In this work, we study a full-duplex (FD) cloud radio access network (C-RAN) from the aspects of infrastructure sharing and information secrecy, where the central unit utilizes FD remote radio units (RU)s belonging to the same operator, i.e., the trusted RUs, as well as the RUs belonging to other operators or private owners, i.e., the untrusted RUs. Furthermore, the communication takes place in the presence of untrusted external receivers, i.e., eavesdropper nodes. The communicated uplink (UL) and downlink (DL) waveforms are quantized in order to comply with the limited capacity of the fronthaul links. In order to provide information secrecy, we propose a novel utilization of the quantization noise shaping in the DL, such that it is simultaneously used to comply with the limited capacity of the fronthaul links, as well as to degrade decoding capability of the individual eavesdropper and the untrusted RUs for both the UL and DL communications. In this regard, expressions describing the achievable secrecy rates are obtained. An optimization problem for jointly designing the DL and UL quantization and precoding strategies are then formulated, with the purpose of maximizing the overall system weighted sum secrecy rate. Due to the intractability of the formulated problem, an iterative solution is proposed, following the successive inner approximation and semi-definite relaxation frameworks, with convergence to a stationary point. Numerical evaluations indicate a promising gain of the proposed approaches for providing information secrecy against the untrusted infrastructure nodes and/or external eavesdroppers in the context of FD C-RAN communications.

Neural networks have been shown to be vulnerable against minor adversarial perturbations of their inputs, especially for high dimensional data under $\ell_\infty$ attacks.To combat this problem, techniques like adversarial training have been employed to obtain models that are robust on the training set.However, the robustness of such models against adversarial perturbations may not generalize to unseen data.To study how robustness generalizes, recent works assume that the inputs have bounded $\ell_2$-norm in order to bound the adversarial risk for $\ell_\infty$ attacks with no explicit dimension dependence.In this work, we focus on $\ell_\infty$ attacks with $\ell_\infty$ bounded inputs and prove margin-based bounds.Specifically, we use a compression-based approach that relies on efficiently compressing the set of tunable parameters without distorting the adversarial risk. To achieve this, we apply the concept of effective sparsity and effective joint sparsity on the weight matrices of neural networks.This leads to bounds with no explicit dependence on the input dimension, neither on the number of classes.Our results show that neural networks with approximately sparse weight matrices not only enjoy enhanced robustness but also better generalization. Finally, empirical simulations show that the notion of effective joint sparsity plays a significant role in generalizing robustness to $\ell_\infty$ attacks.

We consider the reconstruction of group-sparse vectors from sign measurements of random projections. In particular, we establish conditions on the number of measurements under which such signal ensembles can be recovered up to a prescribed accuracy. The results rely on a mixed restricted isometry property as first employed by Foucart in the context of 1-bit compressed sensing, as well as certain results on random hyperplane tessellations. The paper fills a gap in the literature by establishing that group-sparse signals can be estimated from 1-bit observations with the same number of measurements as required for the reconstruction of block-sparse signals from unquantized measurements. We confirm the correct behavior of the recovery schemes in a series of numerical experiments.

We address the problem of reconstructing group-sparse vectors from compressive measurements acquired via subgaussian block diagonal measurement operators. Such results can be obtained by establishing the so-called group restricted isometry property of the underlying measurement matrix. In particular, the problem is reduced to the task of bounding certain geometric objects associated with the suprema of a particular chaos process, which involves estimating Talagrand's γ2-functional via Dudley's metric entropy integral. As part of the proof, we generalize Maurey's empirical method to provide new bounds on the covering number of sets consisting of finite convex combinations of compact sets.

In this work, we study an unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) system with multiple ground users. We aim at solving the non-convex UAV trajectory design problem which maximizes the minimal received energy among all users by determining the UAV's flying path under given UAV speed constraints. To solve such intractable problem, we propose a genetic algorithm (GA) based successive hover-and-fly (SHF) scheme that iteratively searches the optimal hovering points and optimizes the corresponding hovering time. Moreover, we extend the study to scenarios with no-fly zones, for which an improved GA based method with a penalizing strategy is proposed accordingly. Numerical results confirm the performance advantage of the proposed GA based algorithm in comparison to the benchmark algorithms in prior works under a wide range of system parameters.

In this work we study the secrecy energy efficiency (SEE) maximization problem for a multi-carrier and multiple-input-multiple-output (MIMO) multiple eavesdroppers communication system. By utilizing the full-duplex (FD) operation, the system is simultaneously capable of bidirectional communication and jamming to the potential eavesdroppers. In particular, we opportunistically utilize different communication and jamming channel conditions at different subcarriers, raised due to multipath fading or large bandwidth, in order to improve the SEE. Due to the non-convex and non-smooth nature of the resulting optimization problem, we propose an iterative solution with a guaranteed convergence to a stationary point based on the successive inner approximation and Dinkelbach's algorithm. The numerical evaluations show a considerable improvement of the system SEE under the condition that the self-interference of the FD transceivers can be efficiently mitigated.

Low-power wide-area network (LPWAN) multi-radio access technology (RAT) devices promise enabling Internet of Things (IoT) use-cases that simultaneously require high coverage and data rates, and low energy consumption. For such devices, active probing is usually used for discovering if communication between a mobile terminal (MT) and a base station (BS) or a handover of the MT across LPWAN technologies should be initiated. Because of continuous probing, this procedure increases signaling overhead and energy consumption of the MT. Assuming that the location information of the MT is required for enabling an IoT use-case, this information can potentially also be used for enhancing the discovery and vertical handover procedures in heterogeneous LPWANs. Hence, we propose a location-based mechanism for making discovery and handover decisions in outdoor LPWANs. We do that under the assumption that the location of the MT can be estimated with a certain level of localization errors, while the perfectly accurate location information of the BSs are known to the MT. The mechanism grounds the decisions on the expected SNR between the MT and the BS, which removes the need for continuous probing. If the location information of the MT can be estimated with GPS-like accuracy, we demonstrate that the mechanism can achieve more than 90% correct discovery decisions. We also show that the mechanism is highly accurate in determining if a handover between technologies should be initiated. For an order of magnitude less accurate location information (e.g., for SigFox-based fingerprinting), we show that the mechanism can still make reasonable discovery decisions.

Full-duplex (FD) jamming transceivers recently have been shown to enhance the information security of wireless communication systems by simultaneously transmitting artificial noise (AN) while receiving information. In this paper, we investigate whether FD jamming can also improve the system's secrecy energy efficiency (SEE) in terms of securely communicated bits per Joule when considering the additional power used for jamming and self-interference (SI) cancellation. Moreover, the degrading effect of the residual SI is also taken into account. In this regard, we formulate a set of SEE maximization problems for a FD multiple-input-multiple-output multiple-antenna eavesdropper (MIMOME) wiretap channel, considering both cases where exact or statistical channel state information (CSI) is available. Due to the intractable problem structure, we propose iterative solutions in each case with a proven convergence to a stationary point. Numerical simulations indicate only a marginal SEE gain, through the utilization of FD jamming, for a wide range of system conditions. However, when SI can efficiently be mitigated, the observed gain is considerable for scenarios with a small distance between the FD node and the eavesdropper, a high signal-to-noise ratio (SNR), or for a bidirectional FD communication setup.

The mutual coherence provides a basis for deriving recovery guarantees in compressed sensing. In this paper, the mutual coherence of spherical harmonics sensing matrices is examined for a class of sensing patterns common in practice and is used as a figure of merit for designing sensing matrices. We will show that for each sampling pattern, the coherence is lower bounded by the inner product of two Legendre polynomials with different degrees. In some practical situation, it is desirable to have sampling points on a sphere follow a regular pattern, hence, facilitating the measurement process. It will be shown that for a class of sampling patterns, the mutual coherence would be at its maximum, yielding the worst performance. Finally, the sampling strategy is proposed to achieve the derived lower bound.

Cloud-Radio Access Networks (C-RANs) are known for their potential to accommodate the heavy processing required by the exponentially increasing data traffic. Thanks to virtualization techniques, the baseband processing in C-RANs, can now be performed on virtual machines (VMs) at the central unit (CU). This is a priceless tool for improving the processing efficiency, as it provides the opportunity for dynamic allocation and sharing of processing resources. In this work, we investigate the assignment of processing jobs and the allocation of resources among the virtual machines, while reducing the overall power consumption of the VMs. Furthermore, the developed model also accounts for communication overhead, which may incur due to package dependencies. The performance of the proposed technique is investigated with varying system constraints and the obtained results demonstrate the potential power savings made possible using the proposed method.

In this work, joint design of fronthaul compression and precoding in cloud radio access networks (C-RANs) is studied for full-duplex (FD) multiple-input multiple-output (MIMO) underlay cognitive radio networks. In this system, multiple secondary uplink (UL) and downlink (DL) users equipped with multiple antennas communicate with a control unit (CU) in the “cloud” through a set of multi-antenna secondary FD radio units (RU) which are connected to the CU through limited capacity links. We address the sum-rate maximization problem subject to UL and DL fronthaul rate constraints at each RU, power constraints at each RU and UL users, and maximum allowed interference at the primary users. Casting this non-convex problem as a difference of convex (DC) problem, an iterative algorithm based on the Majorization Minimization (MM) approach that guarantees convergence to a stationary point is proposed. Numerical results demonstrate the advantage of the proposed algorithm.

In this work we study the secrecy energy efficiency (SEE) of a multiple-input-multiple-output multiple-antenna eavesdropper (MIMOME) wiretap channel, in terms of the securely communicated bits-per-Joule, where the legitimate receiver is equipped with full-duplex (FD) capability. In particular, we seek answer to the question: if and how the application of an FD jammer can enhance the system SEE, considering the additional power consumption used for jamming and self-interference cancellation, as well as the degrading effect of residual self-interference. In this regard, an SEE maximization problem is formulated. Due to the intractable problem structure, an iterative solution is provided with a guaranteed convergence to a local optimum. Moreover, the proposed solution is extended for a system with a bidirectional communication, where both legitimate nodes are equipped with FD capability. Numerical simulations indicate a marginal SEE gain, via the utilization of FD jamming, for a wide range of system conditions. However, the observed gain is significant for the scenarios with a small distance between the FD node and the eavesdropper, a high signal-to-noise ratio (SNR) condition or for a bidirectional FD communication setup, under the condition that the self-interference can be effectively and efficiently mitigated.

In this work we address the joint design of the wireless backhauling network topology, as well as the frequency/power allocation on the wireless links, where the nodes are capable of full-duplex (FD) operation. The proposed joint design enables the coexistence of multiple access/backhaul links at the same channel, resulting in an enhanced spectral efficiency. Moreover, it enables the usage of FD capability when/where it is gainful. In this regard, a mixed-integer-linear-program (MILP) design framework is proposed, aiming at a minimum cost design for the wireless backhaul network, considering the required rate demand at each base station, as well as the various operational limits. The numerical simulations show a reduction in the network cost via the utilization of the proposed designs, thanks to the coexistence of multiple links on the same channel due to the FD capability.

In this paper, we consider a full-duplex (FD) and amplify-and-forward (AF) relay with multiple antennas, where hardware impairments of the FD relay are taken into account. Due to the inter-dependence of the transmit relay power and the residual self-interference in an FD-AF relay, we observe a distortion loop that degrades the system performance when relay dynamic range is not high. In this paper, we analyze the relay function, and an optimization problem is formulated to maximize the signal to distortion-plus-noise ratio under relay and source transmit power constraints. Due to the problem complexity, we propose a gradient-projection-based (GP) algorithm to obtain an optimal solution. Moreover, a non-alternating sub-optimal solution is proposed by assuming a rank-1 relay amplification matrix, and separating the design of the relay process into multiple stages (MuStR1). The proposed MuStR1 method is then enhanced by introducing an alternating update over the optimization variables, denoted as AltMuStR1 algorithm. Numerical simulations show that compared to GP, the proposed (Alt)MuStR1 algorithms significantly reduce the required computational complexity at the expense of a slight performance degradation. Moreover, as the hardware impairments increase, or for a system with a high transmit power, the impact of applying a distortion-aware design is significant.

In this paper we address a worst-case weighted sum rate maximization problem for a full-duplex (FD) and point-to-point (P2P) system. The effects of channel-state information (CSI) error, as well as the signal distortion due to hardware impairments are jointly taken into account. Due to the intractable structure of the resulting problem, a weighted minimum mean squared error (WMMSE) method is applied to cast the rate maximization into a separately convex optimization problem, which can be iteratively solved with a guaranteed convergence. The provided rate maximization framework is also shown to provide a converging minimum mean squared error (MMSE) design as a special case. Moreover, a methodology to obtain the least favorable error matrices is proposed by casting the resulting non-convex quadratic optimization into a convex problem. The achievable guaranteed (worst-case) rate is then numerically studied, over different levels of CSI error intensity, transceiver accuracy, and available transmit power.

With the abundance of mobile connected devices and coexisting networks, localization solutions are inevitably subject to interference. In this paper, effects of interference on the performance of Fingerprinting Localization Algorithms (FPS) are studied both theoretically and through experimentation. The previously introduced theoretical framework based on Hypothesis Testing (HT) problem is employed to characterize the performance of FPS and provide guidelines for combating negative impact of interference. In particular, it is shown that background interference interestingly can improve the performance of FPS, while the interference in the measurement phase incurs an error by pushing the reported location closer to the anchors. Moreover, the anchors provide the highest interference robustness for locations in their proximity. These results are further verified through simulations and experimentation in realistic setups.

The censored channel is one of the fundamental channels in information theory, which belongs to the class of non-linear channels. It is modeled by cascading an additive noise channel with a clipping operator. This paper is concerned with the information theoretic capacity of this channel. A necessary and sufficient condition for optimality of the input distribution is derived and it is shown that the capacity-achieving input distribution for the amplitude-limited censored channel has only a finite number of mass points. This result holds for a large class of noise distributions including additive Gaussian noise.

In this paper, an optimized cooperative communication scheme based on the recently presented LTE-V2X is introduced. LTE-V2X is the newly proposed technology by 3GPP in its Release 14 in order to obtain a feasible communication scheme between vehicles and infrastructures. This paper investigates the case of centralized network architecture, where the eNodeBs are used as information centers in order to optimize the scheduling scheme by means of clustering the vehicles in their coverage range. The proposed cooperative scheme is based on the shared messages between vehicles using a semi-persistent scheduling (SPS) which fulfills the rigid requirements, i.e., delay and reliability, faced in vehicular communications. In this paper, an enhancement of the SPS is defined using the information gathered by the network infrastructures in order to mitigate the interference. In addition, due to the high mobility of the vehicles, a predictive control model is implemented to estimate the vehicles trajectory, hence, improving the scheduling scheme performance. The aforementioned methods are simulated in a realistic scenario, using a traffic simulator, and compared with the standard LTE-V2X implementation as well as other methods proposed in the literature.

We discuss receiver clock correction and associated performance bounds for passive emitter localization using TDOA measurements from asynchronous sensor networks. In the considered system, passive receiving sensors are augmented with beacons at known locations that perform approximately periodical transmissions, used for calibration of the system synchronization. The precise transmission times as well as the transmission interval of the beacon messages are unknown. Similarly the transmissions of the target are irregular and do not, in general, occur simultaneous with the beacon transmissions. The clocks of each sensor are described by a linear error model. Based on that, we compare different snapshot based approaches for clock correction and derive theoretical limits for the localization of the target based on the modified Cramer-Rao lower bound (MCRLB). Simulation results are presented to illustrate the theoretical findings and show that our proposed estimators perform close to the MCRLB. Additional experimental results verify the analysis and the approach in a realistic large scale scenario.

In this paper we address the linear precoding and decoding design problem for a bidirectional orthogonal-frequency-division-multiplexing (OFDM) communication system, between two multiple-input-multiple-output (MIMO) full-duplex (FD) nodes. The effects of hardware distortions, leading to residual self-interference and inter-carrier leakage, are taken into account. In the first step, the operation of a FD MIMO OFDM transceiver is modeled under the impact of known hardware impairments. An alternating quadratic convex program (AltQCP) is then provided to obtain a minimum-mean-squared-error (MMSE) design for the defined system. The proposed design is then extended to maximize the system sum rate, applying the weighted-MMSE (WMMSE) method. The proposed AltQCP methods result in a monotonic improvement, leading to a necessary convergence to a stationary point. Finally, the performance of the defined system is evaluated under various system conditions, and in comparison to the other approaches in the literature. A significant gain is observed via the application of the proposed method as the hardware inaccuracy, and consequently inter-carrier leakage, increases.

Sensor based automated driving technologies, e.g., Adaptive Cruise Control (ACC), have been developed for many years in order to increase the traffic efficiency. Moreover, in the last decade, there has been a growing interest in further optimizing the traffic efficiency with the help from Vehicle-to-Vehicle (V2V) communications, e.g., Cooperative Adaptive Cruise Control (CACC). This paper investigates the utilization of V2V communication in platooning systems which is one fundamental application in future Intelligent Transport Systems (ITS). A novel V2V assisted platooning system is proposed in this paper, where a proposed prediction based control scheme is used to reduce the required intra-platoon gap under the stability constraint. Moreover, the impact of V2V communications range, delay and reliability on the performance of such platooning systems is discussed. Based on the distributed scheduling scheme in 3GPP Rel. 14 LTE-V2X sidelink (SL), a platoon based scheduling scheme is proposed to increase the intra-platoon V2V communication reliability.

We present a novel algorithm for self-localization in sensor networks without any prior knowledge on the locations of the sensors. We assume that all sensors in the network can receive and transmit, thus we obtain time difference of arrival measurements for all combinations of sensors. Using the full set of these differences in arrival times in the network we are able to obtain the relative location of the sensors nodes, the shape of the network. This leaves us with the problem of anchoring the network to its absolute location, which we solve using additional transmitting beacons at known locations. Experimental results from numerical simulation demonstrate the performance of our approach under various conditions.

We present a system utilizing off-the-shelf hardware components and a software framework, which is able to perform radio frequency localization with high accuracy in the order of a few meters. The architecture consists of highly synchronized receivers that are utilized by a distributed software defined radio stack in order to obtain synchronized multi channel reception of the target signal. This includes transportation of the sampled baseband signals from the receivers over a backbone network and local signal processing at a fusion center. To overcome the large drifts in the synchronization signals provided by GPS, we permanently calibrate the system based on a stationary reference source. The implemented system framework enables us to perform research on localization algorithms and performance evaluation under the influence of various inherent noise and error sources.

Self interference (SI) in full duplex (FD) systems is the interference caused by the transmission stream on the reception stream. Being one of the main restrictive factors for performance of practical full duplex systems, however, not too much is known about its effect on the fundamental limits of relaying systems. In this work, we consider the full duplex three-node relay channel with SI where SI is modeled as an additive Gaussian noise whose variance is dependent on instantaneous input power. The classical achievable rates and upper bounds for the single three-node relay channel no longer apply due to the structure of SI. Achievable rates for Decode-and-Forward (DF) and Compress-and-Forward (CF) and upper bounds on the capacity are derived assuming Gaussian inputs and SI. The deterministic model is also introduced and its capacity is characterized. The optimal joint source-relay distributions is discussed. Numerical results are provided comparing the achievable rates and upper bound.

Vehicular communications are increasingly gaining importance in research and industry as a viable communication technology, which makes cost-efficient and reliable deployment strategies a key aspect for the planning of a communication network infrastructure. The most critical aspect, and at the same time, the biggest challenge is to obtain a total connectivity between the vehicles and the network elements. In this paper, an environment-based deployment strategy is developed, obtaining an optimal number and distribution of RSUs in a selected area while maximizing the connectivity of the network. The main distinctive aspect of the proposed strategy is the use of an environment-based radio channel model, which is specific for a given urban area. Once the site-specific propagation parameters of the proposed radio channel model are obtained, the optimal RSU distribution is achieved by solving a multi-objective, integer linear program. In addition, a metaheuristic algorithm based on tabu-search is proposed for the solution. The network optimization scheme suggested in this work shows an enhanced coverage granting an always on connectivity for vehicle-to-infrastructure networks. Furthermore, due to the optimal number of RSUs, the cost of the network is minimized, which makes the deployment strategy feasible for real scenarios.

Energy consumption in wireless communication systems is exponentially increasing due to growing wireless traffic. Combating adverse effects of excessive energy consumption calls for energy- aware system design, leading to a new research paradigm, green communications. To achieve energy awareness in a full-duplex (FD) bi-directional multiple antenna system, in this paper, we study a Quality-of-Service (QoS) problem, where the total transmit power is minimized subject to minimum rate constraints at each node, and propose two algorithms. We first apply a penalty-based method in order to obtain an efficient optimization strategy. Afterwards, in the second algorithm, we generalize the well-known relationship between Weighted-Sum-Rate (WSR) andWeightedMinimum-Mean- Squared-Error (WMMSE) problems, originally used to solve the sum-rate maximization problem, to tackle the sum-power minimization problem.

A network of three nodes mutually communicating with each other is studied. This multi-way network is a suitable model for three-user device-to-device communications. The main goal of this paper is to characterize the capacity region of the underlying Gaussian three-way channel (3WC) within a constant gap. To this end, a capacity outer bound is derived using cut-set bounds and genie-aided bounds. For achievability, the 3WC is first transformed into an equivalent star channel. This latter is then decomposed into a set of “successive” sub-channels, leading to a sub-channel allocation problem. Using backward decoding, interference neutralization, and known results on the capacity of the star-channel relying of physical-layer network coding, an achievable rate region for the 3WC is obtained. It is then shown that the achievable rate region is within a constant gap of the developed outer bound, leading to the desired capacity approximation. Interestingly, in contrast to the Gaussian two-way channel (TWC), adaptation is necessary in the 3WC. Furthermore, message splitting is another ingredient of the developed scheme for the 3WC, which is not required in the TWC. The two setups are, however, similar in terms of their sum-capacity pre-log, which is equal to 2. Finally, some interesting networks and their approximate capacities are recovered as special cases of the 3WC, such as the cooperative broadcast channel and multiple access channel.

In this paper, we address the energy efficiency maximization problem for a distributed passive radar system, with application in signal classification. Two energy efficiency maximization strategies are studied. Firstly, the total power consumption of the network is minimized, while maintaining a required classification quality. We show that the resulting optimization problem has a similar solution structure to the famous water-filling algorithm and can be obtained analytically. Secondly, the energy efficiency of the network is viewed as the ratio of the observed useful information to the total energy consumption of the network for each estimation process. The optimal solution for the latter case is achieved by converting the original problem into an iterative convex feasibility check with a guaranteed convergence to optimality. Finally, the optimal behavior of the defined system in terms of energy efficiency is examined with respect to different system parameters and design approaches by performing extensive numerical simulations.

We investigate the power allocation problem in distributed sensor networks that are used for target object classification. In the classification process, the absence, the presence, or the type of a target object is observed by the sensor nodes independently. Since these local observations are noisy and thus unreliable, they are fused together as a single reliable observation at a fusion center. The fusion center uses the best linear unbiased estimator to accurately estimate the reflection coefficient of target objects. We utilize the average deviation between the estimated and the actual reflection coefficient as a metric for defining the objective function. First, we demonstrate that the corresponding optimization of the power allocation leads to a signomial program which is in general quite hard to solve. Nonetheless, by using the proposed system model, fusion rule and objective function, we are able to optimize the power allocation analytically and can hence present a closed-form solution. Since the power consumption of the entire network may be limited in various aspects, three different cases of power constraints are discussed and compared with each other. In addition, a sensitivity analysis of the optimal power allocation with respect to perfect and imperfect parameter knowledge is worked out.

The ultimate goal of the present paper is to provide mathematical tools for dealing with the complicated average error probability (AEP) in Nakagami fading channels. This is useful for analytical investigations as well as alleviating computational effort in simulations or on-line computations. We hence thoroughly analyze the mathematical structure of the AEP over Nakagami fading channels. First, the AEP is re-parameterized to obtain a mathematically concise form. The main contributions are then as follows. An ordinary differential equation is set up, which has the AEP as a solution. By this approach, a new representation of the AEP is found, which merely needs integration over a broken rational function. This paves the way to numerous amazing relations of the AEP, e.g., to the Gaussian hypergeometric and the incomplete beta function. Moreover, monotonicity and log-convexity are demonstrated. Finally, asymptotic expansions of the AEP are given.

We investigate the symmetric Degrees-of-Freedom (DoF) of a homogeneous multiple-input multiple-output (MIMO) 3-way channel with MTtransmit antennas and MRreceive antennas at each user. The 3-way channel extends the two-way channel to three users, who exchange six messages in total, i. e., there is one message from each user to each of the two other users. We assume that each user operates in a perfect full-duplex mode. Genie-aided upper bounds on the DoF of the channel are derived and it is shown that those are achievable by combining MIMO interference alignment, null-space beam-forming and zero-forcing. A particular gain from this homogeneous setup is that the symmetric DoF allocation providing complete fairness among all users is sum-DoF optimal.

We consider the problem of cyclic interference alignment (IA) on the 3 - user X- network and show that it is infeasible to exactly achieve the upper bound of equation degrees of freedom for the lower bound of n = 5 signalling dimensions and K = 3 user-pairs. We observe that this infeasibility goes beyond the problem of common eigenvectors in invariant subspaces within spatial IA. In order to gain non-asymptotic feasibility with minimal intervention, we first investigate a communication strategy that enables IA by feedforwarding a subset of messages with minimal rate. In a second step, we replace the proposed feedforward strategy by an analogous cyclic interference alignment and cancellation scheme with a backhaul network on the receiver side and also by its dual cyclic interference neutralization scheme with a backhaul network on the transmitter side.

In this work, the autocorrelation and autocovariance functions of the channel state information (CSI) in a multi-cell mobile network with fast fading is derived analytically. The derivation is based on the Rayleigh fading model. A squared Bessel function is found to be a good approximation of the autocovariance. Simulations confirm both the analytical result and approximation to be accurate for short period. The approximation can be potentially used for a coarse estimation of the moving speed of a mobile user.

In the present work, we investigate the power allocation problem in distributed sensor networks that are used for passive radar applications. The signal emitted by a target is observed by the sensor nodes independently. Since these local observations are noisy and are thus unreliable, they are fused together as a single reliable observation at a remotely located fusion center in order to increase the overall system performance. The fusion center uses the best linear unbiased estimator in order to estimate the present target signal accurately. By using the proposed system model, fusion rule and objective function we are able to optimize the power allocation analytically and can hence present a closed-form solution to the power allocation problem. Since the power allocation problem can be subject to different power constraints, three different cases of power constraints are discussed and compared with each other. Furthermore, we demonstrate that all considered constraints lead to signomial optimization problems which are in general quite hard to solve. The main applications of the proposed results are issues concerning the sensor selection and energy efficiency in passive sensor networks.

Common approaches for partitioning cell site planning problems into smaller instances typically suffer from the lack of knowledge about the number of clusters k that is appropriate for the particular problem. When applying an iterative processing over methods such as the k-means algorithm or the related k-medoids approach to determine the optimum k, the computational effort can be higher than solving the original problem instance directly. This is particularly the case if the optimum k is small, which happens most likely in urban environments where the user density and the number of cells are high. In this paper, we propose a graph-based method using minimum cut operations to partition a planning instance automatically into an appropriate number of k sub-problems. We demonstrate the benefits of this approach by numerical evaluation of exemplary application to the problem of planning LTE cell site locations in an urban environment.

Web 2.0 provides various types of social media applications, e.g., blogs, forums and news sites that allow users to post Web comments. This kind of communication plays an important role in acceptance research. To extract different opinions from such data, it is necessary to build Web comment corpora. Building such corpora requires focused crawling. Many focused Web crawling algorithms are known to build topic-specific Web collections. However, the type of Web pages is typically not considered. In this paper, we introduce a new type-specific focused crawler, which uses a classifier based on HTML meta information. Its application allows for collecting only Web pages that cover Web comments from various domains.

One of the main enablers of dynamic spectrum access is fast and reliable spectrum sensing. Acquiring the occupation status of a spectral band can be accomplished in different ways, one of which is called cyclostationary spectrum sensing. The aforementioned method exploits the prior knowledge of periodicities inherent in most man-made signals for the purpose of detecting their presence in a set of sample data. One prerequisite for the detection is the knowledge of the signal's cyclic autocorrelation (CA), which can be estimated from a finite amount of time-domain samples. This work introduces a new method for estimating the CA using a very small amount of time-domain samples, i.e. a short observation time. This is accomplished by modeling the desired CA vector using a custom dictionary describing its known properties and recovering it by solving a convex optimization problem.

In this work, we investigate the Interference Neutralization scheme on a 2×2×2 relay-interference channel w.r.t. the Cyclic Interference Alignment framework. We formulate sufficient and necessary conditions for this particular Interference Neutralization scheme to achieve the min-cut upper bound, i.e., two degrees of freedom, confirming the work on Aligned Interference Neutralization by Gou et al. Furthermore, we translate our proposed scheme to a generalized version of the asymptotic Aligned Interference Neutralization scheme based on spatial Interference Alignment.

In this paper, we consider one-bit output quantization of a discrete signal with m real signaling points subject to arbitrary additive noise. First, the capacity-achieving distribution is determined for the corresponding channel. For any fixed quantization threshold q it concentrates on the two most distant signaling points, hence leading to an interpretation as binary asymmetric channel. The direct proof of this result allows for an explicit form of the capacity as a function of threshold q. We characterize stationary points as candidates for optimal thresholds by a condition on the differential quotient of the derivative of the binary entropy function. In contrast to intuition, symmetry of the noise distribution does not ensure a unique optimum antipodal threshold.

A two-way relay-interference channel describes a system of four communicating transceivers with two interjacent parallel relays arranged in a bidirectional 2 × 2 × 2 relay-interference network. Two pairs of transceivers are each communicating bidirectionally with the aid of both relays. All transceivers and relays are assumed to operate in full-duplex mode. Since Interference Neutralization is known as a promising method to achieve the cut-set upper bounds on the data rates of the unidirectional relay-interference channel, we investigate a Cyclic Interference Neutralization scheme on the corresponding bidirectional relay-interference channel w.r.t. a conceptual channel model based on a polynomial ring. We show that, if the channel matrix satisfies a certain set of symmetry conditions, a total number of 4 degrees of freedom is asymptotically achievable.

In this work, a downlink power control scheme based on channel quality indicator (CQI) is proposed for LTE femtocells. Unlike the conventional methods, this scheme takes the service types of users into account. Without making strong assumptions, such as full knowledge of the network or perfect coordination among basestations, a completely decentralized solution is provided. Simulation results confirm, that comparing to the conventional schemes, better throughput and coverage can be achieved simultaneously by the proposed algorithm.

Heterogeneous network is a promising solution to improve the energy efficiency of the cellular system. In this work, an energy efficient power management scheme designed for LTE femtocells is presented. Using user and service classification, the proposed algorithm balances the network coverage, average data rate and energy consumption. Simulation results confirms the superiority of proposed algorithm in energy efficiency and coverage.

In this work, a discrete-time stationary Rayleigh flat-fading channel with unknown channel state information at transmitter and receiver side is studied. The law of the channel is presumed to be known to the receiver. For independent identically distributed (i.i.d.) zero-mean proper Gaussian input distributions, the achievable rate is investigated. The main contribution of this paper is the derivation of two new upper bounds on the achievable rate with Gaussian input symbols. One of these bounds is based on the one-step channel prediction error variance but is not restricted to peak power constrained input symbols like known bounds. Moreover, it is shown that Gaussian inputs yield the same pre-log as the peak power constrained capacity. The derived bounds are compared with a known lower bound on the capacity given by Deng and Haimovich and with bounds on the peak power constrained capacity given by Sethuraman et al.. Finally, the achievable rate with i.i.d. Gaussian input symbols is compared to the achievable rate using a coherent detection in combination with a solely pilot-based channel estimation.

Consider the estimation of an unknown parameter vector in a linear measurement model. Centralized sensor selection consists in selecting a set of kssensor measurements, from a total number of m potential measurements. The performance of the corresponding selection is measured by the volume of an estimation error covariance matrix. In this work, we consider the problem of selecting these sensors in a distributed or decentralized fashion. In particular, we study the case of two leader nodes that perform naive decentralized selections. We demonstrate that this can degrade the performance severely. Therefore, two heuristics based on convex optimization methods are introduced, where we first allow one leader to make a selection, and then to share a modest amount of information about his selection with the remaining node. We will show that both heuristics clearly outperform the naive decentralized selection, and achieve a performance close to the centralized selection.

In this paper, we consider a heterogeneous network with one macro node and one pico node. We are concerned with the problem of associating users in the macro cell, to either the macro node or the pico node, in order to maximize the sum rate in the downlink. We formulate a new theoretical framework to study this problem and derive an upper bound on the achievable sum rate using semidefinite relaxation. Furthermore, we propose a randomized heuristic to produce a feasible solution, and most importantly, give an analytic guarantee on its performance. Independently of the problem data, we can ensure a worst case performance for the randomization method. In practice, this guarantee is as good as the standard best SNR heuristic typically used in 3GPP LTE networks.

In this work, we consider a heterogeneous network consisting in several macro nodes and pico nodes. Our goal is to associate users, belonging to this network, to one of the nodes, while maximizing the sum rate of all users. We also want to analyze the load balancing achieved by this association. Therefore, we develop a new theoretical framework to study cell association for the downlink of multi-cell networks and derive an upper bound on the achievable sum rate. We propose a dynamic cell association heuristic, which achieves performance close to optimal. Finally, we verify our results through numerical evaluations and implement the proposed heuristic in an LTE simulator to demonstrate its viability.

In this paper we consider the transmission of jointly sparse signals over a MIMO MAC composed of two transmitters and one receiver. Distributed compression is performed at the transmitters using a liner transformation and joint reconstruction at the receiver is enabled using the theory of distributed compressed sensing. The objective is to minimize the sum MSE between the uncompressed signals at the transmitter and the recovered signals at the receiver. We present a new theoretical framework to study this problem and provide an algorithm enabling to transmit correlated signals over a MIMO MAC, which performs provably close to optimal. To validate our approach we analyze the performance of our system for different noise conditions and varying compression factors. The results show that distributed compressed sensing can be performed reliably over a MIMO MAC.

Bit-interleaved coded modulation (BICM) is a practical approach for reliable communication over the AWGN channel in the bandwidth limited regime. For a signal point constellation with 2mpoints, BICM labels the signal points with bit strings of length m and then treats these m bits separately both at the transmitter and the receiver. BICM capacity is defined as the maximum of a certain achievable rate. Maximization has to be done over the probability mass functions (pmf) of the bits. This is a non-convex optimization problem. So far, the optimal bit pmfs were determined via exhaustive search, which is of exponential complexity in m. In this work, an algorithm called bit-alternating convex concave method (Bacm) is developed. This algorithm calculates BICM capacity with a complexity that scales approximately as m3. The algorithm iteratively applies convex optimization techniques. Bacm is used to calculate BICM capacity of 4,8, 16, 32, and 64-PAM in AWGN. For PAM constellations with more than 8 points, the presented values are the first results known in the literature.

The impact of significantly long delays is a central problem one has to face in the ergodic interference alignment scheme. In this work, we consider the delay-rate tradeoff in a fully connected K-user Gaussian interference channel through the lens of partial interference alignment. In the concept of partial interference alignment, only a subset of user-pairs with a favorable interference pattern is aligned. We explore two flexible communication schemes: At first, we propose a scheme with uniform delay-rate demands. Then we introduce the delay-rate tradeoff for a case of non-uniform demands such that also imbalanced delays and rates can be investigated.

This paper considers the problem of sensing a sparsely occupied wideband spectrum utilizing a set of geographically distributed sensing nodes as well as a fusion center. Exchange of measurement data between the sensing nodes and the fusion center takes up parts of the precious radio spectrum and thus, methods for diminishing the minimum amount of measurements still ensuring a reliable reconstruction of the spectrum at the fusion center are needed. To this end we propose two approaches in the form of convex optimization problems to tackle the problem. The first approach applies classic compressive sampling, while the second approach improves the optimization problem, so that all measurements which have been acquired in a distributed manner can be taken into consideration in a single spectrum recovery operation. This makes it possible, to exploit the inherent diversity gain. The presented approaches to efficient distributed spectrum sensing enable reliable dynamic spectrum access.

This paper considers the problem of associating users, in an heterogeneous network, to either a macro node or a pico node within a tightly coordinated cell cluster. We introduce a new theoretical framework to model this problem for the downlink and derive upper bounds for achievable sum rate and minimum rate using convex optimization. Further we propose heuristics, consisting in dynamic cell association, enabling to achieve performance close to the upper bounds. Finally we implement these heuristics in an LTE simulator and show the potential of such dynamic cell association for a small LTE network.

Many communication channels with discrete input have non-uniform capacity achieving probability mass functions (PMF). By parsing a stream of independent and equiprobable bits according to a full prefix-free code, a modulator can generate dyadic PMFs at the channel input. In this work, we show that for discrete memoryless channels and for memoryless discrete noiseless channels, searching for good dyadic input PMFs is equivalent to minimizing the Kullback-Leibler distance between a dyadic PMF and a weighted version of the capacity achieving PMF. We define a new algorithm called Geometric Huffman Coding (GHC) and prove that GHC finds the optimal dyadic PMF in O(m log m) steps where m is the number of input symbols of the considered channel. Furthermore, we prove that by generating dyadic PMFs of blocks of consecutive input symbols, GHC achieves capacity when the block length goes to infinity.

OFDM-based cognitive radio (CR) systems are shown to be an effective solution for increasing spectrum efficiency by activating the certain group of subcarriers (subbands) in locally available spectrum. However, each CR receiver should synchronize itself to appropriate carrier frequency and to identify currently activated subbands. Moreover, energy and bandwidth efficiency of CR systems can be improved if each CR could provide additional characterization of the local spectral content. In this paper a novel joint frequency synchronization and spectrum occupancy characterization method for OFDM-based CR systems is proposed. The synchronization preamble structure is appropriately modified in order to efficiently perform frequency offset estimation, to identify occupied subbands, and, finally, to provide SNR and interference power estimates as reliable quantitative indicators of spectrum occupancy. The performance of proposed method is evaluated for different amounts of spectrum occupancy and interference levels.

The capacity achieving probability mass function (PMF) of a finite signal constellation with an average power constraint is in most cases non-uniform. A common approach to generate non-uniform input PMFs is Huffman shaping, which consists of first approximating the capacity achieving PMF by a sampled Gaussian density and then to calculate the Huffman code of the sampled Gaussian density. The Huffman code is then used as a prefix-free modulation code. This approach showed good results in practice, can however lead to a significant gap to capacity. In this work, a method is proposed that efficiently constructs optimal prefix-free modulation codes for any finite signal constellation with average power constraint in additive noise. The proposed codes operate as close to capacity as desired. The major part of this work elaborates an analytical proof of this property. The proposed method is applied to 64-QAM in AWGN and numeric results are given, which show that, opposed to Huffman shaping, by using the proposed method, it is possible to operate very close to capacity over the whole range of parameters.

This paper considers the problem of applying compressed sensing ideas to relay networks in order to increase the network throughput. We present a new method to perform forwarding which, when the signals transmitted by several sources are jointly sparse, enables to send a vector of dimension higher than the min-cut of the network. First, the jointly sparse source signals are mapped to a signal of lower dimension which can be transmitted over the network. Second, we allow source nodes to send at a so called single-source min-cut rate which is such that each node transmits without considering other sources transmitting simultaneously. Finally, sinks use compressed sensing in order to recover the transmitted sparse signal. Algebraically, the source signal is multiplied by a network matrix which needs to have the restricted isometry property to provide perfect reconstruction. We present algorithms to choose the network matrix coefficients as well as simulation results to show the validity of our approach.

In this paper, the rate region of the two user MIMO broadcast channel (BC) under linear filtering at high signal-to-noise ratio (SNR) is investigated when time sharing is not available and the transmitter has more antennas than the sum of the receiving antennas. To reach the rate region's boundary, the sum rate is maximized subject to a given ratio between the users rates. The sum rate is first considered asymptotically when the SNR tends to infinity and taken as an affine function of the logarithm of the SNR, with the multiplicative and the additive parameters called the multiplexing gain (MG) and the rate offset (RO), respectively. The maximal MG and the maximal RO are obtained for every rate ratio constraint. Additionally, the asymptotic optimal stream allocations that achieve those values are also derived. Analytical inner and outer bounds, which offer a rough approximation of the boundary but are extremely easy to evaluate even in fading channels, are then developed. The maximization of the rate subject to a rate ratio constraint is then studied at finite SNR. Algorithmic inner and outer bounds for the rate region boundary are derived and shown to be very close to each other and accurate even at intermediate SNR.

We explore an algorithmic approximation of achievable lower bounds for the generalized degrees of freedom on finite-field linear deterministic K-user interference channels. With a heuristic greedy algorithm, derived from results on the maximum independent set problem in graph theory, the max-min generalized degrees of freedom for desired links are approached. The idea of interference alignment is implicitly contained in the algorithm. It is applied to deterministic interference channels with entirely symmetric cross-channel gains, and compared to the generalized degrees of freedom of a currently known coding scheme. The greedy heuristic also generalizes to deterministic interference channels with asymmetric channel gains.

In practical large scale relay networks, it is often an unfeasible task to adapt the strategy of the relays to each source/sink pair communicating through the network. In this paper we consider the strategy of the relays as fixed and chosen in advance. In this context, it is a crucial problem for the source node transmitting information, to know the strategy of the relays in the network. Therefore we optimize the transmit strategy of the source node, assuming only partial relay information knowledge, i.e., channel state information and relay strategies. We also study the impact of getting complete relay information from certain relays only, derive the reduction in the problem uncertainty due to this knowledge and evaluate numerically the performance of the network.

In this paper, we consider a class of models that describe parallel observations of a single source by many noisy sensors, lossy quantization at each sensor, and finally information fusion of the quantized data. Certain phenomena in biophysics and neural information processing, but also in detection networks and modern communications can be elucidated by these models. Mutual information is used as an analytical measure of information exchange. We characterize the optimum information fusion rule by maximum entropy of the corresponding output distribution. For discrete input distributions, this problem can be reduced to a generalized Knapsack problem, which is hard to solve in general. We suggest a heuristic that minimizes the decrease of entropy in each step, and show that for binary information fusion the true optimum is attained for dyadic distributions. The problem of finding optimum quantization rules is an essential part of the model and treated analogously. For input distributions with a density, optimality is achieved by determining appropriate quantization thresholds. Finally, by applying the data processing inequality, an upper bound for the mutual information of arbitrary stochastic pooling channels is found. This bound provides interesting insight into the resilience of parallel noisy information processing in biological systems.

Unequal transition probabilities between input and output symbols, input power constraints, or input symbols of unequal durations can lead to non-uniform capacity achieving input distributions for communication channels. Using uniform input distributions reduces the achievable rate, which is called the shaping gap. Gallager's idea for reliable communication with zero shaping gap is to do encoding, matching, and jointly decoding and dematching. In this work, a scheme is proposed that consists in matching, encoding, decoding, and dematching. Only matching is channel specific whereas coding is not. Thus off-the-shelf LDPC codes can be applied. Analytical formulas for shaping and coding gap of the proposed scheme are derived and it is shown that the shaping gap can be made zero. Numerical results show that the proposed scheme allows to operate off-the-shelf LDPC codes with zero shaping gap and a coding gap that is unchanged compared to uniform transmission.

In this work resource allocation is studied for orthogonal frequency division multiplexing (OFDM) unicasting by multiple base stations (BSs). In the considered scenario, each user is restricted to being covered by one BS, i.e., BS selection, while each subcarrier is assigned to at most one user, i.e., subcarrier assignment. Thus, interference does not exist among these BSs. We aim at maximizing the weighted sum of data rates subject to limited transmission power at each BS, while the minimum rates required by users are satisfied. BS selection and subcarrier assignment are considered jointly. A heuristic method is proposed. It has linear complexity in the number of users, subcarriers and BSs. Simulations demonstrate that the performance loss of the proposed method is relatively small.

We consider an information theoretic model that describes parallel reception of a common signal by many receivers. The signal is distorted by noise and independently decoded by each receiver. Receivers cooperate in the sense that the outcome is conveyed to a decision center, which makes the final decision on the received signal by a majority vote. A potential scenario which could be described by this model is a cluster of base stations jointly receiving signals from a mobile station and conjoining individual decodings. Similar principles apply for cognitive radio setups where a secondary user employs many weak subcarriers, subject to sudden drop out by occupation from the primary users. The whole system is modeled as a cascade of channels, and quality of information exchange is measured by mutual information. This allows for modeling and optimizing quantization and detection in a unifying approach. Numerical evaluations demonstrate that the increase of mutual information by using additional channels is only logarithmic. 4-QAM is investigated as a concrete example and system performance is numerically investigated. The model in this paper is motivated by one used for describing information exchange in biological neural networks, revealing so called stochastic resonance by adding noise to signals.

The reliable detection of unused spectrum while meeting a required probability of detecting primary user activity is a key functionality of cognitive radio systems. In cooperative spectrum sensing, the detection results of multiple cognitive radios are combined to a global result with high reliability. In order to transmit the local decisions a signaling channel is required. It can be realized by ultra-wideband underlay communications in parallel to primary user activity. Due to strict limitations on the transmission power for ultra-wideband communications, it is crucial to carefully allocate power levels to minimize channel errors, which could corrupt the transmission of local decisions. In this paper, an approach for the allocation of transmission power levels for the signaling channel is proposed, which aims to maximize the global probability of detecting spectrum holes while detecting primary user activity with a given reliability. Numerical results based on real spectrum measurements show the feasibility and illustrate the performance of the approach.

Several attractive features of ultra wideband (UWB) communications make it a good candidate for physical-layer of wireless sensor networks (WSN). These features include low power consumption, low complexity and low cost of implementation. In this paper, we present an opportunistic power assignment strategy for distributed detection in parallel fusion WSNs, considering a Nakagami-m fading model for the communication channel and time-hopping (TH) UWB for the transmitter circuit of the sensor nodes. In a parallel fusion WSN, local decisions are made by local sensors and transmitted through wireless channels to a fusion center. The fusion center processes the information and makes the final decision. Simulation results are provided for the global probability of detection error and relative performance gain to evaluate the efficiency of the proposed power assignment strategy in different fading environments.

We analytically allocate to sub-channels costly power and symbol rate to maximise a general throughput function. Link parameters (e.g., modulation order and packet length) are explicitly considered. We provide the structure of the general solution, and for certain scenarios give an explicit solution, which includes the optimal link configuration. A channel can be immediately discarded if its normalised gain is less than the normalised power price. Moreover, when power is "low", all of it should go to the best sub-channel. In general, the best "M" channels operate at maximal symbol rate, with the SNR that satisfies a single-variable optimising condition; and at most one additional channel operate at the "low power" optimum. Previously, we had analysed costly power allocation under a channel performance function that generalises the Gaussian capacity formula. However, the power allocation that would be optimal if each sub-channel operated at "capacity" could differ significantly from the maximiser of actual performance. Hence, the power allocation obtained herein is preferable.

In this paper, a joint iterative channel estimation and low-density parity-check (LDPC) decoding algorithm based on factor graphs and the sum-product algorithm is proposed for orthogonal frequency division multiplexing (OFDM) systems employing multiple transmit and receive antennas (MIMO). By modeling time-varying frequency-selective fading channels as autoregressive processes and approximating messages as Gaussian pdf, this receiver algorithm is able to maintain a low complexity. Moreover, with the help of strong channel coding and proper pilot allocation, the signal overhead can be significantly reduced.

In this paper, the performance of Schmidl and Cox (S&C) timing estimator in cognitive radio systems under a fractional bandwidth (FBW) scenario is investigated. The synchronization preamble is appropriately modified in order to avoid transmission in the subbands where primary users (PU) are active. However, at the receiver, the timing synchronizer processes all received signal samples within the given OFDM band without any knowledge of a PU presence, thus being affected by interference. Statistical properties of timing metric in the presence of interference caused by PU are derived for an AWGN channel.

In this paper, we present a general analytical framework for the exact mutual information (MI) cumulants of multiple-input multiple-output (MIMO) systems with perfect receiver channel state information (CSI) and no transmitter CSI. Our derivation is based on a recent parameterization of the joint ordered eigenvalue probability density function (PDF), that encompasses both uncorrelated/semi-correlated Rayleigh channels as well as uncorrelated Rician channels. In addition, we extend our framework to account for the cumulants of doubly-correlated Rayleigh channels and also to deduce tractable expressions in the high Signal-to-Noise ratio (SNR) regime. The cumulants are particularly useful to study all high-order statistics (HOS) of the MI; in fact, they can be used to express the MI mean and variance as a finite sum of determinants. Our analytical expressions are then validated via Monte-Carlo simulations with the attained accuracy being excellent in all cases.

In adaptive orthogonal frequency division multiplexing (OFDM)systems, different powers and rates can be allocated to subcarriers optimally by water-filling. This strategy has been proved to improve the system performance significantly. However, a large signalling overhead is induced by water-filling to contain the group of presently employed mapping schemes. The faster the channel varies in time, the more frequently the large signalling overhead is needed. In this paper, to avoid such a problem, we propose that a constant rate and different powers are allocated to subcarriers while satisfying the constraints on the transmission rate and the average bit-error rate (BER). First, an approximate relationship between BER and signal-to-noise ratio (SNR) is employed. Then, based on the convex optimization framework, the transmission power is optimally distributed to the used subcarriers with an optimal constant rate. The subcarrier assignment can be determined by an upgraded bisection method, which can be also used for other resource allocation problems. Simulations demonstrate that the proposed resource allocation strategy has better performance than water-filling with the signalling overhead considered in fast time-varying environment.

In this paper, the rate region of the two user MIMO broadcast channel (BC) with linear filtering at high signal-to-noise ratio (SNR) is studied when time sharing is not available and the transmitter has fewer antennas than the sum of the receiving antennas. To reach the boundary of the rate region, the sum rate is maximized subject to a rate ratio constraint. Furthermore, the sum rate is approximated as an affine function of the logarithm of the SNR and the two parameters of this approximation, which are the multiplexing gain (MG) and the rate offset (RO), are derived. This leads directly to the asymptotic rate region, particularly interesting because it is obtained in simple analytical form and offers a good approximation at high but finite SNR. We then consider the rate region boundary at finite SNR and derive algorithmic bounds for it, which are accurate even at intermediate SNR.

A femto access point (FAP) is a low-cost, low-power device intended to be installed by a home owner, to turn the home into a tiny wireless communication cell, that operates underneath a standard cell, in licensed spectrum bands. Femto-cells can improve user experience in indoor locations, and increase overall system capacity with modest monetary investment. But the unplanned dynamic nature of these cells, significantly complicate resource allocation and interference control. We propose a relatively simple, decentralised market-oriented scheme for sub-channel and power allocation when femto-cell users co-exist with those of an OFDMA macro-cell. Our scheme utilises the Dutch auction (price progressively falls until a participant buys the object). Special "confirmation messages" are used to control interference, achieve channel reuse across Femto-cells, and mitigate the "hidden terminal" problem. Secure software inside each terminal may record transactions for eventual payment collection, or the auction can be interpreted as a prioritised decentralised allocation algorithm, without real money exchange.

Allpass transformed filter banks provide a nonuniform frequency resolution and can be used in mobile speech processing systems, e.g., cellular phones or digital hearing aids. The nominal design of such an allpass transformed analysis-synthesis filter bank (AS FB) with near perfect reconstruction (NPR) is achieved by numerical optimization of finite-impulse response (FIR) equalizers in each subchannel. The underlying nominal optimization problem is an equality constrained leasts-quares problem. In a robust design, we take into account coefficient uncertainty in a possible implementation of such a filter bank. We will describe this uncertainty by the choice of two simple set-based worst-case uncertainty models, namely a norm bound error model and a coefficient bound error model. When including these error models, both robust designs can be recast as second-order cone programs (SOCP) and solved efficiently by standard numerical optimization methods. Furthermore, we will provide design examples to show that both robust designs maintain a good overall performance with respect to NPR while offering less sensitivity to quantization errors.

A crucial parameter required for adaptive transmission in orthogonal frequency division multiplexing (OFDM) systems is the signal-to-noise ratio (SNR). In this paper, certain modifications to previously proposed preamble-based SNR estimator for wireless OFDM systems are suggested in order to improve estimation performance. Proposed modifications, related to DFT interpolation, are based on the adaptive selection of significant channel impulse response (CIR) paths utilizing the average noise power estimate obtained in frequency domain. The modified estimator shows performance improvement of average SNR estimation in low SNR regime and considerably outperforms original algorithm for SNR per subcarrier estimation.

The design of wireless sensor networks for signal processing applications has to consider the limited battery power of the sensor nodes. In this paper, a combined routing path selection and power allocation strategy is presented that is especially designed for distributed detection in sensor networks with a serial topology. The objective is to minimize the global probability of detection error of the serial network under a total network power constraint. The cross-layer approach for the selection of a routing path through the network is based on the local observation SNR of the sensors as well as fading conditions and is efficiently implemented by a backward greedy algorithm. After the routing path is established, a subsequently performed power allocation algorithm aims to optimally distribute a total power budget with respect to the detection performance of the sensor network.

Typical applications of wireless sensor networks include infrastructure monitoring and surveillance, where in many cases the geometry of the monitored object determines the topology of the deployed network. For example, important applications like pipeline monitoring and border surveillance feature a linear arrangement of wireless sensors. In this paper, we address the scalable optimization of linear sensor networks for serial distributed detection applications. In serial distributed detection, signal detection is performed collaboratively by multiple sensors arranged in serial until a final detection result is reached. By locally maximizing the Chernoff information at each sensor in the serial network, scalable solutions are obtained which only rely on local information. By considering the problem of detecting a deterministic signal in the presence of Gaussian noise, a detailed numerical study reveals interesting trade-offs and dependencies between communication constraints and detection performance.

This paper is concerned with dynamic resource allocation for heterogeneous downlink in orthogonal frequency division multiple access (OFDMA) systems. First, this paper formulates the resource allocation problem when one terminal may demand both non-real time (or best effort) transmission and real time transmission over the downlink simultaneously. We consider the constraint on the total transmission power of the base station for both types of services and the constraint on the rate requirement of each terminal for a real time transmission. To solve this problem, an efficient instantaneous per-symbol method with dual optimality is proposed based on the convex optimization framework. Compared to the exhaustive search, simulations show that the performance degradation of our method is limited to 0.002% of the optimal solution, when an OFDMA system consists of more than 16 subcarriers. Therefore, this paper illustrates that it is not computationally prohibitive to optimally solve the resource allocation problem for heterogeneous transmission over OFDMA downlink.

This work is inspired by the question "Can 100 speakers talk for 30 minutes each in one room within one hour and with zero interference to each other's audience?" posed by Cadambe and Jafar at the 45th Allerton conference 2007. We consider the problem of how many transmitter-receiver pairs can be placed such that each desired link may use half of the channel time free of interference from unintended transmissions. The answer is given in the title: at least n pairs, i.e., 2n stations can be positioned in the (n-1)-dimensional Euclidean space such that complete interference alignment in time is achieved. Regular patterns with equal distances between receivers and transmitters, respectively, are the solution. The basic methodology for achieving this result is borrowed from the field of distance geometry.

It has been shown that adaptive power and rate allocation for multiuser orthogonal frequency multiplexing (OFDM) improves the system performance significantly. In this paper, the resource allocation that aims at minimizing the total transmission power under certain data transmission constraints is considered. First, the power variation for single-user water-filling while changing the subcarrier assignment is derived. Based on this, a class of methods for multiuser resource allocation is proposed. The presented methods, consisting of tactical processes, can achieve a good balance of computational complexity and performance. Compared to previous works, simulations show that our methods have comparable or better performance and that the computing time for the proposed methods is approximately sublinearly increasing in the number of users K and the number of subcarriers N.

We consider a generalised multi-receiver radio network under a quality-of-service (QoS) constraint that involves generalised carrier-to-interference ratios. This model includes as special cases many well-known schemes, such as those discussed by Yates (JSAC, 13(7): 1341-1348, 1995). A simple feasibility condition for the QoS targets, and a power-vector that yields those targets are given in closed form for the case in which additive noise is negligible and the key functions are homogeneous of degree one. The condition has the simple form, k_i <= q_i, where "i" identifies a terminal, "k_i" its desired QoS level, and "q_i" its quality of service when all power levels equal unity. If the feasibility condition is satisfied, the power levels P_i = k_i / q_i yield or exceed the desired levels of quality. The generalised multi-receiver network can be conservatively represented by an equivalent set of independent transmitter-receiver pairs, with q_i equal to the channel gain of the pair that represents "i". Macro-diversity and multiple-connection reception are some of the specific models discussed as examples.

In this paper, we consider the dynamic power and rate allocation for the heterogeneous transmission over multiuser orthogonal frequency division multiplexing (OFDM) downlink, where users may require real time or non-real time transmission. The data rate for the real time transmission is lower bounded, while the total transmission power for both transmissions is limited to a fixed amount. Solutions to this problem must be computationally efficient in order to adapt to the fast time-varying channels in practice. To accelerate the resource allocation for the considered scenario, efficient approaches are given to update the power or rate variation while changing subcarrier assignments. By iteratively using these approaches, a resource allocation method is proposed to achieve a good balance between the performance and the complexity. Its complexity is linearly increasing in the number of subcarriers and the number of users. Simulation results demonstrate that our method has small performance loss compared to the dual optimum and achieves much better performance compared to previous works.

In wireless sensor networks, the limited energy of the nodes should be utilized in such a way that the performance measure of the sensing application is optimized. In this paper, power-aware design of IR-UWB sensor networks for distributed signal detection is discussed. The design approach consists of two parts which aim at minimizing the global probability of detection error. First, an application-specific node clustering algorithm is performed. Based on the generated topology a resource allocation scheme adapted to distributed detection is carried out. It is based on both, information from the network topology and individual sensor detection quality. Numerical results indicate significant performance gains for sensor networks with realistic resource constraints.

The limited energy of sensor nodes in wireless sensor networks strongly recommends power-aware design methodologies. In this paper, a power-aware sensor selection strategy for wireless sensor networks is presented that is especially designed for distributed detection with soft decision fusion. The objective is to minimize the global probability of error at the fusion center under a total network power constraint. The cross-layer approach for the selection of a proper subset of sensors is based on a measure of individual sensor detection quality as well as location information. It corresponds to a low-complexity power allocation algorithm and enables significant performance gains in terms of reduction of the global probability of error compared to the inclusion of all sensors.

This paper addresses the optimization of wireless sensor networks for distributed detection applications. In general, the jointly optimum solution for the local sensor decision rules and the fusion rule is very difficult to obtain and does not scale well with the number of sensors. In this paper, the joint optimization of the local sensor decision rules and the fusion rule is facilitated by using an upper bound on the global probability of detection error. The bound is derived using Hoeffding's inequality and allows for non-identically distributed sensor observations, multi-bit sensor output, as well as noisy communication channels between the sensors and the fusion center. By considering the problem of detecting a known signal in the presence of Gaussian noise, numerical results reveal dependencies of the obtained solutions on the prior probabilities, the total number of sensors, and the local observation SNR.

This paper addresses the problem of resource allocation for orthogonal frequency division multiple access (OFDMA) downlink that aims at minimizing the total transmission power under data transmission constraints. To accelerate this multiuser resource allocation with small performance loss, an efficient technique is introduced that provides the power variation of single-user water-filling when the subcarrier assignment is changed. Based on this technique, an intelligent resource allocation method for OFDMA downlink is designed. First, a good starting point is determined by estimating the cardinality of the set of subcarriers that will be assigned to each user. Second, the convergence of the resource allocation is accelerated by reassigning those subcarriers first, which have the greatest channel-to-noise ratio (CNR) variation over the users. Compared to previous works, simulations show that the presented method provides an improved balance between performance and computational complexity.

We perform market-oriented management of the reverse link of a CDMA cell populated by data terminals, each with its own data rate, channel gain, willingness to pay (wtp), and link-layer configuration, and with energy supplies that are limited for some, and inexhaustible for others. For both types, appropriate performance indices are specified. A terminal pays in proportion to its fraction of the total power at the receiver, which directly determines its signal-to-interference ratio (SIR), and hence its performance. Hence, a terminal can individually choose its optimal power fraction without knowing the choices made by others. The network sets for each terminal an individual price that drives the terminal to the "revenue per Watt" maximiser. An optimal link-layer configuration can be identified through this analysis. Distinguishing features of our model are: (i) the simultaneous consideration of both limited and unlimited energy supplies, (ii) the performance metrics utilised (one for each type of energy supply), (iii) the generality of our physical model, and (iv) our focus on the fraction of total power at the receiver allocated to a terminal.

An intermittently-connected, cooperative communication network in which mobility is indispensable is studied though an analytical random-walk model, its possible implementation with present-day technology is explored, and applications are discussed. Some important questions are answered, and many more are posed.

Orthogonal frequency division multiplexing (OFDM) offers high data rates and robust performance in frequency selective channels by link adaptation utilizing information about the channel quality. A crucial parameter required for adaptive transmission is the signal-to-noise ratio (SNR). In this paper, we propose a novel SNR estimation algorithm for wireless OFDM systems based on the reuse of the synchronization preamble. The periodic structure of the preamble is utilized for the computationally efficient SNR estimation algorithm, based on the second-order moments of received preamble samples. The performance of the proposed algorithm is compared with the MMSE algorithm and two preamble-based algorithms found in the literature. It is shown that the proposed algorithm is robust against frequency selectivity and may therefore be used for subchannel SNR estimation.

We introduce a model of a generalised multi-receiver radio network with quality-of-service (QoS) constraints. There are two key functions: (1) Ni is non-decreasing and homogeneous and gives i's QoS as function of its carrier-to-interference ratios at each of K receivers, (2) nu_ik is a semi-norm that gives the interference experienced by transmitter i at receiver k as function of the power vector. We utilise "norm" concepts and Banach's well-known fixed-point theorem to characterise the conditions under which a QoS vector is feasible, and the corresponding power-adjustment process converges. The critical power levels equal a_i/h_i where a_i is the QoS target, and h_i is the 'average' channel gain. hi=Ni(h_il,..., h_i,K) where h_ik is the channel gain from transmitter i to receiver k. If the interference experienced by each transmitter i at each receiver k is less than 1 when each power is set to the critical level (i.e., nu_ik(a_l/h_l, a_2/h_2,..., a_N/h_N)

This paper deals with the intertwined problems of feasibility, power allocation and call admission control on the basis of the signal-to-interference-ratio in multi-cell CDMA networks. Positively homogeneous in-cell interference functions are introduced. Under this general concept optimum power allocations are determined by first solving in-cell interference problems with fixed background noise, and subsequently a small system of linear coupling equations. This approach simultaneously clarifies the existence of some power allocation such that minimum SIR requirements are fulfilled. The decomposition is also utilized for designing a decentralized admission control algorithm. Finally, guidelines are given on how required parameters may be estimated in existing network structures.

We consider a stationary Rayleigh flat-fading channel with temporal correlation and a compactly supported power spectral density of the channel fading process. We assume that the channel state is unknown to both transmitter and receiver, while the law of the channel is presumed to be known to the receiver. Given a set of fixed signaling sequences, the optimum input distribution, with respect to the achievable rate, has the property of a constant Kullback-Leibler distance between the output distribution and a mixture of the output distributions conditioned on the different input sequences. Based on this, we determine the set of optimum input distributions for PSK signaling. In addition, we identify the special case of transmitting one pilot symbol to acquire a phase reference as being included in the set of optimum input distributions. We derive an integral expression for the capacity constrained to PSK signaling depending on the autocorrelation of the channel and the SNR. Evaluation of the asymptotic high SNR behavior shows a loss in the constrained capacity with respect to the case of perfect channel knowledge corresponding to at least one signaling dimension, i.e., the information transmitted by one symbol.

In this paper, the saddle point behavior of mutual information is investigated for discrete channel models. We use the fact that mutual information is a convex function of the channel matrix, and a concave function of the input distribution. Interpreting transmission as a game, nature against the transmitter with payoff given by mutual information, equilibria are shown to exist for certain strategy sets of nature. The case that nature makes the channel useless with zero capacity is discussed in detail. If nature uses a singleton nonzero capacity strategy, a characterization of the capacity-achieving input distribution is derived. Relevant channel classes covered by this approach include the binary asymmetric and erasure channel with bounded error probabilities. Furthermore, for the symmetric n-symbol channel two classes of separation constraints are introduced and the according game equilibria are determined.

Adaptive modulation of subcarriers for orthogonal frequency division multiplexing (OFDM) has been shown to improve system performance significantly. As a prerequisite, accurate channel state information (CSI) must be available at the transmitter. For time varying, noisy channels, however, this is difficult to achieve in practical systems. In this paper, we consider optimal subcarrier assignment under incomplete CSI. Soft estimates and the Cramer-Rao bound are used to analyze the effect of CSI errors. As an important control parameter, the frame length is coming in. We investigate analytically its influence on the performance of adaptive modulation in OFDM systems for the rate and power optimization problems. Simulations demonstrate that the frame length may not be neglected as an important overall system performance parameter.

Adaptive power and rate allocation for multiuser orthogonal frequency division multiplexing (OFDM) has been shown to improve the system performance significantly. In this paper, a group of resource allocation methods with low complexity for multiuser OFDM downlink is presented. The proposed methods initialize subcarrier assignments for users independently, which may result in conflicts on subcarriers. Following processes are designed to resolve such conflicts, where the output is further improved by simply sorting the subcarriers with conflicts according to appropriate criteria. Simulation results show that the best proposed method has less than 4% performance degradation with up to 95% complexity reduction compared to a newly suggested reference algorithm.

Resource management is the general topic of the present paper, particularly, we deal with capacity sharing for interference limited wireless networks by power control. Proportional reduction of the signal-to-interference ratio (SIR) requirements is suggested as the control mechanism to accommodate users in the case of overload. For this purpose, we carefully describe the geometrical structure and the asymptotic behavior of the set of feasible power vectors as a proportionality factor tends to its boundaries. In the case that there is no feasible power adjustment, the minimum proportional SIR reduction is determined under general power constraints. We conclude with developing a locally quadratic convergent algorithm for numerical computation of the optimum power assignment. The investigations provide both insight into the theoretical structure of optimum power allocation as well as a practical method for call admission control.

This paper is concerned with linear multiuser receivers for code division multiple access (CDMA). Mainly three are of practical relevance: the matched filter (MF), the successive interference cancellation (SIC) and the minimum mean squared error (MMSE) receiver. For the first time, an explicit representation of the signal-to-interference-plus-noise (SINR) ratio for the linear SIC receiver under general random spreading is given. For the MMSE receiver the SINR is obtained by using the asymptotic large system regime as an accurate approximation. Once the SINR for each receiver type is known, the question arises what quality-of-service (QoS) requirements can be supported by an adequate power assignment. A vector of QoS requirements (gammai) is called feasible, if there exists some power assignment such that the SINR of each user i does not fall short of threshold gammai. It is shown that for each receiver type there exists a componentwise minimal power assignment such that the SINR of each user equals this threshold. This minimal vector may be determined for the MF and SIC receiver by solving a system of linear equations with coefficients explicitly known. For the MMSE receiver in the large system regime an iterative algorithm is derived, which converges to the optimum power allocation.

The present correspondence deals with the rate and power allocation problem in multiuser orthogonal frequency division multiple (OFDM) access systems. We first derive the solution of the single user OFDM power allocation problem explicitly for a class of general rate-power functions by means of directional derivatives. This solution is employed for both designing a new heuristic and obtaining bounds in a branch-and-bound algorithm for allocating power to subcarriers. The branch-and-bound algorithm is used for performance evaluation of our new and two known power allocation heuristics by computing the exact optimum, given the number of allocated subcarriers per user.

The present correspondence deals with the rate and power allocation problem for multi-user orthogonal frequency division multiple access (OFDMA). Using directional derivatives we first derive an explicit solution of the single-user OFDM power allocation problem for a general class of rate-power functions. In a nested algorithm, this solution is used to determine the solution of the closely related rate allocation problem for both the single-user and multi-user case with proportionality constraints. The results are applied to a widely used class of rate-power function.

Evaluating the performance measures of distributed detection in sensor networks is important for design procedures aiming at optimal configurations. Direct computation of the global error probabilities is a difficult problem and feasible only in some special cases. In this paper, we present an approach that provides closed-form upper bounds on the detection error probabilities in the parallel fusion network which are both computationally simple and numerically tight. The bounds are derived by combining a probability inequality formulated by Hoeffding with a multiplicative form factor which is due to Talagrand. We demonstrate that the bounds apply to sensor networks of varying size, an arbitrary number of local quantization levels, and non-identical sensors.

Discrete input distributions are capacity-achieving for a variety of noise distributions whenever the input is subject to peak power or other bounding constraints. In this paper, we consider additive noise with arbitrary absolutely-continuous distribution and ask the question what the optimal input distribution over a set of fixed signaling points would be. The capacity-achieving distribution is characterized by constant Kullback Leibler distance between the shifted noise distribution and a certain mixture hereof. As an application, the optimal input distribution for binary symmetric signaling over exponential noise channels is determined. It further follows that in certain symmetric cases the uniform distribution over all signaling points is capacity-achieving.

Fast radio wave propagation prediction is of tremendous interest for planning and optimization of cellular radio networks. We propose a cube oriented 3D ray launching algorithm for both fast and accurate field strength prediction, particularly suitable for urban scenarios. Our model allows field strength prediction for a 5 km2area with 5 meter resolution in about 8 sec with mean squared error of 7 dB. As urban environments cannot be completely described, we recommend a parameter calibration for different cities using measurement data from test runs.

In this paper, derivatives of mutual information for a general linear Gaussian vector channel are considered. We consider two applications. First, it is shown how the corresponding gradient relates to the minimum mean squared error (MMSE) estimator and its error matrix. Secondly, we determine the directional derivative of mutual information and use this geometrically intuitive concept to characterize the capacity-achieving input distribution of the above channel subject to certain power constraints. The well-known water-filling solution is revisited and obtained as a special case. Also for shaping constraints on the maximum and the Euclidean norm of mean powers explicit solutions are derived. Moreover, uncorrected sum power constraints are considered. The optimum input can here always be achieved by linear precoding.

In this correspondence, we determine the optimal power allocation to antennas in a Gaussian vector channel subject to lscrp-norm constrained eigenvalues. Optimal solutions are characterized by using directional derivatives of the mutual information. As the central result, the optimal power assignment is obtained as the level crossing points of a set of simple monotone functions. The well-known water-filling principle for sum power constraints is retrieved as the limiting case p=1. A nested Newton type algorithm is given for finding numerical solutions

The optimal power allocation for Gaussian vector channels subject to sum power constraints is achieved by the well known water-filling principle. In this correspondence, we show that the discontinuous water filling solution is obtained as the limiting case of p-norm bounds on the power covariance matrix as p tends to one. Directional derivatives are the main vehicle leading to this result. An easy graphical representation of the solution is derived by the level crossing points of simple power functions, which in the limit p = 1 gives a nice dual view of the classical representation

We consider a stable CDMA cellular network of N users with given quality-of-transmission requirements, and ask the question if an additional user can be admitted to the network without violating any of the active quality-of-service thresholds. An important point is that, although the inclusion of a new user impinges on the power adjustment of all mobiles in the network, this decision is made on the basis of information locally collected at the base stations only. We achieve this goal by an appropriate agglomeration of outer- and inner-cell interference, and an according dimensionality reduction scheme. The corresponding admission control algorithm applies an interference prediction phase prior to making a decision about accepting further customers to the wireless network. Capacity is exploited in that users are admitted whenever there is room to. Individual power constraints are included in our system model, which makes the resulting admission control algorithm widely applicable for practical purposes.

When transmitting over multiple-input-multiple-output (MIMO) channels, in the case of total power constraints and complete channel state information (CSI) the optimum power distribution is obtained by water-filling the squared singular values of the channel matrix H. In this paper, we consider the case that nature behaves as an opponent to the optimum transmit strategy in choosing the channel as bad as possible. Interpreting the mutual information as payoff function for two players, the transmitter and a malicious nature, this approach may be seen as a two person zero sum game. We first analyze maximum points of the payoff function for a fixed channel matrix under general power restrictions and characterize such points via directional derivatives. Worst channel behavior must be separated from the zero channel where no transmission is possible at all. Loewner semi-ordering of nonnegative Hermitian matrices is employed to ensure minimum channel quality. It is shown that a Nash equilibrium exists for general power constraints. Concrete results are achieved for a limited total power budget and limiting the maximum available power for each subchannel

In this paper, we define the power region as the set of power allocations for K users such that everybody meets a minimum signal-to-interference ratio (SIR). The SIR is modeled in a multiuser CDMA system with fixed linear receiver and signature sequences. We show that the power region is convex in linear and logarithmic scale. It furthermore has a component-wise minimal element. Power constraints are included by the intersection with the set of all viable power adjustments. In this framework, we aim at minimizing the total expended power by minimizing a component-wise monotone functional. If the feasible power region is nonempty, the minimum is attained. Otherwise, as a solution to balance conflicting interests, we suggest the projection of the minimum point in the power region onto the set of viable power settings. Finally, with an appropriate utility function, the problem of minimizing the total expended power can be seen as finding the Nash bargaining solution, which sheds light on power assignment from a game theoretic point of view. Convexity and component-wise monotonicity are essential prerequisites for this result

The capacity region of code-division multiple access (CDMA) is determined by the set of transmission rates combined with quality-of-service (QoS) requirements which allow for a feasible power allocation scheme for n mobiles in a cellular network. The geometrical and topological properties of the capacity region are investigated in the present paper for the case of unlimited and limited power, respectively. As a central result, we show that the capacity region is convex by breaking the complicated topological structure into characteristic properties of its boundary and interior points, each of interest in itself. Based on these results, we furthermore investigate optimal power assignment schemes in the case that the demand of a community of users is infeasible. Weighted minimax and Bayes solutions are explicitly determined as appropriate means to share the capacity of a cellular network in a reasonable and fair way.

Access-control strategies and rational pricing in code-division multiple-access (CDMA) systems need exact knowledge of the available transmission capacity and its limiting boundaries. We use the term "capacity region" to specify the set of user-transmission demands that can be supported at the desired quality of service (QoS). In this paper, we investigate the geometrical properties of the capacity region for a fixed number of users in a CDMA radio network under general QoS characteristics and general power constraints. It turns out that, under very mild assumptions, the capacity region is convex, and has an appealing monotonicity property. As a side result, we develop an elementary theory for characterizing the existence of solutions to systems of linear equations with nonnegative elements, analogous to Perron-Frobenius' theory, but bypassing irreducibility.

Accurate power control is an essential requirement in the design of cellular code-division multiple-access (CDMA) systems. In this paper, we contribute three main themes to the power control problem. First, we derive an efficient algorithm for computing minimal power levels for large-scale networks within seconds. Nice and intuitive conditions for the existence of feasible power solutions follow from this approach. Second, we define the capacity region of a network by the set of effective spreading gains, or data rates, respectively, which can be supplied by the network. This is achieved by bounding the spectral radius of a certain matrix containing system parameters and mutual transmission gain information. It is shown that the capacity region is a convex set. Finally, we reveal an interesting duality between the uplink and downlink capacity region. In a clear-cut analytical way, it substantiates the fact that the uplink is the more restricting factor in cellular radio networks. The same methods carry over to certain models of soft handover. In the case that the channel gains are subject to log-normal shadowing, we introduce the concept of level-/spl alpha/ capacity regions. Despite the complicated structure, it can still be shown that this set is sandwiched by two convex sets coming arbitrarily close as variance decreases.

In this study, an offline traffic engineering model for multi-protocol label switching (MPLS) networks is introduced. The model aims at mapping the traffic trunks, consisting of both best-effort traffic and traffic with QoS requirements, onto the network. For an optimal network management, three different objectives are taken into consideration, namely minimal routing delay, optimal load-balance in the network, and minimal splitting of traffic trunks. This multi-criteria network optimization problem is formulated as a mixed integer problem. A case study is carried out in order to analyze the basic properties of the model and the trade-off between the objectives for different types of traffic.

In this paper, the minimal base-station density for a code-division multiple-access (CDMA) cellular radio network is determined such that the outage probability does not exceed a certain threshold. Base stations are assumed to be located on a regular triangular grid of minimum distance d, while mobiles are randomly distributed according to a two-dimensional Poisson point pattern. Each mobile may be connected to, at most, one of four surrounding base stations, effectively connecting and applying power control to the one with least attenuation. Thus, we model the use of macroscopic selection diversity. We obtain a normal approximation to the total interference power at a reference base station for a correlated log-normal shadowing law. The base station distance we obtain is proportional to the inverse of the square root of the traffic intensity, and we obtain the constant of proportionality, which is itself a function of the minimum acceptable carrier-to-interference (C/I) ratio and the maximum tolerable outage probability. Our formula for this distance can be used in network planning and design.

We deal with the uplink outage probability for a UMTS cellular network at a reference base station equipped with a smart antenna. Users are assumed to be distributed according to a planar Poisson point process. Their respective transmission rates are modeled by independent random variables. This leads to a compound Poisson process for the total data rate to be served in a cell by code division multiplexing. Outage occurs if the total rate exceeds the effective chip rate (3.84 MChip/s divided by the minimum required bit energy to noise power ratio). Circulant matrices, a normal approximation, and Monte Carlo integration are employed to determine the outage probability as a performance parameter of the system.

No abstract available.

Presents a one-dimensional model to analyze routing strategy NFP (nearest with forward progress) for a multihop packet radio network. It is assumed that each station has adjustable transmission range to address target nodes, distributed on a line according to a nonhomogeneous Poisson process. NFP transmits to the nearest neighbor in the desired direction with transmission range as small as possible, to minimize the probability of collisions. This model is appropriate e.g. for road traffic information systems. The analysis is based on a complete mathematical description, the solution of certain differential equations is one of the key points to arrive at a closed form solution. Results are presented. Graphically, it turns out that NFP has uniformly largest throughput, while progress behaves comparably to other routing strategies proposed in the literature.>