Christos Mavrokefalidis

Low-Power Leading-Zero Counting and Anticipation Logic for High-Speed Floating Point Units

In this paper, a new leading-zero counter (or detector) is presented. New boolean relations for the bits of the leading-zero count are derived that allow their computation to be performed using standard carry-lookahead techniques. Using the proposed approach various design choices can be explored and different circuit topologies can be derived for the design of the leading-zero counting unit. The new circuits can be efficiently implemented either in static or in dynamic logic and require significantly less energy per operation compared to the already known architectures. The integration of the proposed leading-zero counter with the leading-zero anticipation logic is analyzed and the most efficient combination is identified. Finally, a simple yet efficient technique for handling the error of the leading-zero anticipation logic is also presented. The energy-delay behavior of the proposed circuits has been investigated using static and dynamic CMOS implementations in a 130-nm CMOS technology.

Training design in single relay AF cooperative systems with correlated channels

In this paper, training design is studied for a single relay amplify-and-forward cooperative network. The taps of the frequency selective channels are assumed to be correlated and OFDM modulation is used for transmission. Based on the least squares (LS) criterion, conditions for pilot tone positioning and a number of power allocation schemes are described. Analytical closed-form power allocation expressions are provided for both the source and the relay. The theoretical results are fully corroborated by simulations.

Sorter Based Permutation Units for Media-Enhanced Microprocessors

Single or multibit subword permutations are useful in many multimedia and cryptographic applications. Several specialized instructions have been proposed to handle the required data rearrangements. In this paper, we examine the hardware implementation of the powerful permutation instruction group (GRP). The design of the proposed permutation unit is based on the functionality of sorting networks. Two variants of the sorter-based GRP unit are introduced and analyzed and their energy-delay behavior is investigated using static CMOS implementations in a 130-nm CMOS technology.

Optimal training design and placement for channel estimation in cooperative networks

In this paper, optimal training design and placement are studied for channel estimation in a frequency selective, single relay, amplify and forward cooperative system. Using a two-phase transmission protocol, the power distribution between the phases, among the pilot tones as well as the positioning of pilot tones within the transmitted OFDM symbols are examined. The LMMSE and two variants of the LS estimator are analyzed and the optimal training design and placement strategies are derived. Closed-form expressions are derived for the minimum achievable MSE in each case, which are precisely verified through simulations.

Efficient adaptive equalization of doubly dispersive channels in MIMO-FBMC/OQAM systems

The problem of adaptively equalizing doubly dispersive MIMO channels for FBMC/OQAM systems is studied in this paper. The challenges in this type of multicarrier systems include their intrinsic self-interference and the need to cope with time- and frequency-selective subchannels in realistic propagation conditions. An efficient and numerically stable algorithm is adopted, relying on a decision feedback structure that implements BLAST ordering for the input signals recovery. The ability of this algorithm to address the above challenges has been demonstrated. The focus of this paper is on reducing the needs of this equalizer in training information. A channel estimate-based (re-)initialization scheme is developed and shown to be quite effective in lowering the training overhead, at an affordable additional cost in complexity. For the sake of comparison, the MIMO-OFDM problem is also studied. Simulation results for practical scenarios demonstrate the effectiveness of the proposed approach.

Optimal training design for channel estimation in OFDM/OQAM cooperative systems

Channel estimation in OFDM/OQAM-based cooperative systems is considered in this paper. Cooperation is based on a single AF relay, and a well established two-phase protocol is adopted for its operation. The problem of optimally designing the preambles in the two phases is investigated for LS channel estimation, where optimality is in the sense of minimum MSE subject to a transmit energy constraint. Optimal conditions are derived for the energy allocations and positions of the pilot symbols. Equalization is also considered, where a relay-induced interference term is identified at the destination node. A simple cancellation procedure is proposed and evaluated. Simulation results are reported that corroborate the analysis and provide a comparison with the corresponding CP-OFDM system.

Supervised energy disaggregation using dictionary — based modelling of appliance states

In this paper, a supervised energy disaggregation method is proposed. The appliances that are monitored, are modelled by multi-state finite state machines. Each state of an appliance is described by exactly one vector of power consumptions from a carefully designed set of such vectors (called atoms), that comprise a dictionary. The latter is constructed during a training phase, where it is assumed that individual power consumption signals are available. A clustering algorithm is applied on overlapping patches extracted from the training signals to select a fixed number of representatives, i.e., the atoms of the dictionary. Moreover, in the training phase, an appropriate state transition probabilities matrix is constructed. During the operation phase, where the actual disaggregation task is performed, a trellis, with a reduced number of transitions, is used for the acquisition of the disaggregated power consumption signals per appliance. Numerical results, using the REDD dataset, are provided, in order to demonstrate the effectiveness of the proposed method.

Adapt-Align-Combine for diffusion-based distributed dictionary learning

Diffusion-based distributed dictionary learning methods are studied in this work. We consider the classical mixed l2-l1 cost function, that employs an l2 representation error term and an l1 sparsity promoting regularizer. First, we observe that this cost function suffers from an inherent permutation ambiguity. This ambiguity may deteriorate significantly the performance of diffusion-based schemes, since the involved combination step may combine different atoms even when the same atoms exist at all dictionaries. Thus, we propose to align the dictionaries prior to the combination step. Furthermore, we define a new problem, that we call the node-specific distributed dictionary learning problem. The proposed Adapt-Align-Combine algorithm enjoys increased convergence rate as compared with a scheme that does not align the dictionaries prior to the combination. Simulation results support our findings.

Preamble-based channel estimation in single-relay networks using FBMC/OQAM

Preamble-based channel estimation in filter bank-based multicarrier (FBMC) systems using offset quadrature amplitude modulation (OQAM) has been extensively studied in the last few years, due to the many advantages this modulation scheme can offer over cyclic prefix (CP)-based orthogonal frequency division multiplexing (OFDM) and in view of the interesting challenges posed on the channel estimator by the interference effect inherent in such an FBMC system. In particular, preambles of short duration and of both the block (full) and comb (sparse) types were designed so as to minimize the channel estimation mean squared error (MSE) subject to a given transmit energy. In the light of the important role that relay-based cooperative networks are expected to play in future wireless communication systems, it is of interest to consider FBMC/OQAM, and in particular questions associated to preamble-based channel estimation, in such a context as well. The goal of this paper is to address these problems and come up with optimal solutions that extend existing results in a single relay-based cooperative network. Both low and medium frequency selective channels are considered. In addition to optimal preamble and estimator design, the equalization/detection task is studied, shedding light to a relay-generated interference effect and proposing a simple way to come over it. The reported simulation results corroborate the analysis and reveal interesting behavior with respect to channel frequency selectivity and signal-to-noise ratio.

Semi-blind channel estimation schemes based on a cooperative form of the cross relation criterion

In this paper, two channel estimation schemes are derived for specific cooperative scenarios. Both schemes are based on the cross-relation criterion that has been extensively studied in the (semi-) blind literature. As shown in the paper, in a cooperative system, the channel estimator can be constructed in a natural way either by fractionally-spaced or symbol-spaced samples. We investigate the performance of these two schemes using semi-analytic arguments accompanied by corresponding experimental results.