Designing energy-efficient beamformers is a major challenge to the deployment of battery-powered devices transmitting short data packets at millimeter-wave frequencies. In general, directly maximizing energy efficiency will not meet the stringent reliability requirements of certain Internet-of-Things applications. We focus on designing analog beamformers in a device-to-device setting under short packets, a finite encoder set, and imperfect channel knowledge. To guarantee the required error decoding performance, a two-step procedure is proposed such that beamformers are designed to minimize the power consumption subject to reliability constraints under worst-case channel estimation errors, and then the channel encoder maximizing the energy efficiency is selected. The proposed design enjoys a moderate loss under practical low-resolution phase shifters, and exhibits robustness to inaccurate channel estimators, which is crucial to the tradeoff between energy efficiency and error decoding performance.

Reducing the large sidelobes of multicarrier signals is crucial to prevent adjacent channel interference. Spectral precoding is an effective approach toward this goal, at the expense of throughput loss due to precoder redundancy; thus, it is of interest to explore alternative precoder designs with improved performance at lower redundancies. We present a novel precoder which minimizes radiated power within a user-selectable frequency region. The structure of the precoding matrix is chosen to allow efficient mitigation of in-band distortion at the receiver by means of iterative and successive interference cancellation, while completely avoiding distortion to protected and pilot subcarriers. By exploiting the low-rank properties of constituent blocks, computational complexity can be significantly reduced with little impact on sidelobe reduction. Simulation results show the benefits of the proposed design, which is particularly effective in redundancy-limited settings targeting high spectral efficiency.

This study presents a wind retrieval simulator of a floating Doppler Wind Lidar (DWL) with 6 Degrees of Freedom (DoF) motion. The simulator considers a continuous-wave conical-scanning floating DWL which retrieves the wind vector from 50 radial velocities per scan. The 6 DoF motion framework as well as the most important buoy motion equations are summarized. Rotational and translational motion effects over HWS measurements are studied parametrically.

An optimum energy allocation for massive user networks operating under iterative successive interference cancellation (I-SIC) is derived in this work. We analyze an uplink setting where a large network of cost-effective low-power users employ a practical transmission scheme consisting of the same channel encoder followed by spread spectrum modulation. Variational calculus (VC) is utilized to derive the asymptotic large-user energy distribution that favors I-SIC's performance, in terms of spectral efficiency (SE), when at most two decoding attempts per user are performed. The capacity region under random spreading and SIC operating in the infinite blocklength regime is evaluated and contrasted with the maximized SE using short packets.

A multiple access (MA) optimization technique for massive low-rate direct-sequence spread spectrum communications is analyzed in this work. A dense network of users transmitting at the same rate to a common central node under channel-aware energy allocation is evaluated. At reception, successive interference cancellation (SIC) aided by channel decoding is adopted. Our contribution focuses on wireless scenarios involving a vast number of users for which the provided user-asymptotic model holds. Variational calculus (VC) is employed to derive the energy allocation function that, via user-power imbalance, maximizes the network spectral efficiency (SE) when perfect channel state information at transmission (CSIT) is available and both average and maximum per-user energy constraints are set. Monte Carlo simulations at chip-level of a SIC receiver using a real decoder assess the proposed optimization method.

This paper studies the throughput maximization of a dense multiple access network of low-rate subscribers that share the same practical Forward Error Correction (FEC) code and modulation scheme, and transmit to a central node that implements a Successive Soft Interference Cancellation (soft SIC) strategy in order to mitigate Multiple Access Interference (MAI). In the user-asymptotic case, we make use of Variational Calculus (VC) tools to derive, in terms of the Packet Error Rate (PER) of the shared encoder and the Residual Energy (RE) from imperfect cancellation, the optimum energy profile that maximizes the network spectral efficiency, when a sum power constraint at the SIC input is enforced. Comparative performance analyses using a representative encoder are carried out. Simulation results show the benefit of the adopted soft SIC scheme in front of other SIC strategies, obtaining relevant throughput gains under high traffic loads.

We address the problem of spectrum sensing in cognitive radios (CRs) when the secondary user (SU) is equipped with a multiantenna receiver. We consider scenarios with correlation between the received channels at different antennas and unequal per-antenna noise variances to accommodate calibration errors. First, we derive the exact as well as the asymptotic performance of the genie-aided (benchmark) detector with perfect knowledge of the antenna correlation coefficients, the primary user (PU) signal power and the noise covariance matrix. Then, we consider the sensing problem in which the SU is non-cognizant of the per-antenna noise variances, the PU signal power and the correlation of channel gains, starting with a specific treatment of the two-antenna case. For a general multiantenna receiver, we propose combining the derived test statistics among all antenna pairs. The related optimization problem to obtain the optimum combination weights is analyzed, which requires the analytical performance characterization of the constituent two-antenna detector. Thus, we compute the exact performance of the proposed detector in a special case (a particular case of the Hadamard ratio test) in terms of its detection and false alarm probabilities. Performance analyses are verified with simulations, showing that the proposed detector outperforms several recently-proposed multiantenna detectors for CR in the scenarios considered.

A two-scale ad hoc wireless network is envisaged in which the interference power generated in long-range connections is recycled by the surrounding wireless nodes in order to support their own short-range communications. We prove that the network energy efficiency is independent of the transmitters range and, therefore, that long-range transmitters can supply the power required for short-range connectivity without degrading the overall network energy efficiency. As an example, we have studied the feasibility of a short-range multiple-access return-link powered by the interference harvested from neighboring long-range connections.

In this paper, we address the problem of multiantenna spectrum sensing in Cognitive Radios (CRs) by considering the correlation between the received channels at different antennas. First, we derive the optimum genie-aided detector which assumes perfect knowledge of the antenna correlation coefficients, Primary User (PU) signal power and noise variance. This is used as a benchmark for comparing with more practical detectors when some or all of these parameters are unknown to the Secondary User (SU). Two scenarios are considered: 1) the antenna correlation coefficients and PU signal power are unknown to the SU; 2) the antenna correlation coefficients, PU signal power and noise variance are unknown to the SU. To derive sub-optimum detectors for these two scenarios, we apply the Rao test, an asymptotically equivalent test to the Generalized Likelihood Ratio Test (GLRT) that does not require the Maximum Likelihood (ML) estimates of unknown parameters. Additionally, we calculate analytical approximations to the detection and false-alarm probabilities of the proposed detectors and verify them with Monte-Carlo simulations. The simulation results show that these new detectors outperform several recently proposed detectors for CR using multiple antennas.

Dynamic Spectrum Access (DSA) is receiving considerable interest as a means to improve spectral usage in licensed bands. In order to avoid interference to licensed users, spectrum sensing has emerged as an enabling technology for DSA. The requirements for spectrum sensors are stringent, as licensed user detection must be performed reliably at low signal-to-noise ratios (SNR). Sensing performance can be improved by exploiting signal features not present in the background noise. These approaches result in tradeoffs among performance and robustness to departures from the signal model. We consider second-order signal features and develop detectors exploiting spatial (by using multiple antennas) as well as temporal signal correlation, taking advantage of the fact that the power spectrum of the primary signal at each antenna can be known up to a complex scalar representing the unknown propagation channel. A low-SNR Generalized Likelihood Ratio approach is adopted in order to overcome this uncertainty, resulting in different tests intimately related to familiar diversity combining techniques. The performance of the proposed detectors is analyzed and tested in different scenarios.

Recent technological advances in single photon detectors have paved the way for laser-based interplanetary communications, targeting data rates higher than those achievable in the radio part of the spectrum. The development of photon counting receiver algorithms specific to this channel is required. In particular, maximum likelihood (ML) detection must incorporate the typical Bose-Einstein distribution of background noise photons. We overcome the unwieldy expression of the ML detection metric by deriving an asymptotically tight approximation, converging in variance, which is linear in the data and whose coefficients can be explicitly calculated.

OFDM communications require extreme linearity of high power amplifiers (HPA) to avoid intercarrier interference (ICI) and subsequent degradation of the BER and quality of service (QoS). Amplitude (AM/AM) and phase (AM/PM) amplifier nonlinearities can be compensated in a number of ways. Nevertheless, digital adaptive schemes based on least squares optimization criteria need estimate the delay introduced by the analog chains in the generation of the error signal. A sufficiently precise HPA input-output time delay estimate (TDE) is necessary before attempting pre-distortion of the amplifier itself. This paper introduces statistical criteria for this initial acquisition stage.

Pre-distortion algorithms for the compensation of AM/AM and AM/PM non-linearities of high power amplifiers (HPA) require a previous estimation of the delay introduced by the analog chains at the input and output of the amplifier. This paper presents a coarse time delay estimation (TDE) algorithm to within one-sample resolution based on a conditional entropy principle. The delay estimate is computed via one dimensional search on the proposed TDE spectrum.

Efficient use of High Power Amplifiers (HP A) in communications systems, where power consumption is of primary concern, can be achieved taking maximum advantage of the whole available dynamic range. This means that it is desirable to operate the HPA as near saturation as possible most of the time. Thus, any efficiency improvement can only be obtained at the cost of high AM/AM and AM/PM distortion due to the non-linearity of the HPA. This becomes more critical when non-constant amplitude modulations, such as OFDM and MQAM, are applied. In this work we present an efficient pre-distortion scheme based on statistical properties between sets of input and output signal samples at the HPA. The estimation of the cumulative distribution functions (CDF) is obtained by means of order statistics and then applied in the pre-distortion design, providing a self-adaptive compensation of the non-linearity with a very low computational cost.

In this paper we derive a fast blind coarse frequency offset estimator for OFDM systems in frequency selective channels. The estimator takes advantage of the strong structure of the OFDM signal. In packet oriented systems, using the method of Schmild and Cox (see IEEE Trans. on Commun. vol.45, no.12, p.1613-21, Dec. 1997) for timing and fine frequency offset estimation, it is possible to obtain very accurate synchronization using only one known OFDM symbol transmitted at the beginning of the burst. In systems using a continuous stream of data this estimator can solve the ambiguity of multiples of the subcarrier spacing that produces a fine estimator based on cyclic extensions.

In this paper we present an architecture to relax the critical cycle associated with the feedback operations in IIR filtering when high sampling frequencies exceed the computation bandwidth of digital arithmetics. Its complexity, evaluated in terms of multiplications per output sample per pole, equals that of the canonic IIR recursion and provides considerable savings in comparison to existing techniques such as clustered and scattered look ahead. The procedure is shown to yield unconditionally stable implementations.

A fast algorithm based on a butterfly structure is presented that demultiplexes the symbols of a particular type of MC-MA (multi-carrier multiple-access) modulation previously proposed for indoor radio communications. A special transform is used to packetize the different symbols on the down-link such that the code sequences associated with the different symbols are transmitted synchronously at the base station. The input to the FFT bins is defined as a symbol-dependent combination of Walsh-Hadamard codes. We derive a fast architecture that can combine the common redundancy found in the Walsh-Hadamard and the inverse Fourier transform. In the direct implementation, the receiver would compute the IFFT (as in OFDM) before the Walsh-Hadamard transform. The proposed algorithm evaluates both transforms in a single step. A general expression for the butterfly weights is derived and the savings in computational complexity in terms of the frame length is evaluated.

A description of the signal processing stage of an on-board integrated VLSI multi-carrier demodulator at the demultiplexing level is presented, along with a description of the optimization procedure that has been developed for the signal processing functions. The varying adjacent carrier interference and channel noise distribution are modeled to provide the best performing demultiplexing scheme under the given carrier distribution with minimum complexity.

A cost function for adaptive filtering is proposed, based on the statistical reference criterion. The purpose of this criterion is to enforce a given probability density function at the output of a system. The only aprioristical knowledge utilized is that of the target PDF. This makes it particularly suitable for blind adaptation in the context of arrays in contrast to temporal or spatial reference techniques. Applications that are foreseen are: blind channel equalization, adaptive beamforming for single or multiple signals and source separation. The main goal is blind signal recovery in unfavourable wanted signal-to-inference power ratios.

In this paper we derive the optimum linear algorithm for the separation by an array of a given number of cyclostationary signals of the type encountered in digital communications. Spatially and temporally uncorrelated noise is considered at the input to each sensor. It is proved that the optimum signal separator can be expressed as a matrix transfer function in the z-domain. It is intended that this algorithm be used as a benchmark to test the performance of adaptive schemes. Therefore, bounds on the noise estimation covariance are obtained assuming perfect knowledge of the number of sources as well as of their timing and DOA.>