In this paper, we consider a reconfigurable intelligent surface (RIS)-based transmitter operating in a high-frequency band that sends more symbols per channel use than the number of antennas at the receiver. For such a system, we present a comprehensive study on the design and performance of a neural network (NN)-based receiver. We evaluate the performance of the minimum mean squared error (MMSE) receiver, which is a linear receiver, the optimum maximum likelihood (ML) detector, and the NN-based receiver for recovering the transmitted symbols. The numerical simulations carried out for all the detectors considered prove that the classical far-field simplification leads to a performance underestimation in scenarios involving wide transmitters and high-frequency bands, as the one considered in this paper. Moreover, the results show that the NN-based receiver achieves a favorable trade-off between complexity and performance, outperforming the MMSE receiver while maintaining a lower complexity than the ML detector.

In this paper, we propose a reconfigurable intelligence surface (RIS)-aided location system. Through such a system, we exploit that near-field (NF) effects are still significant at long distances in mm-wave bands (i.e., up to the Fraunhofer distance) for large RISs. Specifically, we leverage the wavefront curvature, which depends on the impinging direction of arrival and the distance to the RIS, to locate emitting sources. As the location procedure cannot directly access the signals at a passive RIS, our method uses the signals reflected by the RIS that impinge on an antenna array located close to the RIS. By processing the signals the array collects, we estimate the spatial power density in a given region using a minimum variance distortionless response (MVDR) estimator. As shown in the paper, this technique can precisely estimate the positions of a set of sources within the NF region of the RIS. Even though the signals that are processed are those collected by the antenna array and not the signals at the RIS, the proposed system can provide a much higher location accuracy than a system with only the antenna array without RIS. We also extend the approach to scenarios with multiple RIS-array pairs.

Receive spatial modulation (RSM) has been proposed as an effective transmission technology for next-generation wireless communications systems because of its reduced complexity and low energy consumption at the receiver end. Compared to conventional multiple-input multiple-output (MIMO) transceivers, which require one radio-frequency (RF) chain and analog-to-digital converter (ADC) per receive antenna, RSM can be implemented with one amplitude detector per antenna and a single RF chain, with mild performance degradation in terms of spectral efficiency (SE). However, its implementation using zero-forcing precoders is inadequate in mmWave channels due to the lack of propagation scattering and, hence, rank deficiency in the channel matrix. This has been only partially solved in the past using receiver antenna selection (RAS), which requires additional receive antennas. On the other hand, using reconfigurable intelligent surfaces (RIS), which can effectively create scatter and enhance the channel matrix rank, is an affordable solution that can also cope with shadowing situations and the higher path losses associated with mmWave bands. We propose using a RIS in a single-user MIMO communication with RSM to improve the downlink transmission. This paper shows that RIS-assisted RSM communication is more than a good option in mm Wave channels in terms of bit error rate due to the multipath induced in the channel and the link budget boosting.

Within the framework of 5G, blockage effects occurring in the mmWave band are critical. Previous works describe the effects of blockages in isolated and multiple links for simple blocking objects, modeled with mathematical tools such as stochastic geometry and random shape theory. Our study uses these tools to characterize a scenario with $N$ links, including the possible correlation among them in terms of blocking for several models of blocking objects. We include numerical evaluations highlighting that assuming independence among the links' blocking elements is a too-brief simplification and does not accurately describe the real scenario. This paper also applies the formulation developed for the case of $N$ links to optimize the relay positioning in mmWave cells for coverage enhancement, that is, to minimize the communication failure probability. We also show that both link budget and blockages affect the optimum positioning of the relays as they are both essential for successful transmission.

This paper presents an estimation approach within the framework of uplink massive machine-type-communications (mMTC) that considers the energy limitations of the devices. We focus on a scenario where a group of sensors observe a set of parameters and send the measured information to a collector node (CN). The CN is responsible for estimating the original observations, which are spatially correlated and corrupted by measurement and quantization noise. Given the use of Gaussian sources, the minimum mean squared error (MSE) estimation is employed and, when considering temporal evolution, the use of Kalman filters is studied. Based on that, we propose a device selection strategy to reduce the number of active sensors and a quantization scheme with adjustable number of bits to minimize the overall payload. The set of selected sensors and quantization levels are, thus, designed to minimize the MSE. For a more realistic analysis, communication errors are also included by averaging the MSE over the error decoding probabilities. We evaluate the performance of our strategy in a practical mMTC system with synthetic and real databases. Simulation results show that the optimization of the payload and the set of active devices can reduce the power consumption without compromising the estimation accuracy.

Ultra dense networks (UDNs) allow for efficient spatial reuse of the spectrum, giving rise to substantial capacity and power gains. However, recent findings have demonstrated that the reduction of inter-site distances after a certain threshold, has a detrimental effect on the network performance. In such cases, interference mitigation strategies to counteract the effect of primarily the line of sight (LOS) interference are imperative. In this course, the present paper investigates a minimum coordination strategy among immediate neighbors. The adopted scheme realizes the interference mitigation objective, enhancing system capacity, while being implementable in practice. Building upon this scheme, the present paper provides tractable closed form expressions for the downlink (DL) ergodic capacity of a reference user under the examined coordination scheme. The analysis is performed for a scenario consisting of base stations (BSs) whose positions follow a Poisson point process of a given spatial density. The tractability and accuracy of the derived expressions renders them ideal for quantifying the performance of additional coordination strategies and for revealing trends in complex optimization problems. Thus, constituting a valuable tool for network operators, toward assessing different interference mitigation techniques and coordination schemes in UDNs.

This paper presents long-term resource provisioning schemes for dense multi-cell OFDMA-based networks, where multiple cells with possibly overlapping coverage areas compete for the same set of resources. The optimization is done over the long term, being independent of the specific users connected to each cell but dynamic enough to follow significant variations of the traffic load. In this sense, we focus on the average resource utilization (RU) and propose schemes to minimize the maximum RU of all cells. Firstly, we consider orthogonal resource usage among cells. Optimal closed-form expressions for the long-term resource provisioning are derived for this case. Secondly, we assume that resources can be reused at non-overlapping cells. In this case, the resource provisioning is solved in two steps: i) the number of resources required per cell is obtained by discretizing the optimal solution of a convex problem, and then ii) the specific resources to be utilized by each cell are determined by using graph coloring. In contrast to previous works, graph coloring can be applied to get an implementable solution for any condition of the cell loads. Simulation results show a significant reduction of the maximum RU, which translates into an increase of the served traffic and a reduction of the packet delay, as compared to static resource provisioning schemes.

We propose a novel stochastic radio resource allocation strategy for the uplink that achieves long-term fairness in terms of similar bitrates considering backhaul and air-interface capacity limitations. We focus on a single cell scenario based on WCDMA technology. We propose to use a maximin criteria to introduce fairness among the different users' throughputs. An stochastic approximation is implemented to obtain an online algorithm where the Lagrange multipliers are estimated at each scheduling period. Our results show that the proposed scheme achieves higher fairness among the users and, in some cases, a higher sum-rate compared with the well-known proportional fair scheduler.

In this paper, we present an approach to solve the nonconvex optimization problem that arises when designing the transmit covariance matrices in multiuser multiple-input multiple-output (MIMO) broadcast networks implementing simultaneous wireless information and power transfer (SWIPT). The MIMO SWIPT design is formulated as a nonconvex optimization problem in which system sum rate is optimized considering per-user harvesting constraints. Two different approaches are proposed. The first approach is based on a classical gradient-based method for constrained optimization. The second approach is based on difference of convex (DC) programming. The idea behind this approach is to obtain a convex function that approximates the nonconvex objective and, then, solve a series of convex subproblems that, eventually, will provide a (locally) optimum solution of the general nonconvex problem. The solution obtained from the proposed approach is compared to the classical block-diagonalization (BD) strategy, typically used to solve the nonconvex multiuser MIMO network by forcing no inter-user interference. Simulation results show that the proposed approach improves both the system sum rate and the power harvested by users simultaneously. In terms of computational time, the proposed DC programming outperforms the classical gradient methods.

In this paper, we focus on a broadcast multiuser multiple-input multiple-output (MIMO) system where we consider that some terminals harvest power and, thus, recharge their batteries through wireless power transfer from the transmitter, while others are simultaneously being served with data transmission. The weighted sum-rate of the terminals that are receiving information data is considered as the optimization policy where minimum energy harvesting constraints are taken into account. We propose a procedure for managing the minimum energy to be harvested by the terminals considering the effect in the target system performance.

We consider a system where multiple users are connected to a small cell base station enhanced with computational capabilities. Instead of doing the computation locally at the handset, the users offload the computation of full applications or pieces of code to the small cell base station. In this scenario, this paper provides a strategy to allocate the uplink, downlink, and remote computational resources. The goal is to improve the quality of experience of the users, while achieving energy savings with respect to the case in which the applications run locally at the mobile terminals. More specifically, we focus on minimizing a cost function that depends on the latencies experienced by the users and provide an algorithm to minimize the latency experienced by the worst case user, under a target energy saving constraint per user.

In this paper we address the problem of designing a user selection algorithm in a multiuser multiple-input multiple-output downlink scenario. Linear precoding is adopted at the transmitter and, thus, the number of users to be served at each particular scheduling period is limited by the number of available transmit and receive antennas. The adopted scheduling criterion for the user selection is based on proportional fair criterion. The novelty relies on the fact that the users are considered to have a finite battery capacity and a harvesting source that allows them to recharge their batteries dynamically. The proposed user scheduler mechanism exploits not only the channel dynamics but also the battery fluctuations. We analyze how the intensity of the energy arrivals impacts the performance of the user scheduler. Simulations results show that the average long-term rates approach the requested quality of service if the scheduler controls the amount of energy that is being used by the terminals at each scheduling period.

This paper addresses the problem of resource allocation and precoder design in a multiuser MIMO broadcast system where the terminals are battery-powered devices provided with energy harvesting capabilities. Energy harvesting is a promising technology based on which it is possible to recharge the battery of the terminals using energy collected from the environment. Models for the power consumption of the front-end and decoding stages are discussed and included in the design of the proposed scheme. In addition, the information concerning the battery level plays an explicit role and has an impact on the design of our proposed allocation strategy. Sum-rate maximization is considered as optimization policy and energy-related constraints are taken into account explicitly in the resource allocation in order to increase the lifetime of the batteries. In the first part of the paper, we consider the transmitter to have perfect channel state information (CSI) and battery knowledge, assuming an ideal feedback link. Then, a robust design based on imperfect channel information at the transmitter is studied and its effect on the energy consumed by the terminals is analyzed. Finally, an extended robust approach considering also imperfect battery knowledge (quantized battery status) at the transmitter is also addressed. Simulation results show that our proposed technique not only improves the usage of the batteries when compared with other traditional allocation policies, but also enhances the average data rate.

This paper presents the FPGA design of an interference-aware digital front end tailored for heterogeneous multi-cell LTE-based systems. A resource-optimized RTL architecture has been implemented and validated under realistic operating conditions using the GEDOMIS®testbed. The parallelization and concurrent resource utilization of the joint synchronization and interference detection processing blocks is covered with low-level details.

This paper addresses the problem of precoder design in a MIMO broadcast scenario where the terminals are battery-powered devices provided with energy harvesting capabilities. Models for the power consumption of the RF and decoding stages are discussed and included in the design of the proposed scheme. Sum-rate maximization is taken as optimization policy and energy-related constraints are considered to increase the duration of the batteries and, thus, the network lifetime. Simulation results show that not only the usage of battery is improved when compared with other traditional allocation policies, but also the average data rate is enhanced.

In this paper, we focus on a broadcast multiuser multiple-input multiple-output (MIMO) system in a network with short links. In this scenario we consider that some terminals harvest power and, thus, recharge their batteries, through wireless power transfer from the transmitter while others are simultaneously being served with data transmission. We assume that the nodes are battery powered devices and, consequently, such solution provides a convenient energy supply. The sum-rate for the users being served with data is considered as the optimization policy where power harvesting per user constraints are taken into account. We provide the optimal structure of the resulting transmit covariance matrices and precoders for the users in the general case (i.e., in scenarios where both types of nodes are present in the network) and we also study the case where only harvesting nodes are to be served. Finally, we analyze and characterize the fundamental tradeoff between the data transmission sum-rate and the powers harvested by the network nodes using the concept of the rate-energy (R-E) region.

An energy harvesting transmitter operating in a point-to-point link through a discrete time fading channel is considered in this paper. Assuming non-causal knowledge of the harvested energy and channel state, the resource allocation that maximizes the mutual information is investigated by considering both the radiated power and the circuitry power consumption. A resource allocation strategy that asymptotically maximizes the mutual information along N independent channel accesses is derived. The Boxed Water-Flowing graphical interpretation is presented, which intuitively depicts the asymptotically optimal resource allocation.

This paper addresses the problem of resource allocation in a broadcast scenario with frequency selective channels where the terminals are considered to be batterypowered devices provided with energy harvesting capabilities. Models for the energy consumption of the RF and decoding stages are discussed and included in the design of the proposed resource allocation policies. The allocation is performed discretely over subcarrier, bit loading, and power by means of practical greedy-like algorithms and considering energy-related constraints in order to increase the duration of the batteries and, thus, the network lifetime. Simulation results show that not only the usage of battery is improved when compared with other traditional allocation policies, but also the average bit-rate is enhanced.

This work deals with the problem of radio resource allocation in a point-to-point multiple-input multiple-output (MIMO) communications system with feedback of channel state information. A total pool of radio resources (power and transmission time) is allocated among training, feedback, and data transmission phases in order to maximize system performance in terms of throughput. The energy consumption associated to the base band signal processing and decoding is also evaluated. This resource allocation problem is studied analytically in a two-way time division duplex (TDD) communications scenario with time correlated channels, where the effect of feedback errors and feedback delay is also taken into account.

In this paper we present the FPGA implementation of a multi-antenna Orthogonal Frequency-Division Multiplexing (OFDM) transmitter and receiver featuring a closed-loop (CL) communication scheme. The PHY-layer development follows the specifications of the mobile WiMAX standard, whereas the limited MAC functionality is using custom specifications. In the implemented CL scheme, the transmitter applies a Transmit Antenna Selection (TAS) decision based on the instantaneous Channel State Information (CSI), which is calculated at the receiver and fed back to the transmitter. The RTL implementation addresses the challenge of efficiently accommodating two transmission schemes with optimal reuse of FPGA resources. The adaptive subcarrier allocation can be thoroughly evaluated only when prototyped in a real-time platform, such as the one described in this paper. The results verified that the CL system is performing better than its open-loop counterpart.

This work considers a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing based multiuser broadcast system with precoding at the transmitter and feedback of channel state information. A general framework is presented for the transceiver design, and also for the design of the feedback link based on the quantization of the users' MIMO channel Gram matrices. The proposed design of the feedback link exploits the correlation of the channel response in the frequency domain due to the finite length of the channel time impulse responses to outperform other schemes based on feedback of the per carrier frequency responses. The transceiver design framework is based on a unitary linear transformation applied at the receivers which allows the computation of equivalent triangular channel response matrices at the transmitter. An analytic study of the error propagation due to the channel quantization in the feedback link and the computation of the equivalent triangular channel matrices is also performed. Based on the previous concepts, all the usual transceiver design criteria can be applied within this framework, and the particular case of a space-frequency precoder for robust mean square error minimization is derived as an example. Finally, the benefits of the proposed strategy are evaluated by means of numerical simulations and compared to other existing techniques.

This paper addresses the problem of resource allocation in a multiple access channel where several mobile terminals (MT's) are transmitting simultaneously to a single receiving base station, all of them with multiple antennas and exploiting beamforming. A multi-carrier modulation is assumed, either orthogonal frequency division multiplexing (OFDM) or filter bank multi-carrier (FBMC). The main advantage of FBMC is that the spectrum of the sub-channels is much more concentrated than for OFDM. This property is explicitly exploited in this paper in order to evaluate an asynchronous network where the transmissions from different MT's are not aligned in time. This implies that a frequency guard between groups of carriers assigned to different users has to be included, being larger for OFDM than for FBMC. The improvement in performance due to this fact is evaluated by means of the rate that can be achieved by dynamically allocating carriers to users and by calculating the power and bit loading to be applied.

This paper proposes a differential quantization strategy to be used in the feedback link of a multi-input-multi-output (MIMO) multiuser broadcast communication system. This strategy is based on an algorithm presented for single user MIMO, and is applied to broadcast systems where the transmitter design is based on block diagonalization, i.e., the transmissions are optimized so as not to produce multiuser interference at the receive terminals, which are using more than one antenna each. The algorithm is applied to the channel Gram matrices exploiting geodesic curves and the intrinsic geometry of positive definite Hermitian matrices. Furthermore, the differential nature of the algorithm exploits the correlation in time present in real channels. Simulation results in the paper show that the proposed algorithm outperforms other techniques based on the direct quantization of the channel matrix.

The focus of this paper is on the precoding and feedback strategies for a radio backhaul link between an access base station (ABS) and the hub base station (HBS) in the presence of mobile stations (MSs). In this paper it is assumed that the access and the backhaul links are simultaneous and use the same frequency band. Two feedback links are exploited at the ABS in order to optimize the communication through the radio-backhaul link: the MSs feed back the channel state information (CSI) of the channels between the ABS and the MSs, and the HBS feeds back the statistics of the received interference. Using this information, a transmission strategy is proposed for the ABS in order to transmit to the HBS without interfering to the MSs and at the same time minimizing the effect of the interference received at the HBS.

This paper proposes a differential quantization strategy to be used in the feedback link of a multiple-input multiple-output (MIMO) communication system. This algorithm is applied to the channel Gram matrix using geodesic curves and exploiting the intrinsic geometry of positive definite Hermitian matrices. It also exploits the temporal correlation of the channel, and follows on average the gradient of the cost function associated to the transmitter design criterion. A full description of the algorithm, including the computational cost and a numerical analysis of the effect of delays and errors in the feedback link is presented. Simulation results show that the proposed algorithm improves other techniques based on the direct quantization of the channel response matrix or the quantization of the subspace spanned by the strongest eigenmodes of the MIMO channel, i.e., Grassmannian based techniques. The main drawback of Grassmannian based algorithms is that the transmitter is constrained to apply a uniform power allocation among spatial transmission modes, which is not forced in the algorithm proposed in this paper.

This paper proposes a differential quantization strategy to be used in the feedback link of a multi-input-multi-output (MIMO) communication system. This algorithm is applied to the channel correlation matrix exploiting geodesic curves and the intrinsic geometry of positive definite Hermitian matrices. Simulation results in the paper show that the proposed algorithm improves other techniques based on the direct quantization of the channel response matrix or the quantization of the subspace spanned by the strongest eigenmodes of the MIMO channel, i.e., Grassmannian based techniques. The main drawback of Grassmananian based algorithms is that the transmitter is constrained to apply a uniform power allocation among eigenmodes, which is not forced in the algorithm proposed in this paper.

This paper addresses the problem of estimating and correcting the residual carrier frequency offset in a multi-input-multi- output (MIMO) system employing orthogonal frequency division multiplexing (OFDM). The presented technique is based on the maximum likelihood criterion and exploits the pilot carriers of the OFDM modulation by assuming that no inter-carrier interference is present. Some implementation aspects of the estimator are also discussed, presenting two alternatives: the grid search and the truncated discrete Fourier transform (DFT) approaches. The evaluation of the estimator is performed by deriving and comparing the corresponding Cramer-Rao bound with the mean square error. Additional simulations are provided in terms of symbol error rate for the case of applying orthogonal space-time block coding to the data carriers.

In this paper, we study the design of the transmitter in the downlink of a multiuser and multiantenna wireless communications system, considering the realistic scenario where only an imperfect estimate of the actual channel is available at both communication ends. Precisely, the actual channel is assumed to be inside an uncertainty region around the channel estimate, which models the imperfections of the channel knowledge that may arise from, e.g., estimation Gaussian errors, quantization effects, or combinations of both sources of errors. In this context, our objective is to design a robust power allocation among the information symbols that are to be sent to the users such that the total transmitted power is minimized, while maintaining the necessary quality of service to obtain reliable communication links between the base station and the users for any possible realization of the actual channel inside the uncertainty region. This robust power allocation is obtained as the solution to a convex optimization problem, which, in general, can be numerically solved in a very efficient way, and even for a particular case of the uncertainty region, a quasi-closed form solution can be found. Finally, the goodness of the robust proposed transmission scheme is presented through numerical results. Robust designs, imperfect CSI, multiantenna systems, broadcast channel, convex optimization.

In this letter, we study spatial Tomlinson-Harashima precoding (STHP) from an information theoretic point of view encompassing both the single and the multiuser scenarios. Since the performance of any communications preceding scheme relies on the quality of the channel state information (CSI) that is made available at the transmitter side, in this work we focus our attention on the analysis of the robustness of STHP in terms of rate loss when the CSI is imperfect. Precisely, we present a robust design of the set of moduli used in STHP and, consequently, of the corresponding power allocation, that maximizes the achievable rates for the worst-case errors in the CSI in the small errors regime.

We present the optimum design of a linear transmitter for a multi-input multi-output communication system when the input data consists of two QPSK streams and the receiver performs maximum likelihood detection. The transmitter design is optimal in the sense that it minimizes the worst-case pairwise error probability. We prove that the resulting linear transmitter constructs, as a function of the two input QPSK streams, a new signal constellation. Moreover, we show that this new signal constellation is such that the minimum distance among the points of the received constellation is maximized and, indirectly, the number of neighbors at minimum distance is also maximized.

We address the problem of the design of a transmitter in a multi-input-multi-output (MIMO) space diversity system. We consider a design which is robust to errors in the channel estimate available at the transmitter. The design is based on the minimization of the necessary transmit power while guaranteeing a minimum and specified quality of service (QoS) in terms of signal to noise ratio (SNR) with a certain probability. According to this objective, the transmitter exploits all the eigenmodes of the MIMO channel estimate, distributing the transmit power in a robust way among the channel modes by using orthonormal temporal signatures. In order to guarantee a full rate transmission, the orthonormal temporal signatures designed for real constellations in orthogonal space-time block codes (OSTBC) are used, leading to a system structure based on a combination of OSTBC and orthogonal weighted beamforming.

Orthogonal frequency division multiplexing (OFDM) has been recently established for several systems such as HiperLAN/2 and Digital video/audio broadcasting, due the easy implementation of the modulator/demodulator and the equalizer. Moreover, also increasing interest is currently being put on multiple-input multiple-output (MIMO) channels, based on the use of antenna arrays at both the transmitter and the receiver. Here, we propose two joint beamforming strategies of low computational load for systems combining OFDM and MIMO. The ultimate objective is the maximization of the signal-to-noise and interference ratio (SNIR) over the carriers subject to a total transmit power constraint. Specifically, the maximization of the harmonic SNIR mean and the minimum SNIR over the subcarriers are proposed. The asymptotic behavior of the proposed methods is analyzed to provide a complete comparative and general view of the most relevant and already known transmit power allocation strategies. Finally, a theoretical analysis of the performance degradation of these techniques is carried out for the case in which the channel state information (CSI) is not perfect. Monte Carlo simulation results for the system bit-error rate and performance degradation with imperfect CSI are provided.

This paper addresses the problem of the spatial scheduling of users in a cell for simultaneous downlink transmission from a base statimultiple antennas on (BS) having multiple antennas under a perspective of joint Physical and Medium Access Control (PHY-MAC) design. First of all, we compute the transmit beamvectors for each group according to a zero forcing (ZF) criterion, which gives a simple closed-form solution. We show first that it is equivalent to the minimization of the maximum bit error rate (BER). In this paper, the main contribution lies on the resolution of the NP-complete combinatorial problem that comes up as a cost function if we want to minimize the total transmit power. The solution of the NP-complete problem is performed by the stochastic technique simulated annealing (SA). Additionally, we present two heuristic algorithms that may enable a real-time implementation of this scheduling approach.

In this paper we address the design of a multiple transmit antenna system in which the channel state information (CSI) at the transmitter is not perfect. Two different approaches are analyzed: one based on the minimization of the mean square error (MMSE) and the other based on the application of the maximum likelihood sequence estimation (MLSE). In both cases a Bayesian criterion is used in order to take into account the error between the CSI and the real channel. Finally, some simulation results and conclusions are provided, showing the gain of these approaches when the error between the CSI and the real channel is either Gaussian or uniform, where this last case corresponds to a quantization of the channel time response in order to transmit the CSI through a feedback channel from the receiver to the transmitter.

This paper addresses the joint design of transmit and receive beamvectors for a multicarrier MIMO channel within the general and powerful framework of convex optimization theory. From this perspective, a great span of design criteria can be easily accommodated and efficiently solved even though closed-form expressions may not be available. Among other criteria, we consider the minimization of the average bit error rate (BER) and also of the maximum BER among all carriers for a given signal constellation. We show how to include additional constraints to control the peak-to-average ratio (PAR) in the system design.

In this paper we address the problem of the design of a power allocation strategy for an orthogonal frequency division multiplexing (OFDM) system. We assume no spatial diversity at the transmitter and receiver, that is, a single-input-single-output (SISO) channel is considered. The goal is to allocate all the available power at the transmitter among the different carriers according to a minimum mean bit error rate (BER) criterion. When designing this strategy, we assume that channel state information (CSI) is available at the transmitter. Two different approaches are taken. The first one considers that the channel estimates are perfect, whereas the second approach assumes that there is some estimation error, and therefore, this approach is robust in nature. Finally, we propose an efficient algorithm for computing the power allocated to the carriers for both approaches, and show some simulation results in order to compare them.

Adaptive antenna arrays can be used to improve the performance of Orthogonal Frequency Division Multiplexing systems. They are usually applied in the frequency domain, after the FFT demodulation. In this paper, the selected algorithms are applied in time domain. A blind method is firstly proposed for the maximization of the temporal Signal to Noise plus Interference Ratio (SNIR) in frequency selective channels exploiting the temporal redundancy introduced by the cyclic prefix (CP). This algorithm may cope well with non-stationary interference that appears during the burst transmission. This blind technique is extended so as to take advantage of the preamble that precedes the useful data in the HIPERLAN/2 (HL/2) specification. Simulations show that this latter semiblind approach improves the overall performance of the system with respect to the traditional Sample Matrix Inversion (SMI) algorithm even in interference stationary scenarios. Additionally, a moving pilot structure is proposed to perform more accurate channel estimation, which further improves the performance of the semiblind technique.

Orthogonal Frequency Division Multiplexing (OFDM) has been recently established for several systems such as HIPERLAN/2 and Digital Video and Audio Broadcasting. The success of this modulation is due to the easy implementati9n of the modulator and demodulator, including the equalization process. Moreover, in last years also increasing interest has been put on the study of Multi-Input-Multi-Output (MIMO) channels, based on the use of arrays of antennas at both the transmitter and the receiver. In this paper we propose two new and low computational cost joint beamforming strategies for communication systems that combine OFDM and MIMO channels. An asymptotic analysis of the proposed methods is carried out, providing a complete comparative study among other classical strategies. To demonstrate the potential of the proposed techniques, we present MonteCarlo simulation results of the system BER and make comparisons with other methods.