The time-domain signal in orthogonal frequency division multiplexing (OFDM) has a large peak-to-average power ratio (PAPR). Most existing solutions for this problem are designed for OFDM systems operating with a fully-digital MIMO architecture. However, these solutions cannot be used for an OFDM system operating with a hybrid beamforming architecture. Therefore, in this work we address PAPR reduction in OFDM with hybrid beamforming. We propose to transmit a PAPR reduction signal along with the precoded information signal. For an efficient design of the PAPR reduction signal, we propose to use extra radio frequency (RF) chains. The design of the PAPR reduction signal and the analog beamformers corresponding to the extra RF chains, is written as a constrained optimization problem. An iterative algorithm based on successive convex approximation is proposed to solve the optimization problem. We show that the proposed technique achieves a considerable end-to-end performance gain in the presence of a non-linear power amplifier.

The single carrier modulation is a potential candidate at the sub-THz band, because of the channel sparsity and the necessity for an analog front-end friendly modulation. However, the peak-to-average-power ratio (PAPR) of the single carrier modulation in a multi-user MIMO downlink scenario is still a concern. In this work, we address this problem, by transmitting a PAPR reduction signal (PRS) together with the precoded data symbols. The goal of the PRS is to reduce the PAPR of the continuous-time signal entering the power amplifier attached to each antenna. We propose two different methods to design the PRS. In the first method, we restrict the PRS to the channel null space, so that it does not cause interference with the users’ information symbols at the receiver. As such, this method has limited degrees of freedom for the PAPR reduction. Considering this, we propose a second method for the PRS design, where the PRS can interfere with the users’ information symbols in a constrained manner. This relaxation enables an increased PAPR reduction without reducing the data detection performance. The design of the PRS is formulated as an optimization problem and the performance of the proposed schemes is studied semi-analytically. Our analysis shows that the proposed scheme is able to reduce the PAPR by about 9 dB at the cost of only 0.5 dB SINR. Consequently, the bit-error-rate performance of the proposed scheme is negligibly impacted by non-linear power amplifier distortion.

The time-modulated array is a simple array architecture in which each antenna is connected to an RF switch that serves as a modulator. The phase shift is achieved by digitally controlling the relative delay between the periodic modulating sequences of the antennas. Two factors limit the practical use of this architecture for communication and sensing. First, the switching frequency is high, as it must be a multiple of the sampling frequency. Second, the discrete modulating sequence introduces undesired harmonic replicas of the signal, which are out-of-band interference. This paper proposes the OFDM modulation with an appropriate precoder to facilitate the aliasing of the harmonic components to simultaneously reduce sideband radiation and switching frequency. The transmit signal has a repeated block structure in the frequency domain to facilitate coherent combining of the aliased signal blocks. As a result, a factor A reduction in switching frequency is achieved at the cost of a factor A reduction in communication capacity. Doubling A reduces sideband radiation by around 2.9 dB. The feasibility of the proposed method is experimentally validated for wideband signals. Full-wave simulations are performed to validate the beamforming performance based on the experimental results.

This paper presents a distributed cell-free communication and radar system that operates in the uplink. The system schedules dedicated transmit (Tx) access points (APs) to transmit dedicated radar signals in the uplink together with the user equipment (UE). The receiving (Rx) APs decode the UE payloads while also detecting targets based on the Tx AP signals. To mitigate the added Tx AP interference, the Rx APs use multiuser processing to recover the UE payloads, while a combination of large processing gains, adaptive beamforming, spatial diversity, interference cancellation and power control is used to mitigate the UE interference impacting the radar. The radar introduces few changes to the physical layer and the additional computations needed are comparable to the communication system. The system is validated numerically by using Monte-Carlo simulations, where we highlight the inherent trade-offs between the various system parameters (such as the power control balancing, and the number of Tx APs scheduled and UEs cancelled) and show that both the communication and radar systems can be effectively integrated into the same network at a near optimal performance.

Analog beamforming (ABF) is a key enabler for high-throughput mmWave communication to harvest sufficient beamforming gain from large antenna arrays. It relies on beam alignment and the radio frequency (RF) architecture. In multiuser scenarios, beam alignment becomes challenging because of the inter-beam-interference between different users. Motivated by the large number of available antennas at the base station, we propose a downlink sparse ABF architecture in which only a subset of antennas per RF chain can be scheduled for the beamforming. For this purpose, we model the RF chain-antenna association as a binary association matrix (BAM) and jointly optimize the transmit beams and the BAM subject to RF hardware constraints. To alleviate the complexity of the resulting mixed–integer programming problem, we propose an iterative beam alignment and BAM optimization where the beam alignment and BAM are successively optimized until convergence. The proposed sparse-connected RF architecture performs significantly better than the traditional fully and partially connected architectures in terms of signal-to-interference-plus-noise ratio and bit error rate. The simulation results indicate that optimizing the BAM profiles relaxes the need for optimized beam allocation beyond beamforming along the line-of-sight.

The channel sparsity and the necessity for an analog front-end friendly modulation have made single carrier a considerable candidate for sub-THz communication. However, the peak-to-average-power ratio (PAPR) of the single carrier modulation in a multi-user MIMO scenario is still a concern. In this work, we reduce the PAPR of the single carrier scheme, by transmitting an additional PAPR reduction signal (PRS). The PRS is constrained to lie in the null space of the channel so that it does not cause interference with the users’ information symbols at the receiver. Moreover, the additional transmit power consumed by the PRS is studied. An optimization problem is formulated to design the PRS, and the optimal power allocation for the PRS is analyzed. Our analysis shows that by trading off only 1 % of the total transmitted power, a PAPR reduction of about 3 dB can be achieved. Further, our simulations reveal that when an optimal power is allocated to the PRS, the proposed scheme achieves a significant performance gain compared to the classical single carrier system in the presence of power amplifier distortion.

In this paper we study analytically the sensitivity of analog-beamformed multiuser downlink MIMO systems to imperfect beamforming (BF) state information at the transmitter and the receiver. We consider different distributions of the transmit and receive BF errors and evaluate the end-to-end performance in terms of the statistical distribution of the minimum signal-to-interference plus noise ratio (SINR) and derive accordingly, for small fluctuation errors, analytical approximation of the average SINR and the corresponding symbol error probability (SEP). The analytical approximations of the SEPs are further benchmarked with the simulated error performance for both uncoded and coded transmission and for two BF architectures namely partially (PCA) and fully (FCA) connected architectures. Simulation results in line-of-sight propagation revealed that (i) tight estimates mainly of the transmit BF angles are required in the order of a fraction of one degree for moderate number of transmit antennas especially for FCA, and (ii) the BF accuracy requirement becomes stringent as the system load increases. We argue that with a moderate to high number of transmit antennas, multiuser beam alignment achieving sub-degree BF accuracy will be one of the main challenges of future mmWave communication systems that rely on analog BF.11This research received the support of the COREnext project, funded by the European Union's Horizon Europe Research and Innovation Actions, under Grant Agreement$N^{\circ}l$01092598.

Orthogonal Chirp Division Multiplexing (OCDM), a chirp based waveform, has gathered interest because of its robustness to time and frequency selective channels. In this work, we study the phase noise effect in OCDM, which has not been investigated in the literature yet. We analytically show that phase noise causes a common phase error (CPE) rotation of data symbols and inter-symbol interference (ISI) in OCDM. We investigate the influence of OCDM system parameters on the phase noise effect. For this purpose, a system parameter, chirp frequency offset is defined and its influence on the CPE and ISI is studied. Depending on the value of this chirp frequency offset, either CPE or ISI becomes the dominant phase noise effect. Our findings also show that an OCDM system can be made robust to phase noise at the cost of spectral efficiency by reducing the number of employed chirps. Our performance analysis demonstrates that OCDM has a gain over OFDM and Single Carrier (SC) in a fading channel with phase noise because of the frequency diversity and the different distortion caused by the phase noise generated ISI terms respectively. We also investigate the performance of OCDM in the presence of narrowband interference with phase noise and observe that OCDM has a benefit over OFDM and SC at medium and low interference power.

This paper presents a multiple-access procedure for joint radar and communication systems. Dedicated radar antennas transmit uplink pilots in the same time-frequency resources as the user pilots over several coherence blocks. The radar and communication channels are subsequently estimated wherein the respective systems proceed as conventional. In order to facilitate pilot reuse and mitigate interchannel interference, the radar and user pilots are modulated in time and frequency so that the channels are orthogonal in the range-Doppler domain. This allows the respective channels to be separated without mutual interference or the need to allocate dedicated radar pilots. The main advantage is the time-division duplex compatibility and the near optimal radar performance. The performance trade-offs are discussed and the method is demonstrated with numerical simulations, demonstrating a relatively unaffected EVM while simultaneously recovering the radar channel for sensing.

This work proposes a first-of-its-kind SLAM architecture fusing an event-based camera and a Frequency Modulated Continuous Wave (FMCW) radar for drone navigation. Each sensor is processed by a bio-inspired Spiking Neural Network (SNN) with continual Spike-Timing-Dependent Plasticity (STDP) learning, as observed in the brain. In contrast to most learning-based SLAM systems, our method does not require any offline training phase, but rather the SNN continuously learns features from the input data on the fly via STDP. At the same time, the SNN outputs are used as feature descriptors for loop closure detection and map correction. We conduct numerous experiments to benchmark our system against state-of-the-art RGB methods and we demonstrate the robustness of our DVS-Radar SLAM approach under strong lighting variations.

Event-based neural networks are currently being explored as efficient solutions for performing AI tasks at the extreme edge. To fully exploit their potential, event-based neural networks coupled to adequate preprocessing must be investigated. Within this context, we demonstrate a 4-b-weight spiking neural network (SNN) for radar gesture recognition, achieving a state-of-the-art 93% accuracy within only four processing time steps while using only one convolutional layer and two fully connected layers. This solution consumes very little energy and area if implemented in event-based hardware, which makes it suited for embedded extreme-edge applications. In addition, we demonstrate the importance of signal preprocessing for achieving this high recognition accuracy in SNNs compared to deep neural networks (DNNs) with the same network topology and training strategy. We show that efficient preprocessing prior to the neural network is drastically more important for SNNs compared to DNNs. We also demonstrate, for the first time, that the preprocessing parameters can affect SNNs and DNNs in antagonistic ways, prohibiting the generalization of conclusions drawn from DNN design to SNNs. We demonstrate our findings by comparing the gesture recognition accuracy achieved with our SNN to a DNN with the same architecture and similar training. Unlike previously proposed neural networks for radar processing, this work enables ultralow-power radar-based gesture recognition for extreme-edge devices.

This paper considers the cell-free (CF) massive multiple-input multiple-output (MIMO) architecture with an added virtual uplink radar integration. We consider joint communications and radar (JCR) in the uplink payload of the time-division duplex (TDD) frame and formulate a set of linear interference constraints in order to limit the user equipment (UE) interference imposed on the radar system at each accesspoint (AP) through power control. The constraints are incorporated into the sum spectral efficiency (SSE) and the sum-log-SNR (SLS) policies. Furthermore, a largest large-scale fading (LLSF) heuristic is formulated as an approximate low-complexity solution. Numerical simulations indicate that all methods provide similar performance in terms of spectral efficiency (SE) and that power control allows the communication system to be controlled to satisfy a given worst case radar performance.

This paper considers the cell-free (CF) massive MIMO architecture from a joint communication and radar point of view. We propose a protocol for communication and sensing, where the network allocates a set of access-points (APs) to participate in the uplink together with the users (UEs) to serve the network with a radar signal. This realizes the radar system as a distributed bistatic radar system, where the objective is to recover the radar echoes from the multiuser interference. The imposed cost on the communication system is the loss of one AP and the need to schedule one additional virtual UE per allocated AP. We present two modes of radar sensing, occurring in either the uplink training segment or the data payload segment of the communication frame. A subspace signal model and its corresponding generalized likelihood ratio test is developed in order to evaluate the detection performance. We present expressions for the probably of detection and false alarm, and demonstrate the system numerically. Our main message is that coordinating the network to transmit in the uplink together with the UEs serves as an interesting approach in enabling radar sensing in CF massive MIMO systems.

We present an optimization-based theory describing spiking cortical ensembles equipped with Spike-Timing-Dependent Plasticity (STDP) learning, as empirically observed in the visual cortex. Using this generic framework, we build a class of global and action-based feature descriptors for event-based cameras that we assess on the N-MNIST and the IBM DVS128 Gesture datasets. We report significant accuracy improvements compared to state-of-the-art STDP-based systems (+9.3% on N-MNIST, +7.74% on IBM DVS128 Gesture). In addition to ultra-low-power learning in neuromorphic edge devices, our work contributes towards a biologically-plausible, optimization-based theory of cortical vision.

Multipath is a challenging problem for radar-based localization systems, especially in indoor scenarios. Multipath is caused by the bounces from static objects like walls and furniture in the room creating false alarms (“ghosts”) in target detections. Although solutions for the multipath effect have been proposed for a range of radar sensing problems, the specific case of multipath recognition and mitigation for a colocated multiple-input–multiple-output (MIMO) radar remains unsolved. For MIMO radar, the different direction-of-arrival (DoA) and direction-of-departure (DoD) angles inhibit the use of beamforming with a virtual array for localizing the first-order ghosts. Additionally, the prior knowledge of the multipath geometry model (room layout and boundary) is not always accessible. Classical ray tracing methods to resolve multipath are hence, not practical. In this work, we exploit a linear relationship between the target and multipath ghosts in the range-Doppler map to propose a Hough-transform-based multipath recognition solution. The algorithm does not require prior multipath geometry information and applies to the various indoor environments for an MIMO radar. Simulation and measurement results demonstrate the effectiveness of the proposed algorithm.

In this paper, a novel hybrid architecture is proposed combining expert knowledge for optimal power allocation and deep artificial neural networks (ANN) to address the access-point scheduling problem in cell-free massive multiple-input multiple-output (MIMO) communication systems with a serial bandwidth-limited fronthaul architecture. The scheduling task is formulated as an image segmentation problem for which a supervised encoder-decoder like ANN is proposed. It consists of serially concatenated contraction and expansion layers to maximize the (regularized) cross-entropy, followed by a binary projection to undo the relaxation problem. Besides the robustness to scenarios with a time-varying system load and fronthaul bandwidth, the proposed architecture provides a complexity-efficient solution that fulfills the fronthaul bandwidth constraints and satisfies the real-time considerations. Our experimental results verify the competitive performance of the proposed solution with respect to both nonlinear solvers and state-of-art convex algorithms while the time efficiency of the ANN model outperforms the state of the art, especially, in scenarios with a large number of users.

This paper considers the problem of radar sensing by using a large number of antennas. We use the orthogonal frequency division multiplexing (OFDM) waveform, and show that the large arrays used in massive multiple-input multiple-output (MIMO) communications enable accurate localization in the array near-field, even at the narrow bandwidths typically encountered at low carrier frequencies. We validate our findings experimentally with a massive MIMO testbed operating at 3.5 GHz carrier frequency and 18 MHz OFDM bandwidth in an indoor environment. We consider a single moving cylinder, and demonstrate a median accuracy of (3.4, 5.6) cm in ($x$,$y$) in the near-field. We show that the accuracy is maintained with only a single subcarrier, and that the resolution increases with an order of magnitude when combining all antennas, effectively surpassing the 16.67 m bistatic range resolution set by the OFDM waveform. We use a radar symbol duration of$71.88~\mu $s at an effective transmission period of 2.5 ms, which indicates that the radar and communication systems can be implemented in time-division with a capacity loss of only 2.9%. Our results suggest that near-field radar sensing can be integrated into future massive MIMO systems operating at low carrier frequencies and narrow bandwidths.

Fronthauling the traffic from/to large number of distributed access points (AP)s in the cell-free (CF) massive Multiple-Input Multiple-Output (MIMO) is one of the main challenges hindering the deployment of CF massive MIMO systems. As the point-to-point fronthaul architecture is not cost-effective, there is a need to move towards shared fronthaul mediums or a point-to-multipoint architecture that requires the proper user-AP scheduling to improve the overall performance while a constraint on the fronthaul bandwidth must also be respected. In this paper, a low-bandwidth system with centralized digital beamforming is considered and a novel algorithm is proposed for the joint power and AP scheduling problem. The proposed optimization framework provides a scalable solution for CF massive MIMO under stringent fronthaul bandwidth constraints.

Beyond 5G systems are expected to approach 1 Tb/s throughput. This poses a significant challenge to the channel decoder. In this paper, we propose a multi-core architecture based on full row parallel layered LDPC decoder with frame interleaving. Compared with conventional partially parallel layered architectures, the proposed architecture increases the throughput by applying frame interleaving into the pipeline architecture and by using multi-core architectures. Two high rate medium size QC LDPC codes are designed with fast decoding convergence speed for this architecture. Both codes are implemented with single core and multi-core architectures to explore different trade-offs between code design, communication performance and implementation. The four decoders are implemented in 16 nm CMOS FinFET technology with a clock rate of 1 GHz. The placement and routing implementation results show that the single core decoder for the LDPC (1027, 856) code is able to provide 114 Gb/s throughput at maximum 3 iterations with an area of 0.173 mm2and energy efficiency of 1.56 pJ/bit; the multi-core decoder for the (1032, 860) code is able to provide 860 Gb/s throughput at maximum 2 iterations with an area of 1.48 mm2and energy efficiency of 3.24 pJ/bit. The multi-core decoder achieves the highest throughput in the literature for medium size (1-2k) LDPC codes. When compared with other state-of-the-art fully parallel high speed architectures, the proposed architectures bring a significant gain both in area efficiency and energy efficiency while keeping the ability to offer flexibility in code rate, number of iterations and early stop.

Cell-free (CF) massive Multiple-Input Multiple-Output (MIMO) with large number of distributed access points (APs) has emerged as a new paradigm allowing higher macro diversity for randomly distributed users. However, the fronthaul traffic bandwidth between central processing unit and the APs can explode in particular in the uplink, requiring expensive star-topology with point-to-point fronthaul links. To achieve a scalable CF massive MIMO architecture and a cost-effective fronthauling solution, we consider, in this paper, a point-to-multipoint fronthaul topology where (a subset of) the APs share a serial fronthaul link offering a per-user limited fronthaul bandwidth. We develop a novel unified optimization framework for iterative power control and AP scheduling that provides a systematic user-centric solution towards scalable uplink CF massive MIMO. Experimental results show that power control is not sufficient to guarantee the best objective and, therefore, the appropriate association of the users to the APs is required to improve the overall system signal-to-noise ratio. Under the stringent fronthaul bandwidth, the proposed joint optimization framework results in i) significant 5% outage data rate increase ii) near uniform distribution of the served users per APs and, hence, an increased diversity and iii) fast convergence of the algorithm within a few iterations.

One of the major bottlenecks that hinders the deployment of Cell-free (CF) massive Multiple-Input MultipleOutput (MIMO) in a cost-effective way is the limited capacity of back/front-haul connections. In this case, the traffic between the large number of distributed access points (APs) and the central processing unit can easily explode. Expensive pointto-point fronthauling technologies are certainly not desirable hence a shared fronthaul medium should be foreseen wherein (a subset of) APs share the same bandwidth-limited medium. However, the point-to-multipoint architectures require proper AP scheduling. In this paper, we propose a novel unified optimization framework for iterative power control and AP scheduling in the uplink of the cell-free massive MIMO system, that offers a systematic user-centric solution towards scalable CF massive MIMO. The experimental results show that power control is not sufficient to reach the best minimum throughput and, therefore, an appropriate association of the users to the APs is crucial to improve the overall system performance.

In order to provide an ever increasing throughput for emerging communication systems, more and more high-frequency bands are being considered, where a wide bandwidth is available, especially at mm-wave (30 to 300 GHz) to THz frequencies. Due to differences in propagation and underlying technology limitations, this leads to systems with different architecture from lower frequencies. In order to estimate the power consumption of such systems and to allow architectural and technology tradeoffs, a dedicated power model is proposed. It combines specifically the power consumption of the power amplifier (PA), analog, and digital components. It also includes overhead components such as power supply and the impact of technology scaling towards future CMOS and non-CMOS technologies.Dedicated models for the different components and their integration in the power model are described. The application of the power model is illustrated on two specific use cases. The first one investigates the trade-off between more antennas or more output power per PA for an access point. Power consumption is optimized for two different PA technologies. The second use case investigates extreme throughputs around 1 Tb/s, targeting fronthaul of future cellular systems. It illustrates the feasibility of such systems from the power consumption point of view.

Millimeter-wave is one of the technologies powering the new generation of wireless communication systems. To compensate the high path-loss, millimeter-wave devices need to use highly directional antennas. Consequently, beam misalignment causes strong performance degradation reducing the link throughput or even provoking a complete outage. Conventional solutions, e.g. IEEE 802.11ad, propose the usage of additional training sequences to track beam misalignment. These methods however introduce significant overhead especially in dynamic scenarios. In this paper we propose a beamforming scheme that can reduce this overhead. First, we propose an algorithm to design a codebook suitable for mobile scenarios. Secondly, we propose a blind beam tracking algorithm based on particle filter, which describes the angular position of the devices with a posterior density function constructed by particles. The proposed scheme reduces by more than 80% the overhead caused by additional training sequences.

We address the problem of static clutter removal in Wi-Fi-based passive bistatic radars. Our goal is to detect slowly moving targets in highly cluttered indoor environments, using Orthogonal Frequency-Division Multiplexing signals from the 802.11n and 802.11ac Wi-Fi standards as sources of opportunity. We propose alternatives to the commonly used Extended Cancellation Algorithm (ECA) clutter removal method. Those alternatives are compared to ECA with simulations using an innovative metric based on CA-CFAR detection, and validated with experimental measurements using two Universal Software Radio Peripherals, along with a fan and an electric train as radar targets. The conclusion of that analysis is that, thanks to the decoupled range and Doppler radar processing, simple novel methods such as Average Removal are efficient alternatives to the computationally intensive ECA which is currently the state-of-the-art in CR.

The use of in-band full duplex (IBFD) is a promising improvement over classical TDD or FDD communication schemes. To enable IBFD radios, the electrical balance duplexer (EBD) has been proposed to suppress the direct self-interference (SI) at the RF stage. The remaining SI is typically assumed to be canceled further in the digital domain. In this paper, we show that the non-zero Doppler frequencies can be extracted from the residual SI, giving information about the speed of objects in the environment. As a result, an IBFD radio can be seen as a monostatic Doppler radar which is affected by the communication signal. This paper presents a detailed performance analysis of the different sources of interference affecting the Doppler radar. The performance is also evaluated using a radar-enhanced IBFD prototype consisting of a SDR module and an EBD designed to achieve up to 55 dB Tx-Rx isolation in the 1.74 GHz RF band. A measurable Doppler component is created by moving a 15 dBsm cone at known velocities at 0. 5-1.3 m from the prototype. For an EBD SI rejection of 45 dB, speeds in the range of 200 to 800 mm/s are detected with high accuracy.

Multi-user MIMO at millimeter wave frequencies combines the huge bandwidth availability at millimeter wave with the spatial multiplexing gain of MIMO processing. Hybrid architectures have been proposed to reach this purpose while keeping implementation costs reasonably low. Active power splitter devices are the key components of a hybrid architecture enabling a lower implementation complexity compared to a full-digital solution. They distribute the same signal to several antenna elements shifting part of the precoding operations to the analog domain. Active power splitter devices however suffer from frequency distortion and their characterization is thus essential for new system designs. In this paper we propose a splitter model based on real hardware measurements showing that the splitter design critically affects the system performance. The frequency distortion influencing the digital channel estimation significantly distorts the precoded signal leading to an EVM floor in the high SNR region. However, when dynamically adapting the splitter operating point to control the trade-off between gain and bandwidth adaptively, the system EVM can be drastically reduced.

Given the high throughput requirement for 5G, merging millimeter wave technologies and multi- user MIMO seems a very promising strategy. As hardware limitations impede to realize a full digital architecture, hybrid MIMO architectures using both analog and digital precoding are considered a feasible solution to implement multi- user MIMO at millimeter wave. Real channel propagation and hardware non-idealities degrade the performance of such systems thus experimenting the new architecture is crucial to support system design. Nevertheless, hybrid MIMO systems are not yet understood as the effects of the wide channel bandwidths at millimeter wave, the non-ideal RF front end as well as the imperfections of the analog beamforming using phased antenna arrays are often neglected. In this paper, we present a 60 GHz multi-user MIMO testbed using phased antenna arrays at both transmitter and receivers. The base station equipped with a 32 phased antenna array allocates simultaneously two users. We show that frequency selective hybrid precoding can efficiently suppress inter-user interference enabling spatial multiplexing in interference limited scenario doubling the throughput compared to a SISO scenario and compensating the frequency fluctuation of the channel. In addition, we report an EVM constellation improvement of 6 dB when comparing the hybrid MIMO architecture with a fully analog architecture.

Contemporary surveillance systems mainly use video cameras as their primary sensor. However, video cameras possess fundamental deficiencies, such as the inability to handle low-light environments, poor weather conditions, and concealing clothing. In contrast, radar devices are able to sense in pitch-dark environments and to see through walls. In this paper, we investigate the use of micro-Doppler (MD) signatures retrieved from a low-power radar device to identify a set of persons based on their gait characteristics. To that end, we propose a robust feature learning approach based on deep convolutional neural networks. Given that we aim at providing a solution for a real-world problem, people are allowed to walk around freely in two different rooms. In this setting, the IDentification with Radar data data set is constructed and published, consisting of 150 min of annotated MD data equally spread over five targets. Through experiments, we investigate the effectiveness of both the Doppler and time dimension, showing that our approach achieves a classification error rate of 24.70% on the validation set and 21.54% on the test set for the five targets used. When experimenting with larger time windows, we are able to further lower the error rate.

Distributed massive multiple-input multiple-output (DM-MIMO) gives a higher spectral efficiency and enhanced coverage area, compared to collocated massive MIMO (CM-MIMO). In general, for massive MIMO, time division duplex is preferable as it enables downlink (DL) precoding based on uplink (UL) channel estimation. A time division duplex (TDD) reciprocity calibration is then essential to compensate the gap between the UL-DL channels, which can be done completely in the base station relying on sounding reference signals (SRS). For a collocated array, relying on mutual coupling between antenna elements, each SRS is received with sufficient power in the array, enabling a reliable estimate of the calibration coefficients. Nevertheless, for DM-MIMO, much less power is collected in distant inter-cluster antennas which degrades the accuracy of the estimated calibration. In this paper, we propose a novel inter-cluster combining method (ICCM) which improves the signal-to-quantization-noise ratio (SQNR) of the SRS, and hence achieves a more robust calibration accuracy for practical DM-MIMO systems. Our experimental results of two 32-antenna arrays distributed in an indoor environment show that ICCM outperforms the existing state-of-the-art algorithms in the sense of lower DL error vector magnitude (EVM) by exploiting diversity and array gain efficiently.

Massive MIMO has the potential to bring great spectral and energy efficiency improvements, making it a very promising technology for future wireless systems. Essential to achieve the gains in practice, is the ability to realize the many antenna paths at low complexity. In this paper, we consider the potential of processing at the semiconductor edge by allowing voltage over-scaling and complete antenna signal failures, focusing on the per-antenna digital functionality that dominant the DSP complexity. The impact of the resulting hardware errors on the performance of Massive MIMO transmission is analyzed. It shows that the inherent redundancy in the system brings a solid tolerance to sporadic hardware errors. Potential control tactics are introduced, that could further optimize the operation of the error-prone circuitry. We anticipate that by exploiting the system and circuit margins, up to 40% power reduction could be achieved on the considered DSP functions without sacrificing performance in many traffic scenarios.

Emerging cellular networks integrate the user terminal geo-localization function besides the communication function. The conventional positioning approach is to estimate the terminal location in two-steps: first the distance to all connected base stations is assessed based on signal time-of-flight measurements, then the location is deduced from the distances by multi-lateration. The two-step approach incurs a performance degradation because information is lost from the received signal when the multi-lateration is performed. In this paper, we propose to iterate between the two conventional steps to progressively refine the distance estimates based on the knowledge of the position estimate obtained from the previous iterations. The information exchanged between the two-steps not only consists in the mean of the estimates (distance or position) but also of their variance that convey information about the reliability of the estimates. Simulation results show that the achievable performance after a few iterations is close to the performance of the optimal approach that directly estimates the position based on the observation of the received signal.

This paper presents an energy-efficient digital signal processor (DSP) for rectangular-to-polar conversion in 802.11ad polar transmitter working on 60 GHz band. Firstly, system simulations with a complete transmission chain are conducted with regard to error vector magnitude and output spectrum, which allows to systematically optimize the design requirements on the DSP block. Secondly, algorithm and architecture co-optimization on the DSP block is explored to minimize the power consumption. Finally, the proposed DSP is synthesized using 28 nm CMOS technology, which provides a throughput of 7.04 Giga samples per second with a power consumption of 28 mW, and area of 0.01 mm2.

This paper presents an efficient mitigation approach for duty cycle mismatch of in-phase and quadrature upconversion signals in digital transmitters. This approach is supported by a mathematical analysis of the baseband equivalent impact of duty cycle mismatch. An efficient digital pre-distortion method is proposed to eliminate the distortion impact. Simulation results show that, for both 64-QAM and 256-QAM modulation schemes, the error-vector-magnitude can be improved from -25.1dB to less than -55dB, which leaves substantial design margin for other non-idealities distorting the transmitted signal.

Harmonic rejection mixing is indispensable in software defined radio receivers employing switched mixers. Current analog multi-path mixing solution suffers from phase and gain mismatches along the paths and as a consequence cannot provide sufficient harmonic rejection. In this paper, we present a low complexity flexible digital intensive harmonic rejection architecture and show how it can be used to enhance the rejection of any single harmonic interference by adaptively combining the different mixing paths. Simulation results show that the proposed method can reject any single interferer adaptively by over 80 dB, which is sufficient for practical applications.

The Chinese Digital Television Terrestrial Broadcasting System has a complex PHY layer definition with many different modes including two different block transmission schemes (OFDM and SC) and three different known symbol padding cyclic extensions, some of which with phase rotation between blocks that break the cyclicity. The block sizes with or without cyclic extension are “non power of two” numbers. This plurality of modes and the unusual block sizes make the design of a signal processing architecture very difficult. In addition, the known symbol padding extensions are intended for channel estimation but have poor auto-correlation properties; hence the channel estimation in long multipath channels is degraded and not suitable for high order constellations. We have designed a novel unified receiver architecture supporting all modes of this broadcasting system, capable to start from a poor initial channel estimation. We describe in detail this architecture and provide simulation results supporting our system choices.

Cognitive radios impose challenges on the design of efficient signal detectors, including wide bandwidth sensing and large dynamic range support. The recently considered compressed sensing theory helps in relaxing the constraints on the design of the analog front-end. The maximum likelihood method introduced here is computationally simple since it does not require a signal reconstruction, unlike most methods introduced in the current literature. Moreover, the metric is optimum, works for any modulation scheme and is independent of the emitted signal knowledge and the number of occupied bands. The results are supported with Matlab simulations, a statistical study is performed and the probabilities of misdetection and false alarm are plotted for different scenarios, proving the efficiency of the estimator in a range of plausible SNRs and subsampling factors.

The rapidly evolving and diversifying wireless landscape demands highly flexible wireless chipsets. Due to the ultimate programmability, SDR solutions are becoming more and more attractive. However, the programmability overhead is still a concern for the silicon area cost of SDR solutions. In this work, we prove that, with algorithm and architecture co- design, SDR solutions can be very competitive even when compared to highly optimized ASICs. Specifically, we show a baseband processor design that can support ISDB-T, DVB-T and ATSC, but the area cost is still comparable to the combination of ASICs which handle the three terrestrial digital TV standards respectively.

To enable frequency domain equalization of continuous phase modulations (CPM), a block construction that ensures cyclicity over each block without disrupting the phase continuity between them was proposed in . We formalize and prove the constraints that should be respected to enable the application of this technique to any CPM scheme.

We address the problem of channel tracking in fast vehicular environments for OFDM systems. In modern vehicular OFDM systems such as the IEEE802.11p, the preamble-based channel estimation is not sufficient to guarantee a good equalization until the end of the burst. Indeed, at urban and highway vehicular speeds, a conventional OFDM receiver generates a large number of errors after only a few OFDM symbols and the bit and packet error rate curves show unacceptable flooring. In addition, the number of pilot sub-carriers during the burst is too small to accurately track the channel variations. To alleviate this, we propose an advanced receiver scheme that updates the channel during the burst in a decision-directed fashion. We show that this technique alone is not sufficient and that it must be complemented with channel smoothing to perform satisfactorily at low SNRs. Finally, we also analyze how the system can be further improved with spatial diversity. Simulation results are provided to illustrate the performance of our novel receiver design and some complexity reduction techniques are described.

We address the problem of detecting and locating narrowband tones in an undersampled signal. It is known that uniformly undersampled signals exhibit frequency aliasing, whereby the frequency location is impossible. To alleviate aliasing, non-uniform sampling can be used. This, however, generates a high level of frequency leakage that prevents detection of weaker signals. We introduce a novel iterative frequency excision technique that allows to detect tones below the original noise floor due to leakage. Up to 20dB of leakage reduction has been achieved with this method.

The large bandwidth available at 60 GHz together with the resulting wavelength of only 5 mm allow the design of multi-Gbps wireless devices equipped with large arrays of tiny antennas. This enables wireless communication of large contents multimedia such as high-definition video. However, due to the high cost and power consumption of analog frond-end (AFE) chains at 60 GHz, it is practically infeasible to allocate a dedicated AFE to each antenna. Thus, it is highly desirable to design low complexity multi-antenna architectures in which an analog linear transformation (ALT) is carried out in order to reduce the required number of AFE chains for digital spatial processing, while minimizing the capacity loss. In this paper, we propose a non-iterative algorithm for joint transmit/receive (TX/RX) ALT. The proposed algorithm is designed with the aim of maximizing the capacity of the resulting reduced dimension MIMO system, assuming a frequency selective propagation channel.

We address the problem of co-channel interference (CCI) in wireless mesh networks (WMNs). In such networks, multiple nodes communicate concurrently using the same time/frequency resources. This is recognized as the interference channel (IFC) because the CCI is a major impairment in such a scenario. To mitigate the CCI, non-cooperative beamforming techniques can be employed. Non-cooperative beamformers are simpler to implement than their cooperative counter-parts because they require neither synchronization nor the sharing of information data between the transmit nodes. In this paper, we then propose a non-cooperative beamforming scheme to improve the performance of WMNs with frequency-selective channels. We present an iterative algorithm that maximizes the signal-to-interference-and-noise ratio criterion over all subcarriers of the orthogonal frequency division multiplexing system. The performance of this algorithm is evaluated through simulations.

Multi-antenna architectures, where beamforming processing is shared between analog and digital, are of great interest for future multi-Gbps wireless systems operating at 60 GHz. In this spectrum band, wireless systems can integrate large antenna arrays in a very small volume thanks to a wavelength of about 5 mm and thus provide the required gain to meet the severe link budget. However, the cost and power consumption of an analog front-end (AFE) chain, that carries out translation between radio frequency (RF) and digital baseband, are too high at 60 GHz to afford one AFE for each antenna. In this paper, we consider low cost multi-antenna architectures with a lower number of AFE chains than antenna elements. We propose a joint design of transmit-receive mixed analog/digital beamformers that aim at maximizing the received average signal-to-noise-ratio (SNR). The proposed scheme shows better performance than state-of-art solutions, which combine antenna selection techniques and digital beamforming.

To enable frequency domain equalization (FDE) for continuous phase modulation (CPM), both cyclicity of individual symbol blocks and phase continuity between different blocks have to be guaranteed. In this letter, we present new block constructions that use a subblock of data-dependent symbols, called intrafix, to satisfy both constraints for different CPM-FDE systems: using either a cyclic prefix or a training sequence (TS), both for precoded and nonprecoded CPM. The known symbols of a TS can be used to improve synchronization and channel estimation. Precoding can be applied to a certain class of CPM schemes to halve the bit error rate.

For both outdoor and indoor wireless systems there is an increasing demand of high spectral efficiency at a very low cost and power consumption. In this context, MIMO wireless system adopting spatial multiplexing offer a way of increasing the spectral efficiency of the system. In order to fully exploit this capacity non linear MIMO detectors such as maximum likelihood detectors are required. However, when high order modulation schemes are applied, the complexity of this kind of detector becomes prohibitive for a practical implementation. As a solution to this problem, low complexity maximum likelihood detectors such as sphere detectors are appealing as a low complexity solution for high spectral efficiency transmission. Although sphere decoding provides a lower complexity solution than a classical ML detector, its complexity still remains unpredictable and exponentially dependent on channel propagation conditions. This variability in complexity makes the implementation of sphere decoders not practical. In this paper, a new approach for constraining the ML search space is proposed which provides a predictable upper bound for complexity, hence facilitating its implementation. Moreover, the new approach for computing the constrained search space significantly reduces the complexity of the detection while offering scalability in terms of performance and complexity. Simulation results in a cellular system demonstrate the scalability of our detector and the performance/complexity trade-off that it enables.

Analog beamforming (ABF) with one scalar weight per antenna is an attractive technique for low-cost, low-power 60 GHz multi-antenna wireless communication systems. However, the design of the corresponding joint transmit and receive (Tx/Rx) ABF optimization algorithms is still challenging in the case of multipath channels due to the constraint of having only one scalar weight per antenna. In this paper, we aim at maximizing the average signal to noise ratio (SNR) at the input of the equalizer and analytically derive close-to-optimal Tx/Rx scalar weights. We show that the required channel state information (CSI) for joint Tx/Rx ABF weights computation is the inner product between all Tx/Rx channel impulse response pairs. Taking the channel length into account, a training-based estimation strategy of this CSI is proposed. Simulation results carried out in a typical 60 GHz multipath environment show that the proposed scheme outperforms the existing ABF schemes in term of BER performances.

We present a new scheme for maximizing the signal-to-noise ratio (SNR) in wideband multi-antenna receivers that employ time-interleaved analog-to-digital converters (ADCs). Current wideband receivers interleave a fixed number of slower ADCs into one fast ADC and assign this latter to one antenna according to an antenna selection algorithm. However, this does not always guarantee an optimal trade-off between thermal noise and quantization noise. This results in an overall SNR that is lower than what could be obtained with the same number of ADCs, assigned in a more optimal way. Therefore, we propose to adjust the number of slower ADCs assigned to a certain fast, interleaved ADC dynamically, according to the SNR of every individual antenna. Our proposed algorithm can be implemented at the expense of a very limited hardware complexity increase. The SNR gain of our new scheme can exceed 7 dB, depending on channel conditions and ADC specifications.

In this paper, an efficient computational scheme is proposed to calculate the symbol mean and variance from bit a priori information, when a so-called multilinear mapping is employed. The multilinear mapping is exploited to reduce the number of the terms needed for the calculation of the symbol mean and variance.

In this paper, we develop a new low-complexity linear frequency domain equalization (FDE) approach for continuous phase modulated (CPM) signals. As a CPM signal is highly correlated, calculating a linear minimum mean square error (MMSE) channel equalizer requires the inversion of a nondiagonal matrix, even in the frequency domain. In order to regain the FDE advantage of reduced computational complexity, we show that this matrix can be approximated by a block-diagonal matrix without performance loss. Moreover, our MMSE equalizer can be simplified to a low-complexity zero-forcing equalizer. The proposed techniques can be applied to any CPM scheme. To support this theory we present a new polyphase matrix model, valid for any block-based CPM system. Simulation results in a 60 GHz environment show that our reduced-complexity MMSE equalizer significantly outperforms the state of the art linear MMSE receiver for large modulation indices, while it performs only slightly worse for small ones.

One of the techniques for spectrum sensing is the energy detection of signals from primary transmitters. However, when detecting in a certain frequency band, this technique suffers from the random fading introduced by the channel. This problem can be alleviated by the use of multiple antennas introducing spatial diversity. The performance of the energy detector with multiple antennas has already been analytically analyzed for flat fading channels. In this paper, we propose analytical expressions for the performance of multi-antenna energy detector with multi-paths fading channels. These expressions will enable to compare for different channel power profiles the gains brought by the two sources of diversity: spatial (or antenna) diversity and multi-path diversity. This analytical performance is compared to simulations to show the validity of the used approximations.

Continuous phase modulations (CPM) have a perfectly constant envelope and are inherently robust against analog front-end nonidealities. Therefore, they have recently been proposed for several modern wireless standards. In this paper, we further develop an existing high-performance frequency domain equalization approach for CPM signals. First, we introduce a polyphase matrix model, valid for any block-based CPM system, which has clear advantages compared to existing models. Second, the model is used to reduce the complexity of the existing minimum mean square error channel equalizer. The proposed technique can be applied to any CPM scheme. Simulation results in a 60 GHz environment confirm that it does not cause any noticeable performance degradation.

In this paper, we present an analytical analysis to predict the power spectral density (PSD) at the output of a nonlinear power amplifier (PA). We focus on offset quadrature phase shift keying (OQPSK) waveform band-limited by a square root raised cosine (SRRC) filter. This is one of the waveforms used in wideband code division multiple access (W-CDMA) wireless standard. We show that the PA output PSD obtained by our analytical analysis matches well the simulated PSD. Furthermore, we compare the PA output PSD of QPSK and OQPSK waveforms as a function of the SRRC filter roll-off. We conclude that for small roll-off, both QPSK and OQPSK experience almost the same level of spectral re-growth. As the roll-off increases, OQPSK becomes less sensitive to PA nonlinearity relative to QPSK.

We show how to apply Frequency Domain Equalization (FDE) to precoded Continuous Phase Modulation (CPM) systems. It is well known that differential precoding can be applied to the specific, popular class of CPM schemes with modulation index h = 1/2Q, where Q is any integer. This precoding halves the bit error rate (BER) compared to nonprecoded CPM without any overhead or complexity increase. We apply FDE to a block-based precoded CPM system. Therefore, we show that in addition to a cyclic prefix, two subblocks of data-dependent symbols have to be inserted in each block to cope with the memory in the CPM signal and to enable correct decoding by the receiver. We explain how to calculate these subblocks. Simulation results in a 60 GHz environment confirm that the BER is halved by precoding, and that this precoding is compatible with FDE using our new technique.

ML and near-ML MIMO detectors have attracted a lot of interest in recent years. However, almost all of the reported implementations are delivered in ASIC or FPGA. Our contribution is to co-optimize the near-ML MIMO detector algorithm and implementation for parallel programmable base-band architectures, such as DSPs with VLIW, SIMD or vector processing features. Although for hardware the architecture can be tuned to fit algorithms, for programmable platforms the algorithm must be elaborately designed to fit the given architecture, so that efficient resource-utilizations can be achieved. By thoroughly analyzing and exploiting the interaction between algorithms and architectures, we propose the SSFE (selective spanning with fast enumeration) as an architecture-friendly near-ML MIMO detector. The SSFE has a distributed and greedy algorithmic structure that brings a completely deterministic and regular dataflow. The SSFE has been evaluated for coded OFDM transmissions over 802.11n channels and 3GPP channels. Under the same performance constraints, the complexity of the SSFE is significantly lower than the K-Best, the most popular detector implemented in hardware. More importantly, SSFE can be easily parallelized and efficiently mapped on programmable baseband architectures. With TI TMS320C6416, the SSFE delivers 37.4 - 125.3 Mbps throughput for 4x4 64 QAM transmissions. To the best of our knowledge, this is the first reported near-ML MIMO detector explicitly designed for parallel programmable architectures and demonstrated on a real-life platform.

We present a new symbol block construction which yields a cyclic continuous phase modulated (CPM) signal to enable frequency domain equalization. It is known that in addition to a cyclic prefix, a subblock of data-dependent symbols has to be inserted in each block to cope with the memory in the CPM signal. We propose a new subblock, called intrafix, valid for any CPM scheme. Our intrafix is shorter than what is currently known in the literature, reducing the overhead. Moreover, it can be calculated on a per-block basis, without knowledge of previous blocks. We also prove that there are constraints on the length of both the intrafix and the total block by studying the influence of the modulation index. Simulation results in a 60 GHz environment show that our new block construction satisfies all requirements.

New air interfaces are currently being developed to meet the high spectral efficiency requirements of the emerging wireless communication systems. In this context, OFDM is considered as a promising air interface candidate for both indoor and outdoor communications. Besides spectral efficiency and power consumption, the production cost of the transceiver should also be optimized. Direct-conversion radio frequency receivers are appealing because they avoid costly intermediate frequency hardware. However, they imply analog IQ separation, introducing a phase and amplitude mismatch between the I and Q branches. A communication system based on OFDM is sensitive to synchronization errors, such as CFO, and to front- end non-idealities, such as IQ imbalance. The goal of this paper is to use the iterative EM algorithm to acquire jointly the CFO and the IQ imbalance. The solution relies on a standard compliant repetitive preamble and does not require the knowledge of the propagation channel. Based on a second order approximation of the likelihood function, the complexity of the EM algorithm is significantly reduced. The algorithm is shown to perform extremely well: the estimates of the CFO and of the IQ imbalance converge to their ML estimate after less than 3 iterations. It outperforms state-of-the-art solutions significantly and suffers from a lower computational complexity. While the CFO estimate is robust against variations of the SNR, the IQ imbalance estimate accuracy is reduced at values of the SNR below 10 dB and above 35 dB.

The improvement of spectral efficiency comes at the cost of exponential increment of signal processing complexity [1]. Hence, the energy-efficiency of baseband has recently turned out to be the bottleneck when deploying advanced air interfaces such as that in 4G. We advocate the scalable baseband design as a system level technique to aggressively optimize the average computation-load and associated energy-consumption. The key technique is to dynamically scale the baseband processing itself to the user requirement, the environment, the platform, etc. In this paper, we present the scalable design and VLIW processor based implementation of the OFDMA modulator, which is one of the most energy consuming parts of OFDMA and MIMO- OFDMA transmitters (in IEEE 802.16e , 3GPP LTE, etc.). Our work enables the OFDMA modulator to scale the modulation- accuracy and computation-load, so that the OFDMA modulator can dynamically reconfigure and work with minimal number of operations, whereas the required modulation-accuracy is still firmly guaranteed. Our work brings significant reductions in the average computation-load and associated energy-dissipation on real-life programmable platforms. Specifically, when the user is working with 16QAM and 1/2 coding rate (Turbo Coding) in a half-loaded 8-user system, the proposed scheme reduces 84% of the cycle-count and the associated energy-consumption on TI TMS320C6713, whereas the resulted Relative Constellation Error (RCE) is still lOdB better than the required RCE in IEEE 802.16e specifications.

A new approach for frequency-domain equalization of continuous phase modulated (CPM) signals is presented. In contrast with state-of-the-art receivers, we separate channel equalization on the one hand and CPM demodulation on the other. This separation enables us to calculate independently of the CPM scheme a new low-complexity zero-forcing channel equalizer. We also present a new high-performance minimum mean square error (MMSE) channel equalizer for any CPM scheme and a method to lower its complexity for a popular class of CPM schemes. Simulations show that our new MMSE equalizer significantly outperforms state-of-the-art linear receivers in a 60 GHz multipath environment.

Single-carrier with cyclic prefix (SC-CP) is seen as an interesting air interface to replace orthogonal frequency- division multiplexing (OFDM) because it features a lower peak- to average power ratio (PAPR) while still allowing low complexity frequency domain equalization (FDE). Both air interfaces are highly sensitive to phase noise (PN) that degrades their system performances. In this paper, we study and compare analytically the PN impact on both air interfaces. Simulations are also carried out to validate the analytical results. PN causes the same common phase error (CPE) on both air interfaces, as well as it leads to Inter-Carrier Interference (ICI) in OFDM and inter-symbol interference (ISI) in SC-CP. However OFDM is found to be slightly less affected than SC-CP in both flat channels and frequency selective channels. It is shown also that CPE is the dominate impact if the PN cut-off frequency is smaller than the subcarrier spacing.

Multilevel continuous phase modulated (CPM) signals feature a perfectly constant envelope, attractive spectral properties and excellent power efficiency. However, their non-linear nature makes them less tractable and their processing more complex. Fortunately, a linear decomposition exists, allowing to apply linear signal processing techniques. This decomposition was originally developed for binary CPM schemes only, and is not suited for schemes with an integer modulation index. It was extended to multilevel CPM schemes, by decomposing the multilevel input sequence in a product of binary subsequences, and applying the binary decomposition to these subsequences. When one or more of these subsequences has an integer modulation index though, this technique fails. We present a general solution, and prove how many pulses are needed to represent the CPM signal in this particular case. The decomposition of the quaternary 3RC system with h = 1/2 is given as an example. A receiver based on this solution is presented.

In order to meet the data rate and quality-of-service (QoS) requirements of the future cellular systems, new air interfaces are currently under development. In this paper, we compare two air interfaces of particular interest for the uplink: cyclic-prefix code-division multiple access (CP-CDMA) proposed in the literature as an evolution of direct-sequence code-division multiple access (DS-CDMA) because it enables the low complexity equalization of the multipath channel in the frequency domain, and single-carrier frequency-division multiple access (SC-FDMA), recently proposed in the long term evolution of the 3GPP standard because it enables the easy separation of the users in the frequency domain. We demonstrate analytically that SC-FDMA is a special case of CP-CDMA, in which the CDMA codes have been optimized to minimize the symbol estimation mean square error (MSE) under a constraint of received power. Numerical results show that SC-FDMA outperforms significantly CP-CDMA at high user loads. The transmit power necessary to fulfill the received power constraint is higher in case of SC-FDMA than in case of CP-CDMA when the carrier sub-sets are allocated randomly to the users, and lower when the carrier sub-sets are allocated in an optimized way.

New air interfaces are currently being developed to meet the high spectral efficiency requirements of the emerging wireless communication systems. In this context, OFDM is considered as a promising air interface candidate for both indoor and outdoor communications. Besides spectral efficiency and power consumption, the production cost of the transceiver should also be optimized. Direct-conversion radio frequency receivers are appealing because they avoid costly intermediate frequency hardware. However, they imply analog IQ separation, introducing a phase and amplitude mismatch between the I and Q branches. A communication system based on OFDM is sensitive to synchronization errors, such as CFO, and to front-end non-idealities, such as IQ imbalance. The goal of this paper is to use the iterative EM algorithm to acquire jointly the CFO and the IQ imbalance. The solution relies on a repetitive preamble and does not require the knowledge of the propagation channel. Based on a second order approximation of the likelihood function, the complexity of the EM algorithm is significantly reduced. The algorithm is shown to perform extremely well: the estimates of the CFO and of the IQ imbalance converge to their ML estimate after less than 3 iterations. While the CFO estimate is robust against variations of the SNR, the IQ imbalance estimate accuracy is reduced at values of the SNR below 10 dB and above 35 dB.

Nowadays a lot of effort is spent on developing OFDM- based inexpensive wireless transceivers. Direct-conversion radio frequency transceivers are appealing because they avoid costly IF analog components. This kind of transceivers imply analog RF I/Q separation. The mismatch between the analog components in the in-phase and quadrature branches introduces an unwanted in-band interference. Unfortunately, OFDM-based systems are very sensitive to I/Q mismatch, mostly when high order modulation schemes are applied. A digital compensation of this unwanted effect is required. In this paper, we developed a method for compensating the transmitter/receiver frequency-dependent I/Q imbalance jointly with the propagation channel in the frequency domain. Based on a low complexity ML channel estimator, a pilot design criterion is derived for channel and IQ imbalance coefficients estimation. An advanced equalizer is proposed which compensates for the frequency-dependent IQ imbalance. Both steps, estimation and compensation, are low cost in terms of implementation complexity. The proposed algorithm enables the system to achieve high SNRs.

The huge bandwidth available at 60 GHz will allow short range wireless communication to deliver bit rates over 1 Gbps. However the design of millimeter wave analog blocks is more critical than at lower frequencies, leading to a possible high non ideality of the radio front-end (FE). A suitable air interface for low cost, low power 60 GHz transceivers should thus use a modulation technique that has a high level of immunity to FE non-idealities. In this paper, we compare the sensitivity to FE non-idealities of two promising air interfaces namely offset quadrature phase shift keying (OQPSK) with frequency domain equalization (FDE) and continuous phase modulation (CPM) with time domain equalization (TDE). Our study focus on three main analog imperfections that are likely to have the biggest impact on the overall system performance: phase noise generated by the voltage control oscillator (VCO), clipping and quantization errors caused by the analog-to-digital converter (ADC) and non-linearity in the power amplifier (PA). Results show that CPM based air interface is more robust than OQPSK-based regarding the FE non-idealities. However the latter is less complex to design and offers much higher data rate than the former in the same bandwidth.

Changing user scenarios demand wireless connectivity among different standards. As a result, reconfigurability is becoming a key issue in the design of future wireless terminals. In analog front-ends, reconfigurable components are very expensive in terms of design cost and area. Bandpass sampling and digital front-end solutions in general, move the ADC closer to the antenna, avoiding most of the reconfigurable analog hardware. We consider the extreme case of bandpass sampling at RF and analyze the ADC requirements for a multi-standard radio. Two different models, one based on cascade analysis and the other on time-domain simulations, are used with a representative set of emerging wireless standards to derive the ADC requirements: sampling frequency, resolution and clock jitter. This study shows that, with modest RF filtering, RF bandpass sampling will soon become a reality for low power terminals.

In fast mobile environments, the reception of orthogonal frequency domain modulation (OFDM) signals with tight sub-carrier spacing is challenged by the inter-carrier interference (ICI). This ICI must be compensated for by a suitable equalizer design, which requires the estimation of a larger number of parameters than for static environments. In addition, ICI also degrades the quality of the channel estimation. We propose an analytical approach to estimate the frequency domain channel matrix that relies on its nearly banded structure and a first order Taylor approximation of the channel variation. An additional constraint of this work is to accommodate OFDM systems with large number of sub-carriers and scattered pilots, such as used in DVB-T and DVB-H broadcast. We provide simulation results to demonstrate the performance of the proposed channel estimation method.

A symbol timing synchronization scheme is critical in the design of an OFDM receiver. Large timing errors can result in a loss of orthogonality between subcarriers, ISI and severe bit error degradation. To minimize this degradation, standards incorporate preambles suitable for two kinds of synchronization algorithms: auto-correlation and crosscorrelation. Unfortunately, the performance and complexity tradeoffs between these algorithms have not been well explored. To address this problem, we have built an FPGA implementation of a synchronization system using both autocorrelation and cross-correlation. Based on our results, in this paper we propose a novel cross-correlation synchronizer and hardware architecture. We then compare its performance and complexity to auto-correlation algorithms for HiperLAN/2 and IEEE 802.11a preambles.