John B. Anderson

Bio: John B. Anderson is an academic researcher from Lund University. The author has contributed to research in topics: Convolutional code & Decoding methods. The author has an hindex of 31, co-authored 151 publications receiving 5270 citations. Previous affiliations of John B. Anderson include McMaster University & Rensselaer Polytechnic Institute.

Digital Phase Modulation

Abstract: First text in bandwidth-efficient digital coded communication. Introduced the method of continuous phase modulation (CPM) coding, with full treatment of spectrum, minimum distance, trasnmitters and receivers.

Sequential Coding Algorithms: A Survey and Cost Analysis

Abstract: The cost of a number of sequential coding search algorithms is analyzed in a systematic manner. These algorithms search code trees, and find use in data compression, error correction, and maximum likelihood sequence estimation. The cost function is made up of the size of and number of accesses to storage. It is found that algorithms that utilize sorting are much more expensive to use than those that do not; metric-first searching regimes are less efficient than breadth-first or depth-first regimes. Cost functions are evaluated using experimental data obtained from data compression and error correction studies.

Faster-Than-Nyquist Signaling

Abstract: In this paper, we survey faster-than-Nyquist (FTN) signaling, an extension of ordinary linear modulation in which the usual data bearing pulses are simply sent faster, and consequently are no longer orthogonal. Far from a disadvantage, this innovation can transmit up to twice the bits as ordinary modulation at the same bit energy, spectrum, and error rate. The method is directly applicable to orthogonal frequency division multiplex (OFDM) and quadrature amplitude modulation (QAM) signaling. Performance results for a number of practical systems are presented. FTN signaling raises a number of basic issues in communication theory and practice. The Shannon capacity of the signals is considerably higher.

Constrained Capacities for Faster-Than-Nyquist Signaling

Abstract: This paper deals with capacity computations of faster-than-Nyquist (FTN) signaling. It shows that the capacity of FTN is higher than the orthogonal pulse linear modulation capacity for all pulse shapes except the sinc. FTN signals can in fact achieve the ultimate capacity for the signal power spectral density (PSD). The paper lower- and upper-bounds the FTN capacity under the constraint of finite input alphabet. It is often higher than the capacity for comparable orthogonal pulse systems; sometimes it is superior to all forms of orthogonal signaling with the same PSD.

Multistream Faster than Nyquist Signaling

Abstract: We extend Mazo's concept of faster-than-Nyquist (FTN) signaling to pulse trains that modulate a bank of subcarriers, a method called two dimensional FTN signaling. The signal processing is similar to orthogonal frequency division multiplex(OFDM) transmission but the subchannels are not orthogonal. Despite nonorthogonal pulses and subcarriers, the method achieves the isolated-pulse error performance; it does so in as little as half the bandwidth of ordinary OFDM. Euclidean distance properties are investigated for schemes based on several basic pulses. The best have Gaussian shape. An efficient distance calculation is given. Concatenations of ordinary codes and FTN are introduced. The combination achieves the outer code gain in as little as half the bandwidth. Receivers must work in two dimensions, and several iterative designs are proposed for FTN with outer convolutional coding.

What Will 5G Be

Abstract: What will 5G be? What it will not be is an incremental advance on 4G. The previous four generations of cellular technology have each been a major paradigm shift that has broken backward compatibility. Indeed, 5G will need to be a paradigm shift that includes very high carrier frequencies with massive bandwidths, extreme base station and device densities, and unprecedented numbers of antennas. However, unlike the previous four generations, it will also be highly integrative: tying any new 5G air interface and spectrum together with LTE and WiFi to provide universal high-rate coverage and a seamless user experience. To support this, the core network will also have to reach unprecedented levels of flexibility and intelligence, spectrum regulation will need to be rethought and improved, and energy and cost efficiencies will become even more critical considerations. This paper discusses all of these topics, identifying key challenges for future research and preliminary 5G standardization activities, while providing a comprehensive overview of the current literature, and in particular of the papers appearing in this special issue.

Channel coding with multilevel/phase signals

Abstract: A coding technique is described which improves error performance of synchronous data links without sacrificing data rate or requiring more bandwidth. This is achieved by channel coding with expanded sets of multilevel/phase signals in a manner which increases free Euclidean distance. Soft maximum--likelihood (ML) decoding using the Viterbi algorithm is assumed. Following a discussion of channel capacity, simple hand-designed trellis codes are presented for 8 phase-shift keying (PSK) and 16 quadrature amplitude-shift keying (QASK) modulation. These simple codes achieve coding gains in the order of 3-4 dB. It is then shown that the codes can be interpreted as binary convolutional codes with a mapping of coded bits into channel signals, which we call "mapping by set partitioning." Based on a new distance measure between binary code sequences which efficiently lower-bounds the Euclidean distance between the corresponding channel signal sequences, a search procedure for more powerful codes is developed. Codes with coding gains up to 6 dB are obtained for a variety of multilevel/phase modulation schemes. Simulation results are presented and an example of carrier-phase tracking is discussed.

Capacity Limits of Optical Fiber Networks

Abstract: We describe a method to estimate the capacity limit of fiber-optic communication systems (or ?fiber channels?) based on information theory. This paper is divided into two parts. Part 1 reviews fundamental concepts of digital communications and information theory. We treat digitization and modulation followed by information theory for channels both without and with memory. We provide explicit relationships between the commonly used signal-to-noise ratio and the optical signal-to-noise ratio. We further evaluate the performance of modulation constellations such as quadrature-amplitude modulation, combinations of amplitude-shift keying and phase-shift keying, exotic constellations, and concentric rings for an additive white Gaussian noise channel using coherent detection. Part 2 is devoted specifically to the "fiber channel.'' We review the physical phenomena present in transmission over optical fiber networks, including sources of noise, the need for optical filtering in optically-routed networks, and, most critically, the presence of fiber Kerr nonlinearity. We describe various transmission scenarios and impairment mitigation techniques, and define a fiber channel deemed to be the most relevant for communication over optically-routed networks. We proceed to evaluate a capacity limit estimate for this fiber channel using ring constellations. Several scenarios are considered, including uniform and optimized ring constellations, different fiber dispersion maps, and varying transmission distances. We further present evidences that point to the physical origin of the fiber capacity limitations and provide a comparison of recent record experiments with our capacity limit estimation.

A Viterbi algorithm with soft-decision outputs and its applications

Abstract: The Viterbi algorithm (VA) is modified to deliver the most likely path sequence in a finite-state Markov chain, as well as either the a posteriori probability for each bit or a reliability value. With this reliability indicator the modified VA produces soft decisions to be used in the decoding of outer codes. The inner software output Viterbi algorithm (SOVA) accepts and delivers soft sample values and can be regraded as a device for improving the signal-to-noise ratio, similar to an FM demodulator. Several applications are investigated to show the gain over the conventional hard-deciding VA, including concatenated convolutional codes, concatenation of trellis-coded modulation with convolutional FEC (forward error correcting) codes, and coded Viterbi equalization. For these applications additional gains of 1-4 dB as compared to the classical hard-deciding algorithms were found. For comparison, the more complex symbol-to-symbol MAP, whose optimal a posteriori probabilities can be transformed into soft outputs, was investigated. >

Quantization

Abstract: Quantization is a process that maps a continous or discrete set of values into approximations that belong to a smaller set. Quantization is a lossy: some information about the original data is lost in the process. The key to a successful quantization is therefore the selection of an error criterion – such as entropy and signal-to-noise ratio – and the development of optimal quantizers for this criterion.