Federated learning (FL) has recently emerged as a tool for efficient distributed learning. In edge-assisted internet of things (IoT) paradigm, FL can achieve a promising speed of convergence in the learning process while preserving privacy. To overcome the inherent wireless network connectivity challenge in FL based IoT networks, unmanned aerial vehicles (UAVs) can play a vital role in offloading the learned models to a central learning server or play as a flying learning server. In this paper, we investigate the physical layer aspects of harnessing UAVs as flying FL server for the distributed model averaging. In particular, we study the effect of the UAV-to-ground communication model in the convergence of the model accuracy and loss curves using well-known benchmark datasets to illustrate the effects. We illustrate the effect of altering the deployment geometry of IoT clients simultaneously with the trajectory design of the UAVs.

Using Unmanned Aerial Vehicles (UAVs) in Search and rescue operations (SAR) to navigate challenging terrain while maintaining reliable communication with the cellular network is a promising approach. This paper suggests a novel technique employing a reinforcement learning multi Q-learning algorithm to optimize UAV connectivity in such scenarios. We introduce a Strategic Planning Agent for efficient path planning and collision awareness and a Real-time Adaptive Agent to maintain optimal connection with the cellular base station. The agents trained in a simulated environment using multi Q-learning, encouraging them to learn from experience and adjust their decision-making to diverse terrain complexities and communication scenarios. Evaluation results reveal the significance of the approach, highlighting successful navigation in environments with varying obstacle densities and the ability to perform optimal connectivity using different frequency bands. This work paves the way for enhanced UAV autonomy and enhanced communication reliability in search and rescue operations.

The rapid proliferation of consumer UAVs, or drones, is reshaping the wireless communication landscape. These agile, autonomous devices find new life as UE in cellular networks. This paper explores their integration, emphasizing the myriad applications, standardization efforts, challenges, and research community solutions. Key areas of investigation include the complexities of 3D deployment, channel modelling, and energy efficiency. Moreover, we highlight the open questions and research opportunities these flying UEs present. The evolving landscape of UAV integration into cellular networks promises transformative enhancements for next-generation communications, addressing challenges while fostering innovation across industries. The paper encapsulates the essential aspects of UAV integration within the cellular ecosystem, offering a concise yet comprehensive overview of this dynamic field, where UAVs as UEs redefine wireless communication with promise and complexity.

Federated Learning (FL) has emerged as a promising approach for privacy preservation, allowing sharing of the model parameters between users and the cloud server rather than the raw local data. FL approaches have been adopted as a cornerstone of distributed machine learning (ML) to solve several complex use cases. FL presents an interesting interplay between communication and ML performance when implemented over distributed wireless nodes. Both the dynamics of networking and learning play an important role. In this article, we investigate the performance of FL on an application that might be used to improve a remote healthcare system over ad hoc networks which employ CSMA/CA to schedule its transmissions. Our FL over CSMA/CA (FLCC) model is designed to eliminate untrusted devices and harness frequency reuse and spatial clustering techniques to improve the throughput required for coordinating a distributed implementation of FL in the wireless network.In our proposed model, frequency allocation is performed on the basis of spatial clustering performed using virtual cells. Each cell assigns an FL server and dedicated carrier frequencies to exchange the updated model’s parameters within the cell. We present two metrics to evaluate the network performance: 1) probability of successful transmission while minimizing the interference, and 2) performance of distributed FL model in terms of accuracy and loss while considering the networking dynamics.We benchmark the proposed approach using a well-known MNIST dataset for performance evaluation. We demonstrate that the proposed approach outperforms the baseline FL algorithms in terms of explicitly defining the chosen users’ criteria and achieving high accuracy in a robust network.

Abstract While Doppler resilient complementary waveforms (DRCWs) have previously been considered to suppress range sidelobes within a Doppler interval of interest in radar systems, their ability to provide Doppler resilience can be further improved. A new singular value decomposition (SVD)‐based DRCW construction is proposed, in which both transmit pulse trains (made up of complementary pairs) and receive pulse weights are jointly considered. Besides, using the proposed SVD‐based method, a theoretical bound is derived for the range sidelobes within the Doppler interval of interest. Moreover, based on the SVD solutions, a challenging non‐convex optimization problem is formulated and solved to maximise the signal‐to‐noise ratio (SNR) with the constraint of low range sidelobes. It is shown that, compared with existing DRCWs, the proposed SVD‐based DRCW has better Doppler resilience. Further, the new optimised SVD‐based DRCW has a higher SNR while maintaining the same Doppler resilience.

In this article, we develop a comprehensive framework to characterize the performance of drone assisted Backscatter communication based Internet of things (IoT) sensor network. We consider a scenario such where drone transmits RF carrier which is modulated by IoT sensor node (SN) to transmit its data. The SN implements load modulation which results in amplitude shift keying (ASK) type modulation for the impinging RF carrier. In order to quantify the performance of the considered network, we characterize the coverage probability for the ground based SN node. The statistical framework developed to quantify the coverage probability explicitly accommodates dyadic backscatter channel which experiences deeper fades than that of the one-way Rayleigh channel. Our model also incorporates Line of Sight (LoS) and Non-LoS (NLoS) propagation states for accurately modeling large-scale path-loss between drone and SN. We consider spatially distributed SNs which can be modelled using spatial Binomial Point process (BPP). We practically implement the proposed system using Software Defined Radio (SDR) and a custom designed SN tag. The measurements of parameters such as noise figure, tag reflection coefficient etc., are used to parametrize the developed framework. Lastly, we demonstrate that there exists an optimal set of parameters which maximizes the coverage probability for the SN.

In this paper, we present a detailed analysis of the coverage and spectral efficiency of a downlink cellular network. Rather than relying on the first order statistics of received signal-to- interference-ratio (SIR) such as coverage probability, we focus on characterizing its meta- distribution. Our analysis is based on the alpha- beta-gamma (ABG) path-loss model which provides us with the flexibility to analyze urban macro (UMa) and urban micro (UMi) deployments. With the help of an analytical framework, we demonstrate that selection of underlying degrees-of-freedom such as BS height for optimization of first order statistics such as coverage probability is not optimal in the network-wide sense. Consequently, the SIR meta-distribution must be employed to select appropriate operational points which will ensure consistent user experiences across the network. Our design framework reveals that the traditional results which advocate lowering of BS heights or even optimal selection of BS height do not yield consistent service experience across users. By employing the developed framework we also demonstrate how available spectral resources in terms of time slots/channel partitions can be optimized by considering the meta-distribution of the SIR.

The performance of wideband array signal processing algorithms is dependent on the noise level in the system. A method is proposed for reducing the level of white noise in wideband linear arrays via a judiciously designed spatial transformation followed by a bank of highpass filters. A detailed analysis of the method and its effect on the spectrum of the signal and noise are presented. The reduced noise level leads to a higher signal‐to‐noise ratio for the system, which can have a significant beneficial effect on the performance of various beamforming methods and other array signal processing applications such as direction of arrival estimation. Here the authors focus on the beamforming problem and study the improved performance of two well‐known beamformers, namely the reference signal based and the linearly constrained minimum variance beamformers. Both theoretical analysis and simulation results are provided.

Direct device-to-device (D2D) communication presents as an effective technique to reduce the load at the base station (BS) while ensuring reliable localized communication. In this paper, we propose a large-scale M2M data Aggregation and Trunking (MAT) scheme, whereby the user equipments (UEs) aggregate M2M data from the nearby MTDs and trunk this data along with their own data to the BS in the cellular uplink. We develop a comprehensive stochastic geometry framework by considering a Poisson hard sphere model for UE coverage. The main motivation of this model is to capture the fact that a UE can gather data from short range, low-power MTDs located only in its close proximity while ensuring that an MTD is associated to at most one UE. We explore the inherent trade-off between the time reserved for aggregation and successful trunking of data to the BS and compare our results with the baseline case where no aggregation mechanism is used. We show that while the baseline case of connecting a bulk of MTDs directly with the BS is prohibitive, MAT scheme can efficiently gather data from selected MTDs in a distributed manner.

IEEE 802.11ah has recently emerged as a promising standard for enabling massive machine-to-machine (M2M) communication. In order to support uplink data transmission from dense machine type clients (such as smart meters, IoT end nodes etc.), 802.11ah relies upon the restricted access window (RAW) based Medium Access Control (MAC) protocol. The underlying motivation behind this protocol is to reduce the contention for spectrum access among a large number of devices. The nodes contend with each other in their assigned RAW slot using Carrier Sense Multiple Access with Collision Avoidance (CSMA/CA). In each RAW slot, the throughput depends upon the number of nodes. Current studies have suggested that the duration of each RAW slot should be the same in the entire RAW frame. However in this paper, we argue that the duration of each RAW slot should be chosen according to the size of the group. We present a model where a RAW frame is divided into two sub-frames and the duration of RAW slots in each sub-frame is chosen according to the size of the group. With the help of an analytical framework, we demonstrate that the throughput under our proposed scheme can be significantly enhanced when compared to a conventional implementation.

In this article, we investigate how the performance of Massive MIMO cellular systems can be enhanced by introducing D2D communication. We consider a scenario where the base station (BS) is equipped with large, but finite number of antennas and the total number of UEs is kept fixed. The key design question is that what fraction of users should be offloaded to D2D mode in order to maximize the aggregate cell level throughput. We demonstrate that there exists an optimal user offload fraction, which maximizes the overall capacity. This fraction is strongly coupled with the network parameters such as the number of antennas at the BS, D2D link distance and the transmit SNR at both the UE and the BS and careful tuning of the offload fraction can provide up to 5× capacity gains.1

Mobile robots (MRs) using wireless communications often experience small-scale fading so that the wireless channel gain can be low. If the channel gain is poor (due to fading), the robot can move (a small distance) to another location to improve the channel gain and so compensate for fading. Techniques using this principle are called mobility diversity algorithms (MDAs). MDAs intelligently explore a number of points to find a location with high channel gain while using little mechanical energy during the exploration. Until now, the location of these points has been predetermined. In this paper, we show how we can adapt their positions by using channel predictors. Our results show that MDAs, which adapt the location of those points, can in fact outperform (in terms of the channel gain obtained and mechanical energy used) the MDAs that use predetermined locations for those points. These results will significantly improve the performance of the MDAs and consequently allow MRs to mitigate poor wireless channel conditions in an energy-efficient manner.

While stochastic geometric models based on Poisson point processes (PPPs) provide a tractable approach for the analysis of uniform two-tier network deployments, the performance evaluation of a non-uniform deployment remains an open issue, which we address in this paper. This is due to the fact that smaller cells can be more efficiently deployed in areas where the QoS of traditional macro base stations is poor. Therefore, in this paper, we introduce Stienen's model, which allows us to analyse such non-uniform deployment. In contrast to traditional PPP-based analysis, performance characterization under the Stienen model is more challenging due to location and density dependencies. However, we demonstrate that the performance can be approximated in a tractable manner. The developed statistical framework is employed to characterize the gains in terms of energy efficiency (EE) for non-uniform deployments. Results show an achievable 19% to 124% improvement in the macrocell coverage as compared to a uniform deployment, while the femtocell coverage and system EE are of the same order of magnitude for both deployments. These results are complemented with the fact that OPEX and CAPEX are reduced due to a lesser number of FAPs deployed.

In this paper, we develop a comprehensive analytical framework for cellular networks that are enhanced with coordinated device-to-device (D2D) communication, where the D2D devices are equipped with content caching capabilities. The base station (BS) coordinates the D2D communication by establishing a D2D link between the requesting user and the nearest D2D helper within the same cell if the latter contains the requested content, otherwise, the BS serves the user itself. The motivation behind restricting D2D pairs within a macro cell is to make coordinated D2D communication realizable as the BS can keep track of the content of the devices without the increased overhead of inter-BS coordination. This approach is similar to LTE direct, where D2D pairing is managed by the BS. We model the locations of BS and D2D helpers using a homogeneous Poisson point process (HPPP). The distribution of the distance between the tagged user and its neighboring D2D helper within the cell is derived using disk approximation for the Voronoi cell, which is shown to be reasonably accurate. We fully characterize the cellular and D2D coverage and the link spectral efficiency of such a network. Our results reveal that cache enabled D2D communication becomes more effective as the requesting user moves away from the BS and high performance gains can be achieved compared to conventional cellular networks, especially when the popularity distribution is skewed and most popular files are requested.

In this paper we introduce Stienen's model for analysing the performance of a non-uniform two-tier networks. The topology of the network consists of a set of macro base stations (MBSs) uniformly deployed, and a set of femtocell access points (FAPs) deployed only outside exclusion areas (discs) surrounding the MBSs. The MBSs serve users within the innermost areas of each macrocell, while the femtocells are restricted to serve users located in the outermost areas towards the edge of the macrocells. Results show that the edge user performance in terms of coverage is highly increased by the addition of femtocells. Moreover, the coverage in the macrocell tier can be also increased in comparison with a macrocell-only network if the number of femtocells deployed is judiciously selected. Furthermore, a well balanced network can be achieved, where the same performance is expected throughout the entire area.

In this article we employ the third generation partnership project (3GPP) recommended path loss models for the analysis of cellular networks overlaid with D2D communication and channel inversion power control in the uplink. We characterize the coverage and average network throughput with the help of stochastic geometry. More specifically, we develop tractable expressions for the coverage in cellular and D2D modes. Our theoretical results differ significantly from previous work, which uses simple power law path loss models. The traditional methodology does not account for the presence of line-of-sight (LoS), non-line-of-sight (NLoS) and free space (FS) links. We demonstrate that such classification of links significantly impacts the inference which can be derived from the analysis for the design of overlaid D2D networks. In particular, we show that, contrary to the previous findings, the average throughput of the network does not saturate with the increase in the density of base stations (BS), but there exists an optimal mode selection threshold and BS density which maximizes the average throughput.

Software Defined Networking (SDN) has recently emerged to become one of the promising solutions for the future Internet. With the logical centralization of controllers and a global network overview, SDN brings us a chance to strengthen our network security. However, SDN also brings us a dangerous increase in potential threats. In this paper, we apply a deep learning approach for flow-based anomaly detection in an SDN environment. We build a Deep Neural Network (DNN) model for an intrusion detection system and train the model with the NSL-KDD Dataset. In this work, we just use six basic features (that can be easily obtained in an SDN environment) taken from the forty-one features of NSL-KDD Dataset. Through experiments, we confirm that the deep learning approach shows strong potential to be used for flow-based anomaly detection in SDN environments.

The increasing demand for any-time any-where wireless connectivity has posed a formidable ‘1000 × data challenge’ for service providers. With the envisioned 1000 × explosion in mobile data traffic by the end of year 2020, wireless network architecture needs to rapidly evolve. In particular, the evolution trajectory should be charted such that exponential gains can be realised in network wide resource efficiency. This requires a clean slate design for future 5G wireless networks while provisioning interoperability with the legacy deployment. Both operators and technology providers realise that 5G will not merely be a newer version of 4G simply provisioning faster data transfers. These 5G networks are expected to be more dynamic due to heterogeneity in terms of devices, technologies, spectral bands and deployment models. Heterogeneity is indeed the intrinsic and central feature of the evolving networking paradigm. Now several potential solutions have recently been proposed to meet the aforementioned challenges and all address both network architecture and technologies. On the architectural front, concepts such as (i) cloudification & softwarisation of radio access networks; (ii) split-plane deployment; (iii) licensed shared access; (iv) decoupled uplink and downlink transmissions; and (v) information/content centric networking are all being considered as the enabling candidates. In terms of new technologies: (i) mmWave communications; (ii) massive MIMO; (iii) D2D communications; (iv) small cell deployment; and (v) low power IoT communication technologies (such as Bluetooth Low Energy, 802.11.ah WiFi, LoRA, SIGFOX) are all vital design tools for future 5G HetNets. In addition, the so-called concept of ‘tactile internet’, which has a wide spectrum of requirements ranging from ultra-low latency to ultra-high throughput via deployment of HetNets, cannot be realised without significant advances in signal processing algorithms. Thus, the main objective of this Special Section is to provide a platform for the dissemination of important results in those signal processing techniques necessary for enabling large scale, heterogeneous, 5G wireless networks. The first part of this Special Section presents four contributions. In the first paper, Mumtaz et al. present an energy efficient algorithm for D2D users in the presence of other cellular users (CUs). The authors employ Lagrangian duality theory for optimising both the power and rate of the D2D users while guaranteeing an acceptable quality-of-service (QoS) for the CUs. Finally, the solution of the proposed algorithm is then employed to achieve proportional fairness between the D2D and the CU users. The second paper (Butt et al.) reflects a growing interest in the area of green communication. It has recently been accepted that opportunistic exploitation of ambient energy sources is going to be the cornerstone of future wireless networks. The authors discuss relay selection schemes with the objective of minimising outage probability for a network consisting of a single source, multiple relays and a single destination. The relays are powered by radio frequency (RF) signals from the source and the authors present an optimal relay selection strategy to minimise outage probability for the system. Finally, a numerical solution is developed to determine the optimal number of relays. In the third paper, Gurjar et al. examine the significance of wireless channel estimation error on the performance of an analogue network coding (ANC)-based MIMO two-way relay system employing zero-forcing (ZF) transceivers in a Rayleigh fading environment. An analytical framework has been developed to study the overall outage analysis and some interesting (exact) expressions have been derived for special cases such as when the relay is equipped with less than two antennas. Some of the important contributions of this work are: a) exact expressions for the overall outage probability and the ergodic sum-rate have been derived within the context of channel estimation error; b) the authors have shown that system diversity may reduce to zero in the presence of channel estimation error due to imperfect self-interference cancellation; c) they conclude that a low complexity solution can be further derived by exploiting channel estimation error with ZF transmission/reception for an ANC based MIMO two-way relay system. In the final paper, Li et al. propose a hierarchical precoding approach for multi-cell, multi-user systems with any number of base stations and users, which is suitable for any number of data streams. The key feature of this approach is to align the inter-user interferences within the same cell to the room spanned by the inter-cell interferences, by which both the inter-cell and inter-user interferences are cancelled simultaneously. The effectiveness of this proposed method is demonstrated with an extensive set of simulations. In summary, this Special Section presents some important recent advances in D2D and relay assisted communication networks with a special focus on energy efficiency. Moreover, some of the state-of-the-art methods in multiuser MIMO systems have also been studied. For those interested in future 5G wireless networks, these articles will serve as a good springboard to appreciate further developments in this important topic. Finally, we would like to thank (i) all the submitting authors for considering this Special Section as a potential journal in which to publicise their research work; (ii) the reviewers for their high quality evaluations; and (iii) the Editorial team of the IET Signal Processing journal for their professional support. Syed Ali Raza Zaidi is currently University Academic Fellow (Assistant Professor) at the University of Leeds, UK. Prior to this, he was a Research Fellow in SPCOM Research Group at Leeds. He received his B. Eng. degree in information and communication system engineering from the School of Electronics and Electrical Engineering, NUST, Pakistan in 2008. He was awarded the NUST's most prestigious Rector's gold medal for his final year project. From September 2007 till August 2008, he served as a Research Assistant in Wireless Sensor Network Lab on a collaborative research project between NUST, Pakistan and Ajou University, South Korea. In 2008, he was awarded overseas research student scholarship along with Tetley Lupton and Excellence Scholarships to pursue his PhD at the School of Electronics and Electrical Engineering, the University of Leeds, U.K. He was also awarded with COST IC0902, DAAD and Royal Academy of Engineering grants to promote his research. In 2013, he was conferred with the prestigious F.W. Carter Prize for outstanding Doctoral thesis by the University of Leeds. Dr. Ali was a visiting Research Scientist at Qatar Innovations and Mobility Centre from October to December 2013. He has served as an invited reviewer for IEEE flagship journals and conferences. Dr. Ali is also UK Liaison for the European Association for Signal Processing (EURASIP). He is currently serving as an editor for IEEE Communication Letters and Lead Guest Editor for IET Signal Processing Special Section on 5G Wireless Networks. He is also the general secretary for IEEE Technical Committee on 5G Networks. He has published more than 60 papers in leading IEEE journals and conferences and has chaired several IEEE workshops/conferences. His current research interests are in the area of design and implementation of large scale networks for machine-to-machine communication (including robotics and autonomous systems). Des McLernon received his B.Sc in electronic and electrical engineering and his MSc in electronics, both from the Queen's University of Belfast, N. Ireland. He then worked in industry on radar systems research and development with Ferranti Ltd in Edinburgh, Scotland and later joined Imperial College, University of London, where he took his PhD in signal processing. After first lecturing at South Bank University, London, UK, he moved to the School of Electronic and Electrical Engineering, at the University of Leeds, UK, where he is a Reader in Signal Processing. His research interests are broadly within the domain of signal processing for wireless communications (in which area he has published over 285 journal and conference papers). He has supervised over 35 PhD students, given many invited talks in the UK and abroad and is Associate Editor of the IET Signal Processing journal. He has been a member of various international conference TPC's and conference organisation committees - recent conference organisation includes IEEE SPAWC 2010, European Signal Processing Conference (EUSIPCO) 2013, IET Conference on Intelligent Signal Processing (London, 2013/2015) and IEEE Globecom 2014/2015 (2nd /3rd Workshops on Trusted Communications with Physical Layer Security). His current research projects include distributed sensing, PHY layer security, caching and energy efficiency in heterogeneous networks, energy harvesting, robotic and drone communications, intrusion detection in software defined networks, compressive sensing and time-frequency analysis. Muhammad Ali Imran received his M.Sc. (Distinction) and Ph.D. degrees from Imperial College London, UK, in 2002 and 2007, respectively. He is currently a Reader in Communications in the Institute for Communication Systems (ICS - formerly known as CCSR) at the University of Surrey, UK and an adjunct Associate Professor at the University of Oklahoma, USA. He has lead a number of multimillion-funded international research projects encompassing the areas of energy efficiency, fundamental performance limits, sensor networks and self-organising cellular networks. He is also leading the new physical layer work area for 5G innovation centre at Surrey. He has a global collaborative research network spanning both academia and key industrial players in the field of wireless communications. He has supervised 21 successful PhD graduates and published over 200 peer-reviewed research papers including more than 20 IEEE Transaction papers. He has been giving a series of expert tutorials on emerging Green 5G technologies and networks at IEEE flagship conferences such as WCNC, PIMRC and ICC. Recently, he has been appointed as an area Chair for IEEE ComSoc Technical Committee on Backhaul/Fronthaul Networking and Communications (TCBNC). He secured first rank in his B.Sc. and a distinction in his M.Sc. degree along with an award of excellence in recognition of his academic achievements conferred by the President of Pakistan. He has been awarded IEEE ComSoc's Fred Ellersick award 2014 and FEPS Learning and Teaching award 2014 and twice nominated for Tony Jean's Inspirational Teaching award. He is a shortlisted finalist for The Wharton-QS Stars Awards 2014 for innovative teaching and VC's learning and teaching award in University of Surrey. He is a senior member of IEEE and a Senior Fellow of Higher Education Academy (SFHEA), UK. Muhammad Zeeshan Shakir is a Senior Research Fellow at Carleton University, Canada. In recent years, he has been involved in several joint R&D initiatives with Telus, DragonWave, University of Surrey, KAUST, and TAMUQ. His research interests include design and deployment of diverse wireless communication systems, including hyper-dense heterogeneous networks and related 5G technologies. He has published more than 75 technical journal and conference papers and has contributed to seven books, all in reputable venues. He is an author of three research monographs including one authored book. He earned his PhD degree in electronic and electrical engineering from University of Strathclyde, Glasgow, UK in 2010. He is an Associate Technical Editor of IEEE Communications Magazine and has served as a Lead Guest Editor for IEEE Communications and IEEE Wireless Communications Magazines. He has been serving as Chair/Co-chair of several workshops/symposia in IEEE flagship conferences, such as ICC and GlobalSIP. He has been giving a series of expert tutorials on emerging Green 5G technologies and networks at IEEE flagship conferences such as Globecom, ICUWB, PIMRC and ICC. Recently, he has been appointed as a Chair to IEEE ComSoc Technical Committee on Backhaul/Fronthaul Networking and Communications (TCBNC). He is an active member of IEEE, IEEE ComSoc and IEEE Standard Association. Mounir Ghogho received his MSc degree in 1993 and PhD degree in 1997 from the National Polytechnic Institute of Toulouse, France. He was an EPSRC Research Fellow with the University of Strathclyde, Glasgow (Scotland), from September 1997 to November 2001. Since December 2001, he has been a faculty member with the school of Electronic and Electrical Engineering at the University of Leeds, UK, where he currently holds a Chair in Signal Processing and Communications. Since 2010, he has also been a Research Director at the International University of Rabat (Morocco). He was awarded the UK Royal Academy of Engineering Research Fellowship in September 2000. He is one of the recipients of the 2013 IBM Faculty award. He is currently an Associate Editor of the IEEE Signal Processing magazine. He served as an Associate Editor of the IEEE Transactions on Signal Processing from 2005 to 2008, the IEEE Signal Processing Letters from 2001 to 2004, and the Elsevier's Digital Signal Processing journal from 2011 to 2012. He is currently a member of the IEEE Signal Processing Society SAM Technical Committee. He served as a member of the IEEE Signal Processing Society SPCOM Technical Committee from 2005 to 2010 and a member of IEEE Signal Processing Society SPTM Technical Committee from 2006 to 2011. He was the General Chair of the 11th IEEE workshop on Signal Processing for Advanced Wireless Communications (SPAWC2010) and the 21st edition of the European Signal Processing Conference (EUSIPCO 2013), and the Technical co-Chair of the MIMO symposium of IWCMC 2007 and IWCMC 2008. His research interests are in signal processing and communication networks. He has published over 260 journal and conferences papers. He was awarded the UK Royal Academy of Engineering Research Fellowship in September 2000. He is also one of the recipients of the 2013 IBM Faculty award and is the EURASIP Liaison in Morocco.

Mobile robots that wish to communicate wirelessly often suffer from fading channels. They need to devise an energy-efficient strategy to search for a high-channel-gain position in a near vicinity from which to begin communications. Such a strategy has recently been introduced through the mobility diversity with multithreshold algorithm (MDMTA). In this paper, we establish the theoretical framework for a generalized version of the MDMTA. This allows improved wireless communications in fading channels for mobile robots via intelligent robotic motion with low mechanical energy expenditure.

We address the optimal transmit power allocation problem (from the sensor nodes (SNs) to the fusion center (FC)) for the decentralized detection of an unknown deterministic spatially uncorrelated signal which is being observed by a distributed wireless sensor network. We propose a novel fully distributed algorithm, in order to calculate the optimal transmit power allocation for each sensor node (SN) and the optimal number of quantization bits for the test statistic in order to match the channel capacity. The SNs send their quantized information over orthogonal uncorrelated channels to the FC which linearly combines them and makes a final decision. What makes this scheme attractive is that the SNs share with their neighbours just their individual transmit powers at the current states. As a result, the SN processing complexity is further reduced.

In this paper, the energy efficiency (EE) of different MIMO diversity schemes is analyzed for the downlink of a two-tier network consisting of both macro- and femto-cells. The locations of the base stations (BSs) in both tiers are modeled by spatial Poisson point processes (PPPs). The EE of the system in b/J/Hz is obtained for different antenna configurations under various diversity schemes. Adaptive modulation is employed to maximize both the throughput and the EE across both tiers. Borrowing well established tools from stochastic geometry, we obtain closed-form expressions for the coverage, throughput, and power consumption for a two tier rate adaptive cellular network. Building on the developed analytical framework, we formulate the resource allocation problem for each diversity scheme with the aim of maximizing the network-wide EE while satisfying a minimum QoS in each tier. We consider that both the number of antennas and the spectrum allocated to each tier constitute the network resource which must be efficiently selected for both tiers to maximize network-wide performance. The best performance in terms of the EE is provided by the schemes which strike a good balance between the achievable maximum throughput and the consumed power (both increasing with the number of RF chains used). In addition, the potential savings in EE by using femto-cells with sleeping mode capabilities are analyzed. It is observed that, when the density of active co-channel femto-cells exceeds a certain threshold, the EE of the system can be significantly improved by sleep scheduling.

In this work, we address the antenna tilt optimization problem for a two-tier cellular network consisting of macrocells and femtocells, where both tiers share the same spectrum and their positions are modeled via two independent Poisson point processes (PPPs). First, we derive the coverage probability for a traditional cellular network consisting only of macrocells and obtain the optimum tilt angle that maximizes the overall energy efficiency (EE). Gains of up to 400% in EE were found for a scenario (approximately) equivalent to a hexagonal cell deployment with cell radius of 200 m when the optimum tilt was selected. We then proceed to model the heterogeneous network (HetNet) scenario where femtocells are also deployed in the network's area. We observe that the macrousers performance is highly sensitive to the interference emanating from the femtocell tier. In order to circumvent this issue, interference coordination employing a guard zone for the macrocell user is proposed. Subsequently, we formulate a joint optimization problem where we derive both, the radius of a guard zone protecting the macrouser and the tilt angle that maximize the EE of the network.

In this article we present an angle of arrival (AoA) multi-robot system for rescue purposes which takes advantage of robots' mobility to improve the position estimate of an unknown target. The robots move according to a certain trajectory (a sequence of stopping points) designed to minimize the variance of the AoA estimation. We present two different techniques to generate these optimal trajectories, with each technique having its own advantage.

The authors address the physical layer security in multiple‐input‐single‐output communication systems. This study introduces a robust strategy to cope with the channel state information errors in the main link to convey confidential information towards a legitimate receiver while artificial noise is broadcast to confuse an unknown eavesdropper. The authors study how an eavesdropper physically located in the vicinity of the transmitter can put at risk the network's security, and hence, as a countermeasure, a ‘protected zone’ was deployed to prevent the close‐quarters eavesdropping attacks. The authors determine the size of the protected zone and transmission covariance matrices of the steering information and the artificial noise to maximise the worst‐case secrecy rate in a resource‐constrained system and to minimise the use of resources to ensure an average secrecy rate. The proposed robust masked beamforming scheme offers a secure performance even with erroneous estimates of the main channel showing that a protected zone not only enhances the transmission security but it allows us to make an efficient use of energy by prioritising the available resources.

In this article, we develop a comprehensive analytical framework to characterize the area spectral efficiency of a large scale Poisson cognitive underlay network. The developed framework explicitly accommodates channel, topological and medium access uncertainties. The main objective of this study is to launch a preliminary investigation into the design considerations of underlay cognitive networks. To this end, we highlight two available degrees of freedom, i.e., shaping medium access or transmit power. While from the primary user's perspective tuning either to control the interference is equivalent, the picture is different for the secondary network. We show the existence of an area spectral efficiency wall under both adaptation schemes. We also demonstrate that the adaptation of just one of these degrees of freedom does not lead to the optimal performance. But significant performance gains can be harnessed by jointly tuning both the medium access probability and the transmission power of the secondary networks. We explore several design parameters for both adaptation schemes. Finally, we extend our quest to more complex point-to-point and broadcast networks to demonstrate the superior performance of joint tuning policies.

Sensing-throughput tradeoff under outage detection constraint has been studied before by assuming no uncertainty in estimation of the noise power, but this might not be the case in practice for an energy detector (ED). In this paper we examine analytically the effect of spectrum sensing on both the secondary user (SU) and the primary user (PU) throughputs under outage detection probability constraint in the presence of noise uncertainty and flat fading channels. First, we apply Jensen's Inequality to derive a new tight closed-form bound for the energy threshold that satisfies a certain outage detection probability. In addition, we derive both the secondary and the primary throughputs over flat fading channels in terms of the new threshold. The simulation results show that there exists an optimum sensing time that maximizes the secondary throughput. Finally, we show that the secondary throughput is more sensitive to noise uncertainty compared to the primary throughput.

In this article, we quantify the achievable spatial throughput of a multi-antenna Poisson cognitive radio network (CRN) collocated with a Poisson multi-antenna primary network. CR users employ Slotted-ALOHA medium access control. The success probability (SP) of a primary link is quantified in the presence of the secondary and primary interferers. It is demonstrated that two fold gains are experienced by employing multiple antennas at primary, i.e., (i) the fixed high desired SP threshold is met; (ii) CRs can also be accommodated without QoS deterioration. Further in this paper, the maximum permissible medium access probability (MAP) for CRN is derived from the link SP and primary users QoS constraint. The impact of the number of antennas and modulation employed at the primary on the permissible MAP of the CRN is also explored. Assuming that CR users employ multi-hop communication, QoS aware relaying with a radian sector forwarding area is studied. The average forward progress (AFP) and isolation probability for a CR user with QoS based connectivity is characterized under the permissible MAP. The spatial throughput for the CRN is quantified by the analysis of the AFP and the permissible MAP. It is shown that there exists an optimal MAP which maximizes the spatial throughput of the CRN. This optimal MAP is coupled with the permissible MAP, density of users, number of antennas and modulation schemes employed in both primary and secondary networks. Lastly, a few important design questions are investigated for multi-hop MIMO underlay CRNs.

We address physical layer security in multiple- input-multiple-output (MISO) communications in the presence of an unknown passive eavesdropper. Beamforming and artificial noise broadcasting are chosen to increase communications security. We first study the effect of a close eavesdropper on security and then we define a “Protected Zone” in the transmitter's vicinity. We present an optimisation strategy that intelligently sets the transmission power and the size of the protected zone to probabilistically achieve secrecy at a specified target secrecy rate. The results show that this strategy can achieve a high probability of secrecy by efficiently prioritising the use of the available resources.

The Femtocell is an attractive solution to improve the indoor coverage of cellular systems. By applying spectrum reuse, femtocells can improve the efficiency of spectrum usage. However, since femtocells are deployed by end-users, insufficient coordination between base stations leads to the reuse of the same frequency resources and results in CCI (co-channel interference), which seriously complicates the synchronization problem, particularly in OFDM based systems, such as WiMAX and LTE. In this paper, we investigate the timing synchronization problem in the presence of CCI of an OFDMA macro/femto uplink scenario. We consider the cross-correlation based timing synchronization and propose a pre-filter to mitigate the CCI effect. The simulation results show that this method effectively eliminates the effect of CCI on the cross-correlation based timing synchronization and provides a performance close to that of no CCI case.

In this paper we study the effect of phase noise (PHN) in a single carrier cyclic prefix (SCCP) system operating with a known cyclic prefix. The PHN process is a limiting factor in high throughput communication systems as higher-order constellations are extremely sensitive to it. In this paper we propose a robust and non-iterative scheme to enhance both the estimation of, and compensation for, the PHN process and this also includes channel estimation. In contrast to existing approaches for PHN compensation, in this paper we use a known cyclic prefix, pilot bins and interpolation over the PHN estimates. Simulation results show that the performance of SCCP with a known cyclic prefix is superior to SCCP systems with conventional compensation schemes in the moderate and higher SNR region for all values of PHN.

In this letter, we address physical layer security in MISO communications in the presence of passive eavesdroppers, i.e., the eavesdroppers' channels are unknown to the transmitter. Spatial beamforming and artificial noise broadcasting are chosen as the strategy for secure transmission. With the aim of guaranteeing a given probability of secrecy, defined by quality of service constraints at the intended receiver and at the eavesdroppers, an optimum power allocation strategy between transmitted information and artificial noise is proposed. Both power constrained and power unconstrained systems are considered. Our proposed outage probability-based approach compares favourably to an existing second-order-statistic-based approach.

We first establish the dynamic Radar Cross Section (RCS) signal model for a conical ballistic missile warhead with precession motion. Scintillation is modeled as a log-normal multiplicative noise. The distribution of the obtained RCS signal is nonGaussian and cannot be obtained in closed-form. Hence, the exact Maximum Likelihood Estimation (MLE) of the pertinent parameter, the missile precession frequency, is untractable. We propose three pseudo MLE approaches. The first approach, called GML, enforces a Gaussian distribution on both the additive and multiplicative noise components. The second approach, called ML8, ignores the additive noise in the measured RCS. The third approach, called AOML, ignores the multiplicative noise. Simulations show that accounting for the multiplicative noise in the estimation significantly improves estimation performance.

Time synchronization is an important challenge for femtocell design. An inaccurate clock can introduce interference, degrade spectrum accuracy and disrupt handovers. Among the potential solutions, wireless network-based time synchronization turns out to be cost effective because no extra provisions are required to ensure synchronization even on a regular basis. In wireless networks, receiver-receiver synchronization schemes offer very good performance. In this paper, we design an MS-assisted receiver-receiver synchronization strategy for femtocells. Two schemes are proposed for different scenarios to ensure a more comprehensive availability. Analysis and simulation results demonstrate that our proposed schemes provide sufficient synchronization accuracy for practical scenarios.

Leakage-based methods which are based on the harmonic mean signal-to-interference ratio (SIR) maximization deliver a good tradeoff between the sum rate and bit-error rate (BER). In this paper, we present the joint design of linear transmit and receive beamformers that maximize the weighted harmonic mean signal-to-interference-and-noise ratio (SINR) for the downlink of the multiuser MIMO channel. By exploiting the uplink-downlink SINR duality, the non-convex optimization problem is transformed into a series of simpler problems which can be solved by geometric programming. When the minimum mean square error (MMSE) receive beamformers are employed, it is shown that the harmonic mean SINR maximization becomes a close approximation to the sum MSE minimization problem in the high SINR region. The simulations show that the proposed scheme outperforms other leakage-based schemes in terms of harmonic mean SINR, sum MSE, BER and sum rate.

Opportunistic relaying is an efficient way of acheiving diversity in wireless cooperative communication systems. In this paper, we analyze the performance of a proactive opportunistic non-regenerative relaying protocol, considering both maximum ratio combining (MRC) and selection combining (SC) schemes at the destination. We derive a closed-form expression for the cummulative distribution function (CDF) of the end-to-end signal-to-noise ratio (SNR) in the presence of Nakagami-m fading and a SC receiver. Utilizing this statistical result, we then derive a new closed-form expression for average symbol error probability (ASEP), valid for many generic modulations, assuming identical integer fading parameters. Furthermore, we also derive a new closed-form expression for the moment generating function (MGF) of the end-to-end SNR considering Nakagami-m fading environment and MRC at the destination. Moreover, using the MGF-based approach, we derive and analyze new closed-form expressions for ASEP when MRC is employed at the destination.

We address the problem of estimating doubly-selective channels using pilot clusters that are time-division multiplexed with the data. The pilot clusters consist of zero-padded pilot symbols in order to decouple channel estimation from data detection. Channel estimation is carried out using the basis expansion model (BEM-)based method, where different BEMs are investigated, and the exact MMSE method which requires full knowledge of the channel statistics. For a fixed number of pilot symbols, we attempt to optimize the power and placement of the pilot symbols at the transmitter side used in transmission, and the number of BEM coefficients used in channel estimation, in the sense of minimizing the total mean-square estimation error (MSE) that includes modelling error. Simulation results confirm that for a wide range of SNR and Doppler spread values, this optimization greatly reduces the MSE and the bit-error rate, and that modelling error, which was ignored in existing work on training design, should be taken into account. The effects of uncertainty in the channel statistics are also studied.

One of the fundamental problems in the wireless sensor networks is the coverage problem. The coverage problem fundamentally address the quality of service (surveillance or monitoring) provided in the desired area. In past, several studies have proposed different formulation and solutions to this problem. Nevertheless none of them has addressed minimum cost solution to the coverage problem. In this paper we consider minimum cost wireless sensor network which provides full coverage to any arbitrary geometric profile under deterministic/random deployment. We then formulate probabilistic coverage model which provides k - coverage probability for minimum cost wireless sensor networks.1

In this paper we propose a new multiaccess protocol that combines the concept of splitting tree algorithms for collision resolution with two of the most relevant cross-layer technologies for random access: retransmission diversity (e.g., NDMA-Network Diversity Multiple Access) and multipacket reception (MPR). The proposed protocol is shown to outperform all the existing algorithms based on either NDMA or MPR. Additionally, the protocol formulation provides an important generalization of the model used for the analysis of solutions in these three fields. Unlike conventional NDMA protocols, in which all the colliding users are requested to immediately retransmit in the next time-slot, our proposed algorithm calculates the optimum set of users allowed to retransmit at each one of the following time-slots. The optimization is based on the previous collected transmissions and on the MPR and source separation (SS) probabilities, thus maximizing throughput and minimizing access-delay. Two possible suboptimal algorithms with simplified feedback assumption and hence suitable for distributed resolution are further derived from the original algorithm: an enhanced version of NDMA assisted by MPR and a fair splitting tree algorithm assisted by MPR and SS. The capacity/stability region of the protocol for several system configurations with two active users is employed to assess the benefits of the proposed algorithms.

We address the problem of estimating doubly-selective channels using pilot clusters that are time-division multiplexed with the data. Channel estimation is carried out using different basis expansion models (BEMs), and direct MMSE channel estimation using the channel statistics. For a fixed number of pilot symbols, we attempt to optimize the power and placement of the pilot symbols used in transmission, and the number of BEM coefficients used in channel estimation, in the sense of minimizing the mean-square estimation error (MSE) that includes modelling error, which is normally neglected in existing work. Simulation results confirm that for a wide range of SNR and Doppler spread values, this optimization greatly reduces the MSE and the bit-error rate, and that modelling error should be taken into account when optimizing training. The effects of uncertainly in the channel statistics are also studied.

Spatial multiplexing in the multi-user MIMO (MU-MIMO) downlink allows each user in the system to receive multiple data subchannels simultaneously using the same time and spectral resources. In this paper, we propose an iterative interference minimization scheme for the MU-MIMO downlink which is capable of transmitting multiple data subchannels to each user, at a relaxed constraint compared to schemes that rely on perfect cancellation of the co-channel interference (CCI). The proposed scheme minimizes inter-user interference (IUI) based on signal-to-leakage ratio (SLR) maximization and performs subchannel decoupling to eliminate the inter-symbol interference (ISI) within each user's signal.

Broadband beamforming, including frequency invariant beamforming, is often achieved by processing the received sensor signals through tapped delay-lines. Unlike most of the existing techniques, we propose a novel design method for three-dimensional frequency invariant beamformers without employing tapped delay-lines. The resultant beamformer, which can form a beam steerable along both the elevation angle and the azimuth angle, has a very simple implementation. A design example is provided to show the effectiveness of the proposed method.

In this paper, we propose a new iterative approach for superimposed training (ST) that improves synchronisation, DC-offset estimation and channel estimation. While synchronisation algorithms for ST have previously been proposed in A.G. Orozco-Lugo et al. (2004), J.K. Tugnait and W. Luo (2004) and E. Alameda-Hernandez et al., due to interference from the data they performed sub-optimally, resulting in channel estimates with unknown delays. These delay ambiguities (also present in the equaliser) were estimated in previous papers in a non-practical manner. In this paper we avoid the need for estimation of this delay ambiguity by iteratively removing the effect of the data "noise". The result is a BER performance superior to all other ST algorithms that have not assumed a-priori synchronisation.

In this paper, we propose a new resource allocation mechanism which is designed to improve the multiuser detection of wireless network diversity multiple access (NDMA) protocols. The mechanism consists of allocating an average number of time- slots to the active user population according to a prescribed quality of service requirement. It is dubbed virtual because it does not rely on user scheduling over different time-slots, but instead it is controlled by adjusting the probability of false alarm of each user. The allocation mechanism improves the throughput of conventional NDMA protocols at the expense of both an access delay degradation and an increased system complexity. The system requires to recover the signals from a mixing system with more outputs (the collected network transmissions) than inputs (the collided packets). By setting the average allocated time-slots to remain constant over different traffic loads, both the optimum transmission probabilities and the stability region of the protocol are approximated by relevant closed-form expressions. Also, the proposed analytical formulation extends the expressions of conventional NDMA systems to the asymmetrical user case (i.e. users with different data rates and detection statistics). Finally, it is shown that under extreme traffic loads, multiuser detection conditions and quality of service requirements, the proposed system degrades into the equivalent of the traditional networking protocols TDMA (time division multiple access) and Slotted- ALOHA (S-ALOHA).

In channel estimation based on Data-Dependent Superimposed Training (DDST) certain frequency components are removed from the data symbols, prior to transmission. Since this means information is removed at the transmitter, the receiver may not find it possible to correctly recover the data. In this paper conditions for data identifiability are given when using a QAM constellation, and an analytical expression for the likelihood of correct detection is given for the noise-free case. A new detection method is then proposed, that can allow the use of larger constellations, and its performance is compared to the existing method.

In this work, we derive general formulae for the instantaneous frequency (IF) and instantaneous bandwidth (IB) according to matching pursuit (MP) signal decomposition, irrespective of the kind of dictionary used. We show that via MP decomposition, the IF is now exactly the weighted average IF of the decomposed signal and that it is also always real-valued. In addition, the IB is always positive for all dictionaries with Gaussian envelopes and arbitrary polynomial phase

In this paper, we propose a new scheme for realizing predesigned two-dimensional (2-D) complex-valued seismic migration FIR digital filters, which are used for migrating three-dimensional (3-D) seismic volumes. The realization is based on singular value decomposition (SVD) of such quadrantal symmetrical 2-D FIR filters. In order to simplify the SVD computations for such impulse response structure, we apply a special matrix transformation on the migration filter impulse responses where we guarantee the retention of their wavenumber phase response. Unlike the existing realization methods which are used for this geophysical application, this realization via SVD results in perfect circularly symmetrical magnitude and phase wavenumber responses. It also saves 23.08% of the number of multiplications per output sample as well as 61.54% of the number of additions per output sample when compared to direct implementation with symmetry via true 2-D convolution

We address the problem of frequency-selective channel estimation and symbol detection using superimposed training. Both single and multiple antenna systems are studied. The superimposed training consists of the sum of a known sequence and a data-dependent sequence unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. The performance of the proposed approach is shown to outperform significantly existing methods based on superimposed training.

In this paper, a new subband adaptive beamformer is proposed that is specifically applicable to microphone arrays that can allow up to /spl plusmn/30/spl deg/ target direction of arrival (DOA) range. The proposed subband beamformer contains a blocking matrix (BM) that uses leaky subband adaptive filters to resolve the problem of the signal blocking capability in the BM when the input signals to the microphone array are highly colored. Simulation results show that this proposed subband beamformer is able to give a better interference suppression than the equivalent fullband beamformer.

In this paper, complex-valued multi-dimensional (m-D) FIR digital filters using the vector space projection method (VSPM) is proposed for designing seismic migration digital filters. In general, designing FIR digital filters using the VSPM is able to produce feasible solutions satisfying all desired filter constraints by the use of only two fast Fourier transform (FFT) computations. Furthermore, the design algorithm can be directly extended from one-dimensional (1-D) to m-D filters, an advantage not present in many other filter design techniques. The idea of designing complex-valued FIR filters using VSPM is motivated by the fact that seismic migration filters are complex-valued.

We address the problem of frequency-selective channel estimation and symbol detection using superimposed training. The superimposed training consists of the sum of a known sequence and a data-dependent sequence that is unknown to the receiver. The data-dependent sequence cancels the effects of the unknown data on channel estimation. The performance of the proposed approach is shown to significantly outperform existing methods based on superimposed training (ST).

An algorithm for the recovery of multiple packets is proposed in this paper. The algorithm exploits the fact that every transmitter sends an implicit training sequence along with the information to accomplish multiple user equalization and interference suppression in the challenging scenario of an asynchronous random access ad hoc network. The proposed method has the advantage of allowing the transmission of complex data over time-dispersive multipath channels. A simulation example is presented that corroborates the interference suppression capabilities of the algorithm.

A technique is developed where a wavelet is matched to a 1D or 2D signal, with the goal of increasing coding gain. This is done by maximizing the projection of the signal onto the scaling subspace. For 1D signals, the results show that the scaling subspace (low pass) signal closely matches (subjectively and objectively) the original signal. For 2D signals, the optimized wavelets give superior coding results when compared with the Daubechies wavelets. For low compression ratios the PSNR gain is over 1.5 dB. This gain drops off at higher compression ratios, possibly due to the lower number of vanishing moments in the optimized wavelets.

We consider multiple packet reception (MPR) for asynchronous random access wireless mobile ad hoc networks. An interference cancellation algorithm is proposed that exploits the baseband cyclostationarity properties of the signal, which are induced at the transmitters by means of modulating the symbols with distinct polynomial phase sequences. In contrast to the method presented in van der Veen et al. (2002), the proposed technique does not require knowledge of the starting time of transmission of the desired signal and can be applied to dispersive multipath channels. Also a practical way of assigning the modulating sequences via the use of a common codebook known to all nodes is proposed, and the impact on local throughput of such a scheme is analyzed.

In this paper we present a 3-D parallel filtering algorithm. This algorithm is highly parallel and efficient as it eliminates the overhead associated with the overlapping segments in the block-filtering approach. It also lifts the restrictions on the input size for high efficiency in the block-filtering algorithm, as both the 3-D input data and impulse response of the system are decimated into eight subsections each. These subsections can be simultaneously and independently processed. The results of the implementation of the 3-D parallel filtering algorithm on multiDSP platform is presented and discussed showing a high performance reflected by the highly parallel architecture and good memory distribution of the 3-D parallel algorithm.

We consider multiple packet reception (MPR) for asynchronous random access wireless mobile ad hoc networks. An interference cancellation algorithm is proposed that exploits the base-band cyclostationarity properties of the signal, which are induced at the transmitters by means of modulating the symbols with distinct polynomial phase sequences. In contrast to the method presented in [A.J. van der Veen and L. tong, 2002], the proposed technique docs not require knowledge of the starting time of transmission of the desired signal and can be applied to dispersive multipath channels. Also a practical way of assigning the modulating sequences via the use of a common codebook known to all nodes is proposed, and the impact on local throughput of such scheme is analyzed.

A new method for the stabilization of the slotted ALOHA protocol is presented in this paper. The approach is based on a modified stochastic gradient adaptation equation that updates from time-slot to time-slot the permission probability parameter that limits the load going onto the channel. Tested in the context of the simple slotted ALOHA protocol and then extended to a spread slotted ALOHA system, the new method achieves a near optimal performance with a substantially reduced complexity, as compared to other stabilization methods.

This paper introduces a new formulation for the blind channel estimation problem. In particular, it is found that any possible realization of the discrete time channel impulse response can be expressed as a linear combination of a set of predetermined eigenvectors. These are obtained from a certain “universal” autocorrelation matrix that depends only on the pulse shaping filter and the maximum delay spread of the multi-path channel, and not on the specific characteristics of the power delay profile. This new formulation shows that an interrelation exists between the different sub-channel responses of the single-input multiple-output equivalent model of the over-sampled baseband signal. Thus, the number of free parameters equals the number of signal space eigenvectors and not the number of coefficients in the multi-channel model. Therefore the optimization associated with the CR algorithm [1] can be reformulated as a reduced dimensional minimization problem with a notoriously enhanced estimation performance.

In this work we introduce the concept of random early detection (RED) to a medium access control (MAC) protocol applicable to the W-CDMA UMTS access interface. The concept is in general applicable to MAC protocols that use channel load information to regulate the access to a CDMA channel. By making the analogy of a CDMA channel as if it were a queuing system of certain capacity, RED prevents the onset of outage due to multiple access interference, in the same way that it prevents a burst of packet losses in an IP buffer. As a result, a substantial improvement in the stability of the protocol is achieved, for a wide range of input traffic loads.

This paper addresses the problem of blind channel equalization in the context of digital communications. Recent results have shown that certain operations applied to the source signal at the transmitter help in the blind identification and equalization of the channel at the receiver. For example, cyclostationarity is induced by multiplying the baseband data signal with a periodic sequence. Here, the baseband data signal is multiplied with a chirp sequence. Exploiting certain structural properties arising from this operation, a simple and fast converging algorithm is obtained for estimating the equalizer coefficients. The algorithm is deterministic, i.e. it provides optimal equalizer settings using finite data in the noiseless case.

In cellular mobile systems the on-line assessment of a radio link performance is normally associated with measurements of the received signal power, and is taken before signal demodulation. It determines the thresholds for handover policies, transmitting power control, diversity switching schemes, and other operations. However, a large received signal power does not necessarily mean "good quality" when it comes to considering radio links corrupted by cochannel interference, multipath propagation and background noise. In such cases, a better parameter that can be used to assess the signal quality is the signal-to-interference-plus-noise power ratio (SINR). Although this quality factor parameter is well-known in the literature, little has been written about how to measure it in a practical context. This article presents a method for the SINR estimation applied to a system using the Digital European Cordless Telecommunications (DECT) specification, but it can also be used by any other system employing constant modulus signals.

Radio signals arriving from cellular mobile systems in urban areas can be modelled as a random process having the Nagakami-Rice probability density function. This assumption is made in order to design a receiver for combating the effects of multipath propagation for M-ary PSK modulated signals. A parameter based on the Nagakami fading index is explored and taken to construct a cost function to be minimized. The error surface is investigated and some strategies are examined for designing an equalizer. A modified constant modulus algorithm is presented and simulations are performed to test the efficiency of the proposed filter.>