The radio stripe (RS) concept has emerged as a viable and practical implementation of the cell-free (CF) massive multiple-input-multiple-output (mMIMO) paradigm, where a sequence of distributed access points (APs) sharing a common stripe cooperatively serve user-equipments (UEs) over both uplink (UL) and downlink channels. In such networks, active eavesdroppers can launch pilot-spoofing attacks, fully informed of the legitimate pilot sequences and AP-selection scheme, to contaminate UL channel estimation (CE), thereby degrading overall throughput and leaking confidential data to the adversary. To counteract spoofing, we analyze a bi-objective AP-selection framework that jointly maximizes (1) the network sum spectral efficiency (SE) and (2) the secrecy spectral efficiency (SSE) of the attacked UEs. Our results traces the Pareto frontier of sum SE versus attacked UE SE, revealing the inherent trade-offs between throughput and security.

The ability to provide reliable data rates across the several intended coverage areas has made massive multiple-input multiple-output (m-MIMO) cell-free (CF) a crucial technology for future sixth-generation (6G) systems. However, CF networks also introduce new vulnerabilities, specifically in terms of security and network integrity. For that, to complement the conventional cryptographic algorithms, physical layer security (PLS) can be an effective strategy for acquiring essential wireless network information in order to create important authentication mechanisms against spoofing attacks. To this end, we propose leveraging the wireless channel and the access point selection (APS) allocation schemes as authentication methods to prevent network attacks that attempt to impersonate legitimate user communications. Our results show that monitoring these parameters provides valuable insights about the network performance and the impact under active eavesdropping influence. Consequently, this approach enables a statistical detection of illegitimate spoofing attempts through alarms, which can be further reinforced by upper-layer authentication protocols to enhance the security performance of wireless networks.

The radio stripe (RS) concept has been emerging as a practical implementation of the cell-free (CF) massive multiple-input-multiple-output (MMIMO) network where all access points (APs) that perform the signal processing required to serve the users’ equipment (UEs) share a common stripe. In such a system, an uplink (UL) power optimization must be performed to increase the system’s spectral and power efficiencies (SE and PE). In this work, we develop a set of multi-objective UL power optimization models and algorithms based on the differential evolution (DE) meta-heuristic (MH) considering the optimal sequential linear processing (OSLP) and maximum ratio combining (MRC) detection schemes. The bi-objective approaches aim at deriving nondominated solutions in models to: 1) maximize the max-min fairness (MMF) and max-sum rate (MSR) metrics; 2) maximize the MMF and minimize transmission power (MTP); 3) maximize the MSR and minimize the MTP. A tri-objective approach is also developed to maximize the MMF and MSR and minimize the MTP. Solutions displaying interesting trade-offs are obtained with the proposed models and algorithmic approaches, compared with the case where the UEs employ full power in the UL. Results show that improved SE and PE can be attained with the proposed bi-objective and tri-objective optimization approaches.

The massive adoption of IoT devices, the recent developments in the efficiency of AI systems, and the increase of edge computational power, accelerated the deployment of edge AI systems. The implementation of these systems through the use of low-power embedded devices scattered across the edges of a network allows for reduced latency and cost, compared to traditional cloud-based AI computing systems. As a result of the low-complexity AI models and the available low-power embedded systems on the market, this paper provides a comparative study on the inference performance of convolutional neural networks for different edge devices, by exploiting low-power GPUs and dedicated AI hardware. The benchmark results were able to achieve 864 inferences/s for the Jetson AGX Xavier board on a pre-trained SqueezeNet, while reaching a high power efficiency of 52.6 inferences/s per Watt. For the dedicated Movidius neural stick, the system requires only 1.5 W for processing 24.2 inferences/s.

The current applications’ requirement for higher data rates typically results in an increase in the signal’s Peak-to-Average Power Ratio (PAPR), which harms the transmitter’s Power Amplifier (PA) efficiency. The Magnitude Modulation (MM) technique was developed to address this issue in bandlimited single-carrier (SC) communications, leaving a trade-off between the attainable PAPR reduction and an increase in the bit error rate (BER) upon MM signal demodulation. Because Deep Learning (DL) algorithms are proving to be applicable in the majority of fields, two models based on current state-of-the-art Artificial Neural Networks (ANNs) are proposed in this paper to replace conventional demodulators. It is also shown that DL demodulators can leverage on the symbols’ memory that is embedded within the transmitted MM signal, caused by the MM procedure. Both systems’ BER performance is analyzed and compared, demonstrating that it is possible to mitigate the MM effect on demodulation.

One of the most promising practical implementations of the cell-free massive MIMO network is based on the radio stripe system, which considers that the access points are sequentially connected, sharing the same fronthaul link while serving all user equipment using the same time-frequency resources. This paper considers an uplink power allocation technique that aims to enhance the network spectral efficiency (SE), modelled as an optimization constrained problem explicitly considering the max-min fairness case. Instead of relying on exact globally optimal approaches, with high computational complexity, three different alternative meta-heuristic (MH) based on simulated annealing, differential evolution and particle swarm optimization approaches are explored. The SE performances along with the total run times of the MH approaches are analyzed and compared with the bisection method.

One of the most promising technologies to be employed in beyond 5G networks is based on the concept of cell-free (CF) massive MIMO, in which a predetermined set of access points (APs) jointly cooperate in the data transmission and reception to/from the user equipment (UE). In order to efficiently manage radio resources, the CF central processing unit implement uplink power control policies. These policies aim to optimize a given network utility function. In this paper, we investigate the max-min fairness optimization problem, in which the spectral efficiency performance of the UE with the worst channel conditions is prioritized, taking into account a per-UE power constraint and assuming linear maximum ratio combining at APs. Existing solutions have typically relied on second-order cone programming with convex approximations, which exhibit high computational complexity and scalability issues. Therefore, meta-heuristics (MHs) are explored as alternative optimization schemes, capable of providing (near)-optimal solutions with reasonable computational effort. Three MH approaches with different operation principles are compared. Numerical results show that the differential evolution algorithm exhibits the best trade-off between solution quality and run time, being able to reach (near)-optimal solutions faster than the bisection approach while coping with the scalability issues of the geometric programming-based algorithm.

This work explores the utilization of low-power heterogeneous devices for parallelizing the compute-intensive hyper-spectral and multispectral image compression CCSDS-123 entropy encoders. Multithread processing allows for the near-optimal system's bandwidth to be exploited increasing the system overall performance. The experimental platform consists of a low-power Jetson TX2 GPU equipped with an ARM Cortex-A57 and Denver 2 host processors, reporting more than 1552 Mb/s and, more importantly, 315 Mb/s/W, all running under a global 5 W power budget, which makes it a good candidate for onboard image compression.

The Time-Interleaved Block Windowed Burst Orthogonal Frequency Division Multiplexing (TIBWB-OFDM) waveform enables to achieve greater confinement in the signal spectrum. This improves with the increase of the window roll-off since the out of band (OOB) radiation drops. However, the TIBWB-OFDM block duration also increases, which leads to a decrease in the transmission rate for a given band, limiting, therefore, the spectral efficiency gains of the system. In this paper, we propose an alternative method for TIBWB-OFDM symbol construction by allowing a partial overlap between adjacent windowed OFDM symbols that compose it. This improves the spectral efficiency at the expense of introduced interference between the signal’s blocks, that deteriorates the Bit Error Rate (BER) performance. To avoid these BER performance losses, we propose a modified receiver design that includes the cancellation of block overlapping effects in the time domain. The proposed receiver is shown to be able to achieve excellent performance, even with the strong overlapping effects of a TIBWB-OFDM system with high spectral efficiency.

In this work, the authors consider phase shift keying (PSK)‐type signals with very compacted spectrum and low envelope fluctuations obtained through the use of magnitude modulation (MM) techniques and stringent roll‐off pulse shaping filtering. The analytical bit error rate (BER) of PSK‐type signals combined with MM is assessed, for both flat fading and time‐dispersive channel scenarios. The authors use a Gaussian model to approximate the statistical distribution of the distortion error introduced by MM to enable a closed‐form evaluation of the BER. The proposed expression depends only on the constellation order and the Kullback–Leibler divergence of the MM factors' probability density function from the Gaussian one, and it was found to be very accurate.

This study considers massive multiple‐input multiple‐output (m‐MIMO) schemes employing orthogonal frequency division multiplexing (OFDM) or single‐carrier with frequency‐domain equalisation (SC‐FDE) modulation, concerning an uplink transmission. The authors study the performance and complexity for both conventional zero forcing (ZF) and minimum mean squared error (MMSE) (which require the inversion of channel matrix) and iterative frequency domain equalisation receivers based on equal gain combining (EGC) and maximum ratio combining (MRC) concepts, that do not require matrix inversions. It is shown that, although matrix inversions are generally the more complex operations for relatively balanced systems (i.e. with similar number of transmit and receive antennas), this is not generally true for systems where the channel matrix is ‘very tall’ (i.e. much more receive than transmit antennas), at least in terms of the number of multiplications and sums. This means that the advantage on a reduced complexity of MRC/EGC‐based equalisers with respect to ZF/MMSE is, in fact, limited. However, the performance advantages combined with the possibility of parallel receiver implementations, make those techniques particularly interesting for m‐MIMO schemes, either employing OFDM or SC‐FDE modulations.

Time-Interleaved Block Windowed Burst-OFDM (TIBWB-OFDM) was recently proposed as a new waveform candidate for future 5G systems and beyond. In this paper, it is shown that this technique is, in fact, easily combined with MIMO systems, offering promising gains in terms of power and spectral efficiency. In addition, since TIBWB-OFDM is built on the mature OFDM technique, it enables fast forward implementation of a frequency domain equalization with inter-stream MIMO suppression, maintaining the simplicity of the equalizer at an affordable cost. Bearing the complexity requirement in mind, three equalization techniques are studied, namely low-complexity iterative maximum rate combining (MRC) and equal gain combining (EGC) receivers, that do not require costly matrix operations, and an iterative block decision feedback equalizer (IB-DFE) receiver that, although requiring matrix inversions, can provide excellent results by taking advantage of single-carrier type nature of the TIBWB-OFDM block. We show that, on the one hand, IB-DFE receiver exhibits superior gains and can be successfully employed in scenarios with moderate number of antenna-elements, where its complexity can be actually manageable. On the other hand, it is shown that iterative MRC and EGC offers excellent performances for high values of receiving antenna-elements.

This paper proposes a way of turning the equalisation of multiple- input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) signals simpler through the employment of low complexity iterative frequency domain equalisation (FDE) receivers based on equal gain combining (EGC) and maximum ratio combining (MRC) concepts, that do not require computing the inverses of high dimensional channel matrices. Performance results show that MIMO- OFDM schemes employing this type of techniques can achieve excellent bit error rate (BER) performances over severe time- dispersive channels and, therefore, approach or even outperform receivers that require inverting channel matrices, such as zero forcing (ZF) and minimum mean squared error (MMSE), after a few iterations.

Massive MIMO (m-MIMO) schemes operating at mmWave bands are being proposed to be used in next generation wireless systems, bringing new challenges both in terms of detection techniques and the definition of channel models. In this paper, frequency-domain iterative detection schemes which do not require matrix inversions and are suitable for m-MIMO systems employing Single- Carrier with Frequency-Domain Equalization (SC- FDE) modulations are considered. These techniques are evaluated using a general clustered channel model for multilayer m-MIMO systems. Performance results show that these techniques can approach the Matched Filter Bound (MFB) with just a few iterations, even with significant correlation between different antenna elements.

This paper considers the use of the linear amplification with nonlinear components (LINC) technique for the power amplification of spectrally compact offset quadrature phase shift keying (OQPSK) signals allowing the use of highly efficient, low cost, and strongly nonlinear high power amplifiers (HPAs). However, the performance of the LINC signal separation and power combining procedures decreases with the rise of the signal's peak-to-average power ratio (PAPR). A new ring-type magnitude modulation (RMM) method is proposed for OQPSK signals that limits both its maximum and minimum complex envelope excursions avoiding zero crossings, without spreading the transmitted signal's spectrum. The performance results show that band-limited OQPSK signals whose envelope has low fluctuations produce LINC components with a narrower spectrum, with a considerable impact on the LINC transmitter regardless of the type of combiner chosen: when using a passive/matched combiner, the transmitter's power efficiency is significantly increased without spreading the combined signal's spectrum; for the highly efficient non-linear Chireix combiner, there is a reduction of the amount of spectral leakage produced by nonlinearly combining the LINC signal components. Finally, an iterative decoding scheme is also proposed, which employs estimates of the received symbols' RMM coefficients to compensate the RMM distortion.

The expectations for fifth generation (5G) systems involve handling huge throughputs together with power consumption. In order to address this issue, massive multiple-input and multiple-output (MIMO) schemes were developed exploring the fact that any M-ary constellation can be decomposed as several offset quadrature phase shift keying (OQPSK) signals, to be amplified by efficient saturated amplifiers and transmitted separately. Since nonlinear amplifiers should be employed with constant envelope signals to avoid nonlinear distortion, a ring-type magnitude modulation (RMM) technique was proposed for bandlimited OQPSK signals to mitigate its envelope fluctuations while maintaining its spectral characteristics. This technique was shown to improve the power amplification efficiency of linear amplification with nonlinear components (LINC) transmitters, although with some degradation of the of the detection performance. This paper proposes an iterative decoding scheme relying on the accuracy of the first iteration's log likelihood ratio (LLR) computation to estimate the RMM coefficients of the received symbols. Several LLR computation methods are compared with the ideal case where the receiver knows each symbol's RMM coefficients, and it is shown that a few iterations are enough to obtain a bit error rate (BER) performance close to the best case scenario.

In the next generation wireless systems, where m-MIMO and mmWave should be combined with beamforming techniques, array optimization at receiver ensuring a good SINR level can become a very large problem. Meta-heuristics can be an adequate approach to solve this problem with a mild computational effort. In this work, different meta-heuristics are implemented and applied to this problem, with Differential Evolution presenting promising connection setup time results that expectedly would enable its use in m-MIMO systems.

The growing progress in wireless communication services lead to a demand in high data rates, spectral efficiency and flexibility requirements. The recently proposed Block-Windowed Burst Orthogonal Frequency Division Multiplexing (BWB-OFDM) transceiver proved to be a reliable alternative scheme to face these current demands. BWB-OFDM employs smoother, non- rectangular windows, allowing a power spectral density similar to the filtered OFDM approach; also, it packs together several OFDM symbols, with the addition of a sole zero- padding to accommodate the multipath channel's propagation delay. This means better overall power and spectral efficiencies. Nevertheless, the system has the same drawback of OFDM when transmitting over hostile channel conditions, such as deep fading in time-dispersive channels. To overcome this problem, a new Time-Interleaved BWB- OFDM (TIBWB- OFDM) transceiver is proposed. This scheme employs interleaving on the time-samples of each BWB- OFDM block, thus creating a sort of diversity at the frequency domain, aiming to preserve the data symbols severely corrupted by the channel's deep fades. The new TIBWB-OFDM transceiver presents considerable power gains relatively to the BWB-OFDM,while maintaining their spectral efficiency.

Strongly nonlinear amplifiers have lower implementation complexity, together with much higher amplification efficiency than linear amplifiers. However, they are only recommendable when the signals at their input have very low envelope fluctuations, something that in general is incompatible with signals with high spectral efficiency (i.e. employing high-order constellations). It is proved, although, that spectral efficient M-ary constellations can be decomposed as a sum of several OQPSK signals that can be amplified separately, thus bringing a new life for this modulation scheme. Constant- envelope OQPSK-type signals can be obtained using its equivalent minimum shift keying (MSK) representation, but don't possess the required compact spectrum close to the minimum Nyquist frequency. This can only be achieved by employing narrowband Nyquist filters, which on the other hand causes the signal's envelope to fluctuate. Considering that, this paper proposes a ring-type magnitude modula- tion (RMM) method for narrowband offset quadrature phase shift keying (OQPSK) signals that considerably reduces its envelope fluctuations with- out spreading the transmitted signal's spectrum. Simulation results show that the reduction of the signal's dynamic range and peak-to-average power ratio (PAPR) allows the use of highly efficient, nonlinear saturated high power amplifiers (HPAs) without spreading the signal's spectrum and with minimum bit error rate (BER) performance degradation.

Computing on field-programmable gate arrays (FPGAs) has been receiving continued interest as it provides high performance at relatively low power budgets, while avoiding the high non-recurring engineering (NRE) costs associated with ASIC designs. However, FPGA development is typically performed using register transfer level (RTL) languages which make the design process protracted and error-prone when compared to software design flows. To ease these problems, high-level synthesis (HLS) tools have been introduced which abstract away the RTL architecture description from the designer. In this work we explore the design space of a nonbinary GF (q) low-density parity-check (LDPC) decoder using Vivado HLS and compare it with state-of-the-art RTL designs.

HLS tools have been introduced with the promise of easening and shortening the design cycle of tedious and error-prone RTL-based development of hardware accelerators. However, they do so either by concealing meaningful hardware decisions which model the computing architecture-such as OpenCL compilers-or by abstracting them away into a high-level programming language-usually C-based. In this paper, we show that although Vivado HLS is sufficiently mature to generate a functionally correct FPGA accelerator from a naive description, reaching an accelerator which optimizes the FPGA resource utilization in a way that conveys maximum performance is a process for a hardware architect mindset. We use a highly demanding application, that requires real-time operation, and develop a non-binary LDPC decoder on a state-of-the-art Virtex 7 FPGA, using the Vivado HLS framework. Despite using the same programming syntax as a C-language software compiler, the underlying programming model is not the same, thus, the optimizations required in code refactoring are distinct. Moreover, directive-based optimizations that tweak the synthesized C description hardware must be used in order to attain efficient architectures. These processes are documented in this paper, to guide the reader on how an HLS-based accelerator can be designed, which in our case can come close to the performance achieved with dedicated hand-made RTL descriptions.

Low-Density Parity-Check (LDPC) decoders typically implement a single decoding algorithm or update rule, which narrows down the design space of the decoder and maintains its overall simplicity. However, gear-shift techniques combine multiple decoding algorithms, update rules and quantization of the log-likelihood ratios (LLRs), allowing wider design space explorations as more parameters can be fine-tuned to a particular need. Gear-shift LDPC decoders have been shown to improve both the decoding throughput and the energy efficiency per bit decoded, while achieving similar capacity compared to traditional approaches that only use one algorithm. In this paper, we incorporate gear-shift techniques based on the Min-Sum algorithm (MSA) and Self-Corrected Min-Sum algorithm(SCMSA) using variable quantization steps. The proposed design allows bit error rate (BER) performances close to the more powerful SCMSA running only a selected number of iterations using the most powerful update rule.

Base station (BS) cooperation architectures provide meaningful capacity gains, as they allow mobile terminals (MTs) in neighbor cells to share the same physical channel. Combining uplink signals received by the different BSs by employing iterative frequency-domain algorithms allows for the correct separation of the signals transmitted by each MT. To transmit those signals, MTs rely on single-carrier (SC) modulation with frequency-domain equalization (FDE). Those SC-FDE transmissions combined with iterative block decision feedback equalization (IB-DFE) techniques have been shown to be a effective mean to transmit cyclic-prefix (CP) assisted blocks within broadband wireless systems. In this paper, we aim to combine the aforementioned concepts of uplink transmission based on IB-DFE with BS cooperation in a clever manner. Our proposed scheme improves both the frequency reuse factor and the MTs' performance at cell edges, with further improved performance when low-density parity-check (LDPC) codes and turbo IB-DFE equalization schemes are considered.

This paper presents a new parallel implementation of a previously hyperspectral coded aperture (HYCA) algorithm for compressive sensing on graphics processing units (GPUs). HYCA method combines the ideas of spectral unmixing and compressive sensing exploiting the high spatial correlation that can be observed in the data and the generally low number of endmembers needed in order to explain the data. The proposed implementation exploits the GPU architecture at low level, thus taking full advantage of the computational power of GPUs using shared memory and coalesced accesses to memory. The proposed algorithm is evaluated not only in terms of reconstruction error but also in terms of computational performance using two different GPU architectures by NVIDIA: GeForce GTX 590 and GeForce GTX TITAN. Experimental results using real data reveals signficant speedups up with regards to serial implementation.

Iterative block decision feedback equalization (IB-DFE) algorithms are proven to be powerful tools when detecting several uplink signals sharing the same physical channel. However, those algorithms may face some constraints -- the IB-DFE has some difficulty picking certain signals up if they are considerably weaker than the rest and the computational effort grows exponentially with the number of signals to be detected. In this paper we present a clustered multiuser detection scheme, based on an IB-DFE algorithm. It is demonstrated that if the mobile terminals (MTs) have enough power and are distributed in clusters, with considerable power difference between them, it is possible to successfully detect them all while containing the computational complexity. Our scheme also requires fewer receiving antennas than the original IB-DFE implementations and, with the aid of low-density parity- check (LDPC) and turbo- equalization, it is able to reach high performance levels with low power requirements.

The development of new mobile communications systems faces several important challenges, namely demanding for high bit rates, high spectral efficiency, low energy consumption and the usage of hostile channels. In order to fulfill these requirements for block-based single carrier transmissions, we propose to use high order QAM constellations combined with envelope control Magnitude Modulation (MM) techniques and bit-interleaved coded modulation with LDPC codes. Also, the receiver uses powerful MM specialized IB-DFE solutions to combat the channel distortion and noise. The obtained results have shown significant improvements in terms of energy efficiency and bit error rate.

Power and flexibility are important constraints in the design of new chips. The efficiency extracted from a design is increasingly becoming a dominant question, and several techniques and technological advances can be used to optimize efficiency in its energy and functionality domains. These two characteristics are critical in digital communication systems that must work accordingly with multiple communication standards at different power, throughput and latency requirements. In this work, we focus on the physical layer Forward Error Correcting (FEC) system, due to the tight throughput and latency constraints they are required to meet, and develop specialized processing engines for Low-Density Parity-Check (LDPC) codes decoding, a class of widely standardized codes. The engines were developed for execution on Field-Programmable Gate Array (FPGA) devices by exploring dataflow and wide-pipeline design approaches, and have the design flexibility to target different LDPC codes, since they were implemented using recent High-Level Synthesis (HLS) tools. The generated engines and architectures allow achieving highly efficient decoders with decoding throughputs ranging from 16 Mbit/s to 1.2 Gbit/s at energy efficiencies of 42 to 908 Mbit/Joule/iteration, while the achieved clock frequencies of operation vary from 80 to 300 MHz. Furthermore, our bandwidth analysis shows that workload boundaries do not impose limitations on a system bus.

Magnitude modulation (MM) techniques applied to single carrier (SC) transmissions provide the potential for gains in power efficiency by reducing the signal's peak-to-average power ratio, allowing efficient power amplification at the transmitter's side. This paper presents an accurate analytical model to measure the intrinsic symbol error rate (SER) due to MM distortion for systems employing high-order constellations and proposes a new feedback based soft-iterative magnitude demodulation (SiMD) scheme for the receiver with proper estimation of the MM factors used in transmission in order to compensate for the MM distortion introduced at the transmitter. It is shown that this technique allows the design of MM highly power efficient SC transceivers with high spectral efficiency.

Despite their ability to reach within the channel capacity in shorter codeblock lengths, non-binary LDPC codes have a higher decoding complexity that poses non-trivial barriers to their generalized adoption at algorithmic and compute-intensive levels. In this work, we propose a programmable FFT-SPA decoder that delivers high decoding throughput at low power consumptions, while retaining a design flexibility at the system level which surpasses typical VLSI descriptions, guaranteeing quick retargeting and prototyping of variants of this family of signal processing algorithms with effective decoding throughputs of up to 1 Mbit/s and potential throughputs of dozens of Mbit/s.

The PAPR (Peak-to-Average Power Ratio) is widely employed to measure the magnitude of the envelope fluctuations of a given signal. It is particularly used to define the required amplifier back- off for multi-carrier modulations such as OFDM (Orthogonal Frequency Division Multiplexing) schemes. However, the PAPR increases with the block size (i.e., the number of subcarriers, in the OFDM case), but the signal's distribution converges to a Gaussian distribution for a large number of subcarriers. Therefore, one might ask if the PAPR is indeed the best parameter to define the amplifier's back-off. In this paper we address this issue. It is shown that the required amplifier's back-off for a given non-linear distortion level, i.e. referred to the input amplifier saturation power, is almost independent of the number of subcarriers and, therefore, the PAPR is not the most suitable to define it. Therefore, we recommend the use of a parameter related to the instantaneous envelope power distribution to define the amplifier back-off.

Polyphase magnitude modulation (MM) has been shown to be a robust and effortless mean to improve the efficiency of a transmitter's high power amplifier (HPA), due to the real-time reduction of the peak to average power ratio (PAPR). MM's technique flexibility allows us to include the MM system on any existing single-carrier (SC) based transmission system with clear benefits on the achieved bit error rate \emph{vs} overall signal power to noise ratio. This paper analyzes the efficiency of MM when added to a multi- input multi-output (MIMO) system, using a block-based SC transmission combined with iterative block decision feedback equalization (IB-DFE). To improve the IB-DFE performance for low power signals, we consider an additional scheme where low-density parity-check (LDPC) coding and turbo equalization are added. Simulation results show a net power efficiency enhancement, particularly for systems with channel coding, confirming MM as a major asset for high performance communication systems.

The linear amplification with nonlinear components (LINC) amplification concept is very important on the developing of the modern mobile and satellite communications systems due to the use of inexpensive high power amplifiers (HPA). This paper analyses the effectiveness of combining an efficient PAPR reducing technique based on magnitude modulation (MM) when applied to a LINC amplification system. The simulation results show that the inclusion of the MM on the LINC processing chain is very favourable in terms of spectral regrowth introduced by signal's clipping distortion. The system is also more robust to gain and phase imbalances between amplifiers with only a marginal bit error rate (BER) loss for an uncoded transmission scenario.

This letter presents a new parallel method for hyperspectral unmixing composed by the efficient combination of two popular methods: vertex component analysis (VCA) and sparse unmixing by variable splitting and augmented Lagrangian (SUNSAL). First, VCA extracts the endmember signatures, and then, SUNSAL is used to estimate the abundance fractions. Both techniques are highly parallelizable, which significantly reduces the computing time. A design for the commodity graphics processing units of the two methods is presented and evaluated. Experimental results obtained for simulated and real hyperspectral data sets reveal speedups up to 100 times, which grants real-time response required by many remotely sensed hyperspectral applications.

The control of the envelope's peak power by reducing overall peak-to-average power (PAPR) of transmitted RF signals can improve significantly the power efficiency of wireless communication systems both for single-carrier (SC) and multi-carrier (MC) transmissions. Multi-stage polyphase magnitude modulation (MPMM) has been recently proposed as an efficient PAPR reduction technique for SC signals. In this paper, we present a proof-of-concept prototype implementation in FPGA of the MPMM method. A prior study of the MPMM performance under fixed-point arithmetic is carried on, justifying decision criteria taken on MPMM's design targeted to FPGA.

It is well known that non-binary LDPC codes outperform the BER performance of binary LDPC codes for the same code length. The superior BER performance of non-binary codes comes at the expense of more complex decoding algorithms that demand higher computational power. In this paper, we propose parallel signal processing algorithms for performing the FFT-SPA and the corresponding decoding of non-binary LDPC codes over GF(q). The constraints imposed by the complex nature of associated subsystems and kernels, in particular the Check Nodes, present computational challenges regarding multicore systems. Experimental results obtained on GPU for a variety of GF(q) show throughputs in the order of 2 Mbps, which is far above from the minimum throughput required, for example, for real-time video applications that can benefit from such error correcting capabilities.

The tradeoff between error-correcting performance and numerical complexity of LDPC decoding algorithms is a well-known problem. In this paper we depict the unseen error-floor performance of the Self-Corrected Min-Sum algorithm for long length DVB-S2 codes. We developed a massively parallel simulation using GPUs which allowed a comprehensive BER characterization either in the waterfall or in the error-floor region. We show that the self-correction technique increases the BER performance by 0.5 and 0.2 dB, in the waterfall and error-floor region, when compared to the Min-Sum algorithm. Furthermore, it reaches within 0.2 dB to the Logarithmic Sum-Product BER performance and it also outperforms the Normalized Min-Sum at high SNR, a low complexity decoding algorithm which yields good BER performance.

In this paper, a new parallel method for sparse spectral unmixing of remotely sensed hyperspectral data on commodity graphics processing units (GPUs) is presented. A semi-supervised approach is adopted, which relies on the increasing availability of spectral libraries of materials measured on the ground instead of resorting to endmember extraction methods. This method is based on the spectral unmixing by splitting and augmented Lagrangian (SUNSAL) that estimates the material's abundance fractions. The parallel method is performed in a pixel-by-pixel fashion and its implementation properly exploits the GPU architecture at low level, thus taking full advantage of the computational power of GPUs. Experimental results obtained for simulated and real hyperspectral datasets reveal significant speedup factors, up to 164 times, with regards to optimized serial implementation.

Vertex component analysis (VCA) has become a very popular and useful tool to linear unmix large hyperspectral datasets without the use of any a priori knowledge of the constituent spectra. Although VCA is fast method, many hyperspectral imagery applications require a response in real time or near-real time.

In this paper we consider broadband wireless systems with high power efficiency. For this purpose we employ MM (Magnitude Modulation) techniques to reduce the PAPR (Peak-to-Average Power Ratio) of the transmitted signals, allowing efficient power amplification. Designed transceivers combine MM with SC-FDE (Single-Carrier with Frequency-Domain Equalization) methods upon iterative-block decision feedback equalization (IB-DFE). Since IB-DFE schemes designed for conventional SC signals are not suitable when we employ PAPR-reduction techniques, the effect of MM is taken into account on the estimation reliability factor that drives IB-DFE convergence. Results for the new proposed MM- IBDFE transceiver present gains above 1.5dB in the overall power efficiency, even when considering severely time-dispersive channels.

This paper extends the current concept of multiple description coding (MDC) to the compressed domain, by proposing efficient splitting of standard single description coded (SDC) video into a multi-stream representation. A novel multiple description video splitting (MDVS) scheme is proposed to operate at network edges, for increased robustness in path diversity video streaming across heterogeneous communications chains. It is shown that poor performance of existing methods is mainly due to distortion accumulation, i.e., drift, when decoding is carried out with missing descriptions. The proposed scheme is able to effectively control drift distortion in both intra and inter predictive coding, even when only one description reaches the decoder. This is achieved by generating a controlled amount of relevant side information to compensate for drift accumulation, whenever any description is lost in its path. The simulation results show that any individual description can be decoded on its own without producing drift, achieving significant quality improvement at reduced redundancy cost. The overall performance evaluation, carried out by simulating video streaming over lossy networks with path diversity, also demonstrates that MDVS enables higher quality video in such heterogeneous networking environments, for a wide range of packet loss rates.

It is well known that LDPC decoding is computationally demanding and one of the hardest signal operations to parallelize. Beyond data dependencies that restrict the decoding of a single word, it requires a large number of memory accesses. In this paper we propose parallel algorithms for performing in CPUs the most demanding case of irregular and long length LDPC codes adopted in the Digital Video Broadcasting Satellite 2 (DVB-S2) standard used in data communications. By performing simultaneous multicodeword decoding and adopting special data structures, experimental results show that throughputs superior to 90 Mbps can be achieved when LDPC decoders for the DVB-S2 are implemented in the current CPUs.

Unlike usual VLSI approaches necessary for the computation of intensive Low-Density Parity-Check (LDPC) code decoders, this paper presents flexible software-based LDPC decoders. Algorithms and data structures suitable for parallel computing are proposed in this paper to perform LDPC decoding on multicore architectures. To evaluate the efficiency of the proposed parallel algorithms, LDPC decoders were developed on recent multicores, such as off-the-shelf general-purpose x86 processors, Graphics Processing Units (GPUs), and the CELL Broadband Engine (CELL/B.E.). Challenging restrictions, such as memory access conflicts, latency, coalescence, or unknown behavior of thread and block schedulers, were unraveled and worked out. Experimental results for different code lengths show throughputs in the order of 1 \sim 2 Mbps on the general-purpose multicores, and ranging from 40 Mbps on the GPU to nearly 70 Mbps on the CELL/B.E. The analysis of the obtained results allows to conclude that the CELL/B.E. performs better for short to medium length codes, while the GPU achieves superior throughputs with larger codes. They achieve throughputs that in some cases approach very well those obtained with VLSI decoders. From the analysis of the results, we can predict a throughput increase with the rise of the number of cores.

An efficient multistage polyphase magnitude modulation (MPMM) scheme was recently proposed to solve the problem of controlling the envelope's power peak of single carrier modulated signals, band limited by root-raised cosine (RRC) pulse shaping filters, in order to maximize the efficiency of the transmitter's high power amplifier (HPA). This paper addresses the statistical analysis of MPMM coefficients and performs an analytical study of the average power and the peak-to-average power ratio (PAPR) of the magnitude modulated transmitted signals. It is shown that the average power losses due to MPMM are largely compensated by the outstanding back-off reduction gains, leading to an effective reduction on the transmitted signal's PAPR. The analytical study also shows that these losses are comparable to those observed with the clipping method (less than 1dB), without generating the undesirable spectral regrowth effect.

In this paper we present a new algorithm for chroma prediction in YUV images, based on inter component correlation. Despite the YUV color space transformation for inter component decorrelation, some dependency still exists between the Y, U and V chroma components. This dependency has been previously used to predict the chrominance data from the reconstructed luminance. In this paper we show that a chrominance component can be more efficiently predicted by using the reconstructed data from both the luminance and the remaining chrominance signal. The proposed chroma prediction is implemented and tested using the Multidimensional Multiscale Parser (MMP) image encoding algorithm. It is shown that the new color prediction mode outperforms the originally proposed prediction methods. Furthermore, by using the new color prediction scheme, MMP is consistently better than the state-of-the-art H.264/AVC for coding both for the luminance and the chrominance image components.

This paper proposes a multiple description (MD) transcoding scheme capable of preventing drift by distortion accumulation in temporally predicted motion compensated slices. Drift compensation is achieved by generating a controlled amount of side information to be used for decoding whenever a description fails to reach the end user terminal. The side information is generated by re-encoding the transcoding residue with an independent quantisation parameter which also controls redundancy. A simplified architecture is devised to reduce transcoding complexity in regard to the number of processing functions and buffer requirements. The experimental results show that temporally predicted frames do not suffer from drift and their quality is significantly improved at reduced redundancy cost in comparison with a classic MD transcoding scheme.

In this paper, we propose a new encoder for scanned compound documents, based upon a recently introduced coding paradigm called multidimensional multiscale parser (MMP). MMP uses approximate pattern matching, with adaptive multiscale dictionaries that contain concatenations of scaled versions of previously encoded image blocks. These features give MMP the ability to adjust to the input image's characteristics, resulting in high coding efficiencies for a wide range of image types. This versatility makes MMP a good candidate for compound digital document encoding. The proposed algorithm first classifies the image blocks as smooth (texture) and nonsmooth (text and graphics). Smooth and nonsmooth blocks are then compressed using different MMP-based encoders, adapted for encoding either type of blocks. The adaptive use of these two types of encoders resulted in performance gains over the original MMP algorithm, further increasing the performance advantage over the current state-of-the-art image encoders for scanned compound images, without compromising the performance for other image types.

This paper proposes a multiple description coding scheme based on a multi-loop structure, which prevents drift distortion accumulation in intra predicted slices. The drift is compensated by generating a controlled amount of side information used by the decoder whenever any description is lost in the corresponding network path. The experimental results show that intra predicted slices do not suffer from drift and their quality is significantly improved (e.g., 6–8 dB) at reduced redundancy cost, (e.g., 0.2–0.25), in comparison with the open loop implementation. Error propagation in subsequent frames is also evaluated when one description is lost for a whole frame and for two rows of macroblocks. In both cases the overall video quality is significantly improved and considering normal GOP sizes the redundancy introduced by the proposed scheme is negligible.

In this paper we propose to show how signal processing algorithm designers can understand the nuances of multicore computing engines in order to conveniently exploit these powerful devices. This is illustrated by presenting source and channel coding, two fundamental operations in multimedia signal processing. We describe methods and principles to develop parallel signal processing algorithms to compute motion estimation for advanced video coding, and low-density parity-check code decoding for forward error correction in the channel coding context. The paper will consider general purpose multi-core architectures and accelerators such as the Cell/B.E. and graphics processing units. Experimental evaluation of the multi-core systems allows their performance for signal processing applications to be compared side by side with previous hardware dedicated solutions.

Due to huge computational requirements, powerful Low-Density Parity-Check (LDPC) error correcting codes, discovered in the early 1960s, have only recently been adopted by emerging communication standards. LDPC decoders are supported by VLSI technology, which delivers good parallel computational power with excellent throughputs, but at the expense of significant costs.

Currently, the bandwidth utilization of residential broadband links is highly inefficient. Providing the means for sharing these links will increase the total bandwidth and reduce the fixed costs per client thus promoting the use of Internet services. This paper proposes a low cost communal residential gateway for sharing and remotely managing broadband access links. We first identify and characterize existing problems on residential asymmetric link sharing scenarios and then propose and evaluate the CommGate prototype that overcomes these problems. This solution contributes to an efficient broadband link sharing, a cost breakdown and an increasing Internet access dissemination, particularly in developing countries.

Magnitude Modulation is a technique proposed with success for QPSK and OQPSK in order to maximize power and bandwidth of small satellite earth stations, by reducing PAPR of the modulated signal at the HPA input. To meet the ever growing demand for higher data rates, higher order modulations have been considered. This paper shows that it is possible to use the magnitude modulation concept for the M-QAM case, even considering the huge number of symbol combinations. The constellation and RRC symmetries are explored, allowing considerable reduction on look-up table computation complexity and storage requirements. Experimental results show considerable gains (>2dB) in PAPR and back-off reduction for 16-QAM with negligible BER losses.

In this paper we present a new segmentation method for the multidimensional multiscale parser (MMP) algorithm. In previous works we have shown that, for text and compound images, MMP has better compression efficiency than state-of-the-art transform-based encoders like JPEG2000 and H.264/AVC; however, it is still inferior to them for smooth images. In this paper we improve the performance of MMP for smooth images by employing a more flexible block segmentation scheme than the one defined in the original algorithm. The new partition scheme allows MMP to exploit the image's structure in a much more adaptive and effective way. Experimental tests have shown consistent performance gains, mainly for smooth images. When employing the new block segmentation scheme, MMP outperforms the state-of-the-art JPEG2000 and H.264/AVC Intra-frame image coding algorithms for both smooth and non-smooth images, at low to medium compression ratios.

Low-Density Parity-Check (LDPC) codes are among the best error correcting codes known and have been adopted by data transmission standards, such as DVB-S2 or WiMax. They are based on binary sparse parity check matrices and usually represented by Tanner graphs. LDPC decoders require very intensive message-passing algorithms, also known as belief propagation. This paper proposes a very compact stream-based data structure to represent such a bipartite Tanner graph, which supports both regular and irregular codes. This compact data structure not only reduces the memory required to represent the graph but also puts it in an appropriate format to gather data into streams. This representation also allows to map the irregular processing behavior of the Sum Product Algorithm (SPA) used in LDPC decoding into the stream-based computing model. Stream programs were developed for LDPC decoding and the results show significant speedups obtained either using general purpose processors, or graphics processing units. The simultaneous decoding of several codewords was performed using the SIMD capabilities of modern stream-based architectures available on recent processing units.

Low-Density Parity-Check (LDPC) codes are powerful error correcting codes (ECC). They have recently been adopted by several data communication standards such as DVB-S2 and WiMax. LDPCs are represented by bipartite graphs, also called Tanner graphs, and their decoding demands very intensive computation. For that reason, VLSI dedicated architectures have been investigated and developed over the last few years. This paper proposes a new approach for LDPC decoding on graphics processing units (GPUs). Efficient data structures and an new algorithm are proposed to represent the Tanner graph and to perform LDPC decoding according to the stream-based computing model. GPUs were programmed to efficiently implement the proposed algorithms by applying data-parallel intensive computing. Experimental results show that GPUs perform LDPC decoding nearly three orders of magnitude faster than modern CPUs. Moreover, they lead to the conclusion that GPUs with their tremendous processing power can be considered as a consistent alternative to state-of-the-art hardware LDPC decoders.

In this paper, we exploit a recently introduced coding algorithm called multidimensional multiscale parser (MMP) as an alternative to the traditional transform quantization-based methods. MMP uses approximate pattern matching with adaptive multiscale dictionaries that contain concatenations of scaled versions of previously encoded image blocks. We propose the use of predictive coding schemes that modify the source's probability distribution, in order to favour the efficiency of MMP's dictionary adaptation. Statistical conditioning is also used, allowing for an increased coding efficiency of the dictionaries' symbols. New dictionary design methods, that allow for an effective compromise between the introduction of new dictionary elements and the reduction of codebook redundancy, are also proposed. Experimental results validate the proposed techniques by showing consistent improvements in PSNR performance over the original MMP algorithm. When compared with state-of-the-art methods, like JPEG2000 and H.264/AVC, the proposed algorithm achieves relevant gains (up to 6 dB) for nonsmooth images and very competitive results for smooth images. These results strongly suggest that the new paradigm posed by MMP can be regarded as an alternative to the one traditionally used in image coding, for a wide range of image types.

State-of-the-art decoders for Low-Density Parity-Check (LDPQ codes adopted by the DVB-S2 standard, explore the periodicity M = 360 features of the selected special LDPC- IRA codes. This paper addresses the generalization of a well known M-kernel parallel hardware structure and proposes an efficient partitioning by any factor of M, without memory addressing overhead and keeping unchanged the efficient message mapping scheme. The method provides a simple and efficient way to reduce the decoder complexity. Synthesizing the proposed decoder architecture for N = {45,90,180} parallel processing units using an FPGA family from Xilinx shows a minimum throughput above the minimal 90 Mbps.

This paper deals with conversion from H.264/AVC (advanced video coding) coded video into the MPEG-2 format. The proposed approach exploits similarities between the coding techniques used in both standards in order to achieve a computationally efficient method for transcoding interframe coded slices. The conversion process is based on adaptation of both coding mode and motion information embedded in the H.264/AVC video stream, such that subsequent MPEG-2 encoding takes full advantage of the higher computational effort spent on the first encoding step. The proposed transcoding scheme significantly reduces the computational complexity needed for MPEG-2 interframe coding by reusing relevant information from the H.264 bitstream. The simulation results show that computational complexity savings up to 60%, with a marginal objective quality cost, can be achieved in comparison with a cascaded decoder-encoder.

In this paper we describe the ongoing work on a new paradigm for compressing the motion predicted error in a video coder, referred to as MMP-Video. This new coding algorithm uses the multidimensional multiscale parser image coding algorithm to encode the residue error, in a H.264/AVC based video coder. MMP has shown to perform very well as a universal still image coding method, particularly when it is combined with intra prediction schemes. In addition, previously published preliminary results have also presented MMP as a promising video coding method. In this paper, we propose new dictionary updating techniques for MMP-video. Along with other functional optimizations, these techniques allow for a significant improvement in the encoder performance. Thus, we were able to achieve considerable gains over H.264/AVC for B slices, specially for medium and high bit-rates, while maintaining equivalent performance for the P slices.

MMP-Intra was recently proposed as a recurrent patterns based image encoder that combines the multidimensional multiscale parser (MMP) algorithm with intra prediction techniques. Our results show that this method is able to achieve considerable gains over state-of-the-art transform-based image encoders for a wide variety of types of images, like text, composed (text and graphics) and texture images, while having performance close to the one of traditional algorithms for smooth images. Because of this universal character, MMP-Intra can be regarded as a viable alternative to transform-based image coding. MMP-Intra uses a multiscale adaptive dictionary to approximate the original data blocks. It is composed of dilations, contractions and concatenations of previously encoded patterns. In this work we present a new method for controlling the dictionary adaptability, in which the dictionary is only updated if a certain distortion criterion is met in the block being encoded. Experimental results show that this scheme is able to consistently outperform the original method, while achieving relevant reductions in its computational complexity

In this paper we present a new method for image coding that is able to achieve good results over a wide range of image types. This work is based on the multidimensional multiscale parser (MMP) algorithm (M. de Carvalho et al., 2002), allied with an intra frame image predictive coding scheme. MMP has been shown to have, for a large class of image data, including texts, graphics, mixed images and textures, a compression efficiency comparable (and, in several cases, well above) to the one of state-of-the-art encoders. However, for smooth grayscale images, its performance lags behind the one of wavelet-based encoders, as JPEG2000. In this paper we propose a novel encoder using MMP with intra predictive coding, similar to the one used in the H.264/AVC video coding standard. Experimental results show that this method closes the performance gap to JPEG-2000 for smooth images, with PSNR gains of up to 1.5 dB. Yet, it maintains the excellent performance level of the MMP for other types of image data, as text, graphics and compound images, lending it a useful universal character.

Mobile access to multimedia contents requires video transcoding functionality at the edge of the mobile network for interworking with heterogeneous networks and services. Under hard transcoding conditions, the bandwidth of a coded video stream needs to be drastically reduced such that the original temporal resolution cannot be maintained. Thus consecutive frame skipping is often unavoidable at the transcoder. We propose an efficient mechanism for improving the quality of service (QoS) delivered to the mobile user, by minimising consecutive frame skipping in transcoding systems. The simulation results show that a better signal quality is obtained at the receiver during hard transcoding periods.

We present a new method for motion compensation, which combines transformations in the spatial and luminance domains. In most standardised video compression algorithms, motion information is assumed to be translation only, as used by the traditional block matching algorithm (BMA). Such an assumption is not always correct, as in most scenes, namely head and shoulders, object motions are rotations and zooms, among other effects, which cannot be represented as translations. In order to compensate these complex motion features more accurately, we applied geometric transformations (BMGT) for motion compensation. Nevertheless, spatial transformations alone are unable to compensate situations like uncovered background, masking between objects or changes in lighting conditions. In this sense, we introduced an additional transformation in the luminance domain (BMGTI), which has proven to be appropriate to overcome such problems. Experimental results have shown that BMGTI exceeds the performance of BMGT by about 2 dB, and achieves better results than the global brightness compensation (GBC) technique. This method is implemented using a spatial hierarchical block structure, which allows video encoding at low bit rates and reduction of the computational complexity.

A video codec based on several parallel digital signal processors is described. The digital signal processors (DSPs) can be easily programmed to implement the H.261 algorithm and are organized as a single instruction multiple data (SIMD) computing architecture. Both the encoder and the decoder divide a picture in regions of horizontal strips and use one local processor per region. These local processors code (decode) one horizontal strip of data which, using the terminology of the H.261 standard, corresponds to two group of blocks (GOBs). They also communicate to a central processor which multiplexes (demultiplexes) the coded data from (for) the processors in the encoder (decoder). In the case of the encoder the central processor also controls a data buffer for bit-rate adaptation. Lateral communication between adjacent processors is implemented to allow comparisons between blocks situated in neighbouring regions, as required by most motion estimation algorithms.