Satellite-based automatic dependent surveillance-broadcast (ADS-B) enables global-scale aircraft tracking, but introduces significant challenges in packet detection due to the severe signal attenuation experienced along the propagation path. This paper investigates detection strategies for satellite ADS-B receivers based on the generalized likelihood ratio test (GLRT), providing a statistically sound alternative to existing heuristic solutions. To enhance robustness against residual frequency offsets, we exploit the modulus of the received samples, which is invariant to phase distortions. Additionally, we extend the observation window beyond the packet preamble to improve detection performance. To reduce the computational cost of an exact GLRT implementation, we explore several alternatives, including an asymptotic approximation of the Rician distribution and a Gaussian model for the observation data. Simulation results show that the proposed schemes achieve reliable detection even at low signal-to-noise ratios, and outperform existing methods by leveraging both the preamble and part of the data payload.

This paper investigates the problem of direction-of-arrival (DoA) estimation in integrated orbital angular momentum (OAM) radar-communication systems. Unlike previous studies, we consider a more realistic and challenging scenario where coherent signals result from mainlobe overlap among echoes from multiple targets. To tackle this issue, we propose an iterative DoA estimation framework based on the estimation of signal parameters via rotational invariance techniques (ESPRIT) algorithm. Specifically, the coherent echo signals are forcibly decoupled to form a data set comprising multiple independent components, each associated with the DoA of a specific target. A novel iterative estimation strategy is then introduced, wherein a recursive ESPRIT algorithm is sequentially applied to each component during each iteration to extract the corresponding DoA information. The estimated angles are subsequently used to update the data set for the next iteration. This alternating process continues until the DoA estimates converge to the true values. Simulation results demonstrate the effectiveness and robustness of the proposed method under coherent echo conditions.

This study focuses on the optimization of a single-cell multi-user multiple-input multiple-output (MIMO) system with multiple large-size reconfigurable intelligent surfaces (RISs). The overall transmit power is minimized by optimizing the precoding coefficients and the RIS configuration, with constraints on users’ signal-to-interference-plus-noise ratios (SINRs). The minimization problem is divided into two sub-problems and solved by means of an iterative alternating optimization (AO) approach. The first sub-problem focuses on finding the best precoder design. The second sub-problem optimizes the configuration of the RISs by partitioning them into smaller tiles. Each tile is then configured as a combination of pre-defined configurations. This allows the efficient optimization of RISs, especially in scenarios where the computational complexity would be prohibitive using traditional approaches. Simulation results show the good performance and limited complexity of the proposed method in comparison to benchmark schemes.

To ensure high array gain, RIS is evolving towards the extra-large RIS (XL-RIS). However, XL-RIS encounters significant challenges in high pilot overhead. In this paper, we consider a downlink XL-RIS communication system, which is used for assisting the data transmission from a single base station (BS) to multiple user equipments (UEs). We first propose a beam training strategy by turning on only a few RIS elements to reduce pilot overhead, so that the RIS can determine the UE areas. Then, the position information of the UEs is able to be obtained by estimating the far-field channels between the UEs and RIS. After that, all elements are activated for high- throughput data transmission, in which the near-field XL-RIS beamforming is performed based on the connection between the near-field and the far-field channels that we disclose with the positional information of UEs, ultimately forming multiple beams directed at different UEs with optimal power allocation.

Consider a communication system in which a single-antenna user equipment exchanges information with a multi-antenna base station via a reconfigurable intelligent surface (RIS) in the presence of spatially correlated channels and electromagnetic interference (EMI). To exploit the attractive advantages of RIS technology, accurate configuration of its reflecting elements is crucial. In this paper, we use statistical knowledge of channels and EMI to optimize the RIS elements for 1i) accurate channel estimation and 2) reliable data transmission. In both cases, our goal is to determine the RIS coefficients that minimize the mean square error, resulting in the formulation of two non-convex problems that share the same structure. To solve these two problems, we present an alternating optimization approach that reliably converges to a locally optimal solution. The incorporation of the diagonally scaled steepest descent algorithm, derived from Newton’s method, ensures fast convergence with manageable complexity. Numerical results demonstrate the effectiveness of the proposed method under various propagation conditions. Notably, it shows significant advantages over existing alternatives that depend on a suboptimal configuration of the RIS and are derived on the basis of different criteria.

In high-resolution Earth observation imagery, Low Earth Orbit (LEO) satellites capture and transmit images to ground to create an updated map of an area of interest. Such maps provide valuable information for meteorology and environmental monitoring, but can also be employed for real-time disaster detection and management. However, the amount of data generated by these applications can easily exceed the communication capabilities of LEO satellites, leading to congestion and packet dropping. To avoid these problems, the Inter-Satellite Links (ISLs) can be used to distribute the data among multiple satellites and speed up processing. In this paper, we formulate a satellite mobile edge computing (SMEC) framework for real-time and very-high resolution Earth observation and optimize the image distribution and compression parameters to minimize energy consumption. Our results show that our approach increases the amount of images that the system can support by a factor of$12\times $and$2\times $when compared to directly downloading the data and to local SMEC, respectively. Furthermore, energy consumption was reduced by 11% in a real-life scenario of imaging a volcanic island, while a sensitivity analysis of the image acquisition process demonstrates that energy consumption can be reduced by up to 90%.

Orbital angular momentum (OAM) radars are able to estimate the azimuth angle and the rotation velocity of multiple targets without relative motion or beam scanning. Moreover, OAM wireless communications can achieve high spectral efficiency (SE) by utilizing a set of information-bearing modes on the same frequency channel. Benefitting from the above advantages, in this paper, we design a novel radar-centric joint OAM radar-communication (RadCom) scheme based on uniform circular arrays (UCAs), which modulates information signals on the existing OAM radar waveform. In details, we first propose an OAM-based three-dimensional (3-D) super-resolution position estimation and rotation velocity detection method, which can accurately estimate the 3-D position and rotation velocity of multiple targets without beam scanning. Then, we derive the posterior Cramér-Rao bound (PCRB) of the OAM-based estimates and, finally, we analyze SE of the integrated system. To achieve the best trade-off between imaging and communication, the transmitted integrated OAM beams are optimized by means of an exhaustive search method. Both mathematical analysis and simulation results show that the proposed radar-centric joint OAM RadCom scheme can accurately estimate the 3-D position and rotation velocity of multiple targets while ensuring the SE of the communication receiver, which can be regarded as an effective supplement to existing joint RadCom schemes.

Spectrum slicing of the shared radio resources is a critical task in 5G networks with heterogeneous services, through which each service gets performance guarantees. In this paper, we consider a setup in which a Base Station (BS) should serve two types of traffic in the downlink, enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC), respectively. Two resource allocation strategies are compared: non-orthogonal multiple access (NOMA) and orthogonal multiple access (OMA). A framework for power minimization is presented, in which the BS knows the channel state information (CSI) of the eMBB users only. Nevertheless, due to the resource sharing, it is shown that this knowledge can be used also to the benefit of the URLLC users. The numerical results show that NOMA leads to a lower power consumption compared to OMA for every simulation parameter under test.

Sinusoidal frequency estimation in the presence of white Gaussian noise plays a major role in many engineering fields. Significant research in this area has been devoted to the fine tuning stage, where the discrete Fourier transform (DFT) coefficients of the observation data are interpolated to acquire the residual frequency error$\varepsilon $. Iterative interpolation schemes have recently been designed by employing two$q$-shifted spectral lines symmetrically placed around the DFT peak, and the impact of$q$on the estimation accuracy has been theoretically assessed. Such analysis, however, is available only for some specific algorithms and is mostly conducted under the assumption of a vanishingly small frequency error, which makes it inappropriate for the first stage of any iterative process. In this work, further investigation on DFT interpolation is carried out to examine some issues that are still open. We start by evaluating the Cramér-Rao bound (CRB) for frequency recovery by interpolation of two$q$-shifted spectral lines and assess its dependence on$\varepsilon $and$q$. Such a bound is of primary importance to check whether existing schemes can provide efficient estimates at any iteration or not. After determining the optimum value of$q$for a given$\varepsilon $, we eventually derive the maximum likelihood (ML) DFT interpolator. Since the latter exhibits the best performance at any step of the iteration process, it might attain the desired accuracy just at the end of the first iteration, which is especially advantageous in terms of computational load and processing time.

Frequency estimation of a single complex exponential signal embedded in additive white Gaussian noise is a major topic of research in many engineering areas. This work presents further investigations on this problem with regards to the fine estimation task, which is accomplished through a suitable interpolation of the discrete Fourier transform (DFT) coefficients of the observation data. The focus is on fast real-time applications, where iterative estimation methods can hardly be applied due to their latency and complexity. After deriving the analytical expression of the Cramér-Rao bound (CRB) for general values of the system parameters, we present a new DFT interpolation scheme based on the weighted least-squares (WLS) rule, where the optimum weights are precomputed through a numerical search and stored in the receiver. In contrast to many existing alternatives, the proposed method can employ an arbitrary number of DFT samples so as to achieve a good trade-off between system performance and complexity. Simulation results and theoretical analysis indicate that, at sufficiently high signal-to-noise ratios, the estimation accuracy is close to the relevant CRB at any value of the frequency error. This provides some advantage with respect to non-iterative competing schemes, without incurring any penalty in processing requirement.

5G technology allows heterogeneous services to share the wireless spectrum within the same radio access network. In this context, spectrum slicing of the shared radio resources is a critical task to guarantee the performance of each service. We analyze a downlink communication serving two types of traffic: enhanced mobile broadband (eMBB) and ultra-reliable low-latency communication (URLLC). Due to the nature of low-latency traffic, the base station knows the channel state information (CSI) of the eMBB users while having statistical CSI for the URLLC users. We study the power minimization problem employing orthogonal multiple access (OMA) and non-orthogonal multiple access (NOMA) schemes. Based on this analysis, we propose a lookup table-based approach and a block coordinated descent (BCD) algorithm. We show that the BCD is optimal for the URLLC power allocation. The numerical results show that NOMA leads to lower power consumption than OMA, except when the average channel gain of the URLLC user is very high. For the latter case, the optimal approach depends on the channel condition of the eMBB user. Even when OMA attains the best performance, the gap with NOMA is negligible, showing the capability of NOMA to reduce power consumption in practically every condition.

With the advent of 5G and the research into beyond 5G (B5G) networks, a novel and very relevant research issue is how to manage the coexistence of different types of traffic, each with very stringent but completely different requirements. We propose a Deep Reinforcement Learning (DRL) algorithm to slice the available physical layer resources between ultra-reliable low-latency communications (URLLC) and enhanced Mobile BroadBand (eMBB) traffic. Specifically, in our setting the time-frequency resource grid is fully occupied by eMBB traffic and we train the DRL agent to employ Proximal Policy Optimization (PPO), a state-of-the-art DRL algorithm, to dynamically allocate the incoming URLLC traffic by puncturing eMBB codewords. Assuming that each eMBB codeword can tolerate a certain limited amount of puncturing beyond which is in outage, we show that the policy devised by the DRL agent never violates the latency requirement of URLLC traffic and, at the same time, manages to keep the number of eMBB codewords in outage at minimum levels, when compared to other state-of-the-art schemes.

In this paper we study the opportunistic use of microwave satellite signals in the Ku and Ka bands to detect the presence of rain events and to estimate their intensity. This approach, based on measuring the excess signal attenuation due to rain, has the advantage that the potential number of sensors over the territory could be increased dramatically also allowing prospectively the realization of accurate regional rain maps. Moreover, employing the same satellite network whose signals are opportunistically used, the measurements can be easily fed back and universally distributed, contributing to the effective implementation of a nowcasting platform, which could help the timely detection of catastrophic rain events. Results are very promising and show that the already good estimates obtained by using signals in the Ku band can be further improved by using signals in the Ka band. Unfortunately, as of today, the frequency with which the receivers on the Ka band provide signal measurements is too low for fast changing rain events and how to address this problem is the object of future research.

In this article, we study the problem of power and channel allocation for multicarrier non-orthogonal multiple access (NOMA) full duplex (FD) systems. In such a system there are multiple interfering users transmitting over the same channel and the allocation task is a non-convex and extremely challenging problem. Following a block coordinated descent approach, we propose two algorithms based on the decomposition of the original allocation problem in lower-complexity sub-problems, which can be solved in the Lagrangian dual domain with a great reduction of the computational load. Numerical results show the effectiveness of the proposed approach, which outperforms other schemes designed to address NOMA-FD allocation and attains performance similar to the optimal solution with much lower complexity.

The Fifth Generation of mobile networks is emerging as a key enabler for Ultra Reliable and Low Latency Communications. However, to effectively design and provide safety-critical applications through mobile systems, many research issues still need to be deeply investigated. The most important ones include: (1) the dynamic and flexible management of radio resources of a new Radio Access Network jointly used by many virtual mobile operators, (2) the optimized and realtime configuration of network slices, and (3) the harmonious integration of Multi-access Edge Computing services. Starting from the efficient methodologies and solutions available in the current state of the art, this position paper sheds some important basis for the design of a comprehensive architecture enabling Radio Access Network slicing for Ultra Reliable and Low Latency Communications, including design criteria, system components and their baseline interactions, and critical open issues to be investigated in future research initiatives.

Combined with block diagonalization (BD) methods, Tomlinson-Harashima (TH) precoding and vector perturbation (VP) are attractive non-linear precoding techniques for multi-user multiple-input multiple-output (MU-MIMO) broadcast channels. In this paper, we first briefly review several non- linear precoding schemes including BD-VP, TH and zero-forcing (TH-ZF) and TH-VP, and then derive the achievable sum rate of the hybrid TH-VP precoding. After that, we compare the achievable sum rates and diversity gains of these non-linear precoding schemes with zero-forcing VP (ZF-VP) and dirty paper coding (DPC). The achievable sum rate of TH-VP is shown to approach the sum capacity of DPC asymptotically and outperforms BD-VP, TH-ZF and ZF- VP.

In long-term evolution (LTE) systems, there is an option to extend the size of the cyclic-prefix (CP) when the propagation environment is characterized by severe time dispersion. Such a peculiar feature makes the system more resilient against multipath distortions, but inevitably complicates the downlink synchronization task, which cannot be accomplished through conventional techniques devised for a specified CP size. In this paper, we study the problem of CP length detection in the LTE downlink in the presence of a timing uncertainty and frequency offset. In contrast to previous investigations based on heuristic arguments, our approach relies on the maximum likelihood (ML) estimation criterion. Furthermore, it accounts for the irregular LTE symbol (characterized by a different CP size), which is present in the normal CP operation mode. The resulting scheme operates in the time-domain and computes a different metric for each possible position of the irregular symbol within the observation window. Due to its flexibility, the proposed ML approach can be applied to any future multicarrier standard for the design of a CP length detection algorithm, either in the presence or in the absence of irregular symbols.

Direct conversion receivers (DCRs) are an effective means to obtain user terminals with reduced cost, size, and power consumption. Their major drawback is the possible insertion of I/Q imbalances in the demodulated signal, which can seriously degrade the performance of conventional synchronization algorithms. In this paper, we investigate the problem of carrier frequency offset (CFO) recovery in an OFDM receiver equipped with a DCR front-end. Our approach is based on the best linear unbiased estimation (BLUE) theory and aims at jointly estimating the CFO, the useful signal component, and its mirror image. In doing so, we exploit knowledge of the pilot symbols transmitted within a conventional repeated training preamble appended in front of each data packet. Two solutions are proposed, and both of them provides the CFO in closed-form, thereby avoiding any grid-search procedure. The accuracy of the proposed methods is assessed in a scenario compliant with the 802.11a WLAN standard. Compared with existing solutions, the novel schemes achieve improved performance at the price of a marginal increase of the processing load.

Device-to-device (D2D) communications are capable of enhancing the total cell throughput, reducing power consumption and increasing the instantaneous data rate. In this paper we propose a distributed power allocation scheme for D2D communications overlaying an OFDMA network, so that D2D communications take place on dedicated resources but are supervised by the BS. The proposed scheme addresses the problem of maximizing the users' sum rate subject to power constraints, which is known to be nonconvex. By modelling the power allocation problem as a potential game, we can take advantage of the potential games property of converging under better response dynamics. Accordingly, we propose a fully distributed iterative algorithm, where each user updates sequentially and autonomously its power allocation. The proposed method exhibit performance close to the maximum achievable optimum and outperform other schemes presented in the literature.

The implementation of device-to-device (D2D) underlaying or overlaying preexisting cellular networks has received much attention due to the potential of enhancing the total cell throughput, reducing the power consumption, and increasing the instantaneous data rate. In this paper, we propose a distributed power allocation scheme for D2D OFDMA communications and, in particular, we consider the two operating modes amenable to a distributed implementation: dedicated and reuse modes. The proposed schemes address the problem of maximizing the users' sum rate subject to power constraints, which is known to be nonconvex and, as such, extremely difficult to be solved exactly. We propose here a fresh approach to this well-known problem, capitalizing on the fact that the power allocation problem can be modeled as a potential game. Exploiting the potential games property of converging under better response dynamics, we propose two fully distributed iterative algorithms, one for each operation mode considered, where each user updates sequentially and autonomously its power allocation. Numerical results, computed for several different user scenarios, show that the proposed methods, which converge to one of the local maxima of the objective function, exhibit performance close to the maximum achievable optimum and outperform other schemes presented in the literature.

The direct conversion receiver (DCR) architecture has received much attention in the last few years as an effective means to obtain user terminals with reduced cost, size, and power consumption. A major drawback of a DCR device is the possible insertion of in-phase/quadrature imbalances in the demodulated signal, which can seriously degrade the performance of conventional synchronization algorithms. In this paper, we investigate the problem of carrier frequency offset (CFO) recovery in an orthogonal frequency-division multiplexing receiver equipped with a DCR front-end. Our approach is based on maximum likelihood (ML) arguments and aims at jointly estimating the CFO, the useful signal component, and its mirror image. In doing so, we exploit knowledge of the pilot symbols transmitted within a conventional repeated training preamble appended in front of each data packet. Since the exact ML solution turns out to be too complex for practical purposes, we propose two alternative schemes which can provide nearly optimal performance with substantial computational saving. One of them provides the CFO in closed-form, thereby avoiding any grid-search procedure. The accuracy of the proposed methods is assessed in a scenario compliant with the 802.11a WLAN standard. Compared to existing solutions, the novel schemes achieve improved performance at the price of a tolerable increase of the processing load.

Before establishing a communication link with the serving base station (eNodeB), user equipment (UE) operating in a long-term evolution (LTE) multi-cellular network must acquire some specific information, including the sector identity and cell group identity. For this purpose, two training sequences called primary synchronization signal (PSS) and secondary synchronization signal (SSS) are periodically transmitted in the downlink to convey such information. In this work, we present a novel maximum likelihood (ML) approach for SSS detection assuming that the PSS has been successfully identified at an earlier stage. As we shall see, the resulting scheme turns out to be too complex for practical implementation as it requires perfect knowledge of the channel covariance matrix. Therefore, we look for simpler solutions and propose two reduced-search methods that operate in a mismatched mode. The first scheme exploits channel state information emerging from both the primary and secondary synchronization signals, while the second scheme operates using only the secondary synchronization signal. Numerical analysis indicates that the proposed methods outperform existing alternatives and can be successfully applied even in a severe propagation scenario. The price for such an advantage is a certain increase of the processing requirement.

This paper investigates the subcarrier allocation problem for uplink transmissions in a multi-cell network, where device-to-device communications are enabled. We focus on maximizing the aggregate transmission rate in the system accounting for both inter- and intra-cell interference. This problem is computationally hard due to its nonconvex and combinatorial nature. However, we show that it can be described by a potential game, and thus a Nash equilibrium can be found using iterative algorithms based on best/better response dynamics. In particular, we propose a simple iterative algorithm with limited signaling that is guaranteed to converge to an equilibrium point, corresponding to a local maximum of the potential function. Using extensive simulations, we show that the algorithm converges quickly also for dense networks, and that the distance to the true optimum is often small, at least for the small-sized networks for which we were able to compute the true optimum.

This paper investigates the problem of carrier frequency offset (CFO) recovery in an OFDM receiver affected by frequency-selective in-phase/quadrature (I/Q) imbalances. The analysis is based on maximum-likelihood (ML) methods and relies on the transmission of a training preamble with a repetitive structure in the time domain. After assessing the accuracy of the conventional ML (CML) scheme in a scenario characterized by I/Q impairments, we review the joint ML (JML) estimator of all unknown parameters and evaluate its theoretical performance. In order to improve the estimation accuracy, we also present a novel CFO recovery method that exploits some side-information about the signal-to-interference ratio. It turns out that both CML and JML can be derived from this scheme by properly adjusting the value of a design parameter. The accuracy of the investigated methods are compared with the relevant Cramer-Rao bound. Our results can be used to check whether conventional CFO recovery algorithms can work properly or not in the presence of I/Q imbalances and also to evaluate the potential gain attainable by more sophisticated schemes.

Before establishing a communication link in a cellular network, the user terminal must activate a synchronization procedure called initial cell search in order to acquire specific information about the serving base station. To accomplish this task, the primary synchronization signal (PSS) and secondary synchronization signal (SSS) are periodically transmitted in the downlink of a long term evolution (LTE) network. Since SSS detection can be performed only after successful identification of the primary signal, in this work, we present a novel algorithm for joint PSS detection, sector index identification, and integer frequency offset (IFO) recovery in an LTE system. The proposed scheme relies on the maximum likelihood (ML) estimation criterion and exploits a suitable reduced-rank representation of the channel frequency response, which proves robust against multipath distortions and residual timing errors. We show that a number of PSS detection methods that were originally introduced through heuristic reasoning can be derived from our ML framework by simply selecting an appropriate model for the channel gains over the PSS subcarriers. Numerical simulations indicate that the proposed scheme can be effectively applied in the presence of severe multipath propagation, where existing alternatives provide unsatisfactory performance.

This paper addresses the problem of maximum likelihood (ML) estimation of slot timing, integer carrier frequency offset and primary sequence index for the downlink of Long Term Evolution (LTE) systems. The proposed algorithm is designed to exploit the knowledge of the pilot Zadoff-Chu sequence embedded in the primary synchronization signal (PSS). The estimation process is affected by the presence of a large set of nuisance parameters, which need to be estimated jointly with the parameters of interest. As a consequence, the exact ML solution is extremely complex and we have developed a suboptimal algorithm designed to provide a good balance between estimation accuracy and complexity. In particular, a key finding is a reduced-rank representation for the frequency response of the channel, which is required by the ML estimator but is not available at receiver prior to having acquired synchronization. Compared to existing alternatives, the resulting scheme exhibits improved accuracy in the estimation of all three parameters of interest.

In this work we investigate the joint estimation of the integer carrier frequency offset (CFO) and the preamble index in a multicarrier system compliant with the WiMAX specifications. Since the exact ML solution is prohibitively complex in its general formulation, a suboptimal algorithm is developed to provide a reasonable trade-off between estimation accuracy and processing load. Compared to existing alternatives, the resulting scheme exhibits improved accuracy and reduced sensitivity to residual timing errors.

Many assignment problems, and channel allocation in OFDMA networks is a typical example, can be formulated as bipartite weighted b-matching (BWBM) problems. In this letter we provide a proof of the convergence and the optimality of the reweighted message passing (ReMP) algorithm when applied to solve BWBM problems in a distributed fashion. To this aim, we first show that the ReMP rule is a contraction mapping under a maximum mapping norm. Then, we show that the fixed convergence point is an optimal solution for the original assignment problem.

In multi-cellular WiMAX systems based on orthogonal frequency-division multiple-access (OFDMA), the training preamble is chosen from a set of known sequences so as to univocally identify the transmitting base station. Therefore, in addition to timing and frequency synchronization, preamble index identification is another fundamental task that a mobile terminal must successfully complete before establishing a communication link with the base station. In this work we investigate the joint maximum likelihood (ML) estimation of the carrier frequency offset (CFO) and preamble index in a multicarrier system compliant with the WiMAX specifications, and derive a novel expression of the relevant Cramer-Rao bound (CRB). Since the exact ML solution is prohibitively complex in its general formulation, suboptimal algorithms are developed which can provide a reasonable trade-off between estimation accuracy and processing load. Specifically, we show that the fractional CFO can be recovered by combining the ML estimator with an existing algorithm that attains the CRB in all practical scenarios. The integral CFO and preamble index are subsequently retrieved by a suitable approximation of their joint ML estimator. Compared to existing alternatives, the resulting scheme exhibits improved accuracy and reduced sensitivity to residual timing errors. The price for these advantages is a certain increase of the system complexity.

To cope with the ever increasing demand for bandwidth and increasing number of users, future wireless networks will be designed with the radio resource allocation techniques able to get good performance with low complexity and feedback. In this paper we study an allocation problem for OFDMA networks formulated with the objective of minimizing the load of each cell in the system or maximizing the number of accessing users subject to the constraint that each user meets its target rate. We formulate the two problem as one framework of finding the maximum weighted independent set (MWIS) in graph theory. In addition, we propose a minimal weighted-degree greedy (MWDG) algorithm. The control information requested to perform the allocation is limited and the computational burden is shared between the base station and the user equipments. Simulations have been carried out under constant bit rate traffic model and the results show MWDG has excellent performance and outperforms all other techniques.

This paper focuses on the issue of carrier frequency synchronization for filtered multitone wireless transmission over time-frequency selective fading channels. Rather than either relying only on known pilot symbols multiplexed within the transmitted burst or exploiting the specific signal structure in a blind mode, the estimation algorithm we pursue is derived from the maximum likelihood principle and takes advantage of both pilot symbols and also the unknown information-bearing symbols through specific differential decision-directed processing. When compared to conventional pilot-based methods, the proposed approach improves the frequency acquisition range without degrading estimation accuracy at even lower cost in terms of computational complexity.

In this paper we address the problem of margin adaptive scheduling in the downlink of an orthogonal frequency division multiple access (OFDMA) multiple-input multiple-output (MIMO) system. Optimal resource allocation in MIMO systems requires the joint optimization of: a) linear transmit and receive spatial filters, b) channel assignment and c) power allocation. This problem is not convex and its complexity becomes thus intractable already for small sets of users and subcarriers. To reduce the complexity of the problem at hand, we propose a novel heuristic strategy that partitions the users in different groups according to their average channel quality and addresses the original problem by solving a succession of lower-complexity allocation problems. The spatial dimension is employed to prevent multiple access interference from hindering the performance of the sequential allocation. To further reduce the complexity burden we introduce a linear programming formulation in combination with a waterfilling-based strategy to allocate channels and power to the groups of users. Numerical results and evaluation of the computational complexity show that, though suboptimal, in most cases the proposed algorithm manages to exploit in an original way the inherent multi-user diversity of multi-carrier systems to ease the task of resource allocation with a very limited performance loss from the theoretic optimum.

This paper proposes a distributed and low-complexity resource allocation scheme for cellular OFDMA networks. In particular, we consider ReMP, a reweighted message passing algorithm that perturbs the standard max-sum algorithm by suitably reweighting messages. In a single-cell scenario, such a scheme allows to achieve convergence to a fixed and provably optimum point without employing any central controller. The ReMP algorithm is then adapted to a multi-cell environment. To this aim, we devise X-ReMP, a ReMP-based algorithm that combines cross-cell signaling and the regular ReMP routine that still runs within each cell. The cross-signaling among cells aids ReMP to deal with the inter-cell multiple-access interference, so that X-ReMP allows convergence to a good working point in terms of system throughput even in presence of strong inter-cell interference.

We propose two novel distributed resource allocation (RA) schemes for the uplink of a cellular multi-carrier multi-format system based on the message passing (MP) technique. In the proposed approaches each transmitter iteratively sends and receives information messages to/from the base station with the goal of achieving an optimal RA strategy. The exchanged messages are the solution of small distributed allocation problems. Hence, despite the NP-hardness of the original RA problem, they distribute the computational effort in the cell among all the transmitters and the base station. Specifically, the first algorithm combines MP with a dynamic programming formula solved at each step, while the second method initially solves to optimality a simplified single-format RA via MP, and eventually performs format allocation to satisfy the rate constraints. Compared to alternatives, numerical results assess the validity of MP-based schemes both in terms of efficiency and complexity.

We investigate the problem of carrier frequency offset (CFO) recovery in an OFDM direct-conversion receiver plagued by both dc-offset and frequency-selective I/Q imbalance. In order to enlarge the frequency acquisition range, the CFO is divided into an integer part, which is multiple of the subcarrier spacing, plus a remaining fractional part. The fractional CFO is firstly estimated by resorting to the least-squares (LS) principle using a suitably designed training sequence. Since the exact LS solution requires a complete search over the frequency uncertainty range, we propose a simpler scheme that dispenses from any peak-search procedure. We also derive an approximated closed-form expression of the estimation accuracy that reveals useful for assessing the impact of various design parameters on the system performance. After computing the fractional CFO, the integer frequency error is eventually retrieved by following a weighted LS approach. Numerical simulations and theoretical analysis indicate that the proposed scheme can be used to obtain accurate CFO estimates with affordable complexity.

This paper considers distributed protocol design for resource allocation (RA) problems. We propose a fully decentralized RA scheme based on the min-sum message passing (MP) approach in which each message is the solution of small distributed allocation problems. Due to the presence of cycles in the network graph, the MP routine may not converge to a fixed point. To this end, we introduce a reweighted MP (ReMP) algorithm that perturbs the ordinary min-sum algorithm by suitably re-weighting messages. ReMP distributes the computational effort of achieving an optimal RA among nodes. Such feature makes ReMP particularly attractive in wireless networks allowing the convergence to a fixed and provably optimum point without employing any central controller. Numerical results show that ReMP outperforms conventional MP-based algorithms for RA problems in terms of computation time.

This paper deals with carrier frequency offset recovery for burst-mode filter-bank multicarrier transmission over channels affected by severe time-frequency selective fading. Unlike previously proposed algorithms, whereby frequency is recovered either relying on known pilot symbols multiplexed with the data stream (pilot-aided, or PA, approach), or exploiting specific properties of the multicarrier signal structure in a non-pilot-aided (NPA) fashion, here we present and discuss an algorithm based on the maximum likelihood principle, which can be qualified as combined PA/NPA since it takes advantage both of pilot symbols and also indirectly of data symbols through knowledge and exploitation of their specific modulation format. The algorithm requires the availability of the statistical properties of channel fading up to second-order moments. It is shown that the above approach allows to improve on both estimation accuracy and frequency acquisition range of previously published schemes.

We consider an OFDM system employing bit-interleaved coded-modulation (BICM) and investigate the problem of reliable data detection in the presence of narrowband interference (NBI). Such a scenario may arise in many practical contexts, including cellular applications and wireless transmissions over unlicensed frequency bands. It is known that conventional BICM decoding strategies suffer from significant performance degradation when the received signal is plagued by NBI. To overcome this difficulty, in the present work we model the interference power on each subcarrier as a nuisance parameter which is averaged out from the likelihood function. This approach results into a novel bit-metric that makes use of a suitable estimate of the NBI power obtained from previous data decisions. Numerical simulations indicate that the error rate performance of the proposed scheme is close to that of a maximum likelihood (ML) Viterbi decoder having perfect knowledge of the NBI power across the signal spectrum.

The direct-conversion architecture is a promising solution for the design of low-cost mobile terminals. However, it introduces extra RF impairments that greatly complicate fundamental receiver functions, including the synchronization task. This work deals with pilot-aided carrier frequency offset (CFO) recovery in an OFDM direct-conversion receiver plagued by frequency-selective I/Q imbalance. Since the exact maximum likelihood (ML) solution of this problem requires a complete search over the frequency uncertainty range, we propose a simpler scheme that dispenses from any peak-search procedure. Numerical simulations and theoretical analysis indicate that the proposed scheme attains the relevant Cramer-Rao bound at all signal-to-noise ratios of practical interest.

This paper investigates the estimation of multiple residual carrier frequency offsets (RCFOs) and sampling frequency offsets (SFOs) in the uplink of a multiuser OFDM network with space division multiple access (SDMA). The proposed solutions are based on the presence of some dedicated pilot tones within the signal spectrum, which are traditionally employed in OFDM systems to track channel variations and residual frequency errors. The main obstacle is the large number of parameters involved in the estimation process, which makes the uplink synchronization a rather challenging task. A practical solution to this problem relies on the separation of the received signals before the estimation procedure is started. This way, the RCFOs and SFOs of different users are estimated independently with affordable complexity. We propose two alternative approaches for users' separation. The former is based on the use of orthogonal pilot sequences, while the latter exploits the distinct spatial signatures of the uplink signals. Computer simulations and theoretical analysis are employed to assess the performance of the proposed schemes and to make comparison with existing alternatives.

This paper investigates the joint pilot-assisted estimation of the residual carrier frequency offset (RCFO) and sampling frequency offset (SFO) in an orthogonal frequency division multiplexing (OFDM) system. As it is known, the exact maximum-likelihood (ML) solution to this problem involves a bidimensional grid-search that cannot be pursued in practice. After introducing an enlarged set of auxiliary unknown parameters, however, the RCFO and SFO recovery tasks can be decoupled and the bidimensional search is thus replaced with a simpler mono-dimensional search. This results into an estimation algorithm of reasonable complexity which is suitable for practical implementation. To further reduce the processing load, we also present an alternative scheme yielding frequency estimates in closed-form. Numerical simulations indicate that the proposed methods outperform existing estimators available in the literature in terms of both estimation accuracy and error-rate performance.

We propose a novel distributed resource allocation scheme for the up-link of a cellular multi-carrier system based on the message passing (MP) algorithm. In the proposed approach each transmitter iteratively sends and receives information messages to/from the base station with the goal of achieving an optimal resource allocation strategy. The exchanged messages are the solution of small distributed allocation problems. To reduce the computational load, the MP problems at the terminals follow a dynamic programming formulation. The advantage of the proposed scheme is that it distributes the computational effort among all the transmitters in the cell and it does not require the presence of a central controller that takes all the decisions. Numerical results show that the proposed approach is an excellent solution to the resource allocation problem for cellular multi-carrier systems.

In this paper we present two algorithms for SNR estimation for transmissions over flat-fading time-varying channels. The first method exploits a polynomial approximation of the time-varying channel to derive a joint maximum likelihood estimator of the signal power and noise variance. The second technique is based on a subspace decomposition approach and exploits the inherent properties of the signal correlation matrix. Both algorithms can be implemented with affordable complexity and exhibit excellent performance.

We investigate the problem of channel estimation in an orthogonal frequency-division multiplexing (OFDM) system plagued by unknown narrowband interference (NBI). Such scenario arises in many practical contexts, including cellular applications and emerging spectrum sharing systems, where coexistence of different types of wireless services over the same frequency band may result into remarkable co-channel interference. Estimation algorithms devised for conventional OFDM transmissions are expected to suffer from significant performance degradation in the presence of NBI. To overcome this difficulty, in the present work we follow a novel pilot-aided approach where the interference power on each pilot subcarrier is treated as a nuisance parameter which is averaged out from the corresponding likelihood function. The latter is then maximized in an iterative fashion according to the expectation-maximization (EM) principle or by applying the Jacobi-Newton algorithm. The resulting schemes have affordable complexity and are inherently robust to NBI. Their accuracy is investigated by means of computer simulations and compared with the relevant Cramer- Rao bound.

Carrier frequency offset (CFO) in OFDM systems is normally estimated in two steps. The fractional part of the CFO is recovered first and the remaining ambiguity is subsequently resolved by detecting the integer frequency offset (IFO). Conventional IFO recovery algorithms for OFDM signals are sensitive to multipath distortions as they are derived without explicitly taking into account the frequency selectivity of the transmission channel. In this paper, we propose a novel scheme in which the channel response and IFO are jointly estimated using a maximum likelihood (ML) approach. In doing so we exploit one or more pilot blocks placed at the beginning of the frame and carrying known symbols. Since the complexity of the resulting ML algorithm may be relatively large, we also suggest suboptimal solutions unifying various earlier proposals. Computer simulations are used to demonstrate the superiority of the proposed schemes over existing alternatives. It is shown that excellent performance can be achieved with affordable complexity even in the presence of highly dispersive channels.

Cognitive radio employs spectrum sensing to facilitate coexistence of different communication systems over a same frequency band. A peculiar feature of this technology is the possible presence of interference within the signal bandwidth, which considerably complicates the synchronization task. This paper investigates the problem of carrier frequency estimation in an orthogonal frequency-division multiplexing (OFDM)-based cognitive radio system that operates in the presence of narrowband interference (NBI). Synchronization algorithms devised for conventional OFDM transmissions are expected to suffer from significant performance degradation when the received signal is plagued by NBI. To overcome this difficulty, we propose a novel scheme in which the carrier frequency offset (CFO) and interference power on each subcarrier are jointly estimated through maximum likelihood (ML) methods. In doing so we exploit two pilot blocks. The first one is composed of several repeated parts in the time-domain and provides a CFO estimate which may be affected by a certain residual ambiguity. The second block conveys a known pseudo-noise sequence in the frequency-domain and is used to resolve the ambiguity. The performance of the proposed algorithm is assessed by simulation in a scenario inspired by the IEEE 802.11g WLAN system in the presence of a Bluetooth interferer.

In this paper synchronization of uncoordinated OFDM spectrum sharing systems that operate in the presence of narrowband interference (NBI) is analyzed. Conventional synchronization algorithms based on correlation of synchronization preamble in time domain suffer from considerable performance degradation when NBI is present. To overcome this degradation we propose and analyze a method to detect NBI presence and to suppress its influence on synchronization before estimation of synchronization parameters is performed at the OFDM receiver. We focus on an OFDM WLAN system operating in license-exempt band and analyze synchronization performance when NBI is active. The proposed method does not introduce any additional preamble overhead with respect to existing WLAN preamble and requires only slightly higher computational complexity at the receiver. To illustrate effectiveness of the proposed method, several numerical examples are provided.

Efficient resource allocation in cellular OFDMA systems envisages the assignment of the number of sub-carriers and the relative transmission format on the basis of the experimented link quality. In this way, a higher number of sub-carriers with low per-carrier cshould be assigned to users at cell border. This strategy has already proved its efficiency in the single-cell scenario, while no study has been provided in the multi-cell scenario with reuse factor equal to one, i.e., in presence of severe interference conditions. In this paper we propose an optimum centralized radio resource allocator for the multi-cell scenario of an OFDMA cellular system which allows to highly outperform iterative decentralized allocation strategies based on local optimization criteria. The proposed scheme is characterized by huge implementation complexity, and, hence, it can be hardly implemented in the real world. However, it can help the system designer in catching the essence of interference limitations in OFDMA cellular systems, thus allowing the elaboration of efficient heuristic decentralized approaches. As an example, we prove that the sub-carrier transmission format adaptation is not useful in a multi-cell scenario. This is because users at cell border tends to consume the most of the resources (i.e., they are assigned the most of sub-carriers), thus producing interference for the neighbor cells over a large set of sub-carriers. Hence, since in this case neighbor cells are forced to use those (few) sub-carriers which experiment low interference, the diversity gain tends to be missed.

We propose a simple distributed radio resource allocation algorithm for an OFDMA cellular system, which aims at minimizing the overall transmitted power subject to a rate constraint for each user. In order to reduce the problem complexity we use a single modulation; simulations show that the resulting performance degradation is negligible when the number of users is high enough. Moreover, we propose a simple distributed heuristic that, by reducing the rate constraints, steers the multicell system towards an stable resource allocation. Results show that the proposed system exhibits a great robustness to the destructive effects of multiple access interference.

Packet scheduling and radio resource allocation in an OFDMA system pursue conflicting goals: the latter aims at achieving short-term efficiency, while scheduling aims at guaranteeing fairness among flows in the long term. We propose a scheduler-allocator architecture that successfully manages to integrate both goals in a loose cross-layer strategy. On each time frame the scheduling module selects a list of packets eligible for transmission, with the goal of achieving long-term fairness; the list is then passed to the radio resource allocator, which finds the best allocation given the current channel state. We evaluate the proposed scheme in a single cell scenario. Results point out that our scheme is able to guarantee throughput fairness among flows, while achieving an efficient allocation of radio resources.

In this paper we propose a novel dynamic subcarrier assignment algorithm for the downlink of an OFDMA system. In conventional schemes, subcarriers are assigned to the active users independently of the actual channel conditions. On the other hand, in a frequency-selective fading environment the users are characterized by different channel responses on each subcarrier. Thus, it makes sense to look for an efficient subcarrier allocation algorithm that fully exploits the inherent multiuser diversity and dynamically assigns a set of subcarriers to each user according to some optimality criterion. The proposed algorithm is based on a mini/max approach and aims at minimizing the error probability of the user with the most attenuated allocated subcarrier. Simulation indicate that the resulting scheme can achieve large improvements with respect to other existing alternatives in terms of either error probability or power consumption

This paper presents a new uplink beamforming technique applied to the WCDMA system, here called partial-adaptive beamforming. Instead of concentrating upon the performance of every single user, the radiation pattern aims to maximize the performance of a group of users and only a limited number of beams has to be formed. We present two algorithms that group effectively the users (grouping algorithm) and shape the radiation pattern (shaping algorithm) in order to enhance the performance of all users being part of the same group. The required hardware and the computational complexity of the proposed algorithms compared to the fully-adaptive beamforming technique is drastically reduced. Simulation results show a considerable gain respect to the fixed beam approach in terms of increased capacity and enlarged coverage. Furthermore the algorithms converge at a speed suitable to implement them following the dynamic evolution of the traffic within the sector.