This paper introduces a multicarrier communication technique termed orthogonal chirp-frequency division multiplexing (OCFDM). It relies on a unitary transform named discrete modular chirp transform. The proposed OCFDM amalgamates characteristics from the orthogonal frequency division multiplexing (OFDM) and orthogonal chirp division multiplexing (OCDM), partitioning the frequency spectrum into distinct subbands and populating each of them with orthogonal chirps. By deriving the reconstructed signal at the receiver, the paper shows that the normalized signal-to-noise ratio exhibits changes across subbands, allowing the use of resource allocation, for instance, for power allocation or bit-loading. Moreover, it is shown that the OCFDM introduces an additional degree of freedom through its tile geometries, allowing it to be configured to perform in between OFDM and OCDM and, consequently, to offer novel and balanced trade-offs among symbol error rate, data rate, and robustness to interference. Numerical analyses illustrate those trade-offs and confirm the versatility and efficacy of the OCFDM.

The demand for improved satellite communication (SatCom)-based broadband connectivity has led to significant technological advancements, particularly in non-geostationary orbit (NGSO) satellites. The new SatCom systems are expected to have flexible beam footprints with fully adaptable payloads while being energy-efficient. With this in mind, this paper explores using neuromorphic processors (NPs) for the in-orbit receive digital beamforming design. We specifically address the beamsteering challenges of high-speed user mobility by means of beamforming adaptation. Inspired by thinned antenna arrays, the proposed beamforming solutions are based on the least absolute shrinkage and selection operator (LASSO) and are adapted to NPs using spiking locally competitive algorithms, namely S-LCA and S-LCA with graded spikes. The proposed approaches can benefit from the energy efficiency of NPs and further reduce the SatCom payload’s power consumption by turning off as many radio frequency chains as possible without compromising the beamforming performance. Numerical experiments conducted on a real-world aeronautical dataset demonstrate that the proposed NP-oriented solutions offer performance on par with conventional optimization algorithms, with the promise of a lower energy expenditure after future implementation on dedicated hardware.

Low signal strength, resulting from the distance between satellites and the Earth's surface, remains a primary challenge in integrating them into terrestrial network systems. Nevertheless, the current density of satellite constellations presents an opportunity to design cost-effective receivers capable of utilizing diversity combining techniques to overcome this issue. However, any combining technique relies heavily upon time synchronism between the received signals, which until now has presented a limitation for practical applications in satellite communication systems. This paper proposes a procedure for compensating large symbol time misalignment and estimating the remaining fractional one between two DVB-S2X waveforms (from different satellites) overlapped in time, frequency, and under noise-limited scenarios. We propose a fractional-time-misalignment estimator using the data-aided early late gate (ELG) principle. We evaluate the proposed estimator using the Walsh-Hadamard-based pilot sequences available in the DVB-S2X waveforms. At the same time, we present an estimation of large misalignments based on the correlation properties of the start-of-super-frame (SOSF) field (which uses longer Walsh-Hadamard sequences than the pilots). Simulation results show that the proposed synchronization methods provide an unbiased estimation even when the noise power levels match that of the received signals.

Reconfigurable intelligent surfaces (RISs) have emerged as a game-changing technology to improve wireless network performance by intelligently manipulating and customizing the physical propagation environment. Such capability is especially important for the application of smart cities as it increases wireless service offers and quality to end-users. In this paper, we aim to maximize the number of served users in a challenging RIS-aided smart city street by jointly optimizing the multislot scheduling, precoding, and passive RIS-based beamforming design under quality of service and power constraints. Multislot scheduling is introduced in order to benefit from additional time diversity and thus better exploit the available degrees of freedom. The formulated problem is a mixed integer nonlinear programming, which is NP-hard. To solve the problem with affordable complexity, we develop an efficient iterative algorithm based on binary variable relaxation, alternating optimization, and successive convex approximation techniques. Simulation results demonstrate the superiority of the proposed design over the design without RIS and the design without scheduling, especially in the presence of a large number of users. In addition, results illustrate that by introducing a quality of service margin, the proposed design can improve its robustness to outdated channel state information in mobility scenarios.

The space communications industry is challenged to develop a technology that can deliver broadband services to user terminals equipped with miniature antennas, such as handheld devices. One potential solution to establish links with ground users is the deployment of massive antennas in one single spacecraft. However, this is not cost-effective. Aligning with recent NewSpace activities directed toward miniaturization, mass production, and a significant reduction in spacecraft launch costs, an alternative could be distributed beamforming from multiple satellites. In this context, we propose a distributed beamforming modeling technique for wideband signals. We also consider the statistical behavior of the relative geometry of the swarm nodes. The paper assesses the proposed technique via computer simulations, providing interesting results on the beamforming gains in terms of power and the security of the communication against potential eavesdroppers at non-intended pointing angles. This approach paves the way for further exploration of wideband distributed beamforming from satellite swarms in several future communication applications.

Medium Earth orbit (MEO) constellation is an appealing solution between geostationary equatorial orbit (GEO) and lower Earth orbit (LEO) in terms of latency and number of satellites required. On-board processing of digital beam-former in MEO satellites is an efficient solution for achieving wider bandwidth, increased flexibility, and lower latency. Power constraints, however, make it impractical to digitally create thousands of beams at once. In this paper, area-power efficient digital beamformer architectures are proposed considering key metrics of a typical MEO scenario. The proposed efficient digital beamformer is comprised of a sparse-matrix-based user selection, a 2D discrete Fourier transform (DFT)-based digital beam generation, which is implemented by a fast Fourier transform (FFT) algorithm, and a spatial windowing module for selecting the antenna pattern. Furthermore, architectures of digital beam-former using conventional 2D-FFT approach, fully unrolled 2D-FFT, and an area-power efficient twiddle factor (TF) quantized fully unrolled 2D-FFT are proposed. The spatial windowing architecture concerning 10 × 10 radio frequency chains and sparse matrix architecture for user selection is also proposed. The proposed architectures are implemented targeting Virtex ultrascale FPGA and the area-power utilization is reported. It is noticed that more than 50%-reduction in area and power is achieved with the beamformer incorporating the proposed TF quantized fully unrolled 2D-FFT.

This paper proposes a novel resource allocation scheme for optimizing the downlink of a user-centric cell-free massive multiple-input multiple-output (MIMO) system. The proposed approach aims to optimize the number of users served by each access point based on channel conditions while adapting to variable packet error rate and modulation and coding schemes. To enhance the received signal-to-noise plus interference ratio, the authors use a precoding design approach called the local protective partial zero-forcing that categorizes users based on their channel gain. The problem is formulated as a joint optimization of user assignment, resource allocation, and the precoding design. Closed-form expressions for the data rate are derived, and a new algorithm for resource allocation is introduced that outperforms several different scenarios while keeping the computational complexity reasonable. Compared to fixed parameter schemes, the proposed approach provides an optimal selection of the number of users for each access point and has the potential to significantly improve the system throughput, making it a novel and impactful solution for the practical implementation of user-centric cell-free massive MIMO systems.

In this paper, we tackle the problem of joint symbol level precoding (SLP) and reconfigurable intelligent surface (RIS) phase shift design with constellation rotation in the finite block length regime. We aim to increase energy efficiency by minimizing the total transmit power while satisfying the quality of service constraints. The total power consumption can be significantly minimized through the exploitation of multiuser interference by symbol level precoding and by the intelligent manipulation of the propagation environment using reconfigurable intelligent surfaces. In addition, the constellation rotation per user contributes to energy efficiency by aligning the symbol phases of the users, thus improving the utilization of constructive interference. The formulated power minimization problem is non-convex and correspondingly difficult to solve directly. Hence, we employ an alternating optimization algorithm to tackle the joint optimization of SLP and RIS phase shift design. The optimal phase of each user’s constellation rotation is obtained via an exhaustive search algorithm. Through Monte-Carlo simulation results, we demonstrate that the proposed solution yields substantial power minimization as compared to conventional SLP, zero forcing precoding with RIS as well as the benchmark schemes without RIS.

On-board processing of digital beamforming in satellites is an efficient solution for the higher data rates, more capacity, and lower latency, but the available on-board limited power makes it impractical to digitally create thousands of beams at once. A significant portion of the analog hardware in a satellite communications payload can be replaced with highly integrated digital components, which are often more affordable, lighter, smaller, and reprogrammable by employing digital beamforming. In comparison to matrix-by-vector multiplication beamforming, the discrete Fourier transform (DFT) beamformer enables the finer realization of real-time beamformers with reduced circuit complexity and lower power consumption. Fast Fourier transform (FFT) methods can further reduce the computing cost of the DFT computation. Therefore, in this paper, area-power efficient two-dimensional (2D) FFT digital beamforming techniques are analyzed and implemented. The major implementation challenge is to produce N samples per cycle with lower area-power consumption. Fully unrolled 4-bit twiddle factor (TF) quantized FFT is proposed in this regard. The optimization techniques through quantization, truncation, and complex multipliers are thoroughly discussed for efficient implementation. The behavioral and post-route timing simulations are validated, and implementation results like area and power consumption are estimated and compared among conventional , fully unrolled, and the proposed 4-bit TF quantized 2D-FFT.

The key advantage of successive relaying (SR) networks is their ability to mimic the full-duplex (FD) operation with half-duplex (HD) relays. However, the main challenge that comes with such schemes is the associated inter-relay interference (IRI). In this work, we propose a reconfigurable intelligent surface (RIS)-enhanced SR network, where one RIS is deployed near each of the two relay nodes to provide spatial suppression of IRI, and to maximize the gain of desired signals. The resultant max-min optimization problem with joint phase-shift design for both RISs is first tackled via the semidefinite programming (SDP) approach. Then, a lower-complexity solution suitable for real-time implementation is proposed based on particle swarm optimization (PSO). Numerical results demonstrate that even relatively small RISs can provide significant gains in achievable rates of SR networks, and the proposed PSO scheme can achieve a near optimal performance.

In this work, we investigate the decode-and-forward (DF) relay-aided double reconfigurable intelligent surface (RIS)-assisted networks, where the signal is subject to reflections from two RISs before reaching the destination. Different relay-aided network architectures are considered for maximum achievable rate under a total power constraint. Phase optimization for the double-RIS channels is tackled via the alternating optimization and majorization-minimization (MM) schemes. Moreover, closed-form solutions are obtained for each case. Numerical results indicate that the deployment of two relays, one near each RIS, achieves higher rates at low and medium signal-to-noise ratios (SNRs) compared to placing a single relay between the two RISs; while at high SNRs, the latter approach achieves higher rates only if the inter-relay interference for the former case is considerably high.

In this paper, we examine the potential for a reconfigurable intelligent surface (RIS) to be powered by energy harvested from information signals. This feature might be key to reap the benefits of RIS technology’s lower power consumption compared to active relays. We first identify the main RIS power-consuming components and then propose an energy harvesting and power consumption model. Furthermore, we formulate and solve the problem of the optimal RIS placement together with the amplitude and phase response adjustment of its elements in order to maximize the signal-to-noise ratio (SNR) while harvesting sufficient energy for its operation. Finally, numerical results validate the autonomous operation potential and reveal the range of power consumption values that enables it.

Among a plethora of new wireless communication technologies, reconfigurable intelligent surface (RIS) emerges as one of the revolutionary solutions to provide energy- and cost-efficient signal transmissions. RIS is capable of reflecting electromagnetic signals in a controlled manner. In this paper, we jointly design the active beamforming at the base station and passive beamforming at the RIS to maximize the number of served users in a practical Smart City street scenario, subject to quality of service (QoS) and power constraints. The formulated problem belongs to the difficult class of mixed-integer non-convex programming, which is NP-hard. To arrive at a low-complexity solution, we first decompose the original problem into two subproblems and then propose an alternating optimization algorithm based on successive convex approximation (SCA) to solve them in an iterative manner. Simulation results are provided to verify the performance improvement of the proposed algorithm as compared to baseline schemes.

The satellite component is recognized as a promising solution to complement and extend the coverage of future Internet of Things (IoT) terrestrial networks (TNs). In this context, a study item to integrate satellites into narrowband-IoT (NB-IoT) systems has been approved within the 3rd Generation Partnership Project (3GPP) standardization body. However, as NB-IoT systems were initially conceived for TNs, their basic design principles and operation might require some key modifications when incorporating the satellite component. These changes in NB-IoT systems, therefore, need to be carefully implemented in order to guarantee a seamless integration of both TN and nonterrestrial network (NTN) for a global coverage. This article addresses this adaptation for the random access (RA) step in NB-IoT systems, which is in fact the most challenging aspect in the NTN context, for it deals with multiuser time-frequency synchronization and timing advance for data scheduling. In particular, we propose an RA technique which is robust to typical satellite channel impairments, including long delays, significant Doppler effects, and wide beams, without requiring any modification to the current NB-IoT RA waveform. Performance evaluations demonstrate the proposal’s capability of addressing different NTN configurations recently defined by 3GPP for the 5G new radio system.

This work addresses the issue of interference generated by co-channel users in downlink multi-antenna multicarrier systems with frequency-packed faster-than-Nyquist (FTN) signaling. The resulting interference stems from an aggressive strategy for enhancing the throughput via frequency reuse across different users and the squeezing of signals in the time-frequency plane beyond the Nyquist limit. The spectral efficiency is proved to be increasing with the frequency packing and FTN acceleration factors. The lower bound for the FTN sampling period that guarantees information losslesness is derived as a function of the transmitting-filter roll-off factor, the frequency-packing factor, and the number of subcarriers. Space-time-frequency symbol-level precoders (SLPs) that trade off constructive and destructive interblock interference (IBI) at the single-antenna user terminals are proposed. Redundant elements are added as guard interval to cope with vestigial destructive IBI effects. The proposals can handle channels with delay spread longer than the multicarrier-symbol duration. The receiver architecture is simple, for it does not require digital multicarrier demodulation. Simulations indicate that the proposed SLP outperforms zero-forcing precoding and achieves a target balance between spectral and energy efficiencies by controlling the amount of added redundancy from zero (full IBI) to half (destructive IBI-free) the group delay of the equivalent channel.

This work proposes an efficient batch-based implementation for kernel regression on graphs (KRG) using random Fourier features (RFF) and a low-complexity online implementation. Kernel regression has proven to be an efficient learning tool in the graph signal processing framework. However, it suffers from poor scalability inherent to kernel methods. We employ RFF to overcome this issue and derive a batch-based KRG whose model size is independent of the training sample size. We then combine it with a stochastic gradient-descent approach to propose an online algorithm for KRG, namely the stochastic-gradient KRG (SGKRG). We also derive sufficient conditions for convergence in the mean sense of the online algorithms. We validate the performance of the proposed algorithms through numerical experiments using both synthesized and real data. Results show that the proposed batch-based implementation can match the performance of conventional KRG while having reduced complexity. Moreover, the online implementations effectively learn the target model and achieve competitive performance compared to the batch implementations.

This paper proposes a time-frequency (TF) domain technique for detecting the presence of automatic dependent surveillance-broadcast (ADS-B) interference signals in radio astronomy applications. The proposed technique uses a priori knowledge about the ADS-B signal's frequency information and compares it with the received signal's spectrogram time slices via the cosine similarity function. In the presence of ADS-B signals, the similarity levels are higher, whereas in their absence the levels are lower. Hence, the proposed approach exploits this to detect the presence of such signals. Simulation results using signals from the Parkes radio telescope show the efficacy of the proposed method in detecting the presence of ADS-B signals when compared with other classic detectors.

In this work, we consider an eavesdropping scenario in wireless multi-user (MU) multiple-input single-output (MISO) systems with channel coding in the presence of a multi-antenna eavesdropper (Eve). In this setting, we exploit machine learning (ML) tools to design a hard decoding scheme by using precoded pilot symbols as training data. Within this, we propose an ML framework for a multi-antenna hard decoder that allows an Eve to decode the transmitted message with decent accuracy. We show that MU-MISO systems are vulnerable to such an attack when conventional block-level precoding is used. To counteract this attack, we propose a novel symbol-level precoding scheme that increases the bit-error rate at Eve by obstructing the learning process. Simulation results validate both the ML-based attack as well as the countermeasure, and show that the gain in security is achieved without affecting the performance at the intended users.

This paper tackles the problem of both multi-user and intersymbol interference stemming from co-channel users transmitting at a faster- than-Nyquist (FTN) rate in multi-antenna downlink transmissions. We propose a framework for redundant block-based symbol-level precoders enabling the trade-off between constructive and destructive multi-user and interblock interference (IBI) effects at the singleantenna user terminals. Redundant elements are added as guard interval to handle IBI destructive effects. It is shown that, within this framework, accelerating the transmissions via FTN signaling improves the error-free spectral efficiency, up to a certain acceleration factor beyond which the transmitted information cannot be perfectly recovered by linear filtering followed by sampling. Simulation results corroborate that the proposed spatiotemporal symbol-level precoding can change the amount of added redundancy from zero (full IBI) to half (IBI-free) the equivalent channel order, so as to achieve a target balance between spectral and energy efficiencies.

Set-membership affine projection (SM-AP) adaptive filters have been increasingly employed in the context of online data-selective learning. A key aspect for their good performance in terms of both convergence speed and steady-state mean-squared error is the choice of the so-called constraint vector. Optimal constraint vectors were recently proposed relying on convex optimization tools, which might sometimes lead to prohibitive computational burden. This paper proposes a convex combination of simpler constraint vectors whose performance approaches the optimal solution closely, utilizing much fewer computations. Some illustrative examples confirm that the sub-optimal solution follows the accomplishments of the optimal one.

This paper aims at studying the achievable data rate of discrete cosine transform (DCT)-based multicarrier modulation (MCM) systems. To this end, a general formulation is presented for the full transmission/reception process of data in Type-II even DCT and Type-IV even DCT-based systems. This paper focuses on the use of symmetric extension and zero padding as redundancy methods. Furthermore, three cases related to the channel order and the length of the redundancy are studied. In the first case, the channel order is less than or equal to the length of the redundancy. In the second and third cases, the channel order is greater than the length of the redundancy; the interference caused by the channel impulse response is calculated, and theoretical expressions for their powers are derived. These expressions allow studying the achievable data rate of the DCT-based MCM systems, besides enabling the comparison with the conventional MCM based on the discrete Fourier transform.

The wide availability of mobile devices with embedded microphones opens up opportunities for new applications based on acoustic sensor localization (ASL). Among them, this paper highlights mobile device self-localization relying exclusively on acoustic signals, but with previous knowledge of reference signals and source positions. The problem of finding the sensor position is stated as a function of estimated times-of-flight (TOFs) or time-differences-of-flight (TDOFs) from the sound sources to the target microphone, and the main practical issues involved in TOF estimation are discussed. Least-squares ASL solutions are introduced, followed by other strategies inspired by sound source localization solutions: steered-response power, which improves localization accuracy, and a new region-based search, which alleviates complexity. A set of complementary techniques for further improvement of TOF/TDOF estimates are reviewed: sliding windows, matching pursuit, and TOF selection. The paper proceeds with proposing a novel ASL method that combines most of the previous material, whose performance is assessed in a real-world example: in a typical lecture room, the method achieves accuracy better than 20 cm.

There are two main families of algorithms that tackle the problem of sparse system identification: the proportionate family and the one that employs sparsity-promoting penalty functions. Recently, a new approach was proposed with the l0-IPAPA algorithm, which combines proportionate updates with sparsity-promoting penalties. This paper proposes some modifications to the l0-IPAPA algorithm in order to decrease its computational complexity while preserving its good convergence properties. Among these modifications, the inclusion of a dataselection mechanism provides promising results. Some enlightening simulation results are provided in order to verify and compare the performance of the proposed algorithms.

In licensed shared access (LSA), incumbents (i.e. primary users) control the access to their spectrum bands through temporarily leasing to a third party (called LSA licensees) in a specific spatial region. Such shared access takes place based on spectrum availability information acquired from LSA repositories. In this context, this paper presents the following contributions: 1) The use of a cognitive cooperative framework based on measurement-capable devices (MCDs) in order to provide valuable information both to generate and to update radio-environment maps for LSA repositories, thus enabling more dynamic spectrum-access opportunities. 2) A new distributed and cooperative scheme among cognitive MCDs to detect and classify incumbents and LSA licensees in the LSA network. The proposal suggests a clustering strategy for cooperative spectrum sensing when neighboring MCDs observe different licensed users-whether incumbents or LSA licensees. The clustering considers the differences in underlying hidden Markov models associated with the detection of distinct licensed users. 3) Theoretical expressions for the overall probability of detection and false alarm are derived for hard cooperation strategies that may include failed MCDs in the clustering process.

Self-localization of smart portable devices serves as foundation for several novel applications. This work proposes a set of algorithms that enable a mobile device to passively determine its position relative to a known reference with centimeter precision, based exclusively on the capture of acoustic signals emitted by controlled sources around it. The proposed techniques tackle typical practical issues such as reverberation, unknown speed of sound, line-of-sight obstruction, clock skew, and the need for asynchronous operation. After their theoretical developments and off-line simulations, the methods are assessed as real-time applications embedded into off-the-shelf mobile devices operating in real scenarios. When line of sight is available, position estimation errors are at most 4 cm using recorded signals.

This letter proposes an efficient method based on the steered-response power (SRP) technique for sound source localization using microphone arrays: the refined volumetric SRP (RV-SRP). By deploying a sparser volumetric grid, the RV-SRP achieves a significant reduction of the computational complexity without sacrificing the accuracy of location estimates. In addition, a refinement step improves on the compromise between complexity and accuracy. Experiments conducted in both simulated- and real-data scenarios show that the RV-SRP outperforms state-of-the-art methods in accuracy with lower computational cost.

The increasing scarcity in the available spectrum for wireless communication is one of the current bottlenecks impairing further deployment of services and coverage. The proper exploitation of white spaces in the radio spectrum requires fast, robust, and accurate methods for their detection. This paper proposes a new strategy to detect adaptively white spaces in the radio spectrum. Such strategy works in cognitive radio (CR) networks whose nodes perform spectrum sensing based on energy detection in a cooperative way or not. The main novelty of the proposal is the use of a cost-function that depends upon a single parameter which, by itself, contains the aggregate information about the presence or absence of primary users. The detection of white spaces based on this parameter is able to improve significantly the deflection coefficient associated with the detector, as compared to other state-of-the-art algorithms. In fact, simulation results show that the proposed algorithm outperforms by far other competing algorithms. For example, our proposal can yield a probability of miss-detection 20 times smaller than that of an optimal soft-combiner solution in a cooperative setup with a predefined probability of false alarm of 0.1.

We analyze two algorithms, viz. the affine projection algorithm for sparse system identification (APA-SSI) and the quasi APA-SSI (QAPA-SSI), regarding their stability and steady-state mean-squared error (MSE). These algorithms exploit the sparsity of the involved signals through an approximation of the l0norm. Such approach yields faster convergence and reduced steady-state MSE, as compared to algorithms that do not take the sparse nature of the signals into account. In addition, modeling sparsity via such approximation has been consistently verified to be superior to the widely used l1norm in several scenarios. In this paper, we show how to properly set the parameters of the two aforementioned algorithms in order to guarantee convergence, and we derive closed-form theoretical expressions for their steady-state MSE. A key conclusion from the proposed analysis is that the MSE of these two algorithms is a monotonically decreasing function of the sparsity degree. Simulation results are used to validate the theoretical findings.

We propose two versions of affine projection (AP) algorithms tailored for sparse system identification (SSI). Contrary to most adaptive filtering algorithms devised for SSI, which are based on the l1norm, the proposed algorithms rely on homotopic l0norm minimization, which has proven to yield better results in some practical contexts. The first proposal is obtained by direct minimization of the AP cost function with a penalty function based on the l0norm of the coefficient vector, whereas the second algorithm is a simplified version of the first proposal. Simulation results are presented in order to evaluate the performance of the proposed algorithms considering three different homotopies to the l0norm as well as competing algorithms.

This article describes a physical-layer simulator for both uplink and downlink connections of LTE systems, whose performances are assessed by simulating standardized environments. The simulator is compliant with Release 9 of LTE standard and it is publicly available for educational purposes, allowing students and researchers to test the performance of Signal Processing and Digital Communications techniques in an easy-to-use MATLAB framework. Users may benefit from implemented features such as channel estimation using different demodulation reference signals, channel coding, equalization, multiple access schemes in which multiple cells are employed, as well as diversity, spatial multiplexing, and beamforming transmissions. As an example, we evaluate the impact on the performance of an uplink connection due to inaccuracy in channel estimation and multi-user interference. In addition, we include the evaluation of using diversity and spatial multiplexing transmissions on downlink connections.

Multicarrier and single-carrier block-based transceivers with minimum redundancy have proved to be an alternative to classical orthogonal frequency-division multiplex (OFDM) and single-carrier with frequency-domain equalization (SC-FD) systems. In general, these minimum-redundancy transceivers have superior throughput performance than OFDM and SC-FD systems, requiring the same asymptotic complexity, viz. O(M log2M), for M data symbols. However, the previous proposals of such transceivers rely on the channel-state information (CSI) assumption. In addition, they also assume that the equalizer was previously designed, focusing on the equalization problem only. The aim of this work is to present some theoretical results related to the design of the equalizers that employ minimum redundancy, without assuming CSI. The key result of this paper is to show that it is possible to design those equalizers based on pilot information and using fast-converging iterative algorithms that require O(M log2M) operations per iteration.

Recent works have proposed practical zero-forcing (ZF) and linear minimum mean squared-error (LMMSE) solutions for fixed and memoryless block-based transceivers with minimum redundancy, using only half the amount of redundancy employed in standard systems. Their equalization processes require only O(M log2M) operations. However, it may be difficult to apply LMMSE equalizers with minimum redundancy in some practical systems, given their higher number of operations. This letter proposes novel suboptimal LMMSE equalizers with minimum redundancy that require the same amount of computations of ZF equalizers, with a mild decrease in the throughput performance when compared to the optimal LMMSE solution.

This paper proposes a new semi-blind data-selective affine projection algorithm for channel equalization. This algorithm generalizes the concept of set-membership filtering to be used when the transmitted symbols belong to an M-PSK constellation. Moreover, the criterion for updating the estimate is based on error measure considered relevant to the constellation. The proposed algorithm incorporates appropriate constraints to the set-membership affine projection (SM-AP) algorithm resulting in a generalized adaptive algorithm for M-PSK equalization (SM-AP-CMA) which results in fast convergence while keeping a reduced number of coefficient updates. Simulation results using a UMTS channel model are presented in order to confirm the improved features attained by the proposed algorithm.