In this work, we present a recent investigation on leveraging large reconfigurable intelligent surfaces (RISs) as anchors for positioning in wireless communication systems. Unlike existing approaches, we explicitly address the uncertainty arising from the substantial physical size of the RIS—particularly relevant when a user equipment (UE) resides in the near field—and propose a method that ensures accurate positioning under these conditions. We derive the corresponding Cramér-Rao bound (CRB) for our scheme and validate the effectiveness of our scheme through numerical experiments, highlighting both the feasibility and potential of our approach.

Next-generation wireless communication systems will incorporate millimeter wave (mmWave) as one of the key technologies to increase data rates. These forthcoming mmWave systems will integrate multiple-input multiple-output (MIMO) technology to ensure sufficient link margin and are expected to be deployed in conjunction with sub-6 GHz systems. Configuring a MIMO communication link usually relies on estimating channel state information (CSI), which is difficult to acquire at mmWave frequencies due to the low pre-beamforming signal-to-noise ratio (SNR). In this paper, we propose a novel approach to estimate mmWave MIMO channels by leveraging out-of-band information from a sub-6GHz band. Utilizing this approach, we develop three channel estimation methods and compare these methods with one using only in-band information. We investigate the influence of theK-factor, the SNR and the number of antennas on the performance of the proposed channel estimation methods through simulations. Additionally, we compare these methods in terms of their computational complexity. Finally, we validate the performance of the proposed methods through channel measurements conducted in an outdoor environment. The results demonstrate that the proposed methods outperform in-band mmWave channel estimation in terms of spectral efficiency, particularly in scenarios of low SNR and high K-factor.

Terahertz (THz) integrated sensing and communication (ISAC) aims to integrate novel functionalities, e.g., environmental sensing, into communication systems. Accurate channel modeling is crucial for the design and performance evaluation of future ISAC systems. This paper presents a novel hybrid channel model and a high-precision environment reconstruction framework for THz monostatic sensing. Vector network analyzer (VNA)-based channel measurements using the directional scanning sounding (DSS) scheme are performed in a laboratory environment at 300 GHz with a 20 GHz bandwidth. A low-complexity space-alternating generalized expectation-maximization (SAGE)-based algorithm is proposed to estimate the parameters of the multipath propagation components (MPCs) and to de-embed the antenna pattern. Leveraging geometric principles, the MPCs are classified into specular and diffuse components. In our proposed framework, the specular components are used for material detection and identification, while the diffuse components are leveraged to enhance geometric environment reconstruction. Demonstrations of both geometrical environment reconstruction and material identification are provided to validate the effectiveness of the proposed approach. This work offers valuable insights into THz monostatic sensing channel modeling and the design of future THz ISAC systems.

In this work, we present a wireless localization method that operates on self-supervised and unlabeled channel estimates. Our self-supervising method learns general-purpose channel features robust to fading and system impairments. Learned representations are easily transferable to new environments and ready to use for other wireless downstream tasks. To the best of our knowledge, the proposed method is the first joint-embedding self-supervised approach to forsake the dependency on contrastive channel estimates. Our approach outperforms fully-supervised techniques in small data regimes under fine-tuning and, in some cases, linear evaluation. We assess the performance in centralized and distributed massive multiple-input multiple-output (MIMO) systems for multiple datasets. Moreover, our method works indoors and outdoors without additional assumptions or design changes.

The canonical setup of cell-free massive multiple-input multiple-output (MIMO), where all the access points (APs) serve all the users, does not scale well. In this work, we propose a deep reinforcement learning (DRL) approach to user-centric clustering in which each user is served by only a subset of APs. The clusters are formed such that either a given user demand is satisfied or the network sum rate is maximized. Unlike previous studies, we allow the clusters to vary in size depending on the propagation conditions. We design our DRL framework to be flexible enough to accommodate different performance targets in terms of the sum spectral efficiency, fronthaul capacity and power consumption. By optimizing the AP selection for each user, our proposed scheme is able to achieve the same performance as the canonical setup (upper bound) with significantly lower fronthaul requirements.

In this paper, we consider an access point clustering-based pilot assignment scheme for user-centric cell-free massive MIMO systems. In such a system, the complexity is reduced by performing the pilot assignment for each cluster independently. However, users at the edge of the cluster will experience high interference from users in neighboring clusters. In this paper, we propose two schemes to reduce pilot contamination for cluster-edge users. The proposed schemes can perform per-group pilot assignment with zero or limited inter-cluster cooperation. Numerical results verify that, compared with conventional pilot assignment schemes, the proposed pilot assignment schemes achieve similar performance while having lower complexity and allowing for decentralized processing.

One of the key research directions to increase the capacity of new radio (NR) vehicle-to-everything (V2X) communication systems is extension of employed frequency bands from sub-6 GHz to millimeter wave (mmWave) range. To investigate different propagation effects between sub-6 GHz and mmWave bands in high-mobility scenarios, one needs to conduct channel measurements in both frequency bands. Using a suitable testbed setup to compare these two bands in a fair manner, we perform channel measurements at center frequencies of 2.55 GHz and 25.5 GHz, velocities of 50 km/h and 100 km/h, and at 126 different spatial positions. Furthermore, we conduct a comparative study of the multi-band propagation based on measurement results. We estimate the power delay profile (PDP) and the Doppler power spectral density (DSD) from a large set of measurements collected in a measurement campaign. Finally, we compare measured wireless channels at the two employed frequency bands in terms of root-mean-square (RMS) delay spread and RMS Doppler spread.

We consider a user-centric cell-free massive MIMO system in which many distributed access points are connected to a central processing unit to serve multiple users. In practice, linear precoding schemes can achieve high spectral efficiency in massive MIMO systems. However, for cell-free massive MIMO, these methods suffer from a large training overhead and high computational complexity. We reduce the complexity of the zero-forcing precoder and improve the performance of the maximum ratio transmission precoder by dividing the users into groups and assigning different frequency resources to each group. Further, we reduce the training overhead by assigning highly interfering users to different groups. We propose a graph coloringbased user grouping scheme with an adjustable group size. Numerical results show that the proposed scheme can achieve about 96% spectral efficiency of zero-forcing while requiring only 32% processing complexity of zero-forcing. Further, the proposed scheme improves the performance of maximum ratio transmission by 24%. Finally, we investigate the impact of the coherence block length.

Distinguishing between reconfigurable intelligent surface (RIS) assisted paths and non-line-of-sight (NLOS) paths is a fundamental problem for RIS-assisted integrated sensing and communication. In this work, we propose a pattern alternation scheme for the RIS response that uses part of the RIS as a dynamic part to modulate the estimated channel power, which can considerably help the user equipments (UEs) to identify the RIS-assisted paths. Under such a dynamic setup, we formulate the detection framework for a single UE, where we develop a statistical model of the estimated channel power, allowing us to analytically evaluate the performance of the system. We investigate our method under two critical factors: the number of RIS elements allocated for the dynamic part and the allocation of RIS elements among different users. Simulation results verify the accuracy of our analysis.

In this paper, we consider the problem of activity detection in grant-free code-domain non-orthogonal multiple access (NOMA). We focus on performing activity detection via subspace methods under a setup where the data and pilot spreading signatures are of different lengths, and consider a realistic frame-structure similar to existing mobile networks. We investigate the impact of channel correlation on the activity detection performance; first, we consider the case where the channel exhibits high correlation in time and frequency and show how it can heavily deteriorate the performance. To tackle that, we propose to apply user-specific masking sequences overlaid on top of the pilot signatures. Second, we consider the other extreme with the channel being highly selective, and show that it can also negatively impact the performance. We investigate possible pilots’ reallocation strategies that can help reduce its impact.

Non-orthogonal multiple access (NOMA) transmission techniques promise reduced latency, massive user deployments, and higher data rates. To investigate how these per-formance enhancements translate to practical deployments in a network, a reliable system-level abstraction for downlink two-user power domain NOMA with successive interference cancellation at the receiver is needed. A simple and efficient abstraction is described and then validated through comparison with link-level simulation results. With the developed abstraction, the effects of the deployment of NOMA techniques in a wireless network with a large number of machine type communication users are evaluated in terms of cell throughput, number of users served simultaneously, and fairness.

We propose novel tree-structured quantization approaches for points on Grassmann and Stiefel manifolds. Such manifold quantizers find application, e.g., for channel state information quantization in limited feedback multiple-input multiple-output wireless communications. The proposed quantizers allow to trade-off quantization distortion for complexity by pruning the width of the quantization tree. We compare the rate-distortion performance and quantization complexity of the proposed quantizers to existing single stage and recursive multi stage quantizers, showing that the proposed methods lie in-between these two extremes.

We propose a novel construction for Grassmannian superposition transmission, which allows to transmit multiple data streams non-coherently, i.e., without channel state information at the transmitter and receiver, over single-input multiple-output block-fading channels. This scheme is suitable for multi-resolution broadcasting, as well as, for non-coherent non-orthogonal multiuser transmissions. We present a low-complexity greedy detector for this superposition scheme, as well as, a trellis detector that achieves close-to-optimal performance with significantly reduced complexity compared to maximum likelihood detection. We investigate the performance of the proposed schemes by numerical simulations, showing that the overall symbol error rate performance is very similar to existing single-stream transmission schemes, yet, at the same time supporting the transmission of multiple independent data streams using a non-orthogonal Grassmannian superposition.

Fixed point-to-point microwave links (P-P links) are widely used for vital backhaul connections in cellular networks. One frequency range allocated to P-P links by the International Telecommunications Union ranges from 24.25GHz to 29.5GHz. The same frequency range was also allocated to mobile services, with bands dedicated to 5G new radio (NR). Potential outages or performance drops in P-P links due to interference from 5G NR are therefore a concern to mobile network operators. We characterize the link-level performance of a commercial off-the-shelf (COTS) P-P link experimentally through measurements. We install a COTS P-P link on our campus. Through an additional experimental transmitter, we generate a 5G NR interference signal, allowing us to measure the link performance depending on signal-to-interference ratio (SIR) in the field. We measure the data rate that the P-P link achieves with respect to the interferer’s location and transmit power. We establish a relation between the P-P link’s SIR and data rate based on our measurements. We further measure the P-P link’s antenna pattern in an anechoic chamber. To investigate the impact of interfering 5G NR user equipments (UEs) in typical cellular network deployment, we perform system-level simulations based on our link-level measurements. We simulate the impact of 5G NR users on a P-P links backhaul connection in typical cell geometries. We identify the cell’s key parameter that determines the P-P link’s achievable data rate.

In this paper we report the results from three field measurements and analysis of fifth generation (5G) broadcast systems, conducted in Vienna, Austria. One measurement was conducted under stationary conditions while during the others the receiver was in motion. Based on comparison between computer simulations and the static measurement scenario, we estimate the coverage probability in terms of BLock-Error Ratio (BLER) for various system parametrizations. The results are applicable for more complicated channel conditions for which the measurement procedure is more involved.

The deployment of large antenna arrays at the base station is a prerequisite for operating at the millimeter wave bands. The large antenna arrays are required to achieve high beamforming gains and thus to guarantee sufficient received signal power. In massive multiple input multiple output systems hybrid precoding is utilized to reduce power consumption and cost. Conventional precoding schemes require full channel knowledge at the transmitter, which is hard to obtain, especially in frequency division duplex systems. In this paper, we propose a novel hybrid precoding scheme, which requires only the information acquired during the initial access phase. Furthermore, we derive an analytical expression for characterizing the beam detection capabilities of the system.

In this paper, we investigate the outage performance of an intelligent reflecting surface (IRS)-assisted non-orthogonal multiple access (NOMA) uplink, in which a group of the surface reflecting elements are configured to boost the signal of one of the user equipments (UEs), while the remaining elements are used to boost the other UE. By approximating the received powers as Gamma random variables, tractable expressions for the outage probability under NOMA interference cancellation are obtained. We evaluate the outage over different splits of the elements and varying pathloss differences between the two UEs. The analysis shows that for small pathloss differences, the split should be chosen such that most of the IRS elements are configured to boost the stronger UE, while for large pathloss differences, it is more beneficial to boost the weaker UE. Finally, we investigate a robust selection of the elements’ split under the criterion of minimizing the maximum outage between the two UEs.

Abstract Issues of energy efficiency, latency and reliability have engaged researchers' attention in wireless communications for a long time due to their importance in the dependable delivery of data over wireless networks. The emergence of M2M communication and IoT has intensified this attention because energy efficiency and latency are critical factors affecting their performance. In this paper, a scheduler is designed by utilising the probability density function of the signal‐to‐noise ratio of Rayleigh fading channels to define a threshold used for resource allocation. This threshold, combined with the mean SNR of an M2M device, determines whether or not an M2M device is eligible for scheduling, given its instantaneous channel conditions. The trade‐off between energy efficiency, latency and packet drop of this proposed scheduling strategy is investigated. The performance of the proposed scheduler is compared to round robin, maximum throughput, and proportional fair schedulers. Compared to these standard scheduling strategies, the scheduler provides the advantage of trading off latency for energy efficiency by tuning the threshold parameter.

Uplink non-orthogonal multiple access (NOMA) has been proposed as an efficient technique to support massive connectivity and reduce access-latency. However, due to the inherent multiuser interference within such a system, iterative joint detection is required, which is of high-complexity. In this paper, we exploit the propagation properties of wireless channels to reduce the detection complexity. In particular, when neighboring spreading-blocks on the time-frequency grid experience similar channel conditions, then it is possible to reuse the calculated filter weights between them. We propose four detection strategies and compare them across a wide range of time- and frequency-selectively. Then, assuming the base station is equipped with a sufficient number of antennas, we replace the MMSE filter with a lower-complexity approximation using Neumann series expansion. The results show that our strategies incur only a small performance loss, while substantially cutting down complexity.

With the rising interest in vehicular communications many road safety applications have been developed over the last years. Road safety applications demand low end-to-end latency which can be supported by the large bandwidth available in the millimeter-wave (mm-wave) band. However, with growing carrier frequency the wireless network coverage degrades dramatically. In our work, we focus on enhancing the vehicle-to-infrastructure (V2I) link through idle vehicular users. We enable idle users to act as relays and to boost the signal from the Base Station (BS) to the users with poor quality links and therefore enhance the performance of the entire network. We analyze this approach in a 2-dimensional (2D) Manhattan grid where micro-cells and vehicular users are placed randomly. We consider a part of the users to be idle and select the one who maximizes the coverage improvement to boost the signal from the BS depending on the street, BS and user density. Based on techniques of stochastic geometry, we derive an analytical expression for the coverage probability of the direct link as well as the relay-assisted link and compare the analytical results to Monte Carlo system level simulations in order to validate our model.

Inter-cell interference remains to be a bottleneck for conventional cellular networks as cell-edge users continue to suffer from poor performance. Cell-free massive MIMO is a novel network architecture that suppresses inter-cell interference by eliminating cell boundaries. It promises uniform performance throughout the coverage area, enabled by the coherent joint transmission from multiple distributed antennas. To make the network scalable, a user-centric approach is adopted where each user is served by a cluster of nearby access points (APs). In this work, we study the impact of cluster formation on the total fronthaul requirement, which is a limiting factor in practical coordinated distributed systems. We also investigate its effect on the guaranteed quality of service (QoS) of the network. Using our proposed algorithms, we look into the optimal cluster sizes for a given scenario and show that good performance can be achieved even with relatively small user-centric clusters, which then translates to fronthaul savings.

In this work, we investigate the potential of deep feedforward neural networks for user position estimation for a multi-path directional channel model in presence as well as absence of the line of sight path. Furthermore, we take advantage of a triplet network architecture combined with a triplet loss function, which allows us to exploit intrinsic properties of channel state information. We show that despite the high-dimensional nature of CSI, the proposed network can be trained to learn from the low-dimensional space and with less amount of training samples compared to the long-established approaches in the literature for fingerprinting localization. Finally, in order to investigate the performance and emphasize the benefits of triplet loss, we compare it to another network based on classification.

In frequency division duplex (FDD) multiple-input multiple-output (MIMO) wireless communications, limited channel state information (CSI) feedback is a central tool to support advanced single- and multi-user MIMO beamforming/precoding. To achieve a given CSI quality, the CSI quantization codebook size has to grow exponentially with the number of antennas, leading to quantization complexity, as well as, feedback overhead issues for larger MIMO systems. We have recently proposed a multi-stage recursive Grassmannian quantizer that enables a significant complexity reduction of CSI quantization. In this letter, we show that this recursive quantizer can effectively be combined with deep learning classification to further reduce the complexity, and that it can exploit temporal channel correlations to reduce the CSI feedback overhead.

We propose a novel recursive multi-stage approach to Grassmannian quantization. Compared to the commonly employed single-stage quantization, our method has the advantage of significantly decreasing the number of codebook searches required for quantization and, thus, reducing the complexity. On the downside, the multi-stage approach causes a slight rate-distortion degradation compared to single-stage quantization. We analyze the rate-distortion performance of the proposed recursive quantization approach, considering random vector quantization within the individual stages. We furthermore propose a bit-allocation optimization amongst the stages of the quantizer, given a constraint on the total number of quantization bits.

Delay diversity (DD) is a low-complexity and flexible transmit diversity technology for coded offset quadrature amplitude modulation based filter bank multicarrier (OQAM/FBMC) transmission systems. However, the introduction of delay parameters in DD-OQAM/FBMC systems leads to increased channel frequency selectivity, which makes the channel estimation problem more complicated. In this paper, we analyze and develop preamble-based channel estimation methods in DD-OQAM/FBMC systems from the perspective of frequency-domain, transform-domain, and time-domain aspects. Besides, channel estimators that can obtain the minimum channel estimation mean square error under least squares (LS) and linear minimum mean square error (LMMSE) criteria are derived according to different levels of channel knowledge. Specifically, the proposed frequency-domain method estimates each channel frequency response individually based on a simplified frequency-domain channel model. The transform-domain method first estimates the equivalent single-input-single-output channel observed at the receiver assuming subchannel flatness, and then uses a window function in the time domain to separate the channels of different transmit antennas. The time-domain method is constructed based on an accurate time-domain channel model to directly estimate the channel impulse response without making any assumption about the subchannel flatness. A series of simulation experiments is conducted to verify the effectivity of these channel estimation methods for DD-OQAM/FBMC systems.

Numerical simulation of wireless communications systems is an important means within the process of development and evolution of mobile communications specifications. We offer a software suite to the academic society for free under an academic use license that enables the community to perform simulations of relevant scenarios for 5G and beyond in a reproducible manner. To cover a variety of potential scenarios for future mobile communications systems, we offer the Vienna 5G Link Level (LL) Simulator and the Vienna 5G System Level (SL) Simulator. In this contribution, we perform a verification of our LL and SL simulators which allows us to claim simulation results to be valid. As the Vienna 5G SL Simulator relies on link performance simulation results carried out with the Vienna 5G LL Simulator, we further consider their interaction in the context of verification.

We present a novel localization method based on directional beams, as available in novel massive MIMO transmission techniques instead of radius information, and derive a least squares (LS) estimation method. The new method is a direct LS method that can be solved by a linear set of equations rather than an iterative method required for radius information. In a further step, we also show how to transform radius information into virtual beams to apply the proposed method. Finally, we evaluate the accuracy of the new methods by simulations.

For spreading-based multiple access, whether orthogonal (OMA) or non-orthogonal (NOMA), the spreading sequences (signatures) are selected from a predefined codebook. When operating in a cellular system, intercell interference will be inherently present between close base stations that share the same resources. If the codebook is reused across the different cells, then intercell interference can cause a full collision of the interfering users in the code-domain, thus deteriorating their performance, especially those at the cell-edge. In this paper, we propose a method for reducing intercell interference by means of jointly designed codebooks. The criterion for the code-book design is to minimize the maximum cross-correlation between the signatures of the interfering cells. In order to obtain such code-books, we employ the algorithm of alternating projection. We finally apply the method to two interfering cells for both NOMA and OMA systems, with the users being uniformly distributed in the cells, and show that it can provide a considerable gain to the cell-edge users.

In this paper, we propose a novel optimization framework for resource and numerology allocation in multi-user scenarios. The goal of our optimization is to equalize the users' achievable rates. Our optimization includes intersymbol and intercarrier interference, channel estimation error and interband interference as a result of mixed numerology. The optimization can be formulated as an integer linear program. To reduce the computational complexity of the optimization, we also propose a linear relaxation of the problem. We investigate the performance of our methods by numerical simulations, demonstrating significant gains over an LTE-compliant scenario with fixed numerology and a heuristic approach.

Periodic Channel Quality Indicator (CQI) feedback consumes much of the uplink resources. In scenarios such as urban festivals, sport events, and Olympics football World Cups, which pose additional challenges to the wireless networks due to the heavy traffic load, channel estimation strategies have to be implemented to overcome the problem of signalling overhead while satisfying certain performance bounds. To reduce the CQI feedback overhead, we propose a limited feedback selection scheme. The proposed scheme permits the Base Station (BS) to obtain CQI from a subset of users under substantially reduced feedback overhead and estimate the channel for the remaining users. We cast the problem of CQI estimation by exploiting the theory of Gaussian Process Regression (GPR) which benefits from the correlation property of the macroscopic shadow fading. I.e., users that are close to each other have a correlated channel. The results show that with the proposed approach, a significant reduction in feedback is achieved while keeping the BLock Error Ratio (BLER) below 10% threshold.

In this paper, we propose an optimization method for resource and numerology allocation in multi-user scenarios. We focus on the achievement of packet latency constraints in low latency communications as one of the fundamental service requirements in 5G. We consider two groups of users: low latency users that impose a latency constraint and conventional, non-low latency users that do not impose a latency constraint. Furthermore, we consider users with varying channel conditions characterized by their delay and Doppler spread. Additionally, our optimization includes the effects of channel estimation and inter-numerology interference. The optimization problem is formulated as a multiscenario max-min Knapsack problem and it is given by an integer programming solution.

Grassmannian frames consist of unit-norm vectors with a maximum cross correlation between each other that is minimal. A property like that is desired in many applications, such as in wireless communications, sparse recovery, quantum information theory, and more. In this letter, we present an iterative algorithm targeting the construction of Grassmannian frames, based on a collision-based packing of equal-radius hyperspheres on the surface of a unit-norm hypersphere. Our results show that the algorithm is capable of producing frames with very low coherence, and at a fast convergence rate compared to other methods.

In this paper, we consider a network architecture that supports dynamic allocation of remote radio heads (RRHs) amongst macro base stations, e.g., over dynamically established wireless fronthaul links or a reconfigurable wired fronthaul network. This architecture is similar to cloud radio access networks, yet with the difference that we rely on existing macro base stations rather than outsourcing them to the cloud. We propose a mixed-integer second order cone program (MISOCP) for RRH assignment amongst base stations and joint coordinated beamforming within the dynamically formed distributed antenna systems (DASs); we denote this network architecture as dynamic distributed antenna system (dDAS). Our optimization focuses on minimizing the number of users that are in outage. We propose several relaxations of our optimization problem to determine suboptimal solutions with much reduced computational complexity. We compare the performance of the dDASs to classical heterogeneous networks that employ coordinated beamforming of independent small cells rather than RRHs, demonstrating substantial improvements in terms of outage probability.

We consider beamforming in multiple-input single-output wireless communications under the assumption that the channel is composed of two dominant specular components plus diffuse background scattering. Assuming unknown phases of the two specular components, and thus uncertainty about the multipath interference conditions (constructive/destructive) at the receiver, we propose a beamformer optimization problem that minimizes the signal outage probability, i.e., the probability that the received signal power falls below a prescribed threshold. The obtained optimization problem is non-convex and thus difficult to solve. We consider local optimization on the associated Grassmann manifold, using the negative gradient of the outage probability as local search direction for line search. To simplify the optimization, we propose an approximation of the outage probability which facilitates efficient implementation of the line search.

For real-world performance evaluation, channel models should be accurate in reflecting a realistic behavior between transmitter and receiver. A major concern with the actual standardized channel models, is that they do not consider the time evolution and are relevant only for drop based simulations. The need for spatial consistency of these channel models has been also acknowledged by standardization bodies, e.g., 3GPP, and alternative models are under discussion. In this paper, we compare the statistics generated with the 3GPP 3D channel model to those of ray tracing simulations and validate our method to achieve spatial consistency. Moreover, we estimate the correlation parameters such that the results obtained with our method match the ray-tracing results.

In current wireless cellular networks, the BS tries to provide the highest possible rate to the users that can reliably be decoded. The BS obtains this rate information as a feedback from the users. However, the biased CQI reporting by the users impacts the rate and hence the system performance. The bias is directly related to the estimation error of the predicted SNR obtained by the Gaussian Process Regression (GPR) method in which the spatial correlation of the wireless channel is used for wireless channel prediction, and in particular, for predictive resource allocation. In this paper, the objective is to optimize the predicted SNR-CQI mapping by introducing an offset, adapted to the estimation accuracy relative to the user density. Results show that the proposed CQI mapping enhances the system performance for different user densities.

Spatial wireless channel prediction is crucial for future wireless networks, and in particular, for predictive resource allocation. In this paper, we first predict the channel quality indicator (CQI) at an arbitrary test user based on the Gaussian process regression (GPR) method. Second, in order to limit the overall signalling overhead, we exploit the correlation property of the wireless propagation channel. The performance of the proposed method is evaluated by the Cram'er- Rao bound (CRB). Simulation results not only well demonstrate the potential of our proposed method, but also match with the theoretical analysis.

Channel quality prediction is an essential function for anticipatory and proactive radio resource allocation. In this paper, we propose a channel quality prediction method based on the concept of Gaussian Process Regression (GPR) in which the spatio-temporal correlation of the wireless channel is used for wireless channel prediction. The objective of the paper is to find the optimal channel quality prediction for non-static users. Furthermore, we propose our analytical optimization in the choice of users which enhances the spatio-temporal correlation of the wireless channel and results in performance improvements in terms of BLER and rate loss. Simulation results show the potential of our proposed method.

In order to meet the user requirements in the next generation radio access technology, the ongoing standardization within 3rd Generation Partnership Project (3GPP) offers flexibility in terms of employing mixed numerology. The term numerology refers to the parametrization of the multicarrier modulation scheme. In this paper we consider Single User Single-Input-Single-Output (SU-SISO) Orthogonal Frequency Division Multiplexing (OFDM) under the doubly-selective channel. We investigate the optimal pilot-symbol pattern, accounting not only for Intercarrier Interference (ICI) and Intersymbol Interference (ISI) caused by channel conditions, but also considering the channel estimation error as a consequence of imperfect channel estimation. As a cost function for our optimization we choose the upper bound of the constrained capacity and apply it for different numerology. We show the throughput results in terms of different numerology and compare it to an LTE-compliant scenario.

The performance of indoor wireless cellular networks depends on several parameters such as base station density or arrangement, as well as the blockage of the signal by walls. We investigate the dependence of the network performance in terms of coverage probability and also area spectral efficiency in order to demonstrate the influence of the base station density. We analytically derive expressions for both performance metrics including the blockage by walls and compare these results to those of extensive Monte-Carlo simulations. This is performed for different association strategies as well as for random and regular base station placements. It turns out that in general the addition of walls improves the network performance, and does so even more the smaller the base station density becomes.

In LTE-Advanced (LTE-A) demodulation reference symbols are employed for pilot aided channel estimation to perform coherent detection. Since these reference symbols are allocated on the same time-frequency positions for all users in Multi-User MIMO (MU-MIMO) operation or for all spatial layers in Single-User MIMO (SU-MIMO), they are designed to be code-domain orthogonal in order to be separable at the receiver. This orthogonality is obtained by cyclically shifting a reference signal base sequence, where the specific cyclic shift values are signaled to each user. In this work we show formal equivalence of SU-MIMO and MU-MIMO for LTE-A uplink in the context of channel estimation. We propose a standard compliant mapping that assigns cyclic shifts to users such that SU-MIMO estimation methods are applicable also for MU-MIMO transmissions. Further we show the trade-off between the number of active users and the channel's frequency selectivity in MU-MIMO operation due to the residual channel estimation error.

The tree edit distance (TED), defined as the minimum-cost sequence of node operations that transform one tree into another, is a well-known distance measure for hierarchical data. Thanks to its intuitive definition, TED has found a wide range of diverse applications like software engineering, natural language processing, and bioinformatics. The state-of-the-art algorithms for TED recursively decompose the input trees into smaller subproblems and use dynamic programming to build the result in a bottom-up fashion. The main line of research deals with efficient implementations of a recursive solution introduced by Zhang in the late 1980s. Another more recent recursive solution by Chen found little attention. Its relation to the other TED solutions has never been studied and it has never been empirically tested against its competitors. In this paper we fill the gap and revisit Chen’s TED algorithm. We analyse the recursion by Chen and compare it to Zhang’s recursion. We show that all subproblems generated by Chen can also origin from Zhang’s decomposition. This is interesting since new algorithms that combine the features of both recursive solutions could be developed. Moreover, we revise the runtime complexity of Chen’s algorithm and develop a new traversal strategy to reduce its memory complexity. Finally, we provide the first experimental evaluation of Chen’s algorithm and identify tree shapes for which Chen’s solution is a promising competitor.

Massive MIMO, 3D beamforming and beam tracking strategies for mobile users are among the key strategies for the 5th generation of mobile cellular networks. Their investigation requires channel models that are spatially consistent and evolve smoothly over time. State-of-the-art channel models, such as, 3GPP SCM, WINNER and the 3GPP 3D channel model, do not consider the time evolution and are relevant only for drop based simulations. In this paper we propose two methods to model spatially correlated channels; first, by introducing spatial correlation to LOS/NLOS propagation and indoor/outdoor state of a user, and second, by introducing spatial correlation to the small scale parameters that represent short-term fading characteristics. Our results exhibit realistic behavior of users that move through the network, in terms of LOS/NLOS and indoor/outdoor states and preserve the statistics of the channel model.

In this paper, we investigate multicast transmit beamforming for vehicle to everything (V2X) communication employing LTE's multimedia broadcast single-frequency network (MBSFN) capabilities. LTE-MBSFN enables to broadcast/ multicast information in 4G networks, which can be utilized for efficient dissemination of road traffic related information to vehicles on highways/motorways. MBSFN is currently restricted to single antenna transmission within the LTE standard; this paper aims to present performance benefits obtained with the introduction of multiple transmit antennas. We propose an efficient multi-cell multicast beamforming technique that leads to significant SINR improvements, which enhances reliability, reduces transmission latency and facilitates achieving 5G requirements for vehicular communications.

We consider downlink multi-input single-output transmission in multi-cell mobile wireless networks. We consider a finite-scatterer directional channel model with imperfect channel state information at the transmitter. Specifically, we assume imperfect knowledge of the signal angles of departure of the scattering components, as well as uncertainty about the relative phase-shifts between the signals arriving over these scattering multipath components. For this situation we determine the outage probability of linear beamforming at the transmitters and utilize the results for multi-user scheduling and admission control. The considered channel model relates to Rician fading and to two wave with diffuse power fading for the special cases of a single specular component and two specular components, respectively.

Future wireless systems will be characterized by a large range of possible uses cases. This requires a flexible allocation of the available time-frequency resources, which is difficult in conventional orthogonal frequency division multiplexing (OFDM). Thus, modifications of OFDM, such as windowing or filtering, become necessary. Alternatively, we can employ a different modulation scheme, such as filter bank multi-carrier (FBMC). In this paper, we provide a unifying framework, discussion, and performance evaluation of FBMC and compare it with OFDM-based schemes. Our investigations are not only based on simulations, but are substantiated by real-world testbed measurements and trials, where we show that multiple antennas and channel estimation, two of the main challenges associated with FBMC, can be efficiently dealt with. In addition, we derive closed-form solutions for the signal-to-interference ratio in doubly-selective channels and show that in many practical cases, one-tap equalizers are sufficient. A downloadable MATLAB code supports reproducibility of our results.

In this letter, we revisit single-user multiple-input single-output beamforming with imperfect channel state information (CSI) at the transmitter. We present a CSI estimation model that is suitable for finite-scatterer directional channel models with uncertainty in the relative phase shifts of the scattering components. We show that in this case the distribution of the effective beamformed channel does not follow one of the well-known channel fading distributions and leads to complicated outage calculations. We show that the presented model generalizes popular existing models and we relate our investigations for the special cases of one and two specular components to existing results. We furthermore consider signal outage optimal beamforming for the case of two specular components.

In this paper, we consider downlink multi-user MIMO transmission in massive MIMO systems, assuming massive amount of antennas at transmitter- as well as receiver-side. We investigate the performance of several linear MIMO transceiver architectures of comparatively low complexity, since non-linear schemes of high complexity (e.g., maximum likelihood detection) appear infeasible with large numbers of transmit and/or receive antennas. The focus of our investigation lies on the impact of spatial antenna correlation on the achievable rate of the considered schemes. We derive achievable rate upper bounds that facilitate interpretation and explanation of performance losses observed with increasing antenna correlation and with growing number of antennas and spatially multiplexed users. We identify favourable combinations of low complexity transmitter/receiver filters and show that the impact of antenna correlation strongly depends on the applied transceiver architecture.

The wireless channel behaves markedly different depending on whether a line-of-sight (LOS) between transmitter and receiver exists or not. State-of-the-art wireless channel models for mobile communications, such as, IST WINNER-II and the 3GPP 3D channel model specified in TR 36.873, define distinct macro- and microscopic fading parameters for LOS and non-LOS (NLOS) situations. Whether a user is in LOS/NLOS is randomly determined by a distance-dependent LOS probability; commonly, for each position in the network, the random realization of LOS/NLOS propagation is independently determined from this distance-dependent LOS probability. Since in this case random draws of neighboring positions are statistically independent, base station assignment regions in system-level simulations become highly irregular. To mitigate this problem, we propose an efficient method to generate spatially correlated LOS/NLOS channel maps that follow predefined distance-dependent LOS probability and additionally enable spatial clustering of LOS positions.

In this letter, we derive worst-case approximation results for rank one and rank two leakage-based multicasting (LBM) as recently proposed by Schwarz and Rupp [“Transmit optimization for the MISO multicast interference channel,” IEEE Trans. Commun., vol. 63, no. 12, pp. 4936-4949, Dec. 2015]. Specifically, we provide worst-case lower bounds on the approximation ratios achieved with rank one/two Gaussian randomization of the optimal solution as obtained from a semidefinite relaxation (SDR). We demonstrate the validity of the derived bounds through Monte Carlo simulations and we show that good approximation ratios are achieved even for very large number of multicast users and leakage constraints.

An open problem in slotted Aloha protocols is to optimally estimate the number of participants as such knowledge is crucial to select the optimal frame length. First results are known in literature based on observing the slot fill levels in case of empty slots and single occupancies (singleton slots). Advances in signal processing allow now also to decode successfully slots with higher fill levels, for example, due to multiple antennas. In this paper we derive the maximum likelihood estimator when arbitrary occupancies up to a maximal fill level R have been observed. Due to our novel approach, the derivation is rather simple and its implementation is of low complexity.

Although the LMS algorithm is often preferred in practice due to its numerous positive implementation properties, once the parameter space to estimate becomes large, the algorithm suffers of slow learning. Many ideas have been proposed to introduce some a-priori knowledge into the algorithm to speed up its learning rate. Recently also sparsity concepts have become of interest for such algorithms. In this contribution we follow a different path by focusing on the separability of linear operators, a typical property of interest when dealing with tensors. Once such separability property is given, a gradient type algorithm can be derived with significant increase in learning rate. Even if separability is only given to a certain extent, we show that the algorithm can still provide gains. We derive quality and quantity measures to describe the algorithmic behavior in such contexts and evaluate its properties by Monte Carlo simulations.

We consider downlink multi-user MIMO transmission based on block-diagonalization precoding with quantized channel state information at the transmitter, obtained through limited feedback. We assume that users are equipped with excess receive antennas, i.e., the number of receive antennas is larger than the number of data streams per user, and propose a novel receive antenna combining method that maximizes an estimate of the expected achievable user rate. By means of simulations, we validate our assumptions and demonstrate significant rate gains compared to existing combiners of similar complexity.

In this paper, we investigate downlink physical layer multicast transmit beamforming in wireless cellular networks, considering interference between multiple independent multicast transmitters (base stations). Transmit beamforming can exploit multiple antennas at the transmitter to direct the multicast signal towards the intended users, while minimizing the interference leakage caused to other users of the network. We propose a multicast beamformer optimization problem that maximizes the achievable multicast transmission rate while restricting the interference leakage caused to other users, by applying a semidefinite relaxation to approximate this NP-hard problem with a convex optimization problem that can be solved efficiently. Furthermore, we consider multiple receive antennas at the users and propose an antenna combiner that maximizes the achievable user rate. Finally, we combine the proposed beamforming and receive antenna combining methods via alternating optimization and evaluate the performance using Monte-Carlo simulations.

In this letter, we consider time-varying complex-valued n × m matrices H[k] (m ≤ n) and propose a predictive quantizer for the eigenvectors of the Gramian H[k]H[k]H, which operates on the associated compact Stiefel manifold. The proposed quantizer exploits the temporal correlation of the source signal to provide high-fidelity representations with significantly reduced quantization codebook size compared to memoryless schemes. We apply the quantizer to channel state information quantization for limited feedback based multi-user MIMO, employing regularized block-diagonalization precoding. We demonstrate significant rate gains compared to block-diagonalization precoding using Grassmannian predictive feedback.

In this paper, we consider multiple-input single-output (MISO) physical layer multicasting, where several multiantenna transmitters each simultaneously multicast a common message to a distinct set of single-antenna receivers, causing interference between different multicast messages. To optimize the achievable rate of this MISO multicast interference channel, we propose iterative distributed transmit optimization algorithms that are based on interference leakage control, requiring local channel state information at each transmitter and leakage information exchange among transmitters. We, furthermore, propose extensions of existing coordinated multipoint transmission schemes that have been developed for unicast interference channels, such as signal to leakage and noise ratio beamforming, to the considered multicast system. Such methods are of importance, e.g., for future releases of LTE that will support multiantenna multicasting using MBMS/MBSFN. Numerical simulations confirm the potential of the proposed distributed transmit optimization algorithm.

In this article, we investigate the performance of cellular networks employing remote radio units to enable distributed multi-user MIMO transmission using block-diagonalization precoding. We consider the downlink of an OFDM-based LTE compliant multi-carrier system in which the users provide channel state information (CSI) to the base station via limited capacity feedback links. With limited feedback, residual interference between the spatially multiplexed users cannot be avoided, causing an interference-limitation of the achievable downlink throughput. Efficient CSI quantization is therefore central in such systems to achieve a performance gain over single-user MIMO. Building on Grassmannian CSI quantization concepts, we propose an effective CSI feedback clustering approach in this work to exploit the correlation of the wireless channel in the frequency domain. We furthermore derive the corresponding channel quality feedback and propose a practical greedy multi-user scheduler.

We propose an adaptive quantization algorithm for subspace tracking on the Grassmann-manifold of p-dimensional subspaces in the n-dimensional Euclidean space. This quantization problem arises naturally in limited feedback based wireless communication systems, which apply precoding for interference cancellation and alignment. The proposed algorithm exploits the differential geometry associated with the Grassmann-manifold for efficient differential and predictive quantization. The algorithm is applied to channel state information quantization in a multi-user block-diagonalization based wireless communication system, demonstrating large throughput gains compared to memoryless quantization.

Spatial multiplexing of multiple users in a multi-antenna broadcast system, i.e., multi-user MIMO, is a promising technique for improving the spectral efficiency of cellular networks. Still, practical implementations struggle with the difficulty of obtaining sufficiently accurate channel state information at the transmitter (CSIT), due to restrictions on the capacity of the CSI feedback links from the users. Without accurate CSIT, residual multi-user interference diminishes the potential performance gain of multi-user MIMO, challenging its value for practical realizations. In this work we show that the CSI feedback overhead can be significantly reduced if the number of spatially multiplexed data streams per user is less than the number of receive antennas, and the antenna outputs are appropriately combined. We propose an interference-unaware antenna combining algorithm that minimizes the CSI quantization error, which has the effect of minimizing multi-user interference at the price of reducing the received signal power. We mathematically analyze the performance of the proposed algorithm, substantiating its value for limited feedback systems. Numerical simulations confirm the potential of the proposed algorithm in comparison to a traditional technique that maximizes the received signal power.

In this paper, we present algorithms for the quantization of a correlated unit norm random vector process. Such algorithms are important, e.g., for channel vector quantization in wireless communication systems. Starting from a quantization codebook that uniformly quantizes the unit sphere, we propose to iteratively adapt the codebook to the channel statistics, in order to improve the quantization accuracy. This is achieved by increasing the density of the quantization code vectors in that area of the unit sphere where the next realization of the random process is expected to lie, without increasing the codebook size. Additionally, we propose spherical prediction algorithms for the considered random process. Combining the codebook adaptation techniques with this prediction leads to improved quantization accuracy. The performance of the algorithms is demonstrated by employing them in an LTE system for providing accurate transmitter channel knowledge used for multiuser beamforming.

Many candidate technologies for future wireless communication systems (e.g., multi-user MIMO, interference alignment), rely on accurate Channel State Information (CSI) at the Transmitter (CSIT) to determine the appropriate multi-antenna pre-processing steps. Thus, in frequency division duplexing systems, a dedicated feedback link has to be provided to each attached user for CSI reporting. In order to keep the feedback rate moderate, CSI quantization is required. Additionally, CSI is typically only provided for a subset of all available time/frequency resources, implicating the need for an interpolation algorithm at the base station. Furthermore, to compensate for the processing delay of the feedback link, CSI prediction is required as well. In this work, we treat these problems by incorporating our previously presented CSI feedback algorithm in a 3GPP LTE-A compliant wideband OFDM simulation environment, and augmenting it with appropriate interpolators and predictors. As an example, the performance of multi-user zero-forcing beamfoming is investigated, under practical feedback delay- and time-frequency granularity-constraints, demonstrating substantial throughput gains, in case of low to moderate user mobility.

We present a large-scale cell based measurement analysis of the user behavior in a live operational HSDPA network. The motivations are: first, to understand the statistical properties of users in cells for refining network planning procedures; and second, to provide realistic traffic models for simulations of cellular packet-oriented networks. We provide an analysis of mean cell load over daytime, as well as models for short-term cell load in terms of user activity and throughput. Furthermore, an evaluation of user-sessions with respect to duration and mean throughput is given. Our findings lead to four different models reflecting realistic user-traffic load of cellular networks. To verify the concept we present respective simulations which investigate multiuser-scheduling in an LTE-network. They show that conventional simulation settings can lead to an overestimation of performance.

We address the problem of downlink multiuser scheduling in practical wireless networks under a desired fairness constraint. Wireless networks such as LTE, WiMAX and WiFi provide partial channel knowledge at the base station/access point by means of quantized user equipment feedback. Specifically in 3GPP's LTE, the Channel Quality Indicator (CQI) feedback provides time-frequency selective information on achievable rates. This knowledge enables the scheduler to achieve multiuser diversity gains by assigning resources to users with favorable channel conditions. However, focusing only on the possible diversity gains leads to unfair treatment of the individual users. To overcome this situation we propose a method for multiuser scheduling that operates on the boundary of the achievable multiuser rate region while guaranteeing a desired long term average fairness. Our method is based on a sum utility maximization of the α-fair utility functions. To obtain a given fairness, quantified with Jain's fairness index, it is necessary to find an appropriate α, which we obtain from the observed CQI probability mass function.

The UMTS Long Term Evolution (LTE) allows the pilot symbol power to be adjusted with respect to that of the data symbols. Such power increase at the pilot symbols results in a more accurate channel estimate, but in turn reduces the amount of power available for the data transmission. In this paper, we derive optimal pilot symbol power allocation based on maximization of the post-equalization Signal to Interference and Noise Ratio (SINR) under imperfect channel knowledge. Simulation validates our analytical mode for optimal pilot symbol power allocation.