In this paper we consider a heterogeneous LTE network where femto cells are randomly deployed in a macro network. Femto cells are modeled as closed cells, namely only group member UEs can be associated with the femto cells. We demonstrate that inter-cell interference may prevent reliable operations for non-member UEs that are in proximity of a closed cell, which thus experience outage. We show how some of the novel features introduced in the Rel-10 specifications of the LTE standard can be leveraged by a suitable inter-cell interference coordination scheme (ICIC), which relies upon resource partitioning among different nodes to reduce the inter-cell interference problem. Additional significant improvements can be achieved when the proposed ICIC scheme is associated to a simple yet effective autonomous power control algorithm, described in detail in the paper, and further gains are demonstrated for UEs employing interference cancellation of broadcast interfering signals. We finally propose an enhanced ICIC method, based on a tighter coordination between macro and femto nodes, whose significant performance improvements advocate for suitable updates to the future LTE specifications.

In this paper we consider a heterogeneous LTE network where femto cells are randomly deployed in a macro network according to the two models described in [1], namely dual-strip and NxN. Femto cells are modeled as closed cells, namely only group member UEs can be associated with the femto cells. Therefore, dominant interference may be experienced by non-member UEs that are in proximity of a closed cell and leave these UEs in outage. We show geometry performance in a practical deployment, and discuss an autonomous power control method, which allows to dramatically reduce the outage probability, and we evaluate a resource partitioning method between macro and femto cells that further improves the performance of macro users.

We investigate the information rate of channels affected by phase noise, aiming at predicting the ultimate performance limits in this scenario. Moreover, a closed-form upper bound is also derived for phase shift keying (PSK) modulations. Finally, we consider the design of nonsystematic irregular repeat-accumulate (RA) codes for this channel trying to give new insights on the codes to be employed for such an application.

Orthogonal frequency division multiplexing (OFDM) and single-carrier (SC) data transmission are compared as potential candidates for the next generation coherent long-haul 100 Gbps optical communication systems. The comparison takes into account impairments either general or mainly related to coherent detection, e.g., group velocity dispersion (GVD), polarization mode dispersion (PMD), non-linear effects, phase noise due to transmit and receive lasers, uncompensated frequency offset, other synchronization aspects, overall complexity, power and spectral efficiency, technological constraints.

We consider a differentially encoded phase-shift keying (DPSK) signal transmitted over a channel affected by correlated Rayleigh fading. For this problem, we derive the exact maximum a posteriori (MAP) symbol detection algorithm. By analyzing its properties, we demonstrate that it can be implemented by a forward-backward estimator of the channel, followed by a symbol-by-symbol completion to produce the a posteriori probabilities of the information symbols. The resulting algorithm can be employed as a soft-input soft-output (SISO) block in turbo-iterative receivers and exhibits an excellent performance despite its limited computational complexity. The performance of the proposed algorithm is evaluated for time-domain modulations over frequency-flat channels and for frequency-domain modulations over time-invariant channels.

We investigate the spectral efficiency, achievable by a low-complexity symbol-by-symbol receiver, when linear modulations based on the superposition of uniformly time- and frequency-shifted replicas of a base pulse are employed. Although orthogonal signaling with Gaussian inputs achieves capacity on the additive white Gaussian noise channel, we show that, when finite-order constellations are employed, by giving up the orthogonality condition (thus accepting interference among adjacent signals) we can considerably improve the performance, even when a symbol-by-symbol receiver is used. We also optimize the spacing between adjacent signals to maximize the achievable spectral efficiency. Moreover, we propose a more involved transmission scheme, consisting of the superposition of two independent signals with suitable power allocation and a two-stage receiver, showing that it allows a further increase of the spectral efficiency. Finally, we show that a more involved equalization algorithm, based on soft interference cancellation, allows to achieve an excellent bit-error-rate performance, even when error-correcting codes designed for the Gaussian-noise limited channel are employed, and thus does not require a complete redesign of the coding scheme.

We investigate the spectral efficiency of continuous phase modulations (CPMs). To this end, we need an effective bandwidth definition for a CPM signal, whose power spectral density has in principle an infinite support. The definition we adopt is based on the spacing between adjacent carriers in a frequency division multiplexed CPM system. We consider the inter-channel interference, which depends on the channel spacing, and we evaluate the spectral efficiency achievable by a single-user receiver in the considered multi-channel scenario. We then optimize the channel spacing with the aim of maximizing the spectral efficiency, showing that impressive improvements with respect to the spectral efficiencies reported in the literature and obtained by heuristic approaches can be achieved.

We consider a terrestrial wireless channel, whose statistical model under flat-fading conditions is due to Clarke. A lot of papers in the literature deal with receivers for this scenario, aiming at estimating and tracking the time-varying channel, possibly with the aid of known (pilot) symbols. A common approach to derive receivers of reasonable complexity is to resort to a Kalman filter which is based on an approximation of the actual fading process as autoregressive moving-average (ARMA) of a given order. The aim of this paper is to show that the approximation of the actual fading process, usually exploited in the literature, is far from effective. Thus, we present a novel technique, based on an off-line minimization of the mean square error of the channel estimate, which ensures a considerable gain in terms of bit-error rate for Kalman-based receivers without increasing the receiver complexity. Moreover, we also propose a novel approximation, to be employed in Kalman smoothers proposed for iterative detection schemes, which allows further performance improvements without a significant increase of the computational complexity.

We address the design of low-complexity algorithms for soft-output detection over channels impaired by intersymbol interference. Unlike most works with similar aims, which assume the presence of the whitened matched filter at the receiver (Forney approach), algorithms that can directly work on the matched filter output (Ungerboeck approach) are considered. We introduce a novel (cyclic) factor graph describing the channel and, by applying the sum-product algorithm to it, we derive soft-output detection schemes that can provide impressive complexity reductions with respect to the benchmark algorithms, since their complexity is linear, instead of exponential, in the channel memory. Finally, we report simulation results proving that the performance of the proposed algorithms makes them appealing for turbo equalization in various practical scenarios.

We consider block-based transmissions over doubly-selective wireless channels. It is well-known that orthogonal frequency division multiplexing decomposes linear time-invariant channels into a set of parallel (non-interfering) channels via a Fourier basis. In contrast, for time-varying channels, inter-channel interference occurs and may have a dramatic impact on the performance. Following a mean square error criterion, we propose an approximate eigenmode decomposition for linear time-varying channels, defined directly in the discrete-time domain. We show the effectiveness of the proposed waveform design by evaluating the mutual information (with Gaussian inputs) for doubly-selective terrestrial Rayleigh wireless channels.

We investigate the spectral efficiency, achievable by a low-complexity symbol-by-symbol receiver, when linear modulations based on the superposition of uniformly time- and frequency-shifted replicas of a base pulse are employed. Although orthogonal signaling with Gaussian inputs achieves capacity on the additive white Gaussian noise channel, we show that, when finite-order constellations are employed, by giving up the orthogonality condition (thus accepting interference among adjacent signals) we can considerably improve the performance, even when a symbol-by-symbol receiver is used. We also optimize the spacing between adjacent signals to maximize the achievable spectral efficiency. Moreover, we propose a more involved system model, consisting of the superposition of two independent signals and a receiver based on successive interference cancellation, showing that it allows a further increase of the spectral efficiency.

We investigate the spectral efficiency of continuous phase modulations (CPMs). To this end, we need an effective bandwidth definition for a CPM signal, whose power spectral density has in principle an infinite support. The definition we adopt is based on the spacing between adjacent carriers in a frequency division multiplexed CPM system. We consider the inter-channel interference, that depends on the channel spacing, and we evaluate the spectral efficiency achievable by a single- user receiver in the considered multi-channel scenario. We then optimize the channel spacing with the aim of maximizing the spectral efficiency, showing that impressive improvements with respect to the spectral efficiency achieved without optimizing the channel spacing can be achieved. I.

We propose a novel iterative method, similar to the generalized Blahut-Arimoto algorithm recently proposed by Kavcic, to evaluate the Markov capacity of a continuous phase modulated signal over an additive white Gaussian noise channel. One of the novelty of our approach is that we maximize, with respect to the input distribution, the spectral efficiency in bps/Hz rather than the mutual information in bits per channel use. We address this problem by taking into account the bandwidth occupancy of the CPM signal by means of the Carson's rule bandwidth definition, and solving a linearly constrained nonlinear optimization problem. The results show that Markov capacity obtained with the proposed input optimization algorithm strongly outperforms the capacity for independent and uniformly distributed input.

We consider a terrestrial wireless channel, whose statistical model under flat-fading conditions is due to Clarke. A lot of papers in the literature deal with receivers for this scenario, aiming at estimating and tracking the time-varying channel, possibly with the aid of known (pilot) symbols. A common approach to derive receivers of reasonable complexity is to resort to a Kalman filter which is based on an approximation of the actual fading process as autoregressive moving-average (ARMA) of a given order. The aim of this paper is to show that the approximation of the actual fading process, usually exploited in the literature, is far from optimal. Thus, we present a novel technique, based on an off-line minimization of the mean square error of the channel estimate, which ensures a considerable gain in terms of bit-error rate for Kalman-based receivers without increasing the receiver complexity. Moreover, we also propose a novel approximation, to be employed in Kalman smoothers proposed for iterative detection schemes, which allows to further improve the performance without a significant increase of the computational complexity.

We consider rotationally invariant (RI) trellis-coded modulations transmitted over channels affected by phase noise. To describe the main ideas of this paper, we first concentrate, as a case study, on the simplest RI scheme, namely the differentially encodedM-ary phase-shift keying signal. For this problem, we use the framework based on factor graphs and the sum–product algorithm to derive the exact maximuma posteriorisymbol detection algorithm. By analyzing its properties, we demonstrate that it can be implemented by a forward–backward estimator of the phase probability density function, followed by a symbol-by-symbol completion to produce thea posterioriprobabilities of the information symbols. To practically implement the forward–backward phase estimator, we propose a couple of schemes with different complexity. The resulting algorithms exhibit an excellent performance, and in one case, only a limited complexity increases with respect to the algorithm that perfectly knows the channel phase. The properties of the optimal decoder and the proposed practical decoding schemes are then extended to the case of a generic RI code. The proposed soft-output algorithms can also be used in iterative decoding schemes for concatenated codes employing RI inner components. Among them, in the numerical results, we consider repeat-accumulate codes and other serially concatenated schemes recently proposed in the technical literature.

We consider rotationally invariant (RI) trellis-coded modulations (TCMs) transmitted over channels affected by phase noise. To describe the main ideas of this paper, we first concentrate, as a case study, on the simplest RI scheme, namely the differentially encoded M-ary phase-shift keying (M-PSK) signal. For this problem, we use the framework based on factor graphs (FGs) and the sum-product algorithm (SPA), to derive the exact maximum a posteriori (MAP) symbol detection algorithm. By analyzing its properties, we demonstrate that it can be implemented by a forward-backward estimator of the phase probability density function, followed by a symbol-by-symbol completion to produce the a posteriori probabilities of the information symbols. To practically implement the forward-backward phase estimator, we propose a couple of schemes with different complexity. The resulting algorithms exhibit an excellent performance and, in one case, only a limited complexity increases with respect to the algorithm that perfectly knows the channel phase. The properties of the optimal decoder and the proposed practical decoding schemes are then extended to the case of a generic RI code. The proposed soft-output algorithms can also be used in iterative decoding schemes for concatenated codes employing RI inner components. Among them, in the numerical results, we consider repeat-accumulate (RA) codes and other serially concatenated schemes recently proposed in the technical literature.

We present a new algorithm for joint detection and decoding of iteratively decodable codes transmitted over channels affected by a time-varying phase noise (PN) and a constant frequency offset. The proposed algorithm is obtained as an application of the sum-product algorithm to the factor graph representing the joint a posteriori distribution of the information symbols and the channel parameters given the channel output. The resulting algorithm employs the soft-output information on the coded symbols provided by the decoder and performs forward-backward recursions, taking into account the joint probability distribution of phase and frequency offset. We present simulation results for high-order coded modulation schemes based on low-density parity-check codes and serially concatenated convolutional codes, showing that, despite its low complexity, the algorithm is able to cope with a strong PN and a significant uncompensated frequency offset, thus avoiding the use of complicated data-aided frequency-estimation schemes operating on a known preamble. The robustness of the algorithm in the presence of a time-varying frequency offset is also discussed

We propose novel techniques to reduce the complexity of the well-known Bahl–Cocke–Jelinek–Raviv (BCJR) algorithm when it is employed as a detection algorithm in turbo equalization schemes. In particular, by also considering an alternative formulation of the BCJR algorithm, which is more suitable than the original for deriving reduced-complexity techniques, we describe three reduced-complexity algorithms, each of them being particularly effective over one of the three different classes of channels (minimum-phase, maximum-phase, and mixed-phase channels) affected by intersymbol interference. The proposed algorithms do not explore all paths on the trellis describing the channel memory, but they work only on the most promising ones, which are chosen according to the maximuma posterioricriterion. Moreover, some optimization techniques for improving the effectiveness of the proposed solutions are described. Finally, we report the results of computer simulations showing the impressive performance of the proposed algorithms, and compare them with other solutions in the literature.

We propose novel techniques to reduce the complexity of the well-known Bahl, Cocke, Jelinek, and Raviv (BCJR) algorithm when it is employed as a detection algorithm in turbo equalization schemes. In particular, by also considering an alternative formulation of the BCJR algorithm, which is more suitable than the original one for deriving reduced-complexity techniques, we describe three reduced-complexity algorithms, each of them particularly effective over one of the three different classes of channels affected by intersymbol interference (minimum-phase, maximum-phase, and mixed-phase channels). The proposed algorithms do not explore all paths on the trellis describing the channel memory, but they work only on the most promising ones, chosen according to the maximum a posteriori criterion. Moreover, some optimization techniques improving the effectiveness of the proposed solutions are described. Finally, we report the results of computer simulations showing the impressive performance of the proposed algorithms, and we compare them with other solutions in the literature.

We consider the carrier frequency offset estimation in a digital burst-mode satellite transmission affected by phase noise. The corresponding Cramer-Rao lower bound is analyzed for linear modulations under a Wiener phase noise model and in the hypothesis of knowledge of the transmitted data. Even if we resort to a Monte Carlo average, from a computational point of view the evaluation of the Cramer-Rao bound is very hard. We introduce a simple but very accurate approximation that allows to carry out this task in a very easy way. As it will be shown, the presence of the phase noise produces a remarkable performance degradation of. the frequency estimation accuracy. In addition, we provide asymptotic expressions of the Cramer-Rao bound, from which the effect of the phase noise and the dependence on the system parameters of the frequency offset estimation accuracy clearly result. Finally, as a by-product of our derivations and approximations, we derive a couple of estimators specifically tailored for the phase noise channel that will be compared with the classical Rife and Boorstyn algorithm, gaining in this way some important hints on the estimators to be used in this scenario

We consider continuous phase modulations (CPMs) in iteratively decoded serially concatenated schemes. Although the overall receiver complexity mainly depends on that of the CPM detector, almost all papers in the literature consider the optimal maximum a posteriori (MAP) symbol detection algorithm and only a few attempts have been made to design low-complexity suboptimal schemes. This problem is faced in this paper by first considering the case of an ideal coherent detection, then extending it to the more interesting case of a transmission over a typical satellite channel affected by phase noise. In both cases, we adopt a simplified representation of an M-ary CPM signal based on the principal pulses of its Laurent decomposition. Since it is not possible to derive the exact detection rule by means of a probabilistic reasoning, the framework of factor graphs (FGs) and the sum-product algorithm (SPA) is used. In the case of channels affected by phase noise, continuous random variables representing the phase samples are explicitly introduced in the FG. By pursuing the principal approach to manage continuous random variables in a FG, i.e., the canonical distribution approach, two algorithms are derived which do not require the presence of known (pilot) symbols, thanks to the intrinsic differential encoder embedded in the CPM modulator.

We present innovative techniques to reduce the complexity of the well known BCJR detection algorithm applied on channels affected by intersymbol interference. On a general channel with mixed phase, we independently perform two reduced trellis searches in the forward and backward recursions, and propose a modified completion stage for combining them. When iterative detection/decoding is performed, the reduced searches are defined on the basis of a probabilistic criterion which provides a noticeable performance improvement with respect to the other reduced-complexity algorithms. On the other hand, for channels with minimum phase, we show that the simplified backward recursion can operate on the same set of paths selected during the forward search. Computer simulations confirm the effectiveness of the proposed algorithms in various turbo equalization schemes.

We consider rotationally invariant (RI) trellis-coded modulations (TCMs) transmitted over channels affected by phase noise. For this problem, we use the framework based on factor graphs (FGs) and the sum-product algorithm (SPA) to derive the exact maximum a posteriori (MAP) symbol detection algorithm. We demonstrate that it can be implemented by a reduced number of forward-backward estimators of the phase probability density function, followed by a symbol-by-symbol completion to compute the a posteriori probabilities of the information symbols. To practically implement the forward-backward phase estimators, we propose a couple of schemes with different complexity. The resulting algorithms exhibit an excellent performance and a limited complexity. The proposed soft-output algorithms can be also used in iterative decoding schemes for concatenated codes employing RI inner components

We consider the carrier frequency offset estimation in a digital burst-mode transmission affected by phase noise. The corresponding Cramer-Rao lower bound is analyzed for linear modulations under a Wiener phase noise model and in the hypothesis of knowledge of the transmitted data. Even if we resort to a Monte Carlo average, from a computational point of view the evaluation of the Cramer-Rao bound is very hard. We introduce a simple but very accurate approximation that allows to carry out this task in a very easy way. As it is shown, the presence of the phase noise produces a remarkable performance degradation of the frequency estimation accuracy. In addition, we bound and we also gain some important hints on the estimators to be used in this scenario.

In this paper, we derive low-complexity MAP symbol detection algorithms for continuous phase modulations (CPMs). To obtain this result, the well-known problem of a transmission over an additive white Gaussian noise (AWGN) channel affected by known intersymbol interference (ISI) is first considered. We show that the maximum a posteriori (MAP) symbol detection strategy, usually implemented by using the Forney observation model, can be equivalently implemented based on the samples at the output of a filter matched to the received pulse, i.e., based on the Ungerboeck observation model. Although interesting from a conceptual viewpoint, the derived algorithm has a practical relevance in turbo equalization schemes for partial response signalling, where the implementation of a whitening filter can be avoided, and in the derivation of the simplified algorithms for CPM signals.

In this paper, we present an iterative decoding algorithm for turbo and LDPC codes transmitted over channels affected by a time-varying phase noise and an uncompensated frequency offset. The proposed algorithm is obtained as an application of the sum-product algorithm to the factor graph representing the joint a posteriori distribution of the information symbols and the channel parameters given the channel output. The resulting algorithm employs the soft-output information on the coded symbols provided by the decoder and performs forward-backward recursions taking into account the joint probability distribution of phase and frequency offset. We present numerical results for binary LDPC codes and LDPC-coded modulation schemes, showing that, despite its low complexity, the algorithm is able to cope with a strong phase noise and a significant uncompensated frequency offset, thus avoiding the use of complicated data-aided frequency estimation schemes operating on a known preamble.

We present two new iterative decoding algorithms for channels affected by strong phase noise and compare them with the best existing algorithms proposed in the literature. The proposed algorithms are obtained as an application of the sum-product algorithm to the factor graph representing the joint a posteriori probability mass function of the information bits given the channel output. In order to overcome the problems due to the presence in the factor graph of continuous random variables, we apply the method of canonical distributions . Several choices of canonical distributions have been considered in the literature. Well-known approaches consist of discretizing continuous variables or treating them as jointly Gaussian, thus obtaining a Kalman estimator. Our first new approach, based on the Fourier series expansion of the phase probability density function, yields better complexity/performance tradeoff with respect to the usual discretized-phase method. Our second new approach, based on the Tikhonov canonical distribution, yields near-optimal performance at very low complexity and is shown to be much more robust than the Kalman method to the placement of pilot symbols in the coded frame. We present numerical results for binary LDPC codes and LDPC-coded modulation, with particular reference to some phase-noise models and coded-modulation formats standardized in the next-generation satellite Digital Video Broadcasting (DVB-S2). These results show that our algorithms achieve near-coherent performance at very low complexity without requiring any change to the existing DVB-S2 standard.

This paper proposes new algorithms for joint iterative decoding and parameter estimation in Bayesian and nonBayesian method. The low-density parity-check (LDPC) codes in the presence of phase noise is focused in this paper. The nonBayesian algorithm for an unknown phase channel is based on the application of the expectation-maximization (EM) algorithm. The proposed Bayesian algorithms are obtained as an application of the sum-product algorithm to the factor graph. Comparing the performance of the considered algorithms in the case of LDPC code, the EM-KL (EM-Karhunen-Loeve) algorithm performs better than EM-SW (sliding window) algorithm.