To fully unleash the potentials of the cognitive radio (CR) paradigm, new challenges must be addressed. Specifically, as regards the network layer, the problem of the route priority, i.e., the problem of prioritizing the routes for the CR packet transmission, is crucial, since the communication opportunities provided by a route are deeply affected by the primary-user (PU) activity. Furthermore, whenever the CR network layer exploits proactively acquired information on the PU activity, update packets need to be exchanged among the CR users, inducing so a route overhead independently of the adopted routing protocol. Hence, in this paper, we analytically derive the optimal route priority rule, i.e., the route priority rule maximizing the achievable capacity, by jointly accounting for the PU activity and the route overhead. To this aim, at first, we formulate the optimal route priority problem, and we prove that its computational complexity through exhaustive search is exponential. Then, we provide the closed-form expressions of the achievable capacity. Stemming from these expressions, we derive the optimal route priority, and we design a computational-efficient search algorithm. All the theoretical results are derived by adopting two routing strategies and two PU activity models.

In the last several years, many algorithms trying to mimic biological processes have been proposed to enhance the performance of communication networks. However, the bio-inspired algorithms represent only the very first step toward the design of a smart adaptive communication network since: 1) they model only a limited set of the rules underlying the biological processes, thus, omitting fundamental functionalities; 2) they are executed on traditional computer architectures, thus, failing to achieve the intrinsic parallelism exhibited by biological processes. To overcome these issues, in this paper, the BioNetwork paradigm is proposed, a novel communication network paradigm in which the traditional network nodes are replaced by living organisms. The BioNetwork paradigm provides very attractive features over traditional network paradigms, such as efficiency, adaptivity, reliability, self-organization, and scalability. Moreover, it has a huge potential since it can be adopted in many different applications, such as health and military ones. In the paper, this potential is shown by proving that a BioNetwork can solve one of the most fundamental NP-hard problems in networks, i.e., the Steiner tree problem. To this aim, a BioNetwork constituted by a unicellular organism, the Physarum polycephalum slime mold, is designed. Throughout the paper, it is proven that a Physarum BioNetwork can solve the Steiner tree problem with an exponential convergence rate toward the optimal solution. The theoretical solutions are validated through a case study.

This paper proposes a blind channel shortening algorithm for uplink reception of a single-carrier interleaved frequency-division multiple-access (SC-IFDMA) system, affected by timing offsets (TOs) and carrier frequency offsets (CFOs), and transmitting over a highly dispersive channel. When the cyclic prefix (CP) cannot completely compensate for channel dispersion and TOs, a convenient strategy is to shorten the channel by timedomain equalization (TEQ) in order to ease signal reception. The proposed receiver exhibits a three-stage structure: the first stage performs blind channel shortening without requiring a priori knowledge of the channels or preliminary compensation of the CFOs; the second stage performs multiuser CFO compensation; finally, to alleviate noise amplification effects possibly arising from CFO compensation, the third stage implements blind peruser signal-to-noise ratio (SNR) maximization. The performance of the proposed method are assessed and compared with those of existing algorithms by Monte Carlo computer simulations.

A key issue of the spectrum sensing functionality in Cognitive Radio (CR) networks is the ability of tuning the sensing time parameters, i.e., the sensing time and the transmission time, according to the Primary User (PU) network dynamics. In fact, these parameters influence both the spectrum sensing efficiency and the PU interference avoidance. This issue becomes even more challenging in presence of PU mobility. In this paper, an optimal spectrum sensing design for mobile PU scenarios is proposed with the aim to achieve the following important features: i) to determine the optimal mobility-aware transmission time, i.e., the transmission time value that jointly maximizes the spectrum sensing efficiency and satisfies the PU interference constraint; ii) to determine the optimal mobility-aware sensing time threshold, i.e., the maximum sensing time value assuring efficient spectrum sensing. First, closed-form expressions of both the optimal transmission time and the optimal sensing time threshold are analytically derived for a general PU mobility model. Then, the derived expressions are specialized for two widely adopted mobility models, i.e., the Random Walk mobility Model with reflection and the Random Way-Point mobility Model. Practical rules for the sensing parameter tuning are provided with reference to the considered mobility models. The analytical results are finally validated through simulations.

Cooperative spectrum sensing techniques are mainly based on two different decision approaches, according to the role of the decision maker: i) in the Combining Decision approach, the decision maker combines the sensing information collected from its cooperators, without participating in the sensing of the monitored band; ii) in the Sensing & Combining Decision approach, the decision maker combines both the sensing information of its cooperators and its own local sensing information. The choice of the decision approach deeply affects the performance of any cooperative spectrum sensing technique. However, the key issue of choosing the decision approach that guarantees the higher detection accuracy independently of the underlying cooperative sensing architecture is still an open problem. For this, in this paper, the criteria for an effective decision-approach selection are analytically derived with the object of maximizing the detection accuracy in presence of realistic channel propagation effects. Specifically, through a theoretical analysis, it is proven that the detection accuracy exhibits a threshold behavior as a function of the adopted decision approach. Closed-form expressions of such a threshold are analytically derived and practical insights for the decision approach choice are provided. Finally, the theoretical analysis is validated through simulations.

This paper proposes a blind channel shortening algorithm for uplink reception of a single-carrier interleaved frequency-division multiple-access (SC-IFDMA) system transmitting over a highly-dispersive channel, which is affected by both timing offsets (TOs) and frequency offsets (CFOs). When the length of the cyclic prefix (CP) is insufficient to compensate for channel dispersion and TOs, a common strategy is to shorten the channel by means of time-domain equalization, in order to restore CP properties and ease signal reception. The proposed receiver exhibits a three-stage structure: the first stage performs blind shortening of all the user channel impulse responses (CIRs), by adopting the minimum mean-output energy criterion, without requiring neither a priori knowledge of the CIRs to be shortened, nor preliminary compensation of the CFOs; the second stage performs joint compensation of the CFOs; finally, to alleviate noise amplification effects, possibly arising from CFO compensation, the third stage implements per-user signal-to-noise ratio (SNR) maximization, without requiring knowledge of the shortened CIRs. A theoretical analysis is carried out to assess the effectiveness of the proposed shortening algorithm in the high-SNR regime; moreover, the performances of the overall receiver are validated and compared with those of existing methods by extensive Monte Carlo computer simulations.

This paper develops a solution for the problem of uncorrelated user selection in mobile cognitive radio ad hoc networks, with the objective to increase the performance of cooperative spectrum sensing. For this, a fully distributed user selection algorithm is developed by adaptively selecting uncorrelated cognitive radio users, which is able to account for dynamic changes in the network topology and in the channel conditions. Since the proposed user selection is based on the evaluation of the correlation experienced by the cognitive radio users, it is mandatory to have a parameter able to measure the correlation among them. For this, a spatial correlation coefficient is proposed to express the correlation characteristics of mobile cognitive radio users in different environments. Performance evaluation is conducted through simulations, and the results reveal the benefits of adopting the proposed correlation-aware user selection for cooperative spectrum sensing.

Two main issues affect the existing routing metrics for cognitive radio ad hoc networks: i) they are often based on heuristics, and thus they have not been proved to be optimal; ii) they do not account for the route diversity effects, and thus they are not able to measure the actual cost of a route. In this paper, an optimal routing metric for cognitive radio ad hoc networks, referred to as OPERA, is proposed. OPERA is designed to achieve two features: i) Optimality: OPERA is optimal when combined with both Dijkstra and Bellman-Ford based routing protocols; ii) Accuracy: OPERA exploits the route diversity provided by the intermediate nodes to measure the actual end-to-end delay, by taking explicitly into account the unique characteristics of cognitive radio networks. A closed-form expression of the proposed routing metric is analytically derived for both static and mobile networks, and its optimality is proved rigorously. Performance evaluation is conducted through simulations, and the results reveal the benefits of adopting the proposed routing metric for cognitive radio ad hoc networks.

This paper deals with multiple-input multiple-output (MIMO) broadband wireless communication systems, employing orthogonal frequency-division multiplexing (OFDM) with cyclic prefix (CP) in combination with space-time-frequency block coding (STFBC). In order to exploit the benefits of OFDM and STFBC in highly frequency-selective channels, without incurring in a significant increase of receiver complexity, a channel shortening prefilter is inserted at the receiver, aimed at reducing the length of the MIMO channel impulse response before CP removal. Two MIMO channel shorteners are proposed, both relying on linearly-constrained minimization of the mean-output-energy of the signal at the output of the channel shortener, where the linear constraints are optimally chosen so as to maximize the signal-to-noise ratio either at the output of the channel shortener or at the input of the STFBC maximum-likelihood decoder. Unlike other solutions proposed in the literature, these shortening designs are blind ones, i.e., a priori knowledge of the MIMO channel impulse response to be shortened is not required, and can be carried out in closed-form, i.e., iterative computation of the prefilter weights is not necessary. Numerical simulations show that, without a substantial increase in computational complexity, receivers equipped with the proposed blind channel shorteners pay only a negligible performance penalty with respect to non-blind channel shorteners, while being significantly more robust to finite sample-size effects.

In this paper, the problem of synthesizing a blind channel shortening algorithm for orthogonal frequency-division multiplexing (OFDM) systems is addressed. In particular, a commonly adopted assumption in the channel shortening framework is discussed, showing that, when it is violated, the data statistics usually needed for the synthesis of the shortening algorithms turn out to be periodically time varying (PTV) rather than time-invariant. Elaborating on this point, and considering in particular the recently proposed minimum-mean output-energy (MMOE) blind channel shortening algorithm, it is shown how its synthesis must be modified in order to account for the PTV nature of the data statistics. Numerical results assessing the performance of the proposed blind PTV-MMOE channel shortening algorithm are reported.

In this paper, the effects of the primary-user (PU) mobility on spectrum sensing in Cognitive Radio (CR) networks are studied. To this aim, first, the spectrum sensing problem is reformulated to account for the PU mobility. Then, the effects of the PU mobility are studied with the objective to determine the parameters that affect the spectrum sensing functionality. For this, two performance metrics are analytically derived: i) the detection capability, which measures the PU mobility impact on the CR user detection probability; ii) the mobility-enabled sensing capacity, a new metric that measures the expected transmission capacity achievable by a CR user in the presence of PU mobility. The mathematical analysis is carried out in different scenarios, by using mobility and spectrum occupancy models. The results show that the detection capability is affected by five parameters: the PU protection range, the network region size, the PU mobility model, the CR spatial distribution, and the number of PUs that use the same spectrum band. Moreover, it is shown that the sensing capacity can significantly increase in the presence of PU mobility if the PU protection range is smaller than the network region size. The mathematical results are derived by considering the dynamic PU traffic, and validated through simulations.

Cooperative approaches have been proposed as an effective way to improve the spectrum sensing accuracy. Generally, cooperative spectrum sensing techniques require two successive stages: sensing and reporting. The reporting channels are usually assumed ideal. In this paper, we remove this assumption and we investigate the effects of reporting channels affected by temporal dispersion on cooperative spectrum sensing. To this aim, we propose two fusion schemes: a Widely Linear scheme and a Linear one. For both the schemes, closed-form expressions of the detection and the false alarm probabilities are derived. The performance are also evaluated numerically, and the results show that the Widely Linear detector outperforms the Linear one in operative conditions of practical interest. Moreover, for the sake of completeness, a theoretical comparison of the proposed detectors is carried out for reporting channels affected by multipath frequency non-selective fading. Surprisingly, the analysis proves that the two detectors perform exactly the same under this assumption. Therefore, there is not anymore advantage in using the Widely Linear scheme, which exhibits higher, although limited, computational complexity. The theoretical analysis is validated numerically.

Abstract The paper proposes a Distributed Hash Table (DHT)‐based multi‐path routing protocol for scalablead hocnetworks. Specifically, we propose a multipath‐based improvement to a recently proposed DHT‐based shortest‐path routing protocol, namely the Dynamic Address RouTing (DART). The resulting protocol, referred to as multi‐path DART (M‐DART), guarantees multi‐path forwarding without introducing any additional communication or coordination overhead with respect to DART. The performances of M‐DART have been evaluated by means of numerical simulations across a wide range of environments and workloads. The results show that M‐DART performs the best or at least comparable with respect to widely adopted routing protocols in all the considered scenarios. Moreover, unlike these protocols, it is able to assure satisfactory performances for large networks by reducing the packet loss by up to 75%. Copyright © 2010 John Wiley & Sons, Ltd.

The Spectrum Sensing is a fundamental functionality of the Cognitive Radio (CR) paradigm since a reliable detection of the presence of spectral holes is a mandatory requirement to avoid harmful interferences to licensed users. In this paper, we address this problem by proposing a widely linear (WL) cooperative strategy at a fusion center side where the CR user statistics have been received by means of multipath fading channels. The proposed strategy jointly elaborates the received signal and its conjugate version to take advantage by the information contained in the statistical pseudo-covariance function of the received signal. We design the WL strategy to maximize a modified deflection coefficient and we compare it with a strictly linear (SL) cooperative strategy. Results of numerical simulations show that the WL fusion-rule outperforms the SL one in operative conditions of practical interest.

In this paper we address the issue of the optimal candidate-set selection in the opportunistic routing paradigm. More specifically, although several algorithms for selecting the optimal candidate set have been proposed, to the best of our knowledge none of them has never considered the problem of selecting the optimal constrained candidate set, namely the optimal candidate set with a fixed maximum set size. In this paper we contribute to this problem by providing an analytical framework to model both the optimal constrained and unconstrained candidate-set selection. Moreover, we propose two algorithms for optimal candidate-set selection for distance vector routing, one for the constrained and one for the unconstrained case. Simulations based on experimental data validate our proposal.

The problem of channel shortening equalization (CSE) for multi-input multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) systems is addressed. Relying on constrained minimization of the mean-output-energy (MOE) at the CSE output, we propose a blind channel shortening technique, which does not require a priori knowledge or estimation of the MIMO channel impulse responses to be shortened. Numerical results showing the attainable performance gain of the proposed method are reported.

The paper deals with the performance analysis of a recently proposed QoS Management Architecture (QMA) for a communication infrastructure used in an emergency scenario. The performances are evaluated in terms of the most significant QoS parameters by means of a measurement testbed specifically set up in laboratory to emulate a realistic emergency scenario where different operators have to cooperate during the rescue procedure. The QoS parameters are assessed both in absence and presence of the QMA, considering several traffic types. The comparison of the achieved results shows the effectiveness of the considered QMA.

Mobile Ad hoc NETworks (MANET) and Peer-To-Peer (P2P) systems are emerging technologies sharing a common underlying decentralized networking paradigm. However, the related research activities have been mainly developed by different research communities, nullifying therefore the idea of an unitary approach able to assure effectiveness integrated solutions. In this paper, we propose a DHT-based routing protocol which integrates at the network layer both traditional direct routing, i.e. MANET routing, and indirect key-based routing, i.e. P2P routing. The feature of our proposal is the ability to build an overlay network in which the logical and physical proximity agree. The effectiveness of the proposed solution has been proved by numerical simulations.

In this paper, the problem of estimating the link quality in mesh networks has been considered. Such a process is a major task to develop an efficient network layer, since it allows routing protocols to efficiently use neighbors as relays for multi-hop communications. In the last years, a number of link-quality aware routing metrics have been proposed and analyzed. However, such metrics usually adopt simple link-quality estimators based on moving average filters, which lead to poor performances due to their static nature. In this paper, we propose to improve the estimation of the link quality resorting to a bio-inspired estimator based on the neural network paradigm. The effectiveness of the proposal has been proved by means of a numerical performance comparison between the proposed estimator and the traditional ones under several environmental conditions.

The paper addresses the low-complexity design of minimum mean square error (MMSE) multistage decision-feedback (DF) equalizers for multiple input/multiple output (MIMO) channels. More specifically, the multistage DF equalization strategy is combined with the widely-linear (WL) processing in order to exploit the properties of the rotationally variant transmitted signals. The optimization (in the MMSE sense) of WL-DF equalizer is re-examined in terms of the classical least-square approach and it is generalized to thecase of the MIMO multistage DF receivers, which require at least one equalization stage to initialize the input of the feedback filter. The performance analysis is aimed at comparing the performances of the linear multistage DF equalizers and their WL counterparts in terms of the symbol error rate (SER), the number of equalization stages required to minimize the SER and the number of floating point operations required to optimize the MMSE feedforward and feedback filters.

In ad hoc networks scalability is a critical requirement if these technologies have to reach their full potential. Most of the proposed routing protocols do not operate efficiently with networks of more than a few hundred nodes. In this paper, we propose an augmented tree-based address space structure and a hierarchical multi-path routing protocol, referred to as augmented tree-based routing (ATR), which utilizes such a structure in order to solve the scalability problem and to gain good resilience against node failure/mobility and link congestion/instability. Simulation results and performance comparisons with existing protocols substantiate the effectiveness of the ATR.

Constrained optimization techniques, based on the maximum signal-to-noise-plus-interference (SINR) criterion,, are considered for joint equalization and narrowband interference (NBI) suppression in orthogonal frequency-division multiplexing (OFDM) systems. Specifically, we show that a recently proposed linear receiver (D. Darsena et al., 2005), which mitigates, in the minimum mean-output-energy sense, the NBI contribution at the receiver output, can be regarded as the solution of a constrained maximum-SINR optimization criterion and, moreover, admits an interesting three-stage decomposition. This decomposition improves the receiver robustness against errors in the estimated statistics of the received data and, most important, allows one to greatly simplify the channel estimation problem. Computer simulations are earned out to illustrate the performance improvements achievable by adopting the constrained maximum-SINR equalizer, in comparison with its unconstrained counterpart

We consider in this letter the problem of perfectly equalizing finite-impulse response (FIR) channels in digital communication systems employing nonredundant precoding at the transmitter. In this case, as it is well known, there is no linear FIR zero-forcing (ZF) block equalizer that is capable of perfectly recovering information symbols in the absence of noise. We show that, when the symbol constellation is improper, it is possible to design widely linear FIR-ZF block equalizers that, unlike their linear counterparts, process both the received signal and its complex-conjugate version. In addition, we propose a simple procedure to synthesize universal modulation-induced cyclostationary (MIC) precoders that assures perfect symbol recovery, regardless of the channel-zero locations.

The paper deals with the problem of blind mitigation of intersymbol interference (ISI) as well as multiple-access interference (MAI) in asynchronous high data-rate direct-sequence code-division multiple-access systems. A blind adaptive multiuser receiver based on the constant-modulus algorithm (CMA) is proposed, which demodulates each desired user by exploiting only the knowledge of its spreading code, without requiring estimation of the users's channels and timings. In order to overcome the CMA interference capture problem, which arises in a multiuser scenario, a two-stage adaptive receiver is adopted: In the first stage, partial MAI and ISI suppression is blindly achieved by exploiting the desired user signature structure properties; in the second stage, the residual MAI and the ISI are removed by using the CMA, and the information symbols of the desired user are reliably recovered. Theoretical analysis and simulation results show that the first stage is an effective blind adaptive strategy which allows the CMA detector in the second stage to lock on the desired-user symbol, at a particular delay. The proposed blind receiver achieves a significant performance gain in comparison with existing blind methods.

In this paper, a new method for blind spatial signal filtering is proposed. It utilizes the selectivity property of the higher-order cyclostationary statistics exhibited by the signal of interest to accurately estimate its unknown steering vector, also in the presence of strong frequency-overlapped interfering signals, which would render the standard stationarity-based techniques ineffective. The method is useful when the desired signal cannot be accurately extracted by exploiting its second-order cyclostationarity properties, as proposed in Schell and Gardner (1993). A performance analysis, carried out by computer simulations in the case of QAM-modulated signals with partially overlapping bands, substantiates the effectiveness of the proposed method.

This paper addresses the problem of narrowband interference suppression in direct-sequence spread-spectrum (DS/SS) techniques, which have been adopted to implement code division multiple access (CDMA) systems for wireless mobile communications. The theory of cyclic Wiener filtering, based on the cyclostationarity assumption for the signals involved in the reception problem, is applied to design single-channel adaptive frequency-shift filters which exploit both temporal and spectral correlation properties, i.e., the correlation between time- and frequency-shifted versions of the received signal. The numerical results show that receiving structures based on the proposed cyclostationarity-based interference suppression schemes largely outperform receivers that utilize conventional linear time-invariant suppressors, when they operate in highly contaminated interference environments.

The paper deals with signal extraction performed by processing data received by an array of sensors. The proposed method is blind, i.e., it does not require any a priori knowledge of directional information associated with the signals of interest (SOI's). Such information is obtained directly from the received data by exploiting the higher-order cyclostationarity (HOCS) properties exhibited by most communication signals. The proposed method is inherently tolerant to both possibly non-stationary Gaussian disturbances as well as non-Gaussian interferences not exhibiting the same HOCS properties presented by the SOI's. Simulation results confirm the effectiveness of the method when it operates in severely degraded disturbance environments.

The paper deals with two-sensor interception of cyclostationary signals in the presence of additive non-Gaussian noise. The locally optimum approach is considered as a starting point to derive cyclostationarity-exploiting receiver structures for detecting a weak signal and locating its emission source through time-difference-of-arrival (TDOA) measurements. To obtain analytical information about the detection performance of the proposed receivers, the deflection in both constant and variable noise-level environments is evaluated. Monte Carlo simulations, aimed at evaluating the detection performance in terms of detection probability and false-alarm probability, and the TDOA estimation accuracy, have been carried out. The results show that the cyclostationarity-exploiting receivers can significantly outperform the radiometric receivers in both signal detection and source location, when the noise level is unknown or variable and/or strong interfering signals are present.

Maras and Goutis (IEE Proc. I, Commun., Speech Vision, vol. 136, Aug. 1989, p.233-40) commented on the paper by Izzo and Paura (IEEE Trans. Commun., vol. COM-34, p. 97-103, Feb. 1986). The present paper summarizes the comments of Maras and Goutis and replies to each.

The interception of weak signals in nonGaussian noise is discussed. The spectral correlation property exhibited by all cyclostationary signals is exploited to synthesize multi-cycle and single-cycle detectors which assure a superior tolerance (as compared to radiometric techniques) to one of the most challenging problems in interception, namely accommodating unknown and changing noise level and interference activity. The proposed detectors perform a maximum likelihood estimate of the noise level and use the estimate to form the detection statistic. To obtain some analytical information about the performance of the multi-cycle and single-cycle detectors, the deflection, which is a useful measure of the output (signal-to-noise ratio), especially appropriate for weak-signal SNR interception, has been evaluated. The receiver operating characteristics, carried out via computer simulation, confirm the superiority of the proposed cycle detector with respect to the traditional radiometer.>

The theory of asympoticalllly optimum detection in nonGaussian noise is considered to synthesize a space-diversity structure for the noncoherent detection of a bandpass signal subject to amplitude fluctuations. An asymptotic sufficient statistic is obtained for arbitrary fading laws on the diversity branches and, then, for Rayleigh-distributed amplitude fluctuations, the asymptotically optimum detector is synthesized. The performance of the proposed detection structure is obtained and numerical results and comments are presented. Moreover, a comparison between the asymptotically optimum detector and a simple suboptimum structure is made.

The structure of the locally optimum space-diversity detector for coherent detection of a bandpass signal subject to amplitude fluctuations in the presence of narrow-band non-Gaussian noise is derived and its large-sample performance is evaluated. For easy implementation a suboptimum structure is proposed. It consists of local detectors performing independent local decisions and of a central processor that, working according to simple logical schemes (OR or AND), performs the global decision based on the local ones. Numerical results and a comparison between the performances of both proposed structures are presented and discussed.

The effects of slow and nonselective signal fading on the performance of multilevel noucoherent FSK (NCFSK) systems in an additive mixture of Gaussian and highly impulsive noise are analyzed. For binary systems the bit error rate is derived; forM-ary NCFSK systems upper and lower bounds of the character error probability are obtained. The analysis is performed considering the maximum likelihood receiver for additive white Gaussian noise.