To encourage further research and to facilitate fair comparisons in the development of deep learning-based radio propagation models, in the less explored case of directional radio signal emissions in indoor propagation environments, we have launched the ICASSP 2025 First Indoor Pathloss Radio Map Prediction Challenge. This overview paper describes the indoor path loss prediction problem, the datasets used, the Challenge tasks, and the evaluation methodology. Finally, the results of the Challenge and a summary of the submitted methods are presented.

Metadata geolocation, i.e., mapping information collected at a cellular Base Station (BS) to the geographical area it covers, is a central operation in the production of statistics from mobile network measurements. This task requires modeling the probability that a device attached to a BS is at a specific location, and is presently addressed with simplistic approximations based on Voronoi tessellations. As we show, Voronoi cells exhibit poor accuracy compared to real-world geolocation data, which can, in turn, reduce the reliability of research results. We propose a new approach for data-driven metadata geolocation based on a teacher-student paradigm that combines probabilistic inference and deep learning. Our Deepmend model: ($i$) only needs BS positions as input, exactly like Voronoi tessellations; (ii) produces geolocation maps that are 56% and 33% more accurate than legacy Voronoi and their state-of-the-art VoronoiBoost calibration, respectively; and, (iii) generates geolocation data for thousands of BSs in minutes. We assess its accuracy against real-world multi-city geolocation data of 5, 947 BSs provided by a network operator, and demonstrate the impact of its enhanced metadata geolocation on two applications use cases.

Heterogeneous networks have emerged as a popular solution for accommodating the growing number of connected devices and increasing traffic demands in cellular networks. While offering broader coverage, higher capacity, and lower latency, the escalating energy consumption poses sustainability challenges. In this paper a novel optimization approach for orthogonal heterogeneous networks is proposed to minimize transmission power while respecting individual users’ throughput constraints. The problem is formulated as a mixed integer geometric program, and optimizes at once multiple system variables such as user association, working bandwidth, and base stations transmission powers. Crucially, the proposed approach becomes a convex optimization problem when user-base station associations are provided. Evaluations in multiple realistic scenarios from the production mobile network of a major European operator and based on precise channel gains and throughput requirements from measured data validate the effectiveness of the proposed approach. Overall, our original solution paves the road for greener connectivity by reducing the energy footprint of heterogeneous mobile networks, hence fostering more sustainable communication systems.

This article proposes a novel methodology for interpolating path-loss radio maps, which are vital for comprehending signal distribution and hence planning indoor wireless networks. The approach employs generative diffusion models and proves to be highly effective in generating accurate radio maps with only a small number of measurements. The experimental outcomes demonstrate an average root-mean-square error of 4.23 dB using only 10 percent of the reference points, highlighting the ability of the generative diffusion model to achieve significant interpolation accuracy in radio map generation.

Deep learning (DL) has been recently leveraged for the inference of characteristics related to wireless communication channels, such as path loss (PL). This paper presents how a deep convolutional encoder-decoder, namely a path loss prediction net (PPNet) based on SegNet, can be trained to transform information related to an outdoor propagation environment into a PL heatmap. This work is a part of the 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing First Pathloss Radio Map Prediction Challenge. The DL model is trained with synthetic data generated with a high-performance ray tracing simulator and it is illustrated that PPNet can indeed learn to predict the PL distribution and that it generalizes well to previously unseen outdoor propagation environments.

Indoor cellular networks (ICNs) are anticipated to become a principal component of 5G and beyond systems. ICNs aim at extending network coverage and enhancing users' quality of service and experience, consequently producing a substantial volume of traffic in the coming years. Despite the increasing importance that ICNs will have in cellular deployments, there is nowadays little understanding of the type of traffic demands that they serve. Our work contributes to closing that gap, by providing a first characterization of the usage of mobile services across more than 4, 500 cellular antennas deployed at over 1,000 indoor locations in a whole country. Our analysis reveals that ICNs inherently manifest a limited set of mobile application utilization profiles, which are not present in conventional outdoor macro base stations (BSs). We interpret the indoor traffic profiles via explainable machine learning techniques, and show how they are correlated to the indoor environment. Our findings show how indoor cellular demands are strongly dependent on the nature of the deployment location, which allows anticipating the type of demands that indoor 5G networks will have to serve and paves the way for their efficient planning and dimensioning.

Cell densification through the installation of smallcells and femtocells in indoor environments is an emerging solution to enhance the operation of wireless networks. The deployment of new components within the heart of the radio access network calls for expedient tools that assist and ensure their optimal placement within the existing network infrastructure. In this paper, we introduce metrics that can characterize indoor wireless network performance (IWNP) in terms of coverage and capacity, and we evaluate them via physics-based propagation models. In particular, we exploit a deterministic propagation model, i.e., a ray-tracer, as well as a novel machine learning-based propagation model. We demonstrate that data-driven propagation models can be leveraged for the rigorous evaluation of the IWNP metrics, yielding a remarkable computational efficiency compared to the conventional deterministic models. The use of physics-based site-specific propagation models allows for the particularities of each indoor geometry to be taken into account, and also makes feasible the consideration of uncertainties related to the indoor environment. In this case, the IWNP metrics are expressed as stochastic quantities and a stochastic solution is derived through an efficient polynomial chaos expansion representation, enabling on-the-fly computation of the IWNP metrics statistics.

Recently, the topic of indoor outdoor detection (IOD) has seen its popularity increase, as IOD models can be leveraged to augment the performance of numerous Internet of Things and other applications. IOD aims at distinguishing in an efficient manner whether a user resides in an indoor or an outdoor environment, by inspecting the cellular phone sensor recordings. Legacy IOD models attempt to determine a user’s environment by comparing the sensor measurements to some threshold values. However, as we also observe in our experiments, such models exhibit limited scalability, and their accuracy can be poor. Machine learning (ML)-based IOD models aim at removing this limitation, by utilizing a large volume of measurements to train ML algorithms to classify a user’s environment. Yet, in most of the existing research, the temporal dimension of the problem is disregarded. In this article, we propose treating IOD as a multivariate time-series classification (TSC) problem, and we explore the performance of various deep learning (DL) models. We demonstrate that a multivariate TSC approach can be used to monitor a user’s environment, and predict changes in its state, with greater accuracy compared to conventional approaches that ignore the feature variation over time. Additionally, we introduce a new DL model for multivariate TSC, exploiting the concept of self-attention and atrous spatial pyramid pooling. The proposed DL multivariate TSC framework exploits only low power consumption sensors to infer a user’s environment, and it outperforms state-of-the-art models, yielding a higher accuracy combined with a smaller computational cost.

In massive machine-type communications (mMTC), the conflict between millions of potential access devices and limited channel freedom leads to a sharp decrease in spectrum efficiency. The nature of sporadic activity in mMTC provides a solution to enhance spectrum efficiency by employing compressive sensing (CS) to perform multiuser detection (MUD). However, CS-MUD suffers from high computation complexity and fails to meet the strict latency requirement in some critical applications. To address this problem, in this paper, we propose a novel deep learning (DL) based framework for grant-free non-orthogonal multiple access (GF-NOMA), where we utilize the information distilled from the initial data recovery phase to further enhance channel estimation, which in turn improves data recovery performance. Besides, we design an interpretable and structured Model-driven Prior Information Aided Network (M-PIAN) and provide theoretical analysis that demonstrates the proposed M-PIAN can converge faster and support more users. Experiments show that the proposed method outperforms existing CS algorithms and DL methods in both computation complexity and reconstruction accuracy.

Cooperative caching among nodes is a hot topic in Content Centric Networking (CCN). However, the cooperative caching mechanisms are performed in an arbitrary graph topology, leading to the complex cooperative operation. For this reason, hierarchical CCN has received widespread attention, which provides simple cooperative operation due to the explicit affiliation between nodes. In this study, the authors propose a heuristic cooperative caching algorithm for maximising the average provider earned profit under the two‐level CCN topology. This algorithm divides the cache space of control nodes into two fractions for caching contents which are downloaded from different sources. One fraction caches duplicated contents and the other caches unique contents. The optimal value of the split factor can be obtained by maximising the earned profit. Furthermore, they also propose a replacement policy to support the proposed caching algorithm. Finally, simulation results show that the proposed caching algorithm can perform better than some traditional caching strategies.

Scene recognition remains one of the most challenging problems in image understanding. With the help of fully connected layers (FCL) and rectified linear units (ReLu), deep networks can extract the moderately sparse and discriminative feature representation required for scene recognition. However, few methods consider exploiting a sparsity model for learning the feature representation in order to provide enhanced discriminative capability. In this paper, we replace the conventional FCL and ReLu with a new dictionary learning layer, that is composed of a finite number of recurrent units to simultaneously enhance the sparse representation and discriminative abilities of features via the determination of optimal dictionaries. In addition, with the help of the structure of the dictionary, we propose a new label discriminative regressor to boost the discrimination ability. We also propose new constraints to prevent overfitting by incorporating the advantage of the Mahalanobis and Euclidean distances to balance the recognition accuracy and generalization performance. Our proposed approach is evaluated using various scene datasets and shows superior performance to many state-of-the-art approaches.

Unsupervised Domain Adaptation (UDA) aims to transfer domain knowledge from existing well-defined tasks to new ones where labels are unavailable. In the real-world applications, as the domain (task) discrepancies are usually uncontrollable, it is significantly motivated to match the feature distributions even if the domain discrepancies are disparate. Additionally, as no label is available in the target domain, how to successfully adapt the classifier from the source to the target domain still remains an open question. In this paper, we propose the Re-weighted Adversarial Adaptation Network (RAAN) to reduce the feature distribution divergence and adapt the classifier when domain discrepancies are disparate. Specifically, to alleviate the need of common supports in matching the feature distribution, we choose to minimize optimal transport (OT) based Earth-Mover (EM) distance and reformulate it to a minimax objective function. Utilizing this, RAAN can be trained in an end-to-end and adversarial manner. To further adapt the classifier, we propose to match the label distribution and embed it into the adversarial training. Finally, after extensive evaluation of our method using UDA datasets of varying difficulty, RAAN achieved the state-of-the-art results and outperformed other methods by a large margin when the domain shifts are disparate.

We address the problem of image feature learning for the applications where multiple factors exist in the image generation process and only some factors are of our interest. We present a novel multi-task adversarial network based on an encoder-discriminator-generator architecture. The encoder extracts a disentangled feature representation for the factors of interest. The discriminators classify each of the factors as individual tasks. The encoder and the discriminators are trained cooperatively on factors of interest, but in an adversarial way on factors of distraction. The generator provides further regularization on the learned feature by reconstructing images with shared factors as the input image. We design a new optimization scheme to stabilize the adversarial optimization process when multiple distributions need to be aligned. The experiments on face recognition and font recognition tasks show that our method outperforms the state-of-the-art methods in terms of both recognizing the factors of interest and generalization to images with unseen variations.

The aim of single image super-resolution (SR) is to generate a high-resolution (HR) image from a low-resolution (LR) observable image. In this paper, we address this task by integrating sparse coding and dictionary learning schemes into an end-to-end deep architecture. More specifically, we propose a new non-linear dictionary learning layer composed of a finite number of recurrent units to solve the sparse codes and also to yield the relevant gradients to update the dictionary. In addition, we present a new deep network architecture using the proposed non-linear layers, where two separate parallel dictionaries are adopted to represent the LR and HR images respectively. The whole network is optimized by back propagation, constraining not only reconstruction errors between the restored and the ground truth HR images but also between the sparse codes of the LR and HR image pairs. Various datasets are used to evaluate the performance of the proposed approach and it is shown to outperform many state-of-the-art single image super-resolution algorithms.

This paper focuses on the problem of causally reconstructing compressive sensing (CS) captured video. The state-of-the-art causal approaches usually assume that the signal support is static or changing sufficiently slowly over time, where magnetic resonance imaging is widely used as a motivating example. However, such an assumption is too restrictive for many other video applications, where the signal support changes rapidly. In this paper, we propose a framework that combines motion estimation (ME), the Kalman filter (KF), and CS to adapt the reconstruction process to motions in the video so that the slowly changing assumption on the signal support is relaxed and consequently is more suitable for video reconstruction. Explicit and implicit ME are designed to provide motion-aware predictions, upon which a modified KF procedure is applied. Furthermore, three CS algorithms with embedded ME and KF are developed, and theoretical analyses are conducted via reconstruction error upper bounds to characterize the various factors that affect reconstruction accuracy. Extensive simulations utilizing actual videos are carried out, and the superiority of our methods is demonstrated.

This paper focuses on the reconstruction of a tensor captured using Compressive Sensing (CS). Instead of processing the signals via vectorization as is done in conventional CS, in tensor CS high dimensional signals are kept in their original formats, which benefits hardware implementation and eases memory requirements. In addition, more structures exist in a tensor along its various dimensions than in its vectorized format. Utilizing these various structures, this paper proposes a general reconstruction approach for tensor CS. Employing the proximity operator of a nonconvex norm function, a special case for a tensor with low rank and sparse structures is elaborated, which is shown to outperform the state-of-art tensor CS reconstruction methods when applied to magnetic resonance imaging and hyper-spectral imaging.

Compressed sensing (CS) is a signal acquisition paradigm that utilises the finding that a small number of linear projections of a sparse signal have enough information for stable recovery. This paper develops a Bayesian CS algorithm to simultaneously recover multiple signals that follow the Type-3 joint sparse model [1], [2], where signals share a non-sparse common component and have distinct sparse innovation components. By employing the expectation-maximization (EM) algorithm, the proposed algorithm iteratively updates the estimates of the common component and innovation components. In particular, we find that the update rule for the non-sparse common component in the proposed algorithm, differs from all the other methods in the literature, and we provides an interpretation that gives a valuable insight into why the proposed algorithm is successful in estimating the non-sparse common component. The superior performance of the proposed algorithm is demonstrated by numerical simulation results.

Cooperative transmission can significantly reduce the power consumption associated with long distance transmission in wireless sensor networks (WSNs). In this paper, we analyze the optimal power consumption of cluster-based multi-hop transmission for a cooperative WSN. With specific Quality of Service (QoS) constraints on delay and channel capacity, we show that the power optimization problem of the whole network has no closed-form solution in a slow flat Rayleigh fading environment. Thus we propose a dynamic power control and optimization (DPCO) scheme that can jointly determine the optimal number of cooperative sensors and their transmission power. We further propose a channel approximation algorithm that can significantly reduce the computational complexity of the DPCO scheme.

Compressive sensing (CS), as a new sensing/sampling paradigm, facilitates signal acquisition by reducing the number of samples required for reconstruction of the original signal, and thus appears to be a promising technique for applications where the sampling cost is high, e.g., the Nyquist rate exceeds the current capabilities of analog-to-digital converters (ADCs). Conventional CS, although effective for dealing with one signal, only leverages the intrasignal correlation for reconstruction. This paper develops a decentralized Bayesian reconstruction algorithm for networked sensing systems to jointly reconstruct multiple signals based on the distributed compressive sensing (DCS) model that exploits both intra- and intersignal correlations. The proposed approach is able to address-networked sensing system applications with privacy concerns and/or for a fusion-center-free scenario, where centralized approaches fail. Simulation results demonstrate that the proposed decentralized approaches have good recovery performance and converge reasonably quickly.

Real-world wireless communication channels are typically highly underspread: their coherence time is much greater than their delay spread. In such situations, it is common to assume that, with sufficiently high bandwidth, the capacity without channel state information (CSI) at the receiver (termed the noncoherent channel capacity) is approximately equal to the capacity with perfect CSI at the receiver (termed the coherent channel capacity). In this paper, we propose a lower bound on the noncoherent capacity of highly underspread fading channels, which assumes only that the delay spread and coherence time are known. Furthermore, our lower bound can be calculated recursively, with each increment corresponding to a step increase in bandwidth. These properties, we contend, make our lower bound an excellent candidate as a simple method to verify that the noncoherent capacity is indeed approximately equal to the coherent capacity for typical wireless communication applications. We precede the derivation of the aforementioned lower bound on the information capacity with a rigorous justification of the mathematical representation of the channel. Furthermore, we also provide a numerical example for an actual wireless communication channel and demonstrate that our lower bound does indeed approximately equal the coherent channel capacity.

Distributed compressive sensing (DCS) concerns the reconstruction of multiple sensor signals with reduced numbers of measurements, which exploits both intra- and inter-signal correlations. In this paper, we propose a novel Bayesian DCS algorithm based on variational Bayesian inference. The proposed algorithm decouples the common component, that characterizes inter-signal correlation, from innovation components, that represent intra-signal correlation. Such an operation results in a computational complexity of reconstruction which is linear with the number of signals. The superior performance of the algorithm, in terms of the computing time and reconstruction quality, is demonstrated by numerical simulations in comparison with other existing reconstruction methods.

This article focuses on the problem of reconstructing dynamic sparse signals from a series of noisy compressive sensing measurements using a Kalman Filter (KF). This problem arises in many applications, e.g., Magnetic Resonance Imaging (MRI), Wireless Sensor Networks (WSN) and video reconstruction. The conventional KF does not consider the sparsity structure presented in most practical signals and it is therefore inaccurate when being applied to sparse signal recovery. To deal with this issue, we derive a novel KF procedure which takes the sparsity model into consideration. Furthermore, an algorithm, namely Sparsity-fused KF, is proposed based upon it. The method of iterative soft thresholding is utilized to refine our sparsity model. The superiority of our method is demonstrated by synthetic data and the practical data gathered by a WSN.

Subspace segmentation is the process of clustering a set of data points that are assumed to lie on the union of multiple linear or affine subspaces, and is increasingly being recognized as a fundamental tool for data analysis in high dimensional settings. Arguably one of the most successful approaches is based on the observation that the sparsest representation of a given point with respect to a dictionary formed by the others involves nonzero coefficients associated with points originating in the same subspace. Such sparse representations are computed independently for each data point via \ell_1-norm minimization and then combined into an affinity matrix for use by a final spectral clustering step. The downside of this procedure is two-fold. First, unlike canonical compressive sensing scenarios with ideally-randomized dictionaries, the data-dependent dictionaries here are unavoidably highly structured, disrupting many of the favorable properties of the \ell_1 norm. Secondly, by treating each data point independently, we ignore useful relationships between points that can be leveraged for jointly computing such sparse representations. Consequently, we motivate a multi-task learning-based framework for learning coupled sparse representations leading to a segmentation pipeline that is both robust against correlation structure and tailored to generate an optimal affinity matrix. Theoretical analysis and empirical tests are provided to support these claims.

Conventional dictionary learning frameworks attempt to find a set of atoms that promote both signal representation and signal sparsity fora class of signals. In distributed compressive sensing (DCS), in addition to intra-signal correlation, inter-signal correlation is also exploited in the joint signal reconstruction, which goes beyond the aim of the conventional dictionary learning framework. In this letter, we propose a new dictionary learning framework in order to improve signal reconstruction performance in DCS applications. By capitalizing on the sparse common component and innovations (SCCI) model, which captures both intra- and inter-signal correlation, the proposed method iteratively finds a dictionary design that promotes various goals: i) signal representation; ii) intra-signal correlation; and iii) inter-signal correlation. Simulation results showthat our dictionary design leads to an improved DCS reconstruction performance in comparison to other designs.

In this paper, we propose an active node selection framework for compressive sleeping wireless sensor networks (WSNs) in order to improve the signal acquisition performance and network lifetime. The node selection can be seen as a specialized sensing matrix design problem where the sensing matrix consists of selected rows of an identity matrix. By capitalizing on a genie-aided reconstruction procedure, we formulate the active node selection problem into an optimization problem, which is then approximated by a constrained convex relaxation plus a rounding scheme. The proposed approach also exploits the partially known signal support, which can be obtained from the previous signal reconstruction. Simulation results show that our proposed active node selection approach leads to an improved reconstruction performance and network lifetime in comparison to various node selection schemes for compressive sleeping WSNs.

We propose a novel dynamic reweighted ℓ2(DRℓ2) algorithm in the regime of dynamic compressive sensing. Our analysis shows that aiming to solve a Type II optimization problem, DRℓ2is effectively minimizing a `convex-concave' penalty in the coefficients that transitions from a convex region to a concave function using knowledge of past estimations. DRℓ2thus provides superior reconstruction performance compared with state-of-the-art dynamic CS algorithms.

In this paper, we propose a Generalized Kalman Filtered Compressive Sensing (Generalized-KFCS) framework to reconstruct a video sequence, which relaxes the assumption of a slowly changing sparsity pattern in Kalman Filtered Compressive Sensing [1, 2, 3, 4]. In the proposed framework, we employ motion estimation to achieve the estimation of the state transition matrix for the Kalman filter, and then reconstruct the video sequence via the Kalman filter in conjunction with compressive sensing. In addition, we propose a novel method to directly apply motion estimation to compressively sensed samples without reconstructing the video sequence. Simulation results demonstrate the superiority of our algorithm for practical video reconstruction.

To deploy effective communication systems in vehicle cavities, it is critical to understand the time variation of the in-vehicle channel. Initially, rapid channel variation is addressed, which is characterized in the frequency domain as a Doppler spread. It is then shown that, for typical Doppler spreads, the in-vehicle channel is underspread, and therefore, the information capacity approaches the capacity achieved with perfect receiver channel state information in the infinite bandwidth limit. Measurements are performed for a number of channel variation scenarios (e.g., absorptive motion, reflective motion, one antenna moving, and both antennas moving) at a number of carrier frequencies and for a number of cavity loading scenarios. It is found that the Doppler spread increases with carrier frequency; however, the type of channel variation and loading appear to have little effect. Channel variation over a longer time period is also measured to characterize the slower channel variation. Channel variation is a function of the cavity occupant motion, which is difficult to model theoretically; therefore, an empirical model for the slow channel variation is proposed, which leads to an improved estimate of the channel state.

This paper advocates the use of the emerging distributed compressive sensing (DCS) paradigm in order to deploy energy harvesting (EH) wireless sensor networks (WSN) with practical network lifetime and data gathering rates that are substantially higher than the state-of-the-art. In particular, we argue that there are two fundamental mechanisms in an EH WSN: i) the energy diversity associated with the EH process that entails that the harvested energy can vary from sensor node to sensor node, and ii) the sensing diversity associated with the DCS process that entails that the energy consumption can also vary across the sensor nodes without compromising data recovery. We also argue that such mechanisms offer the means to match closely the energy demand to the energy supply in order to unlock the possibility for energy-neutral WSNs that leverage EH capability. A number of analytic and simulation results are presented in order to illustrate the potential of the approach.

In this paper, we propose a novel Compressive Sensing (CS)-enhanced spectrum sensing approach for Cognitive Radio (CR) systems. The new framework enables cyclic feature detection with a significantly reduced sampling rate. We associate the new framework with a novel model-based greedy reconstruction algorithm: interdependent matching pursuit (IMP). For IMP, the hidden block sparsity owing to the symmetry present in the cyclic spectrum is exploited which effectively reduces the degree of freedom of problem. Compared with conventional CS with independent support selection, a remarkable spectrum reconstruction improvement is achieved by IMP.

Sparse signals can be sensed with a reduced number of random projections and then reconstructed if compressive sensing (CS) is employed. Traditionally, the projection matrix has been chosen as a random Gaussian matrix, but improved reconstruction performance can be obtained by optimizing the projection matrix. In this paper, we are interested in projection matrix designs for sensing sparse signals in overcomplete dictionaries. In particular, we put forth a closed form design that stems from the formulation of an optimization problem, which bypasses the complexity of iterative design approaches.

Compressive sensing (CS), a new sampling paradigm, has recently found several applications in wireless sensor networks (WSNs). In this paper, we investigate the design of novel sensing matrices which lead to good expected-case performance - a typical performance indicator in practice - rather than the conventional worst-case performance that is usually employed when assessing CS applications. In particular, we show that tight frames perform much better than the common CS Gaussian matrices in terms of the reconstruction average mean squared error (MSE). We also showcase the benefits of tight frames in two WSN applications, which involve: i) robustness to data sample losses; and ii) reduction of the communication cost.

This paper proposes an opportunistic nonorthogonal amplify-and-forward (ONAF) scheme, assisted by intelligent relay selection. Through analysing the mutual information of the scheme, the novel optimal relay selection criterion is proposed along with its implementation strategy. In order to reduce the system complexity, a sub-optimal selection criterion is then provided. The diversity-multiplexing tradeoff (DMT) analysis shows that the proposed scheme can achieve a diversity gain of the order of the number of candidate relays, and a maximal multiplexing gain of 1. Since the previous work on opportunistic relaying were established under the orthogonal constraint, ONAF is one of the most advanced opportunistic relaying schemes. Our numerical results show the ONAF scheme can outperform the existing nonorthogonal and opportunistic relaying schemes where the relays forward the message using the amplify-and-forward (AF) mode. More importantly, it is a flexible cooperative scheme that can reduce network power consumption, alleviate interference caused among relays' re-transmissions and avoid the negative impact of the weak source-relay-destination channels.

This paper investigates the characteristics of wind-induced slow fading in fixed wireless links where the first Fresnel Zone is partially obstructed by trees. Based on results from long term propagation measurements at 5.8GHz, we show that, besides seasonal and fast fading, the received signal in foliated fixed wireless links also experiences temporal fading of the order of minutes. This is contributed by the temporal shadowing effect owing to the mean deflection of the tree canopy under the influence of mean wind speed and direction. The characteristics of the slow fading observed during on- and off-leaf seasons will be presented. The correlation among received signal strength variation, tree canopy movement and wind will be discussed. A simple knife-edge diffraction model will be used to explain the trends observed in the slow fading. The analyses presented are useful for long-term operation of foliated fixed wireless links, the design of slow adaptive fade mitigation schemes and the development of vegetation fading simulators.

We propose a novel relationship that characterizes the fundamental tradeoff between the capacity and the outage performance of a multi-user cooperative network. As far as we are aware, no such tradeoff has previously been explicitly investigated in cooperative networks that utilize realistic modulation and channel codes. We show that increased repetition cooperation maps to regions of increased transmission reliability but degrades the system capacity. Careful system design is essential to balance reliability and capacity performance and this can only be achieved through optimization with the aid of these tradeoff curves. We present both theoretical and simulation results on the tradeoffs. Furthermore, we also optimize the tradeoff relationship by employing closed-form optimized partner selection and power allocation schemes, and show that large gains can be made in both outage performance and capacity compared with blind cooperation. The proposed methodology presented in this paper can also be extended to address different system scenarios and performance metrics.

This letter considers the design of sensing matrices with good expected-case performance for compressive sensing applications. By capitalizing on the mean squared error (MSE) of the oracle estimator, whose performance has been shown to act as a benchmark to the performance of standard sparse recovery algorithms, we demonstrate that a unit-norm tight frame is the closest design-in the Frobenius norm sense-to the solution of a convex relaxation of the optimization problem that relates to the minimization of the MSE of the oracle estimator with respect to the sensing matrix. Simulation results reveal that the MSE performance of a unit-norm tight frame based sensing matrix surpasses that of other standard sensing matrix designs in various scenarios, which include sparse recovery with basis pursuit denoise (BPDN), the Dantzig selector and orthogonal matching pursuit (OMP). This also has important practical implications because a unit-norm tight frame based sensing matrix can be designed very efficiently.

Compressive sensing leverages the compressibility of natural signals to trade off the convenience of data acquisition against computational complexity of data reconstruction. Thus, CS appears to be an excellent technique for data acquisition and reconstruction in a wireless sensor network (WSN) which typically employs a smart fusion center (FC) with a high computational capability and several dumb front-end sensors having limited energy storage. This paper presents a novel signal reconstruction method based on CS principles for applications in WSNs. The proposed method exploits both the intra-sensor and inter-sensor correlation to reduce the number of samples required for reconstruction of the original signals. The novelty of the method relates to the use of the Frechet mean of the signals as an estimate of their sparse representations in some basis. This crude estimate of the sparse representation is then utilized in an enhanced data recovering convex algorithm, i.e., the penalized ℓ1minimization, and an enhanced data recovering greedy algorithm, i.e., the precognition matching pursuit (PMP). The superior reconstruction quality of the proposed method is demonstrated by using data gathered by a WSN located in the Intel Berkeley Research lab.

In this paper, we propose a penalized ℓ1minimization algorithm for reconstructing a time-varying signal based on compressive sensing (CS) principles. The time-varying signal can be seen as a sequence of slow-changing frames. In the proposed algorithm, all frames of the sequence are sampled at an equal rate, which makes the encoder simpler than frame-categorized methods. We introduce a specialized Fréchet mean of the target frame and several adjacent frames as the penalty vector to make the algorithm close to ℓ0minimization. We prove that the specialized Fréchet mean is a good approximation of the target frame for a sequence of slow time-varying signals. Experimental results demonstrates the superior reconstruction quality of the proposed algorithm.

This paper presents a novel signal reconstruction method based on the distributed compressive sensing (DCS) framework for application to wireless sensor networks (WSN). The proposed method exploits both the intra-sensor correlation and the inter-sensor correlation to reduce the number of samples required for recovering the original signals. An innovative feature of our method is using the Fr' echet mean of the signals to discover the common support of their sparse representations in some basis. Then a new greedy algorithm, called precognition matching pursuit (PMP), is proposed to further reduce the number of required samples with the knowledge of the common support. The superior reconstruction quality of the proposed method is demonstrated by both computer-generated signals and real data gathered by a WSN located in the Intel Berkeley Research lab.

Distributed spatial diversity systems, such as multi-relay and multi-user networks, have drawn significant attention from the research community. However, their feasibility in a practical environment remains an open concern. Most notably, how to optimize cooperation between multiple users with individual interests and how practical systems issues can erode diversity gains. Whilst existing information theoretical analysis may yield insightful bounds, they provide an inadequate solution for optimal power allocation in realistic systems due to the mutual information saturation of non-Gaussian inputs. In our work, we use feasible modulation and error correction codes to implement a system and demonstrate how multiple users cannot only improve their performance through cooperation, but also optimize their performance through power allocation with imperfect feedback. We do so, by considering an evolution game theoretic (EGT) approach, whereby the status-quo between users change with each decision.

Interest is increasing in the use of Wireless Sensor Networks (WSNs) for intra-vehicle network applications owing to the desire of manufacturers to deliver advanced in-car services. Operating WSNs in the unlicensed 2.4 GHz frequency band that is shared with other wireless technologies, e.g., Bluetooth and WiFi, brings additional challenges owing to interference and multipath fading. Therefore, a comprehensive understanding of the intra-vehicle wireless channel is essential for Radio Frequency (RF) resource allocation management, and to establish if various diversity techniques will assist in mitigating the effects of interference and fading in this environment. In this paper, we present our preliminary results in measuring the wireless channel for different scenarios within a car and also assessing the performance benefits that are potentially available from the use of Frequency Diversity (FD) for Line-of-Sight (LOS) and Non Line-of-Sight (NLOS) cases. A cross correlation based analysis of the results shows that FD offers a route to improve Intra-Vehicle Communication (IVC) quality by the use of smart frequency agile MAC protocols.

The increasing demand for wireless access in vehicular environments (WAVE) supporting a wide range of applications such as traffic safety, surveying, infotainment etc., makes robust channel access schemes a high priority. The presence of selective fading, variable topologies, high density of nodes and feasibility issues represent important challenges in vehicular networks. We present Multi-Carrier Burst Contention, a cross-layer protocol based on a contention scheme that spans both time and frequency domains, employing short and unmodulated energy bursts and a randomized and recursive node-elimination mechanism in order to resolve collisions. It can overcome many of the vehicular environment challenges and provide desirable WAVE features such as scalability, robustness, prioritized access and others. We address physical layer related challenges, present an analytical model, hardware implementation and performance results from theoretical analysis, hardware measurements and simulations, which were run in comparison with the IEEE 802.11p. The results show high scalability and resilience to channel fading and variable topologies and a considerable performance improvement over IEEE 802.11p.

This paper introduces a less is more approach to the modelling of iterative decoding for bit-interleaved coded modulation (BICM). It considers modelling the iterative exchange of soft a priori information between the demapper and decoder with a binary erasure channel model. This simplification, unlike the usual additive white Gaussian noise (AWGN) channel model, allows us to express the extrinsic information transfer (EXIT) chart curves for the demapper with polynomials whose order makes explicit the degrees of freedom available for design. The accuracy of our expressions simplifies the design process enabling us to compare in a more manageable manner the key performance parameters: the BER performance and the convergence threshold. This enabled us to find the pairing of memory-two recursive systematic convolutional (RSC) codes and mappers that achieve the earliest convergence threshold for a target BER floor at convergence: for 8-PSK semi-set partitioning (SSP) was optimal and converged at a signal-to-noise ratio (SNR)= 3.0 dB for a BER target of at least 10-5; two new mappings were the optimal pairings for 16-QAM, where the L4b2 mapping converged at 2.9 dB for a BER floor ≤ 3.46 × 10-4and the H2b2 mapping converged at 3.1 dB for a BER floor ≤ 2.44 × 10-5.

We consider decode-and-forward cooperative networks and we derive analytical expressions as well as tractable asymptotic approximations for the outage probability of a network node. Our analysis sheds more light on the interplay between the channel conditions, the network size and the adopted transmission scheme, and provides a useful tool for the design of cooperative networks.

This paper proposes a new distributed Amplify-and-Forward (AF) scheme which is integrated with the Ring-Trellis Coded Modulation (TCM) codes in order to achieve both high spectral efficiency and large diversity gain. In the distributed AF scheme, more users cooperate with each other. Fach user still uses half of its transmission freedom for relaying others' signal. However, different to the conventional AF scheme, each user further partitions its relay transmission into smaller divisions in order to help more users. As a result, for each user, the distributed AF scheme will have the same spectral efficiency as the conventional AF scheme, but creating more diverse transition paths and providing better diversity gain. In the scheme, output symbols are demultiplexed into several subframes, each of which will be relayed by a different user. As a result, each output symbol from a trellis transition branch can be relayed by a different user, assisting error-correction performance of the decoder. This distributed coding scheme is suitable for different wireless access systems, such as WiLAN and WiMAX systems. Our simulation results show that with the same spectral efficiency, the distributed coded AF scheme can significantly outperform the conventional coded AF scheme.

In this paper, we put forth a model to generate closed-form expressions for EXIT chart curves for BICM-ID systems. This model is based on the representation of the a-priori AWGN channels by BEC channels. We present various applications of the model in the estimation of key performance metrics in BICM-ID systems. This also includes the selection of suitable mapper-encoder pairings for quasi-static fading channels that exhibit the best known performance.

In this paper, we consider cooperative networks of users that implement an unselfish protocol to decode and forward packets of their partners. In unselfish cooperation, a user that has successfully retrieved the source data of a partner, will unconditionally assist that partner in its transmission to the destination. We use a threshold-based model to derive an analytical expression for the end-to-end packet error probability and we validate our approach comparing theoretical to simulation results. Furthermore, we explore the interplay between various network parameters, such as the number of users, the transmission scheme and the quality of the interconnecting channels, and we briefly discuss the impact of power allocation on the overall performance.

We derive an adaptive power control method for a collaborative network utilizing partner selection that aims to minimize the frame error rate (FER). We model a decode-and-forward (DF) collaborative network under block fading conditions, which contains M independent users utilizing codes, whose performance can be expressed by a signal to noise (SNR) threshold, such as turbo codes. We show that partner selection can reduce system complexity and power allocation can improve the FER performance. We use both a search method as well as a convex deterministic method to demonstrate our power allocation scheme. This research extends other work in which adaptive power allocation is only applied to limited scenarios. We conclude that power control can greatly benefit a DF collaborative network in a block fading environment.

Node placement plays a significant role in the effective and successful deployment of Wireless Sensor Networks (WSNs), i.e., meeting design goals such as cost effectiveness, coverage, connectivity, lifetime and data latency. In this paper, we propose a new strategy to assist in the placement of Relay Nodes (RNs) for a WSN monitoring underground tunnel infrastructure. By applying for the first time an accurate empirical mean path loss propagation model along with a well fitted fading distribution model specifically defined for the tunnel environment, we address the RN placement problem with guaranteed levels of radio link performance. The simulation results show that the choice of appropriate path loss model and fading distribution model for a typical environment is vital in the determination of the number and the positions of RNs. Furthermore, we adapt a two-tier clustering multi-hop framework in which the first tier of the RN placement is modelled as the minimum set cover problem, and the second tier placement is solved using the search-and-find algorithm. The implementation of the proposed scheme is evaluated by simulation, and it lays the foundations for further work in WSN planning for underground tunnel applications.

In this study, the authors investigate the performance-complexity tradeoff of convolutional codes for broadband fixed wireless access systems by considering the effects of quantisation and path metric memory in practical Viterbi decoding implementations. They show that in systems with limited antenna diversity, low-memory codes achieve a better error-rate performance compared to that of high-memory codes. Only in systems with considerable antenna diversity, can the performance of a convolutional code be improved by increasing its memory size. Nevertheless, the authors demonstrate that the coding advantage offered by the high-memory codes is not large enough to justify the significant increase in implementation complexity. In particular, memory-2 convolutional codes achieve a coding gain of up to 1.2 dB over their memory-8 counterparts in single-input single-output fixed wireless access systems. The situation is reversed when multiple antennas are used, but the decoder of memory-8 codes occupies at least 130 times more silicon area than that of memory-2 codes.

We derive theoretical bit and frame error rate expressions for decode-and-forward (DF) collaborative networks containing M users, employing a variety of block codes over a Rayleigh block faded channel. With the aid of these expressions, we explore the performance of adaptive power control for such systems. This extends previous work by optimizing power allocation for all relay and direct channels. We further extend our work to a variety of cooperation mechanisms and we conclude that power control can greatly benefit a DF collaborative network in a fading environment.

Wireless Sensor Networks (WSNs) which utilise IEEE 802.15.4 technology operate primarily in the 2.4 GHz globally compatible ISM band. However, the wireless propagation channel in this crowded band is notoriously variable and unpredictable, and it has a significant impact on the coverage range and quality of the radio links between the wireless nodes. Therefore, the use of Frequency Diversity (FD) has potential to ameliorate this situation. In this paper, the possible benefits of using FD in a tunnel environment have been quantified by performing accurate propagation measurements using modified and calibrated off-the-shelf 802.15.4 based sensor motes in the disused Aldwych underground railway tunnel. The objective of this investigation is to characterise the performance of FD in this confined environment. Cross correlation coefficients are calculated from samples of the received power on a number of frequency channels gathered during the field measurements. The low measured values of the cross correlation coefficients indicate that applying FD at 2.4 GHz will improve link performance in a WSN deployed in a tunnel. This finding closely matches results obtained by running a computational simulation of the tunnel radio propagation using a 2D Finite-Difference Time-Domain (FDTD) method.

With the prevalence of wireless capability in mobile devices and the increasing number of wireless network deployments where protocols using carrier sensing (such as IEEE 802.11) are employed, it is important to understand the impact of fading during the channel sensing period and its direct effects on performance in realistic environments. In this paper we evaluate the performance of IEEE 802.11 and that of our proposed cross-layer Multi-Carrier Burst Contention (MCBC) protocol, which we have implemented in hardware, in realistic indoor fading environments with no direct line of sight. We present thorough simulation results, backed up by hardware and real-world measurements, performance issues for each protocol and describe methods for increasing resilience to fading during channel sensing in order to improve performance.

In a cooperative communication network, individual users are encouraged not only to transmit their own data, but also relay other user's data. This relaying transmission creates spatial diversity to combat the effect of individual severe fading and path loss. Since cooperative users utilize some degree of their transmission freedom for relaying other user's data, cooperative transmission results in lowering each user's transmission spectral efficiency. Therefore, a coding scheme with high spectral efficiency and optimized performance would be desirable for a cooperative network. This paper proposes the Trellis Coded Modulation (TCM) scheme to be incorporated with the cooperative Amplify-and- Forward (AF) systems. A criterion for designing good TCM codes for AF systems is also derived and two cooperative AF systems achieving 1 bits/sec/Hz and 1.5 bits/sec/Hz for each user are presented. Analyses in this paper show that cooperative TCM schemes can not only achieve high spectral efficiency, but also outperform convolutional codes with a high order modulation scheme.

The objective of this paper is two-fold. Initially, we present an analytic technique to rapidly evaluate an approximation to the union bound on the bit error probability of turbo codes. This technique exploits the most significant terms of the union bound, which can be calculated straightforwardly by considering the properties of the constituent convolutional encoders. Subsequently, we use the bound approximation to demonstrate that specific punctured rate-1/2 turbo codes can achieve a lower error floor than that of their rate-1/3 parent codes. In particular, we propose pseudo-random puncturing as a means of improving the bandwidth efficiency of a turbo code and simultaneously lowering its error floor.

Convolutional block codes, which are commonly used as constituent codes in turbo code configurations, accept a block of information bits as input rather than a continuous stream of bits. In this paper, we propose a technique for the calculation of the transfer function of convolutional block codes, both punctured and nonpunctured. The novelty of our approach lies in the augmentation of the conventional state diagram, which allows the enumeration of all codeword sequences of a convolutional block code. In the case of a turbo code, we can readily calculate an upper bound to its bit error rate performance if the transfer function of each constituent convolutional block code has been obtained. The bound gives an accurate estimate of the error floor of the turbo code and, consequently, our method provides a useful analytical tool for determining constituent codes or identifying puncturing patterns that improve the bit error rate performance of a turbo code, at high signal-to-noise ratios.

A new framework for efficient exact maximum-likelihood (ML) decoding of spherical lattice codes is developed. It employs a double-tree structure: The first is that which underlies established tree-search decoders; the second plays the crucial role of guiding the primary search by specifying admissible candidates and is our present focus. Lattice codes have long been of interest due to their rich structure, leading to decoding algorithms for unbounded lattices, as well as those with axis-aligned rectangular shaping regions. Recently, spherical Lattice Space-Time (LAST) codes were proposed to realize the optimal diversity-multiplexing tradeoff of MIMO channels. We address the so-called boundary control problem arising from the spherical shaping region defining these codes. This problem is complicated because of the varying number of candidates to consider at each search stage; it is not obvious how to address it effectively within the frameworks of existing decoders. Our proposed strategy is compatible with all sequential tree-search detectors, as well as auxiliary processing such as the MMSEGDFE and lattice reduction. We demonstrate the superior performance and complexity profiles achieved when applying the proposed boundary control in conjunction with two current efficient ML detectors and show an improvement of 1dB over the state-of-the-art at a comparable complexity.

With the rapid growth of WLAN capability for mobile devices such as laptops, handhelds, mobile phones and vehicles, we will witness WLANs with very large numbers of active nodes for which very efficient medium access control techniques will be needed to cope with high loads and mobility. We propose a high performance solution based on an innovative node elimination algorithm that uses short and unmodulated bursts of energy during contention - no data is exchanged. We also present a modified OFDM PHY layer, based on IEEE 802.11a, which allows sensing and bursting on individual subcarriers. We show that the protocol maintains a very low overhead and collision probability which lead to high and virtually constant network throughput at all analyzed network loads, even beyond 500 nodes. The protocol is validated by extensive simulation, comparing it against the IEEE 802.11a and SYN-MAC protocols.

Broadband fixed wireless access (BFWA) systems enable services such as high-speed data communication, high quality voice/video conferencing and high-speed internet access in areas where a wired link is not possible. However, the BFWA channel is a slow-fading channel having deep frequency-selective fading caused by clusters of scatterers in the environment that introduce inter-symbol interference (ISI) at the receiver. Low density parity check (LDPC) codes, optimised for the single-input single-output BFWA channel, are designed using the structured balance incomplete block design method. The use of both quadrature phase shift keying (QPSK) and 16-quadrature amplitude modulation (16-QAM) are investigated theoretically. To help overcome the ISI effects of the channel, equalisation techniques are employed separately with LDPC decoding for a system employing QPSK and 16-QAM modulation schemes. The equaliser single carrier approach is then replaced with orthogonal frequency division multiplexing (OFDM) and the performance of these two approaches is evaluated in terms of bit-error rate. The simulation results show that equalisation with LDPC coding has a measurable performance gain over LDPC coding with OFDM.

The performance of turbo codes in quasi-static fading channels both with and without antenna diversity is investigated. In particular, simple analytic techniques that relate the frame error rate of a turbo code to both its average distance spectrum as well as the iterative decoder convergence characteristics are developed. Both by analysis and simulation, the impact of the constituent recursive systematic convolutional (RSC) codes, the interleaver size and the number of decoding iterations on the performance of turbo codes are also investigated. In particular, it is shown that in systems with limited antenna diversity different constituent RSC codes or interleaver sizes do not affect the performance of turbo codes. In contrast, in systems with significant antenna diversity, particular constituent RSC codes and interleaver sizes have the potential to significantly enhance the performance of turbo codes. These results are attributed to the fact that in single transmit–single receive antenna systems, the performance primarily depends on the decoder convergence characteristics for Eb/N0 values of practical interest. However, in multiple transmit–multiple receive antenna systems, the performance depends on the code characteristics.

Broadband fixed wireless access (BFWA) is an ideal solution for providing high data rate communications where traditional landlines are either unavailable or too costly to be installed. In this paper we consider a number of alternative techniques to achieve high data rate and high quality of services requirements in these systems, including orthogonal frequency division multiplexing (OFDM), turbo equalization as well as multiple-input multiple-output (MIMO) techniques. In particular, the frequency domain OFDM scheme and time domain turbo equalization will be studied and compared in a MIMO BFWA context, in an attempt to provide some guidelines on how to design high data rate BFWA applications.

A novel equalization algorithm utilizing improper nature of the intersymbol interference (ISI) is introduced in this paper. We show that full exploitation of the available information on the second-order statistics of the observed signal entails widely linear processing and that previously known linear minimum mean square error (MMSE) equalizers represent sub-optimum solutions. The proposed scheme is generally applicable for both real and complex signal constellations. The results show that accounting for the improper nature of the ISI leads to significant performance gain compared to conventional equalization schemes.

It has been observed that particular rate-1/2 partially systematic parallel concatenated convolutional codes (PCCCs) can achieve a lower error floor than that of their rate-1/3 parent codes. Nevertheless, good puncturing patterns can only be identified by means of an exhaustive search, whilst convergence towards low bit error probabilities can be problematic when the systematic output of a rate-1/2 partially systematic PCCC is heavily punctured. In this paper, we present and study a family of rate-1/2 partially systematic PCCCs, which we call pseudo-randomly punctured codes. We evaluate their bit error rate performance and we show that they always yield a lower error floor than that of their rate-1/3 parent codes. Furthermore, we compare analytic results to simulations and we demonstrate that their performance converges towards the error floor region, owning to the moderate puncturing of their systematic output. Consequently, we propose pseudo-random puncturing as a means of improving the bandwidth efficiency of a PCCC and simultaneously lowering its error floor.

In this paper, we investigate in detail the performance of iterative demapping and decoding techniques over quasi-static fading channels both with and without antenna diversity. In particular, we consider the effect on the system performance of various mapping schemes, different coding schemes, the inter- leaver size as well as space diversity. Results demonstrate that over quasi-static fading channels characterized by significant antenna diversity, mappings traditionally optimized for iterative receivers (e.g., Boronka mapping) outperform mappings more appropriate for non-iterative receivers (e.g., Gray mapping). In contrast, over quasi-static fading channels characterized by limited antenna diversity, Gray mapping always outperform Boronka mapping for all Eb/N0and all iterations. Strikingly, this situation is in sharp contrast to that in the AWGN case. We note that we can further improve the performance of non-iterative systems (e.g., Gray mapping) having limited antenna diversity by increasing the memory size and removing the interleaving.

A key component of intelligent transportation is the provision of adequate network infrastructure to support vehicle-to-vehicle and vehicle-to-roadside communication. In this paper we report on performance evaluations carried out using the IEEE 802.11a protocol at 5.2 GHz between a moving vehicle and a fixed base station. We concentrate our evaluation on realistic urban speeds and environments, observing that performance at very low speeds is degraded due to the presence of null zones. We vary the modulation scheme and analyse the spread of resulting throughputs. Our results have implications for multimedia and other real-time applications that will utilise vehicle-to-roadside connectivity.

Multiple input multiple output-orthogonal frequency division multiplexing-code division multiplexing (MIMO-OFDM-CDM) techniques are considered to improve the link reliability/spectral efficiency of very high data rate communication systems. In particular, lattice-reduction-aided receivers are proposed for MIMO-OFDM-CDM systems. Simulation results show that the proposed receivers significantly outperform the conventional zero-forcing, minimum mean-squared error, or vertical Bell Labs layered space–time receivers without severely compromising system complexity.

The systems under investigation are broadband fixed wireless access (BFWA) systems operating in multipath fading channels. Conventional detection methods, for example, coherent detection for single-input single-output systems and the Alamouti algorithm for multiple-input multiple-output systems are examined theoretically and shown to yield unsatisfactory performance. The theoretical analyses are validated by Monte-Carlo simulations and are demonstrated to be accurate. The asymptotic performance of the space–time block coded (STBC) system with the–Alamouti transmission scheme is also evaluated. However, the results indicate that the performance lower bound cannot be obtained in an uncoded system due to the error propagation problem, which can be tackled by concatenating the STBC system with an outer channel code and applying the turbo processing principle. Theoretical performance analysis provides an insight into the physical limitations imposed by BFWA channels and suggest solutions to improve the capacity and performance of future BFWA systems.

In this correspondence, we present a new method for the iterative equalization and decoding of multilevel trellis coded modulation (TCM) signals over frequency selective channels. Results show that the proposed algorithm achieves better performance compared to the previous work on the MMSE filter- based turbo equalization for a non-binary coded modulation scheme. The performance gain is accomplished by utilizing the combined modulation and coding nature of TCM and passing the refined signal obtained from different paths to the TCM decoder as the channel value in addition to the a priori probabilities.

A novel approach for the calculation of the transfer function of a terminated punctured convolutional code, which can be seen as a convolutional block code, is presented in this paper. The transfer function of the convolutional block code can be then used to evaluate the transfer function of a punctured turbo code and derive a tight upper bound on the bit error probability. Furthermore, the approach is used to find puncturing patterns that generate punctured turbo codes with optimal performance.

This paper develops a framework for the efficient maximum-likelihood decoding of lattice codes. Specifically we apply it to the spherical Lattice Space-Time (LAST) codes recently put forward by El Gamal et al. that have been proven to achieve the optimal diversity-multiplexing tradeoff of MIMO channels. Our solution addresses the so-called boundary control problem within the same search tree structure as existing suboptimal LAST decoders. We demonstrate its performance and complexity by applying two of the most efficient tree-based ML detectors currently reported in the literature to the spherical LAST code proposed for the 2 × 2 MIMO channel of block length 2. Our optimal decoders exhibit improved performance over the naive lattice decoder with MMSE-GDFE pre-processing at a comparable complexity.

Even though numerous proposals have been made for mobility management schemes in heterogeneous wireless IP networks, no single solution has emerged that is capable of supporting seamless and low latency handovers required to satisfy the stringent delay requirements of real-time multimedia applications. In this paper, we present a location-based mobility management architecture for realtime applications in a heterogeneous network environment. Handover latency is minimised by integrating optimised application layer mobility management signalling using the session initiation protocol (SIP) with location-based information of nearby access points (APs). A newly defined access point location protocol (APLP) is used to disseminate geospatial information and network parameters associated with various APs to location-aware mobile devices that use this information to construct a virtual map of the APs. An intelligent decision engine (DE) implemented in the mobile node (MN) combines the information provided by APLP with knowledge of the user's mobility pattern and policy preferences to anticipate handovers.

We investigate the design of a multiple input multiple output (MIMO) channel sounder appropriate for the broadband fixed wireless access (FWA) scenario. To this end a 4 transmit 4 receive antenna system was simulated in order to investigate the effect of equipment imperfections on the estimation of channel parameters, for example, K-factor, power delay profile (PDP), antenna correlation coefficients (ACC) and signal angle of arrival (AOA). The channel employed in the simulation is based on the SUI-3 model [8]. The accuracy of the channel estimation results is dependent upon the frequency offset between the phase lock loop (PLL) reference oscillators used at the transmitter and receiver. Consequently, high cost rubidium reference sources are usually used since they can reduce the frequency offset to almost zero owing to their frequency stability (aging) of about 5times10-10per year [13], equivalently, 5times10-4ppm per year. We wish to determine how much frequency offset can be tolerated before the estimation results become unacceptable. To this end we have conducted extensive system simulations that have revealed that acceptable accuracy can be achieved using relatively low performance and therefore inexpensive reference sources.

In this paper, we presents a novel method of turbo equalization and decoding multi-level trellis coded modulation (TCM) signals over frequency selective channels. Results show that the proposed algorithm achieves better performance with reduced complexity compared to previous work on the MMSE filter-based turbo equalization for non-binary coded modulation scheme. The performance gain is accomplished by passing the refined signal from different paths to the TCM decoder as channel value in addition to the a prior information. While the computational complexity is reduced by avoiding matrix inversion for each symbol estimate.

This paper presents a novel pre-processing stage that offers significant improvement in the computational efficiency of sphere decoding by imposing a geometrically-inspired ordering on the columns of the channel matrix. By studying the performance of a genie decoder, which has knowledge of the optimal radius, we find that the optimal ordering depends not only on the channel matrix, but also on the received point. Analysis of this idealized problem leads to the proposal of an enhanced ordering. We demonstrate via simulation that it closely matches with the optimal ordering and more importantly that it results in a dramatic increase in sphere decoding efficiency over a 4/spl times/4 MIMO flat fading channel. We emphasize that the performance benefit is particularly great at low SNRs and for high modulation orders, two traditionally challenging regimes for sphere decoders. We conclude by briefly discussing the polynomial complexity of the new ordering algorithm.

In this paper, we investigate in detail the performance of turbo codes in quasi-static fading channels both with and without antenna diversity. First, we develop a simple and accurate analytic technique to evaluate the performance of turbo codes in quasi-static fading channels. The proposed analytic technique relates the frame error rate of a turbo code to the iterative decoder convergence threshold, rather than to the turbo code distance spectrum. Subsequently, we compare the performance of various turbo codes in quasi-static fading channels. We show that, in contrast to the situation in the AWGN channel, turbo codes with different interleaver sizes or turbo codes based on RSC codes with different constraint lengths and generator polynomials exhibit identical performance. Moreover, we also compare the performance of turbo codes and convolutional codes in quasi-static fading channels under the condition of identical decoding complexity. In particular, we show that turbo codes do not outperform convolutional codes in quasi-static fading channels with no antenna diversity; and that turbo codes only outperform convolutional codes in quasi-static fading channels with antenna diversity

This paper presents a new MIMO detector based on successive projection of the received signal onto the faces of the lattice induced by the channel matrix. Analysis of the relationship between successive algorithms like V-BLAST and the maximum likelihood (ML) sphere decoder, which share a common underlying tree structure, leads to the design of the successive projection algorithm (SPA). Although it is a suboptimal detector, we prove theoretically that when ML detection can be realized in a successive manner, i.e., without back-tracking, ML performance is also achieved by the SPA. The advantageous implications of this result are illustrated via simulation of the average bit error rates attained over a 4 times 4 MIMO flat fading channel. For instance, at a target error rate of 10-3using 16-QAM modulation, a 2.8 dB improvement over the popular V-BLAST detector is observed at a comparable complexity. We also demonstrate a parameterized SPA, which offers performance profiles approaching that of an ML detector

We propose a novel and plausibly realistic application scenario for mobile ad hoc networks in the form of a radio dispatch system. We evaluate the system from both financial and technical perspectives to gain a complete picture of its feasibility. Using a realistic mobility and propagation model drawn from real world data we investigate the effects of node density, connection times and traffic congestion on the network coverage. We discuss design considerations in the light of the results. These findings are not limited to this particular scenario but are applicable to any mobile ad hoc system operating in similar conditions.

In this paper, we propose using selective mapping (SLM) and partial transmit sequences (PTS) based on an intermodulation distortion (IMD) reduction strategy to improve the error probability performance of non-linearly distorted orthogonal frequency division multiplex (OFDM) signals. Simulation results demonstrate that in the presence of nonlinearities OFDM systems using SLM or PTS with IMD reduction perform better than those with peak-to-average power ratio (PAPR) reduction. Additionally, simulation results demonstrate that the average out-of-band power generated by IMD reduction strategies is lower than that generated by PAPR reduction strategies. Finally, we propose a sub-optimum solution for the practical realization of SLM OFDM and PTS OFDM systems based on IMD reduction which does not result in a significant loss in performance.

Orthogonal frequency division multiplexing (OFDM) significantly reduces receiver complexity in wireless systems with multipath propagation and therefore has recently been proposed for use in wireless broadband multi-antenna (MIMO) systems. The performance of the maximum likelihood (ML) detector in MIMO-OFDM system is optimal, however, its complexity is prohibitive. A number of other detectors, both linear and non-linear, may offer substantially lower complexity, however, their performance is significantly worse. This paper uses a class of lattice-reduction-aided (LRA) receivers for MIMO-OFDM systems with an arbitrary number of antennas that can achieve near maximum likelihood detector performance with low complexity. Performance comparisons between the LRA receiver and other popular receivers, including linear receivers and V-BLAST in both independent and correlated channels, are provided. It will be shown that the performance of the LRA receiver is superior to that achieved by sub-optimal detection methods, especially when the channel is correlated.

Orthogonal frequency division multiplexing (OFDM) systems are very sensitive to phase noise caused by oscillator instabilities. In this paper the phase noise is resolved into two components, namely the common phase error (CPE), which affects all the subchannels equally and the intercarrier interference (ICI), which is caused by the loss of orthogonality of the subcarriers. We present a technique to estimate and correct the CPE component and demonstrate its effectiveness when applied to a broadband fixed wireless access (BFWA) data transmission system for the multichannel multipoint distribution service (MMDS) band. We show a performance increase up to 6 dB when applying the CPE correction in terms of the tolerance to the phase noise variance, /spl sigma//sub /spl psi///sup 2/.

Orthogonal frequency division multiplexing (OFDM) system performance remains acceptable under timing synchronisation errors as long as the start of each symbol is determined to lie within a certain length into the cyclic prefix (CP). The Schmidl and Cox algorithm (SCA) is quite robust in estimating the timing and frequency offset synchronisation for systems with large OFDM symbol lengths. It uses two OFDM symbols for training with the first one having two identical halves. The start of the frame is estimated by correlating a received sequence of samples equal to half the OFDM symbol length with the following received samples. The effect of additive white gaussian noise (AWGN) in the estimation process is mitigated only if the number of samples used in the correlation, and hence the OFDM symbol size is large. However to be successfully applied to broadband fixed wireless access (BFWA) systems, OFDM should perform well even with smaller symbol lengths. In this paper we present the iterative symbol offset correction algorithm (ISOCA), which uses the SCA for initial coarse timing synchronisation and follows it with a two stage symbol offset correction algorithm by tracking the phase of the second training symbol. We show through simulation that the ISOCA achieves virtually perfect estimation of the start of frame (SOF), even with very low received SNR.

Orthogonal frequency division multiplexing (OFDM) systems are very sensitive to frequency offset caused by tuning oscillator instabilities and Doppler shifts induced by the channel. The Schmidt and Cox (1997) algorithm (SCA) is quite robust in estimating the frequency offset for systems with large OFDM symbol lengths. It uses two OFDM symbols for training with the first one having two identical halves. The frequency offset is estimated by correlating a received sequence of samples equal to half the OFDM symbol length with the following received samples. The effect of additive white Gaussian noise (AWGN) in the estimation process is mitigated only if the number of samples used in the correlation, and hence the OFDM symbol size is large. However to be successfully applied to broadband fixed wireless access (BFWA) systems, OFDM should perform well even with smaller symbol lengths. We present the residual frequency offset correction algorithm (RFOCA), which uses the SCA for initial frequency offset acquisition and follows it with a stage which reduces the initial residual frequency offset by tracking the phase of the decoded data. We show through simulation that the RFOCA achieves an error variance that is many orders of magnitude lower than at the end of the acquisition stage using the SCA alone.

A payoff function used in game theory is derived and a mixed strategy is applied to the fully distributed dynamic channel allocation (DCA) problem for a broadband fixed wireless access (BFWA) network using packet reservation multiple access (PRMA). DCA using least interfered (LI) and random channel allocation (RND) are simulated and their performance compared with the proposed DCA using game theory (GT).

In this paper, the use of orthogonal frequency division multiplexing (OFDM), combined with a time domain equalizer (TEQ), is investigated for broadband fixed wireless access (BFWA) systems. OFDM systems use a cyclic prefix (CP) that is inserted at the beginning of each symbol to convert the linear convolution of data and channel into a circular one. If the CP is longer than the channel length, intersymbol interference (ISI) is avoided. However the use of the CP reduces the efficiency of the system. A TEQ is often used to reduce the channel length, enabling a shorter CP to be used. The TEQ schemes that have been proposed to date result in the effective channel impulse response (EIR), (i.e. including the TEQ) having spectral nulls. This prevents some of subchannels; from being used for data transmission. We present the frequency scaled time domain equalization (FSTEQ) algorithm which optimises the TEQ coefficients in both the time and frequency domains resulting in a flatter spectral response. We show that the algorithm when applied to Stanford University Interim (SUI)-2 channels for the MMDS band yields an improvement of up to 100 fold in the BER at an SNR of 20 dB.

We investigate the use of a multi-element antenna array (MEA) at the base station (BS) in a time division multiple access (TDMA) broadband fixed wireless access (BFWA) system. At high data rates the intersymbol interference (ISI) induced by dispersive channels becomes a technological bottleneck. Space-time processing at the BS offers a solution to improve the wireless link quality in the uplink. First, we investigate the performance of a space-time decision feedback equalizer (DFE) trained with the LMS algorithm for a single user BFWA system (i.e. one user per frequency channel and per time slot). Then we propose an algorithm for space division multiple access (SDMA) in the uplink based on a combination of adaptive space-time equalization and successive interference cancellation (SIC). It is shown via simulations that this combined approach offers an improvement in the probability of symbol error (P/sub s/) for a two-user system (i.e. two users per frequency channel and per time slot) compared to a space-time DFE without SIC. The RLS algorithm is used for the two-user system to ensure fast convergence. The proposed iterative approach offers a low complexity solution compared to minimum mean square error solutions based on direct matrix inversion, which would be costly for the expected delay spreads associated with BFWA systems.