel exhibit that shaping 2 amplitude bits of 16-ASK have almost the same performance as shaping 3 bits, which is 1.3 dB more power- efficient than uniform signaling at a rate of 3 bit/symbol. In this way, required storage and computational complexity of shaping are reduced by factors of 6 and 3, respectively.\u3c/p\u3

Enumerative sphere shaping for wireless communications with short packets

Probabilistic amplitude shaping (PAS) combines an outer shaping layer with an inner, systematic forward error correction (FEC) layer to close the shaping gap. Proposed for PAS, constant composition distribution matching (CCDM) produces amplitude sequences with a fixed empirical distribution. We show that CCDM suffers from high rate losses for small block lengths, and we propose to use Enumerative Sphere Shaping (ESS) instead. ESS minimizes the rate loss at any block length. Furthermore, we discuss the computational complexity of ESS and demonstrate that it is significantly smaller than shell mapping (SM), which is another method to perform sphere shaping. We then study the choice of design parameters for PAS. Following Wachsmann et al., we show that for a given constellation and target rate, there is an optimum balance between the FEC code rate and the entropy of the Maxwell-Boltzmann distribution that minimizes the gap-to-capacity. Moreover, we demonstrate how to utilize the non-systematic convolutional code from IEEE 802.11 in PAS. Simulations over the additive white Gaussian noise (AWGN) and frequency-selective channels exhibit that ESS is up to 1.6 and 0.7 dB more energy-efficient than uniform signaling at block lengths as small as 96 symbols, respectively, with convolutional and low-density parity-check (LDPC) codes