This paper leverages the potential of Large Intelligent Surfaces (LIS) for radio sensing in 6G wireless networks. By taking advantage of arbitrary communication signals occurring in the scenario, we apply direct processing to the output signal from the LIS to obtain a radio map that describes the physical presence of passive devices (scatterers, humans) which act as virtual sources due to the communication signal reflections. We then assess the usage of machine learning and computer vision methods including clustering, template matching and component labeling to extract meaningful information from these radio maps. As an exemplary use case, we evaluate this method for passive multi-human detection in an indoor setting. The results show that the presented method has high application potential as we are able to detect around 98% of humans passively even in quite unfavorable Signal-to-Noise Ratio (SNR) conditions.

Due to a production error in[1], there is an error with the definition of the real and imaginary part operators. They should be defined as Re{} and Im{}. However, the {} are missing in all the equations. We apologize for this error.

This paper evaluates the performance of multi-user massive multiple-input multiple-output (MIMO) systems in which the base station is equipped with a dynamic metasurface antenna (DMA). Due to the physical implementation of DMAs, conventional models widely-used in MIMO are no longer valid, and electromagnetic phenomena such as mutual coupling, insertion losses and reflections inside the waveguides need to be considered. Hence, starting from a recently proposed electromagnetic model for DMAs, we formulate a zero-forcing optimization problem, yielding an unconstrained objective function with known gradient. The performance is compared with that of full-digital and hybrid massive MIMO, focusing on the impact of insertion losses and mutual coupling.

While initial beam alignment (BA) in millimeter-wave networks has been thoroughly investigated, most research assumes a simplified terminal model based on uniform linear/planar arrays with isotropic antennas. Devices with non-isotropic antenna elements need multiple panels to provide good spherical coverage, and exhaustive search over all beams of all the panels leads to unacceptable overhead. This paper proposes a location- and orientation-aware solution that manages the initial BA for multi-panel devices. We present three different neural network structures that provide efficient BA with a wide range of training dataset sizes, complexity, and feedback message sizes. Our proposed methods outperform the generalized inverse fingerprinting and hierarchical panel-beam selection methods for two considered edge and edge-face antenna placement designs.

We study the design of receivers in extra-large scale MIMO (XL-MIMO) systems, i.e. systems in which the base station is equipped with an antenna array of extremely large dimensions. While XL-MIMO can significantly increase the system’s spectral efficiency, they present two important challenges. One is the increased computational cost of multi-antenna processing. The second one is the presence of spatial non-stationarities in the channel response, which imply that the mean energy of a given user’s signal varies across the array. Such non-stationarities limit the performance of the system. In this paper, we propose a distributed receiver for such an XL-MIMO system that can address both challenges. Based on variational message passing (VMP), we propose a set of receiver options providing a range of complexity-performance characteristics to adapt to different requirements. Furthermore, we distribute the processing into local processing units (LPU), that can perform most of the complex processing in parallel, before sharing their outcome with a central processing unit (CPU). Our designs are specifically tailored to exploit the spatial non-stationarities and require lower computations than linear receivers. Our simulation study, performed with a channel model accounting for the special characteristics of XL-MIMO channels, confirms the superior performance of our proposals compared to the state of the art methods.

Dynamic metasurface antennas (DMAs) arise as a promising technology in the field of massive multiple-input multiple-output (mMIMO) systems, offering the possibility of integrating a large number of antennas in a limited —and potentially large— aperture while keeping the required number of radio-frequency (RF) chains under control. Although envisioned as practical realizations of mMIMO systems, DMAs represent a new paradigm in the design of signal processing techniques (such as beamforming) due to the constraints inherent to their physical implementation, for which no complete models are available yet. In this work, we propose a complete and electromagnetic-compliant narrowband communication model for a generic DMA based system. Specifically, the model accounts for: i) the wave propagation and reflections throughout the waveguides that feed the antenna elements, ii) the mutual coupling both through the air and the waveguides, and iii) the insertion losses. Also, we integrate the electromagnetic model in the conventional digital communication model, providing a complete and useful framework to design and characterize the performance of these systems. Finally, the accuracy of the model is verified through full-wave simulations.

Position-aided beam selection methods have been shown to be an effective approach to achieve high beamforming gain while limiting the overhead and latency of initial access in millimeter wave (mmWave) communications. Most research in the area, however, has focused on vehicular applications, where the orientation of the user terminal (UT) is mostly fixed at each position of the environment. This paper proposes a location- and orientation-based beam selection method to enable context information (CI)-based beam alignment in applications where the UT can take arbitrary orientation at each location. We propose three different network structures, with different amounts of trainable parameters that can be used with different training dataset sizes. A professional 3-dimensional ray tracing tool is used to generate datasets for an IEEE standard indoor scenario. Numerical results show the proposed networks outperform a CI-aided benchmark such as the generalized inverse fingerprinting (GIFP) method as well as hierarchical beam search as a non-CI-based approach. Moreover, compared to the GIFP method, the proposed deep learning-based beam selection shows higher robustness to different line-of-sight blockage probability in the training and test datasets and lower sensitivity to inaccuracies in the position and orientation information.

This paper proposes a communication model for multiuser multiple-input multiple-output (MIMO) systems based on large intelligent surfaces (LIS), where the LIS is modeled as a collection of tightly packed antenna elements. The LIS system is first represented in a circuital way, obtaining expressions for the radiated and received powers, as well as for the coupling between the distinct elements. Then, this circuital model is used to characterize the channel in a line-of-sight propagation scenario, rendering the basis for the analysis and design of MIMO systems. Due to the particular properties of LIS, the model accounts for superdirectivity and mutual coupling effects along with near field propagation, necessary in those situations where the array dimension becomes very large. Finally, with the proposed model, the matched filter transmitter and the weighted minimum mean square error precoding are derived under both realistic constraints: limited radiated power and maximum ohmic losses.

In a wireless system with Intelligent Reflective Surfaces (IRS) containing many passive elements, we consider the problem of channel estimation. All the links from the transmitter to the receiver via each IRS elements (or groups) are estimated. We show that the estimation performance are dependent on the setting of the IRS, and design an optimal channel estimation scheme where the IRS elements follow an optimal series of activation patterns. The optimal design is guided by results for the minimum variance unbiased estimation. The IRS setting during the channel estimation period mimics the discrete Fourier transforms. We show theoretically and with simulations that the estimation variance is one order smaller compared to existing methods with on/off IRS activation patterns as proposed in the literature.

Location-aided beam alignment methods exploit the user location and prior knowledge of the propagation environment to identify the beam directions that are more likely to maximize the beamforming gain, allowing for a reduction of the beam training overhead. They have been especially popular for vehicle to everything (V2X) applications where the receive array orientation is approximately constant for each considered location, but are not directly applicable to pedestrian applications with arbitrary orientation of the user handset. This paper proposes a deep neural network based beam selection method that leverages position and orientation of the receiver to recommend a shortlist of the best beam pairs, thus significantly reducing the alignment overhead. Moreover, we use multi-labeled classification to not only capture the beam pair with highest received strength but also enrich the neural network with information of alternative beam pairs with high received signal strength, providing robustness against blockage. Simulation results show the better performance of the proposed method compared to a generalization of the inverse fingerprinting algorithm in terms of the misalignment and outage probabilities.

In this paper, we propose a decentralized robust transmit beamforming and power splitting design for multi-relay assisted simultaneous wireless information and power transfer (SWIPT) systems with imperfect channel state information (CSI). Under the constraints of target rates at relays and users, and energy harvesting (EH) at users, a total transmit power minimization problem is proposed by considering the non-linear EH model. We first reformulate this nonconvex problem into an semidefinite program (SDP) by semidefinite relaxation (SDR) and S-procedure, and then propose an alternating direction method of multipliers (ADMM)-based decentralized algorithm. Numerous simulations are provided to demonstrate the effectiveness of the proposed design. Results show that the proposed design outperforms traditional design in terms of quality of service (QoS) and the feasibility of practical implementation.

The future connectivity landscape, and notably, the 5G wireless systems will feature Ultra-Reliable Low Latency Communication (URLLC). The coupling of high reliability and low latency requirements in URLLC use cases makes the wireless access design very challenging, in terms of both the protocol design and of the associated transmission techniques. This paper aims to provide a broad perspective on the fundamental tradeoffs in URLLC, as well as the principles used in building access protocols. Two specific technologies are considered in the context of URLLC: massive MIMO and multi-connectivity, also termed interface diversity. This paper also touches on the importance of the proper statistical methodology for designing and assessing extremely high-reliability levels.

Millimeter-wave (mmWave) bands will play an important role in 5G wireless systems. The system performance can be assessed by using models from stochastic geometry that cater for the directivity in the desired signal transmissions as well as the interference, and by calculating the signal-to-interference-plus-noise ratio (SINR) coverage. Nonetheless, the accuracy of the existing coverage expressions derived through stochastic geometry may be questioned, as it is not clear whether they would capture the impact of the detailed mmWave channel and antenna features. In this paper, we propose an SINR coverage analysis framework that includes realistic channel model and antenna element radiation patterns. We introduce and estimate two parameters, aligned gain and misaligned gain, associated with the desired signal beam and the interfering signal beam, respectively. The distributions of these gains are used to determine the distribution of the SINR which is compared with the corresponding SINR coverage, calculated through the system-level simulations. The results show that both aligned and misaligned gains can be modeled as exponential-logarithmically distributed random variables with the highest accuracy, and can further be approximated as exponentially distributed random variables with reasonable accuracy. These approximations can be used as a tool to evaluate the system-level performance of various 5G connectivity scenarios in the mmWave band.

Machine-type communication requires rethinking of the structure of short packets due to the coding limitations and the significant role of the control information. In ultra-reliable low-latency communication (URLLC), it is crucial to optimally use the limited degrees of freedom (DoFs) to send data and control information. We consider a URLLC model for short packet transmission with acknow 1-edgement (ACK). We compare the detection/decoding performance of two short packet structures: (1) time-multiplexed detection sequence and data; and (2) structure in which both packet detection and data decoding use all DoFs. Specifically, as an instance of the second structure we use superimposed sequences for detection and data. We derive the probabilities of false alarm and misdetection for an AWGN channel and numerically minimize the packet error probability (PER), showing that for delay-constrained data and ACK exchange, there is a tradeoff between the resources spent for detection and decoding. We show that the optimal PER for the superimposed structure is achieved for higher detection overhead. For this reason, the PER is also higher than in the preamble case. However, the superimposed structure is advantageous due to its flexibility to achieve optimal operation without the need to use multiple codebooks.

We present a novel access protocol for crowd scenarios in massive multiple-input multiple-output (MIMO) systems. Crowd scenarios are characterized by a large number of users with intermittent access behavior, whereas orthogonal scheduling is infeasible. In such scenarios, random access is a natural choice. The proposed access protocol relies on two essential properties of a massive MIMO system, namely, asymptotic orthogonality between user channels and asymptotic invariance of channel powers. Signal processing techniques that take advantage of these properties allow us to view a set of contaminated pilot signals as a graph code on which iterative belief propagation can be performed. This makes it possible to decontaminate pilot signals and increase the throughput of the system. Numerical evaluations show that the proposed access protocol increases the throughput by 36%, when there are 400 antennas at the base station, compared to the conventional method of slotted Additive Links On-line Hawaii Area. With 1024 antennas, the throughput is increased by 85%.

Low cost and high efficiency, defined as energy efficiency (EE) and spectral efficiency (SE), have raised more and more attention in the fifth generation (5G) communication systems due to steadily rising hardware cost, energy consumption, and mobile traffic. This paper studies the hybrid architecture of multiuser massive MIMO systems, where the digital domain utilizes the zero‐forcing (ZF) precoding scheme and the analog domain uses discrete Fourier transform (DFT) processing that significantly reduces hardware cost and energy consumption. We derive analytical expressions on the total achievable SE and EE, as well as offering insight into some engineering parameters in the system performance. Our aim is to achieve low cost and high efficiency massive MIMO system, with constraints on the overall transmit power, the number of users, and the number of radio frequency (RF) chains. Results exhibit that the total achievable SE of the hybrid architectures with DFT precessing is inferior to the full digital architectures and hybrid architectures with the ideal phase shifters, but the performance attenuation can be compensated by providing the more input SNR and higher number of RF chains. Moreover, we find that the total achievable EE of hybrid architectures with DFT precessing outperforms other massive MIMO architectures that include a full digital implementation, ideal phase shifters, and a switched network.

Millimeter-wave (mmWave) bands have been attracting growing attention as a possible candidate for next- generation cellular networks, since the available spectrum is orders of magnitude larger than in current cellular allocations. To precisely design mmWave systems, it is important to examine mmWave interference and SIR coverage under large-scale deployments. For this purpose, we apply an accurate mmWave channel model, derived from experiments, into an analytical framework based on stochastic geometry. In this way we obtain an analytical expression for the SIR coverage probability in mmWave cellular networks.

In this article we propose an iterative training scheme that approximates optimal beamforming between two transceivers equipped with hybrid digital-analog antenna arrays. Inspired by methods proposed for digital arrays that exploit algebraic power iterations, the proposed training procedure is based on a series of alternate (ping-pong) transmissions between the two devices over a reciprocal channel. During the transmissions, the devices update their digital beamformers by conjugation and normalization operations on the received digital signal, while the analog beamformers are progressively updated by a simple “beam split and drop” strategy that tracks the directions from which signals with largest magnitude are being received. The resulting scheme has minimal computational complexity and converges with only a handful of iterations. As shown in the numerical assessment, the method approximates the top singular mode of the channel, hence performing very closely to optimal beamforming.

The massive multiple-input multiple-output (MIMO) technology has great potential to manage the rapid growth of wireless data traffic. Massive MIMO achieves tremendous spectral efficiency by spatial multiplexing many tens of user equipments (UEs). These gains are only achieved in practice if many more UEs can connect efficiently to the network than today. As the number of UEs increases, while each UE intermittently accesses the network, the random access functionality becomes essential to share the limited number of pilots among the UEs. In this paper, we revisit the random access problem in the Massive MIMO context and develop a reengineered protocol, termedstrongest-user collision resolution(SUCRe). An accessing UE asks for a dedicated pilot by sending an uncoordinated random access pilot, with a risk that other UEs send the same pilot. The favorable propagation of massive MIMO channels is utilized to enable distributed collision detection at each UE, thereby determining the strength of the contenders’ signals and deciding to repeat the pilot if the UE judges that its signal at the receiver is the strongest. The SUCRe protocol resolves the vast majority of all pilot collisions in crowded urban scenarios and continues to admit UEs efficiently in overloaded networks.

A massive MIMO system, represented by a base station with hundreds of antennas, is capable of spatially multiplexing many devices and thus naturally suited to serve dense crowds of wireless devices in emerging applications, such as machine-type communications. Crowd scenarios pose new challenges in the pilot-based acquisition of channel state information and call for pilot access protocols that match the intermittent pattern of device activity. A joint pilot assignment and data transmission protocol based on random access is proposed in this paper for the uplink of a massive MIMO system. The protocol relies on the averaging across multiple transmission slots of the pilot collision events that result from the random access process. We derive new uplink sum rate expressions that take pilot collisions, intermittent device activity, and interference into account. Simplified bounds are obtained and used to optimize the device activation probability and pilot length. A performance analysis indicates how performance scales as a function of the number of antennas and the transmission slot duration.

Massive MIMO systems, where the base stations are equipped with hundreds of antenna elements, are an attractive way to attain unprecedented spectral efficiency in future wireless networks. In the "classical" massive MIMO setting, the terminals are assumed fully loaded and a main impairment to the performance comes from the inter-cell pilot contamination, i.e., interference from terminals in neighboring cells using the same pilots as in the home cell. However, when the terminals are active intermittently, it is viable to avoid inter-cell contamination by pre-allocation of pilots, while same-cell terminals use random access to select the allocated pilot sequences. This leads to the problem of intra-cell pilot contamination. We propose a framework for random access in massive MIMO networks and derive new uplink sum rate expressions that take intra-cell pilot collisions, intermittent terminal activity, and interference into account. We use these expressions to optimize the terminal activation probability and pilot length.

We investigate the performance of an energy detection (ED) system with a large number of receiver antennas in multi-path propagation channels. Contrast to existing works focusing on inter-symbol-interference (ISI) free scenarios, in multi-path environments with resolvable path components, ISI jeopardizes the performance of the ED receiver. Asymptotically, when the number of receiver antennas is sufficiently large, we show that a zero-forcing equalizer, requiring only the energy of channel taps to compute the filter coefficients, can be employed to effectively remove the ISI. This significantly reduces the burden on acquiring channel state information (CSI), since only the energy of each channel taps is demanded instead of complete CSI. As a result of asymptotic properties, to decode symbols at the output of the equalizer, decision threshold values take rather simple forms, reducing the computational complexity. Monte Carlo simulation results show that compared with using instantaneous channel energies for channel equalization, employing average channel energy leads to resembling symbol error rate, but demanding a lower filter coefficients updating frequency.

Wireless networks with many antennas at the base stations and multiplexing of many users, known as Massive MIMO systems, are key to handle the rapid growth of data traffic. As the number of users increases, the random access in contemporary networks will be flooded by user collisions. In this paper, we propose a reengineered random access protocol, coined strongest-user collision resolution (SUCR). It exploits the channel hardening feature of Massive MIMO channels to enable each user to detect collisions, determine how strong the contenders' channels are, and only keep transmitting if it has the strongest channel gain. The proposed SUCR protocol can quickly and distributively resolve the vast majority of all pilot collisions.

This paper introduces three key features in geometry-based stochastic channel models in order to include massive MIMO channels. Those key features consists of multiuser (MU) consistency, non-stationarities across the base station array and inclusion of spherical wave modelling. To ensure MU consistency, we introduce the concept of “user aura”, which is a circle around the user with radius defined according to the stationarity interval. The overlap between auras determines the share of common clusters among users. To model non-stationarities across a massive array, sub-arrays are defined for which clusters are independently generated. At last, we describe a procedure to incorporate spherical wave modelling, where a cluster focal point is defined to account for distance between user and cluster.

A multi-user massive MIMO measurement campaign is conducted to study the channel propagation characteristics (e.g. user correlation, sum of eigenvalues and condition number), focusing on the stability over frequencies and the impact of the array aperture. We use 3 arrays with 64 antennas (6m linear array, 2m linear array and 25cm by 28cm squared 2D array) serving 8 users holding a handset with 2 antennas. The study of the measurements shows that the propagation characteristics of the channel are stable for all the measured frequencies. We also observe that user proximity and user handgrip reduce the dispersion of the studied properties of the channel across frequencies, and in such cases the larger the aperture of the array is, the less dispersed the properties are. Increasing the number of base station antennas improves the propagation characteristics of the channel and stabilizes the properties in the frequency domain.

This work concerns wireless cellular networks applying massive multiple-input multiple-output (MIMO) technology. In such a system, the base station in a given cell is equipped with a very large number (hundreds or even thousands) of antennas and serves multiple users. Estimation of the channel from the base station to each user is performed at the base station using an uplink pilot sequence. Such a channel estimation procedure suffers from pilot contamination. Orthogonal pilot sequences are used in a given cell but, due to the shortage of orthogonal sequences, the same pilot sequences must be reused in neighboring cells, causing pilot contamination. The solution presented in this paper suppresses pilot contamination, without the need for coordination among cells. Pilot sequence hopping is performed at each transmission slot, which provides a randomization of the pilot contamination. Using a modified Kaiman filter, it is shown that such randomized contamination can be significantly suppressed. Comparisons with conventional estimation methods show that the mean squared error can be lowered as much as an order of magnitude at low mobility.

By using large point-to-point multiple input multiple output (MIMO), spatial multiplexing of a large number of data streams in wireless communications using millimeter-waves (mm-waves) can be achieved. However, according to the antenna spacing and transmitter-receiver distance, the MIMO channel is likely to be ill-conditioned. In such conditions, highly complex schemes such as the singular value decomposition (SVD) are necessary. In this paper, we propose a new low complexity system called discrete Fourier transform based spatial multiplexing (DFT-SM) with maximum ratio transmission (DFT-SM-MRT). When the DFT-SM scheme alone is used, the data streams are either mapped onto different angles of departures in the case of aligned linear arrays, or mapped onto different orbital angular momentums in the case of aligned circular arrays. Maximum ratio transmission pre-equalizes the channel and compensates for arrays misalignments. Simulation results show that, although the DFT-SM-MRT scheme has a much lower complexity than the SVD scheme, it still achieves large spectral efficiencies and is robust to misalignment and reflection.

Each user in the uplink of an Division Multiple Access (OFDMA) system may experience a different carrier frequency offset (CFO). These uncorrected CFOs destroy the orthogonality among subcarriers, causing inter-carrier interference and multi-user interference, which degrade the system performance severely. In this paper, novel time-domain multi-user interference cancellation schemes for OFDMA uplink are proposed. They employ an architecture with multiple OFDMA-demodulators to compensate for the impacts of multi-user CFOs at the base station's side. Analytical and numerical evaluations show that the proposed schemes achieve a significant performance gain compared to the conventional receiver and a reference frequency-domain multi-user interference cancellation scheme. In a particular scenario, a maximum CFO of up to 40% of the subcarrier spacing can be tolerated, and the CFO-free performance is maintained in the OFDMA uplink.

Two-way relaying in wireless systems has initiated a large research effort during the past few years. In particular, structured codes and lattices are instrumental for achieving high rates when using Physical Layer Network Coding (PLNC). In an attempt to bring the gains of PLNC beyond the classical traffic pattern of two-way relaying, in this paper we consider a scenario with four-way relaying, where each of the two Mobile Stations (MSs) has a two-way connection to the same Base Station (BS), while each connection is through a dedicated Relay Station (RS). The two RSs are in the range of the BS, but they are at antipodal positions within the cell and do not interfere with each other, i. e. achieve a perfect spatial reuse. We introduce communication schemes for serving the four communication flows in two transmission phases. Each phase consists of combined broadcast and multiple access. The main design ingredients are dirty paper coding nested lattice code codes. We compare the performance with a reference scheme that utilizes Decode-and-Forward (DF). The results show that the usage of structured codes in the four-way relaying scenario can significantly increase the achievable rate region.

There are two basic principles used in wireless network coding to design throughput-efficient schemes: (1) aggregation of communication flows and (2) interference is embraced and subsequently cancelled or mitigated. These principles inspire design of many novel multi-flow transmission (MFT) schemes. Such are the Coordinated Direct/Relay (CDR) schemes, where each basic transmission involves two flows to a direct and a relayed user. Usage of MFT schemes as building blocks of more complex transmission schemes essentially changes the problem of scheduling, since some of the flows to be scheduled are coupled in a signal domain and they need to be assigned a communication resource simultaneously. In this paper we define a novel framework that can be used to analyze MFT schemes and assess the system-level gains. The framework is based on cellular wireless users with two-way traffic and it sets the basis for devising composite time-multiplexed MFT schemes, tailored to particular optimization criteria. Those criteria can be formulated by adapting well-known schedulers in order to incorporate MFT schemes. The results show rate advantages brought by the CDR schemes in pertinent scenarios. Another key contribution is the proposed framework, which can be used to evaluate any future multi-flow transmission scheme.

The recent years have brought a significant body of research on wireless Two-Way Relaying (TWR), where the use of network coding brings an evident advantage in terms of data rates. Yet, TWR scenarios represent only a special case and it is of interest to devise similar techniques in more general multi-flow scenarios. Such techniques can leverage on the two principles used in Wireless Network Coding to design throughput-efficient schemes: (1) aggregation of communication flows and (2) embracing and subsequently cancel/mitigate the interference. Using these principles, we investigate Coordinated Direct/Relay (CDR) schemes, which involve two flows, of a direct and a relayed user. In this paper we characterize a CDR scheme by deriving/bounding the Diversity-Multiplexing Trade-off (DMT) function. Two cases are considered. In the first case a transmitter knows the Channel State Information (CSI) of all the links in the network, while in the second case each node knows only CSI of the links towards its neighbors. The results show that the new CDR scheme outperforms the reference scheme in terms of DMT characterization. Several interesting features are identified with respect to the impact of the CSI knowledge to the improvement in diversity or multiplexing brought by the CDR scheme.

Spectrum sensing is a key component for enabling the cognitive radio paradigm. In this paper, we propose a novel totally-blind spectrum sensing technique for cognitive radio device equipped with multiple antennas, namely the Space Frequency Cross Product Sensing (SFCPS) algorithm. Existing correlation-based spectrum sensing techniques rely on the assumption that the received signals are correlated and their performance becomes poor when the signal correlation is low. By appropriately combining the received signals from multiple antennas, the proposed method creates new signals that are fully correlated and on which a sensing method is developed. SFCPS performs better than existing correlation-based techniques and with a lower computational complexity for small number of observed samples.

This paper proposes a low-complexity design for the linear weighted MMSE (WMMSE) transmit filters of a coordinated multi-cell system with multiple users per cell. This design is based on a modified WMMSE approach applied to each transmitting base station individually incorporating the signals sent to the cell of interest and the signals leaked to the other cells. This novel design allows for closed-form expressions of the associated Lagrange multipliers and offers a lower computational complexity compared to known methods. In addition, we incorporate this design in an iterative algorithm to find the linear transmit filter maximizing the weighted sum-rate of the multi-cell system. This algorithm is based on WMMSE where the MSE weights are optimally adjusted so that the WMMSE optimum coincides with the WSR optimum.

We propose a low complexity design of the linear transmit filters for a MIMO interference channel. This design is based on a minimum mean squared error (MMSE) approach incorporating the signal and the interference leakage for each transmitter. Unlike the previous methods, it allows a closed-form expression of the regularization factor for the MMSE transmit filter. Hence, it requires a lower computational complexity compared to the conventional MMSE approach that is optimally achieved by solving a polynomial equation to find the regularization factor. Furthermore, the mean squared error (MSE) performance of the proposed design is verified by simulations to have nearly no loss compared to the conventional MMSE approach.

Joint processing of multiple communication flows in wireless systems has given rise to a number of novel transmission techniques, notably the two-way relaying, but also more general traffic scenarios, such as coordinated direct and relay (CDR) transmissions. In a CDR scheme the relay has a central role in managing the interference and boosting the overall system performance. In this letter we consider the case in which an amplify-and-forward relay has multiple antennas and can use beamforming to support the coordinated transmissions. We focus on one representative traffic type with one uplink user and one downlink user. Two different criteria for relay beamforming are analyzed: maximal weighted sum-rate and maximization of the worst-case weighted SNR. We propose iterative optimal solutions, as well as low-complexity near-optimal solutions.

The ideas of wireless network coding at the physical layer promise high throughput gains in wireless systems with relays and multi--way traffic flows. This gain can be ascribed to two principles: (1) joint transmission of multiple communication flows and (2) usage of a priori information to cancel the interference. In this paper we use these principles to devise new transmission schemes in wireless cellular systems that feature both users served directly by the base stations (direct users) and users served through relays (relayed users). We present four different schemes for coordinated transmission of uplink and downlink traffic in which one direct and one relayed user are served. These schemes are then used as building blocks in multi--user scenarios, where we present several schemes for scheduling pairs of users for coordinated transmissions. The optimal scheme involves exhaustive search of the best user pair in terms of overall rate. We propose several suboptimal scheduling schemes, which perform closely to the optimal scheme. The numerical results show a substantial increase in the system--level rate with respect to the systems with non--coordinated transmissions.

This paper studies linear transmit filter design for weighted sum-rate (WSR) maximization in the multiple input multiple output broadcast channel (MIMO-BC). The problem of finding the optimal transmit filter is non-convex and intractable to solve using low complexity methods. Motivated by recent results highlighting the relationship between mutual information and Minimum Mean Square Error (MMSE), this paper establishes a relationship between weighted sum-rate and weighted MMSE in the MIMO-BC. The relationship is used to propose a low complexity algorithm for finding a local weighted sum-rate optimum based on alternating optimization. Numerical results studying sum-rate show that the proposed algorithm achieves high performance with few iterations.

Packet retransmission strategies are presented for MIMO eigenmode transmission where adaptive modulation and coding (AMC) is implemented. The retransmission design is based on weighted linear MMSE. It includes the transmit and receiver filter, the power and eigenmode allocation and AMC level when new packets are transmitted. The weight matrix of WMMSE is used to appropriately weight streams with different AMC levels in order to maximize the system throughput. Simulations show that the choice of the weight factors has a major impact on the performance.

This paper proposes a generalized carrier frequency offset (CFO) estimator for the uplink of orthogonal frequency division multiple access (OFDMA) wireless systems. Using the maximum likelihood criterion, the estimator estimates CFOs using the phase shift between two observation windows at distance less than the length of OFDMA symbol. The scheme offers the possibility to shorten the OFDMA preamble used in synchronization, and thus reduce overhead in packet-based wireless communications.

This paper studies various cooperative diversity schemes for orthogonal frequency division multiple access (OFDMA)- time division duplex (TDD) based two-hop cellular networks in low mobility scenarios, where the instantaneous channel state information is available at the base station. A user scheduling and radio resource allocation technique is developed in order to efficiently integrate various cooperative diversity schemes for the emerging IEEE 802.16j based systems. The analysis of the system with this scheduler shows that a simple cooperative diversity scheme which dynamically selects the best scheme between conventional relaying and direct transmission is promising in terms of throughput and implementation complexity. The conventional relaying refers to the scheme where the destination relies solely on the signals received through the relay.

This paper studies linear transmit filter design for Weighted Sum-Rate (WSR) maximization in the Multiple Input Multiple Output Broadcast Channel (MIMO-BC). The problem of finding the optimal transmit filter is non-convex and intractable to solve using low complexity methods. Motivated by recent results highlighting the relationship between mutual information and Minimum Mean Square Error (MMSE), this paper establishes a relationship between weighted sum-rate and weighted MMSE in the MIMO-BC. The relationship is used to propose two low complexity algorithms for finding a local weighted sum-rate optimum based on alternating optimization. Numerical results studying sum-rate show that the proposed algorithms achieve high performance with few iterations.

Orthogonal frequency division multiplexing based wireless systems are spectrally efficient, but they are vulnerable to inter-carrier interference (ICI). In a wide area scenario, users will experience varying signal strength due to different individual path loss and also varying amount of Doppler spread because of their independent velocities. Therefore, the ICI among users will vary over a wide range. It is proposed in this work to use dynamically adaptive sub carrier bandwidth (ASB) along with adaptive bit loading to mitigate ICI in such conditions, which will keep receivers simple while maintaining maximum throughput in each situation. Results show that ASB can provide higher throughput than its fixed sub carrier bandwidth counterpart when both may use adaptive bit loading per sub carrier.

We introduce a new two-step relaying scheme based on superposition coding, SC-relaying. In Step 1, the source ASbroadcasts a message, created by superposition coding, to the relay ARand the destination AD. After decoding the information from AS, ARrelays a part of this message in Step 2, by using a codebook that is adapted to the link AR- AD. The information-theoretic design shows that the proposed scheme can improve the spectral efficiency, attaining almost optimal value. We demonstrate that the SC-relaying can be ported even to the case of uncoded systems, where it is shown how to select the transmission scheme in order to maximize the throughput between the source and the destination.

Packet retransmission strategies are presented for 2x2 MIMO spatial multiplexing systems where adaptive modulation and coding (AMC) is implemented based on outdated CSI at the transmitter. Antennas for retransmission are selected to maximize the throughput of the system. A sign change or conjugate operation is allowed for retransmitted packets. The proposed schemes perform antenna selection based on the CSI at the receiver and feed back relevant information to the transmitter. When 2 packets are decoded with errors, retransmission follows a space time block code structure, allowing a seamless switching between spatial multiplexing and space time block coding.

In a real cellular system, existence of a number of multi-antenna schemes in neighboring cells means that different multi-antenna schemes will experience co-channel interference (CCI) from the same or other multi-antenna schemes. In this work, we have summarized our analysis and simulations related to cellular interference of some specific multiple input multiple output (MIMO) schemes on the same and other MIMO schemes. The goal is to study the impact of interference from MIMO schemes at a user located in the cell edge. Semi-Analytical evaluations of signal to interference and noise ratio (SINR) is done to find out the SINR statistics of different combinations of desired and interfering links. We have studied linear combining receivers for all the link combinations. Based on the current analysis, it is found that space-time block code (STBC) is a severe interferer compared to others, and specific attention is needed to counter the interference introduced by STBC interfering link.

In this paper we establish end-to-end achievable sum-rates of different linear and non-linear pre-processing techniques employing amplitude-shift keying (ASK)-modulation in a flat-fading multiple input multiple output (MIMO) broadcast system with single antenna terminals. The obtained results reveal that the minimum mean square error vector precoding (MMSE-VP) technique achieve close to capacity performance in various system settings. Thus it achieves a loss of merely 2-3 dB compared to the sum-capacity in the mid to high SNR range including the effects of practical system implementations, such as a non-ideal modulation set and a sub-optimal solver at the transmitter. When the SNR is low or when the system experience a small load, simpler pure linear techniques achieve similar performance.

Orthogonal frequency division multiplexing (OFDM) is widely used in wireless systems because of its high spectral efficiency, though it is known that it is highly vulnerable to frequency synchronization errors, which severely impede its performance. Such errors arise due to carrier frequency offset and Doppler frequency spread. They destroy the orthogonality among sub carriers and introduce inter carrier interference (ICI). The interference power is proportional to the received signal strength and amount of frequency offset. Several proposal to reduce the impact of ICI in OFDM use very complex signal processing and some have associated bandwidth efficiency loss. Since ICI is inversely proportional to the sub carrier bandwidth, it is interesting to investigate the gains in a system with adaptable sub carrier bandwidth. A system which can support variable sub carrier bandwidths simultaneously meeting the needs of different ICI conditions is compared against a normal OFDM system which uses fixed carrier bandwidth (FSB). Results show that the VSB schemes can be preferred over FSB OFDM system when users with different receive signal strength and Doppler condition have to be served simultaneously.

The wireless relay systems represent an important segment in the wireless communication systems. In a simple wireless relay scenario, a source node ASneeds to communicate to a destination AD, with the help of a relay AR. In this paper we introduce a new two-step relaying scheme based on superposition coding, SC-relaying. In step 1, ASbroadcasts a message, created by superposition coding, to ARand AD. After decoding the information from AS, ARrelays a part of this message in step 2, by adapting the transmission rate with respect to the link quality towards AD. The unique point of the proposed scheme is that the signal format in the two steps is chosen in a way that enables a simple combining at AD. The numerical results show that the proposed scheme can bring a notable gain in spectral efficiency over the conventional multi-hop transmission with adaptive modulation, even in the case when the instantaneous SNR about all the links is not fully known at the source. In addition, the proposed scheme performs very closely to the scheme that is optimal in an information-theoretic sense.

Co-Channel Interference (CCI) caused by different Multiple Input Multiple Output (MIMO) signalling schemes impact the same or other MIMO schemes in different ways. Due to the symbol-by- symbol processing in a desired Single Input Multiple Output (SIMO) link, an interfering Space-Time Block Code (STBC) link may become equivalent to an interfering Spatial Multiplexing (SM) link. Using this knowledge and understanding, we propose an interference cancellation receiver robust to different types of MIMO interferers at cell edge for the Downlink (DL) of cellular systems. The receiver systematically performs a multiple symbol processing: this is the appropriate processing when the signal of interest or the signal of interferer is correlated across symbols, which is the case for STBC transmission. We evaluated different link combinations in terms of Signal to Interference and Noise Ratio (SINR) statistics and Bit Error Rate (BER) performance in cellular Orthogonal Frequency Division Multiple Access (OFDMA) . We have found that the proposed multiple-symbol linear interference cancellation receiver performs satisfactorily when any kind of single 'logical' stream MIMO scheme is present as interferer.

Cyclic delay diversity can be used to increase the frequency selectivity of the wireless channel seen from the receiver. In this work, we have used this simple technique on top of a spatial multiplexing based orthogonal frequency division multiplexing system. In our analysis, it is found that nearly 2 b/s/Hz of 10% outage capacity improvement can be achieved on a basic 2times2 system. This is achieved with little changes at any standard spatial multiplexing system. Also, the number of transmit antennas can be increased to increase the link capacity without making any changes to the receiver. Thus, the proposed technique can be used with any existing MIMO-OFDM system. We have shown significant improvement in FER by using this scheme compared to a simple spatial multiplexing scheme.

This paper addresses channel state information (CSI) estimation in a wireless relay network, which consists of a base station (BS), a relay station (RS), and a mobile station (MS). We propose a method for the BS to obtain the instantaneous CSIs of the three links (BS-RS, BS-MS, and RS-MS links) with minimized overhead. The CSI availability plays an important role for the BS to adapt its transmission strategies to the given channel conditions. Our proposed method exploits the fact that the link condition between fixed BS and fixed RS tends to be stable, and the frequent CSI update is not necessary for this link. In order for the BS to obtain the CSI of the distant link (i.e. RS-MS link), RS amplifies a pilot signal received from MS with a pre-defined amplification factor, and forwards it to BS. This enables the BS to obtain the CSI of the RS-MS link based on the received pilot signal and pre-knowledge on the CSI of BS-RS link. We show by computer simulation that our proposed method can reduce the required overhead for CSI estimation with little degradation on the accuracy of the obtained CSI.

In this article the performance of orthogonal frequency division multiplexing (OFDM) systems with dynamically adaptable sub carrier bandwidth to mitigate inter carrier interference (ICI) is compared against standard OFDM systems with fixed sub carrier bandwidth. In current OFDM system, the sub carrier bandwidth or sub carrier spacing is a fixed design parameter. This is not efficient since the optimum sub carrier bandwidth depends on ICI, which is a function of several parameters such as residual frequency offset, Doppler frequency spread, received signal strength and carrier frequency. Existing algorithms to combat ICI are very complex and sometimes reduce bandwidth efficiency. As an alternative approach, it is worth verifying the benefits of using adaptive bandwidth for sub carrier (ABS). A method for implementing the above scheme is given. Results show that this type of adaptive OFDM scheme has the potential to provide a significant improvement in performance over OFDM systems which use fixed sub carrier bandwidth.

In this paper, we propose a hybrid forwarding scheme for cooperative relaying in orthogonal frequency division multiplexing (OFDM) based networks. The proposed hybrid forwarding scheme changes the type of the forwarding scheme used by a relay terminal on a per sub-carrier basis. It adaptively decides to use either "Amplify and Forward (AF)" or "Decode and Forward (DF)" or "no relay" according to the instantaneous Signal to Noise Ratio (SNR) conditions between the source, relay and destination terminals. This decision is made based on analytically derived equations, which makes it possible to take into account all the link conditions in a relay network with low computational complexity. The bit error performance is presented for cooperative receive diversity with AF, with DF, with proposed hybrid forwarding scheme and for noncooperative transmission. Our results show that the proposed hybrid forwarding scheme significantly outperforms conventional cooperative relaying with AF and DF, and direct transmission schemes.

An algorithm for implementing variable guard interval (VGI) in Orthogonal Frequency Division Multiplexing (OFDM) based wireless networks is presented in this article. The required guard interval is derived as a function of the root mean square (rms) delay spread of the channel, time duration of discrete fourier transform used in OFDM and the required signal to noise ration (SNR). In this work the algorithm has been verified in a highly dynamic channel condition and results show that the algorithm can reduce guard interval significantly and is also found to be robust against time variation of rms delay spread and mean excess delay of the channel.

The design of spectrally efficient and robust schemes is vital to overcome the harsh wireless environment when delivering high data rate services. In the present paper, the performance of a constant power and discrete rate adaptation scheme is evaluated in a multiple antenna scenario. Such a technique has low complexity, and needs a low rate feedback channel to adapt the transmission rate. However, the feedback can become outdated when the channel is rapidly changing. In the light of the feedback delay, the authors propose to analyze the effect of outdated channel information. Closed form expressions are obtained for the bit-error-rate (BER) performance for MIMO diversity, and spatial multiplexing (SM) schemes. Through simulation results, we analyze the impact of feedback delay on the BER. The feedback delay proves to have different impact on each of the MIMO scheme when coupled to a rate adaptive system. We demonstrate that antenna selection suffers more from feedback delay than other diversity schemes. The results can be used to assess the performance of different MIMO techniques, and may be applied to a switching mechanism, where, at each instant, the channel behavior is analyzed and the best fit MIMO mode is selected

We consider the uplink of an orthogonal frequency division multiple access (OFDMA)-based system, where each mobile station (MS) experiences a different carrier frequency offset (CFO). Unconnected CFOs destroy the orthogonality among subcarriers, which could cause severe inter-carrier interference (ICI) and degrade the system performance considerably. In this paper, we propose a novel multi-user interference (MUI) cancellation scheme for uplink OFDMA, which utilizes multiple orthogonal frequency division multiplexing (OFDM)-demodulators architecture to correct and then compensate the negative effects of multiple CFOs at the receiver's side. Numerical evaluation has shown a considerable performance gain compared to the conventional receiver

In this work, we have combined quasi-orthogonal space-frequency block code (QSFBC) and spatial multiplexing (SM) in one joint diversity and multiplexing (JDM) structure. Minimum mean square error (MMSE) and zero forcing (ZF) nulling based ordered successive interference cancellation (OSIC) receivers are derived for above mentioned JDM scheme. Performance comparison is done in realistic wireless scenario. It is observed through simulations that our JDM scheme performs very well in presence of spatial correlation caused by antenna separation and by line of sight (LOS) situation. The gains in frame error rate (FER) and outage spectral efficiency for JDM schemes with studied receivers are very high compared to corresponding SM only schemes.

The goal of this paper is to highlight the benefits of using variable guard interval (VGI) in orthogonal frequency division multiplexing (OFDM) based wireless local area network (WLAN) systems. An algorithm is derived and its performance merit for implementing VGI in the presence of carrier frequency offset is provided. OFDM system needs to maintain orthogonality among sub carriers, which may be lost due insufficient guard interval and carrier frequency offset (due to local oscillator mismatch and Doppler effects). Static guard interval is usually kept large enough to tolerate worst case channel delay spread conditions, which in turn causes loss in efficiency of the system. The use of variable guard interval is expected to improve the system throughput. Since insufficient guard interval and carrier offset together cause orthogonality loss among sub carriers, the dynamic selection of guard interval in the presence of carrier frequency offset is considered in this work. Results are presented to analyze achievable gains. It has been shown that significant improvement in throughput is achievable by this scheme.

Multiuser transmission methods for digital subscriber line (DSL) systems have become of interest with the potential for increased data rate and loop reach. These methods often assume that the set of crosstalk interferers, called the crosstalk profile, and their associated channel responses are known. For DSL systems, the interferers are often uncoordinated, so that in a dynamic environment where DSL transmitters can energize and deenergize, the crosstalk profile cannot be transmitted to the user of interest. While the crosstalk channel estimation problem in a dynamic environment can be intractable for general transmission systems, channel and crosstalk analysis can make use of the specific DSL environment. Namely, the physical channels in a DSL system do not change rapidly, and hence estimates of the crosstalk channel can be saved for future reference. For this reason, we introduce the concept of a channel profile. We develop several algorithms to detect the crosstalk profile and investigate the asymptotic behavior of the new algorithms. Simulations show that for typical crosstalk interference scenarios, the observation time to determine the correct crosstalk profile at probability of error less than 10/sup -3/ can be less than 2 ms.

Accurate identification of crosstalk interferers can increase throughput by proper loading or multiuser detection. In some systems, such as cable based delivery systems, crosstalk channel characteristics are finite and static. Furthermore, the type of service on each line does not change. The combination of the type of service and the channel characteristic is known as a crosstalk profile. Specific methods are introduced that allow for the detection of a crosstalk profile and further minimize the probability of error in selecting from the set of possible crosstalk interference scenarios.

Certain blind channel estimation techniques allow the identification of the channel up to a scale or phase factor. This results in singularity of the Fisher information matrix (FIM). The Cramer-Rao bound, which is the inverse of the FIM, is then not defined. To regularize the estimation problem, one can impose constraints on the parameters. In general, many sets of constraints are possible but are not always relevant. We propose a constrained CRB, the pseudo-inverse of the FIM, which gives, for a minimum number of constraints, the lowest bound on the mean squared estimation error.

The purpose of semi-blind channel identification methods is to exploit the information used by blind methods and the information coming from known symbols. The main focus of this paper is the study of deterministic quadratic semi-blind algorithms which are of particular interest because of their low computational complexity. The associated criteria are formed as a linear combination of a blind and a training sequence based criterion. Through the examples of subchannel response matching and subspace fitting based semi-blind criteria, we study how to construct properly such semi-blind criteria and how to choose the weights of the linear combination. We provide a performance study for these algorithms and give theoretical conditions for the semi-blind performance to be independent of the weights.

We investigate receive diversity for OFDM-based broadband communication systems. We consider the use of multiple antennas to combat time-selective fading and OFDM transmission in combination with an appropriate guard interval to suppress intersymbol interference. The receive diversity scheme is located in the frequency-domain and we derive different linear estimators, hereafter referred to as diversity equalizers. We show that the carrier-based solution for the optimum linear MMSE estimator-which is the least complex solution that maximizes the overall SNR is the well known maximum ratio combiner (MRC). Furthermore, we present a performance analysis for the proposed diversity equalizers and compare theoretical results to a simulated system which uses channel state information for MRC.

We investigate maximum likelihood (ML) methods for blind and semi-blind estimation of multiple FIR channels. Two blind deterministic ML (DML) strategies are presented. In the first one, we propose to modify the iterative quadratic ML (IQML) algorithm in order to "denoise" it and hence obtain consistent channel estimates. The second strategy, called pseudo-quadratic ML (PQML), is naturally asymptotically denoised. Links between these two approaches are established and their global convergence is proved. Furthermore, we propose semi-blind ML techniques combining PQML with two different training sequence estimation methods and compare their performance. These semi-blind techniques, exploiting the presence of known symbols, outperform their blind version. They also allow channel estimation in situations where blind and training sequence methods fail separately. Simulations are presented to demonstrate the performance of all the proposed algorithms, and comparisons between them are discussed in a blind and/or semi-blind context.

We present two approaches to stochastic maximum likelihood identification of multiple FIR channels, where the input symbols are assumed Gaussian and the channel deterministic. These methods allow semi-blind identification, as they accommodate a priori knowledge in the form of a (short) training sequence and appears to be more relevant in practice than purely blind techniques. The two approaches are parameterized both in terms of channel coefficients and in terms of prediction filter coefficients. Corresponding methods are presented and some are simulated. Furthermore, Cramer-Rao Bounds for semi-blind ML are presented: a significant improvement of the performance for a moderate number of known symbols can be noticed.

We pursue our iterative quadratic maximum likelihood (IQML) approach to blind estimation of multiple FIR channels. We use a parameterization of the noise subspace in terms of linear prediction quantities. This parameterization is robust w.r.t. a channel length mismatch. Specifically, when the channel length is overestimated, no problems occur. Underestimation leads to a reduced-order channel estimate. We introduce two matched filter bounds (MFBs) to characterize the performance of receivers using reduced-order channel models. The first one (MFB1) uses the channel model to perform the spatio-temporal matched filtering that yields data reduction from multichannel to single-channel form. The rest of the processing remains optimal. MFB2 on the other hand bounds the performance of the Viterbi algorithm with the reduced channel model. It is shown that the reduced model provided by IQML is the one that maximizes MFB1. We also propose some low complexity techniques for obtaining consistent estimates with which to initialize IQML.