We explore the potential of multi-connectivity to enhance the Extended Reality (XR) capacity of cellular networks in the downlink (DL). Specifically, we investigate the impact of XR tethering group (TGr) on XR capacity, where an XR device maintains both a direct connection to the cellular network and an indirect connection via a 5th generation-advanced (5G-A) tethering device. This setup enables cooperation between devices of the TGr to improve data reception. To efficiently deliver XR data, we employ point-to-multipoint (PTM) multicast transmission and analyze selection combining (SC) across TGr devices, coupled with a joint hybrid automatic repeat request (HARQ) feedback processing algorithm and link adaptation (LA) per TGr at the network side. Additionally, we propose a joint Outer Loop Link Adaptation (OLLA) algorithm for optimizing the modulation and coding scheme (MCS) for the PTM multicast transmission. Two variations for MCS selection based on CQI reports for PTM multicast transmission to XR TGr are also analyzed. Our findings demonstrate that XR TGrs enhance the XR capacity of the network by 12% to 17% compared to single-link-connected XR devices in a dynamic multi-cell, multi-user system level simulation. Furthermore, in coexistence scenarios with enhanced mobile broadband (eMBB) user, XR TGrs increase eMBB throughput by 18%, as they utilize fewer resources.

In-Body Subnetworks (IBS) present a promising platform for delivering eXtended Reality (XR) services, provided that intelligent scheduling mechanisms are employed to mitigate potential interference—particularly when IBS operate as an underlay to 5G and beyond cellular networks supporting enhanced Mobile Broadband (eMBB) users. This paper addresses the coexistence challenge between eMBB and IBS users by proposing a novel downlink transmission framework that leverages beam-domain information to enable efficient spatial resource reuse. The framework aims to meet the stringent delay and reliability requirements of XR users while improving the experienced throughput of eMBB users. Simulation results demonstrate that the proposed spatial reuse algorithm can enhance eMBB throughput by up to 50%, without reducing the number of supported XR users.

The concept of subnetworks has been recently identified as a key component of 6 G, enabling mission-critical services with hyper-reliability. In this paper, we address the challenges posed by both inter-subnetwork interference and external interference in hyper-dense and dynamic industrial environments. We focus on in-factory subnetworks for industrial wireless control applications and propose an advanced frequency-power resource allocation scheme using Gradient Descent-based Resource Allocation (GDRA). The proposed GDRA scheme efficiently mitigates interference while optimizing resource allocation. We evaluate the performance of the proposed scheme and compare it with state-of-the-art approaches, demonstrating significant improvements in spectral efficiency and reliability.

Conventional hybrid automatic repeat request (HARQ) uses single-bit feedback to boost retransmission reliability; however, its limited granularity restricts advanced link adaptation (LA) for optimizing performance beyond reliability. This paper presents a novel LA approach for retransmissions in 6G networks, targeting spectral efficiency (SE) optimization through effective modulation and coding scheme selection within the HARQ framework. In a dense urban setting with urban macro cell deployment and file transfer protocol model 3 (FTP3) traffic, our optimal LA solution improves average downlink (DL) physical resource block utilization by 31.37 % over Chase combining (CC), a gain inherently linked to SE optimization. Following 3GPP release 18 study item technical report's recommendation to include LA information in HARQ feedback, we address feedback design limitations of the optimal solution with a quantized LA metric. This metric is incorporated into our novel enriched HARQ feedback (EHF) at the user equipment, enabling EHF to surpass single-bit conventional feedback that only conveys transport block decoding status. Evaluated under varying feedback bit counts and LA quantization levels, our design achieves significant gains-e.g., a 20.26 % increase over CC in average DL throughput per FTP3 packet with 4-bit feedback-offering flexibility in application-specific bit sizing.

In-body Subnetworks (IBS) are set to become key enablers for standalone eXtended Reality (XR) applications, allowing users to leverage smartphones and XR display devices (XRDD) to create immersive virtual and augmented environments. However, supporting reliable XR services over IBS poses significant challenges due to dense deployment, resource constraints, and stringent communication requirements. To address these challenges, we propose a resource allocation algorithm specifically designed for transmitting XR video frames over IBS. We formulate the problem as a reliability maximization task, modeled as a Partially Observable Markov Decision Process (POMDP). A discrete version of the Soft Actor-Critic (SAC) algorithm is employed to derive a near-optimal policy for joint power control and sub-band allocation. Our approach utilizes a multi-agent framework, where each IBS acts as an agent responsible for resource allocation decisions. Our results demonstrate that, despite relying on partial information of the network, the proposed algorithm surpasses distributed benchmark and performs comparable to centralized algorithms, achieving satisfaction rates of 55% and 98% for the case of 8000 IBSs per square kilometer and packet success rate (PSR) thresholds of 99% and 95%, respectively.

Channel State Information (CSI) is critical for optimizing wireless communication systems, particularly in Industrial Internet of Things (IIoT) networks where real-time performance and reliability are paramount. In high-density 6G-enabled factory environments, the mobility of subnetworks and dense deployments exacerbate interference, necessitating proactive and efficient resource management. This paper introduces a dual attention-based channel prediction framework, combined with an AI-driven resource allocation model, to mitigate the challenges of outdated CSI in IIoT subnetworks. By employing spatio-temporal attention mechanisms, the proposed framework effectively predicts CSI and integrates sub-band allocation with power control for optimized resource utilization. Simulation results highlight significant gains in spectral efficiency, enhanced Quality of Service (QoS) adherence, and superior robustness to delay, outperforming state-of-the-art methods. These advancements make the proposed solution a scalable and reliable choice for next-generation IIoT deployments.

The forthcoming sixth-generation (6G) Industrial Internet of Things (IIoT) subnetworks are expected to support ultrafast control communication cycles for numerous IoT devices. However, meeting the stringent requirements for low latency and high reliability poses significant challenges, particularly due to signal fading and physical obstructions. In this article, we propose novel time-division multiple access (TDMA) and frequency-division multiple access (FDMA) communication protocols for cooperative transmission in IIoT subnetworks. These protocols leverage secondary access points (sAPs) as decode-and-forward (DF) and amplify-and-forward (AF) relays, enabling shorter cycle times while minimizing overall transmit power. A classification mechanism determines whether the highest gain link for each IoT device is a single-hop or two-hop connection, and selects the corresponding sAP. We then formulate the problem of minimizing transmit power for DF/AF relaying while adhering to the delay and maximum power constraints. In the FDMA case, an additional constraint is introduced for bandwidth allocation to IoT devices during the first and second phases of cooperative transmission. To tackle the nonconvex problem, we employ the sequential parametric convex approximation (SPCA) method. We extend our analysis to a system model with reconfigurable intelligent surfaces (RISs), enabling transmission through direct and RIS-assisted channels, and optimizing for a multi-RIS scenario for comparative analysis. Simulation results show that our cooperative communication approach reduces the emitted power by up to 4.5 dB while maintaining an outage probability and a resource overflow rate below$10^{-6}$. While the RIS-based solution achieves greater power savings, the relay-based protocol outperforms RIS in terms of outage probability.

Subnetworks are expected to enhance wireless pervasiveness for critical applications such as wireless control of plants, however, they are interference-limited due to their extreme density. This paper proposes a goal-oriented joint power and multiple sub-bands allocation policy for interference coordination in 6G in-factory subnetworks. Current methods for interference coordination in subnetworks only focus on optimizing communication metrics, such as the block error rate, without considering the goal of the controlled plants. This oversight often leads to inefficient allocation of the limited radio resources. To address this, we devise a novel decentralized inter-subnetwork interference coordination policy optimized using a Bayesian framework to ensure the long-term stability of the subnetwork-controlled plants. Our results show that the proposed decentralized method can support more than twice the density of subnetwork-controlled plants compared to centralized schemes that aim to minimize the block error rate while reducing execution complexity significantly.

In this paper, we investigate the multi-connectivity for Extended Reality (XR) devices, having both a direct link and an indirect link to the same Next-Generation Node B (gNodeB). The indirect link is established through a cooperating 5th generation (5G) tethering device positioned in proximity to the XR device. Our study focuses on point-to-multipoint (PTM) multicast transmission to the XR device and the nearby 5G tethering device, emphasizing their cooperative interaction. We conduct this investigation under a spatially consistent channel model, quantifying the performance in terms of block error rate (BLER). Furthermore, we propose algorithms for processing joint hybrid automatic repeat request (HARQ) feedback (FB) in 5G multicast sessions. These algorithms are designed for multicast sessions, where User Equipment (UE)s actively cooperate, aiming to enhance the spectral efficiency of multiconnected XR devices by minimizing HARQ retransmissions. The resulting increase in spectral efficiency contributes to the overall capacity improvement of the network. Our link-level simulations demonstrate that multi-connectivity for XR yields a 0.8 dB gain for a 10% BLER target. Additionally, the proposed joint HARQ FB processing algorithm provides approximately a 7% gain in spectral efficiency.

Recently, 6G in-X subnetworks have been proposed as low-power short-range radio cells to support localized extreme wireless connectivity inside entities such as industrial robots, vehicles, and the human body. Deployment of in-X subnetworks within these entities may result in rapid changes in interference levels and thus, varying link quality. This paper investigates distributed dynamic channel allocation to mitigate inter-subnetwork interference in dense in-factory deployments of 6G in-X subnetworks. This paper introduces two new techniques, Federated Multi-Agent Double Deep Q-Network (F-MADDQN) and Federated Multi-Agent Deep Proximal Policy Optimization (F-MADPPO), for channel allocation in 6G in-X subnetworks. These techniques are based on a client-to-server horizontal federated reinforcement learning framework. The methods require sharing only local model weights with a centralized gNB for federated aggregation thereby preserving local data privacy and security. Simulations were conducted using a practical indoor factory environment proposed by 5G-ACIA and 3GPP models for in-factory environments. The results showed that the proposed methods achieved slightly better performance than baseline schemes with significantly reduced signaling overhead compared to the baseline solutions. The schemes also showed better robustness and generalization ability to changes in deployment densities and propagation parameters.

In this paper, we develop a novel power control solution for subnetworks-enabled distributed control systems in factory settings. We propose a channel-independent control-aware (CICA) policy based on the logistic model and learn the parameters using Bayesian optimization with a multi-objective tree-structured Parzen estimator. The objective is to minimize the control cost of the plants, measured as a finite horizon linear quadratic regulator cost. The proposed policy can be executed in a fully distributed manner and does not require cumbersome measurement of channel gain information, hence it is scalable for large-scale deployment of subnetworks for distributed control applications. With extensive numerical simulation and considering different densities of subnetworks, we show that the proposed method can achieve competitive stability performance and high availability for large-scale distributed control plants with limited radio resources.

In this paper, we present an unsupervised approach for frequency sub-band allocation in wireless networks using a graph-based learning method. We consider a scenario of dense deployment of subnetworks in the factory environment. The limited number of sub-bands must be optimally allocated to coordinate inter-subnetwork interference. Traditional iterative solutions may not scale to the large scale and density of subnetwork deployment due to their execution overhead limitations. Hence, we consider a data-driven approach based on graph neural networks. We model the subnetwork deployment as an interference graph and propose an unsupervised learning approach to optimize the sub-band allocation using graph neural networks. This approach is inspired by the graph colouring heuristic and the Potts model. The numerical evaluation shows that the proposed method achieves close performance to the centralized greedy colouring sub-band allocation heuristic with lower computational time complexity. In addition, it incurs reduced signalling overhead compared to iterative optimization heuristics that require all the mutual interfering channel information. We further demonstrate that the method is robust to different network settings.

This article proposes a novel deep learning (DL)based enhancement of iterative detection and decoding technique in multiple-input multiple-output (MIMO) wireless systems. The receiver integrates a classical soft-input soft-output (SISO) minimum mean squared error successive interference cancellation (SIC) algorithm into its detector and an optimized low-density parity-check channel decoder within a deep-unfolded iterative (DUI) framework. Termed DUI-SISO-SIC, this receiver offers two significant contributions. Firstly, it introduces learnable parameters to enhance the detector’s a-priori information, thereby improving overall performance. Secondly, it employs a data-driven DL approach to address the computational complexity associated with the crucial MMSE-based symbol ordering process during the SIC step in detection. The simulation results demonstrate the receiver’s superiority over state-of-the-art counterparts in a 5G-compliant coded multi-user MIMO orthogonal frequency-division multiplexing system, regardless of perfect channel knowledge availability. Furthermore, the symbol ordering enhancement during detection demonstrates a substantial reduction in its asymptotic upper bound, particularly with larger MIMO dimensions, compared to the classical MMSE-based approach.

Deep learning (DL) methods have shown potential in tackling the performance-complexity trade-off in multiple-input multiple-output (MIMO) detection. Unlike most studies that evaluate state-of-the-art (SoA) DL receivers in uncoded scenarios, our paper focuses on realistic coded MIMO systems. After a comprehensive literature review, three representative SoA model-based DL receivers viz: DetNet, OAMPNet2, and DUIDD (MMSE-PIC and LoCo-PIC) were selected and comprehensively evaluated. Our findings indicate that DL receivers such as DetNet and OAMPNet2, which base their classical designs on the principle of symbol denoising, fail to sustain their superior performances from uncoded systems to coded systems due to inaccurate residual noise statistics. In contrast, DUIDD, specifically designed for coded systems, achieves effective interference cancellation, resulting in improved coded bit error rates across i.i.d. Gaussian channels, suggesting promising avenues for future research. However, LoCo-PIC, which simplifies MMSE-PIC with a linear solution, suffers performance degradation in correlated urban microcell channels, highlighting the importance of considering the non-linear correlation impacts during detection. Additionally, this simplification leads to further degradation in out-of-distribution channel scenarios, emphasizing the need to address these impacts in realistic wireless systems with varying channel conditions.

Deep learning has been used to tackle problems in wireless communication including signal detection, channel estimation, traffic prediction, and demapping. Achieving reasonable results with deep learning typically requires large datasets which may be difficult to obtain for every scenario/configuration in wireless communication. Transfer learning (TL) solves this problem by leveraging knowledge and experience gained from one scenario or configuration to adapt a system to a different scenario using smaller dataset. TL has been studied for various stand-alone parts of the radio receiver where individual receiver components, for example, the channel estimator are replaced by a neural network. There has however been no work on TL for receivers where the entire receiver chain is replaced by a neural network. This paper fills this gap by studying the performance of fine-tuning based transfer learning techniques for various configuration mismatch cases using a deep neural single-input-multiple-output(SIMO) receiver. Simulation results show that overall, partial fine-tuning better closes the performance gap between zero target dataset and sufficient target dataset.

The use of Intelligent Reflecting Surfaces (IRSs) is considered a potential enabling technology for enhancing the spectral and energy efficiency of beyond 5G communication systems. In this paper, a joint relay and intelligent reflecting surface (IRS)-assisted communication is considered to investigate the gains of optimizing both the phase angles and selection of relays. The combination of successive refinement and reinforcement learning is proposed. Successive refinement algorithm is used for phase optimization and reinforcement learning is used for relay selection. Experimental results indicate that the proposed approach offers improved achievable rate performance and scales better with number of relays compared to considered benchmark approaches.

In-body subnetworks (IBS) are envisioned to support reliable wireless connectivity for emerging applications including extended reality (XR) in the human body. As the deployment of in-body sub-networks is uncontrollable by nature, the dynamic radio resource allocation scheme in place becomes of the uttermost importance for the performance of the in-body sub-networks. This paper provides a comparative study on the performance of the state-of-the-art interference-aware sub-band allocation algorithms in in-body sub-networks supporting the XR applications. The study identified suitable models for characterizing in-body sub-networks which are used in a snapshot-based simulation framework to perform a comprehensive evaluation of the performance of state-of-art sub-band allocation algorithms, including greedy selection, sequential greedy selection (SG), centralized graph coloring (CGC), and sequential iterative sub-band allocation (SISA). The study shows that for XR requirements, the SISA and SG algorithms can support IBS densities up to 75% higher than CGC.

Transmit power control (TPC) is a key mechanism for managing interference, energy utilization, and connectivity in wireless systems. In this paper, we propose a simple low-complexity TPC algorithm based on the deep unfolding of the iterative projected gradient descent (PGD) algorithm into layers of a deep neural network and learning the step-size parameters. An unsupervised learning method with either online learning or offline pretraining is applied for optimizing the weights of the DNN. Performance evaluation in dense device-to-device (D2D) communication scenarios showed that the proposed method can achieve better performance than the iterative algorithm with more than a factor of 2 lower number of iterations.

6th Generation (6G) industrial wireless subnetworks are expected to replace wired connectivity for control operation in robots and production modules. Interference management techniques such as centralized power control can improve spectral efficiency in dense deployments of such subnetworks. However, existing solutions for centralized power control may require full channel state information (CSI) of all the desired and interfering links, which may be cumbersome and time-consuming to obtain in dense deployments. This paper presents a novel solution for centralized power control for industrial subnetworks based on Graph Neural Networks (GNNs). The proposed method only requires the subnetwork positioning information, usually known at the central controller, and the knowledge of the desired link channel gain during the execution phase. Simulation results show that our solution achieves similar spectral efficiency as the benchmark schemes requiring full CSI in runtime operations. Also, robustness to changes in the deployment density and environment characteristics with respect to the training phase is verified.

Sixth generation (6G) in-X subnetworks are recently proposed as short-range low-power radio cells for supporting localized extreme wireless connectivity inside entities such as industrial robots, vehicles, and the human body. The deployment of in-X subnetworks in these entities may lead to fast changes in the interference level and hence, varying risks of communication failure. In this paper, we investigate fully distributed resource allocation for interference mitigation in dense deployments of 6G in-X subnetworks. Resource allocation is cast as a multi-agent reinforcement learning problem and agents are trained in a simulated environment to perform channel selection with the goal of maximizing the per-subnetwork rate subject to a target rate constraint for each device. To overcome the slow convergence and performance degradation issues associated with fully distributed learning, we adopt a centralized training procedure involving local training of a deep Q-network (DQN) at a central location with measurements obtained at all subnetworks. The policy is implemented using Double Deep Q-Network (DDQN) due to its ability to enhance training stability and convergence. Performance evaluation results in an in-factory environment indicated that the proposed method can achieve up to 19% rate increase relative to random allocation and is only marginally worse than complex centralized benchmarks.

This paper investigates efficient deep learning based methods for interference mitigation in independent wireless subnetworks via dynamic allocation of radio resources. Resource allocation is cast as a mapping from interference power measurements at each subnetwork to a class of shared frequency channels. A deep neural network (DNN) is then trained to approximate this mapping using data obtained via application of centralized graph coloring (CGC). The trained network is then deployed at each subnetwork for distributed channel selection. Simulation results in an environment with mobile subnetworks have shown that relatively small-sized DNNs can be trained offline to perform distributed channel allocation. The results also show that regardless of the choice of initialization, a DNN for distributed channel selection can achieve similar performance as CGC up to a probability of loop failure (PLF) of 6 × 10–5in diverse environments with only aggregate interference power measurements as input.

This paper presents a novel Bayesian Synthetic Likelihood (BSL) method for calibration of stochastic radio channels without multi path parameter estimation. To calibrate a stochastic channel model, we apply a Markov Chain Monte Carlo (MCMC) algorithm with a Metropolis accept/reject criterion and synthetic likelihood obtained from data generated using the model. The proposed method is applied to calibrate the Turin model and the polarized propagation graph model. Simulation examples show that the BSL method yield similar calibration accuracy to the state-of-the-art method based on Approximate Bayesian Computation (ABC).

Dense industrial wireless cells have to ensure minimum survival data rates such that the mission critical functionalities can at least be supported regardless of the deployment and interference conditions. In this paper, we propose a novel spectrum allocation mechanism meant to efficiently support minimum survival data rates but without compromising the average network performance. The presented approach is based on a two-phase graph construction and coloring, where cells that are still experiencing mutual interference upon a first frequency allocation are further orthogonalized. Simulation results show that our solution can lead to an increase of up to ~250% of the survival data rates with negligible impact on the average network throughput.

This paper proposes a distributed deep reinforcement learning (DRL) methodology for autonomous mobile robots (AMRs) to manage radio resources in an indoor factory with no network infrastructure. Hence, deep neural networks (DNN) are used to optimize the decision policy of the robots, which will make decisions in a distributed manner without signalling exchange. To speed up the learning phase, a centralized training is adopted in which a single DNN is trained using the experience from all robots. Once completed, the pre-trained DNN is deployed at all robots for distributed selection of resources. The performance of this approach is evaluated and compared to 5G NR sidelink mode 2 via simulations. The results show that the proposed method achieves up to 5% higher probability of successful reception when the density of robots in the scenario is high.

Ultra-wideband (UWB) technology offers the potential for unparalleled support of short-range broadband communication over a multi-gigahertz spectrum and are expected to enable several applications with extreme requirements in future wireless networks. Enabling these systems in the unlicensed spectrum requires efficient co-existence management and adequate understanding of the characteristics and spatio-temporal dynamics of interference signals over the multi-GHz bandwidth. This paper investigates the suitability of Gaussian, Middleton canonical class A, symmetric alpha stable and Gaussian Mixture distributions for modelling radio frequency interference from systems in the UWB spectrum based on measurements. We evaluate the closeness of fit of the distributions to measured interference data and provide insights on the applicability of these models for characterizing interference in the UWB spectrum. Results show that the Gaussian Mixture distribution (GMD) yielded the best fit to the measured interference evaluated with Kullback-Leibler (KL) divergence below 0.05. Results also show that interference signals generated from the GMD agree closely with the measurements.

Ultra-wide band (UWB) radio systems are expected to operate in co-existence with a myriad of other systems over a large unlicensed bandwidth. Thus, UWB devices need to incorporate efficient inter-system interference mitigation mechanisms. In this paper, we present interference measurements covering the UWB spectrum from 3GHz to 11GHz conducted at two locations (indoor and outdoor) on the campus of Aalborg University, Denmark. We analysed the measurements in terms of occurrence probability, interference power distribution and inter-arrival time statistics. The goal is to understand the characteristics of signals emanating from systems operating on this ultra-wide bandwidth as a basis for the development of models and methods for interference characterization and mitigation. Results indicate that signal activity vary significantly across the spectrum with the 5GHz - 6GHz and 9GHz - 10GHz sub-bands having the strongest power levels in the indoor and outdoor measurements, respectively. Statistical analysis results further show significant variation of the power distribution, occurrence probability and inter- arrival time statistics for the various signals detected in the measurements. Results also show that time between interference occurrence is exponentially distributed for most of the sources.

This paper presents the results of wireless channel measurement campaign in the 3 GHz to 8 GHz frequency range. The measurements were performed with focus on the short-range with a transmitter-receiver separation distance less than 9 m in two typical industrial environments: a low clutter density manufacturing space, and a high clutter density one. We analyzed the statistical properties of the most important temporal and large-scale propagation characteristics including total received energy, path loss exponent, maximum excess delay (MED) and root mean square (RMS) delay spread based on the measurements. Statistical models for the RMS delay spread and MED are also presented using the log-normal and Gamma distributions.

This paper presents a low complexity method for computation of the transfer matrix of wireless channels in complex indoor environments using propagation graphs. Multi-room indoor environments can be represented in a vector signal flow graph with with rooms in the complex structure as nodes and propagation between rooms as branches. The transfer matrix can be computed using Masons theorem which lead to a much-reduced computational complexity.

In this paper, we investigate downlink resource allocation in two-tier OFDMA heterogeneous networks comprising a macrocell transmitting at a microwave frequency and dual band small cells utilizing both microwave and millimeter wave frequencies. A non - cooperative game theoretic approach is proposed for adaptively switching the small cell transmission frequency based on the location of small cell users and interference to macrocell users. We propose a resource allocation approach which maximizes the sum rate of small cell users while minimizing interference to macrocell users and the total power consumption. The performance of the proposed resource allocation solution is evaluated via rigorous MATLAB simulations.

In this paper, we derive an asymptotic closed-form expression for the error bound on extrapolation of doubly selective mobile MIMO wireless channels. The bound shows the relationship between the prediction error and system design parameters such as bandwidth, number of antenna elements, and number of frequency and temporal pilots, thereby providing useful insights into the effects of these parameters on prediction performance. Numerical simulations show that the asymptotic bound provides a good approximation to previously derived bounds while eliminating the need for repeated computation and dependence on channel parameters such as angles of arrival and departure, delays and Doppler shifts.

Simple and accurate channel models are required to evaluate the performance of multi-antenna systems with polarized antenna arrays. This paper presents a graph based model for polarized multi-antenna wireless channels by incorporating polarization diversity into the classical propagation graph based model. In addition to classical propagation effects such as attenuation, delay and phase shifts in non-polarized channel models, the proposed model incorporated antenna and scatterer based depolarization effects including polarization coupling and polarimetric responses. An illustration of the procedure for generating the transfer function and impulse response from the model is given for dual polarized channels. Simulation results show that the power delay profile exhibit exponential decay with almost equal decay rate across the co-and cross-polar channels. Our results also show that antenna orientation, relative antenna height and depolarization affect the power-delay properties of the channel.

We investigate the prediction of wideband MIMO spatial channels. We propose a two-stage long range parametric prediction scheme that exploits the temporal, spatial and frequency correlations in a realistic cluster based fading channel. The proposed scheme utilizes the frequency correlation in an ESPRIT-like approach to estimate the cluster delays and scattering coefficients. The spatial and temporal correlations are then used to jointly estimate the angles of arrival, angles of departure and Doppler shifts via a 3D ESPRIT algorithm. Simulation results using the standardized 3GPP/WINNER II spatial channel model show that the proposed algorithm offers improved prediction performance over previous methods and can achieve longer prediction range.

A novel prediction scheme for polarized narrowband MIMO channels is proposed in this paper. The prediction scheme is based on estimation of the parameters of a double directional polarized propagation model. The proposed algorithm transforms the channel impulse response matrix in such a manner that a multidimensional extension of the ESPRIT algorithm can be utilized to jointly estimate the angles of arrival, angles of departure, Doppler shifts and complex polarimetric weights of the dominant multipath components. Simulation results show that the proposed algorithm outperforms repeated application of one dimensional ESPRIT based approach with similar computational complexity.

In this letter, we derive simple expressions for the lower bound on the prediction error variance for narrowband MIMO channel with uniform linear array at both ends of the link. The derived bounds show the relationship between the achievable prediction performance and prediction algorithm design parameters, thereby providing useful insights into the development of fading channel prediction algorithms.

In this paper, we propose an ESPRIT-based parametric prediction scheme for narrowband MIMO systems that fully exploits both temporal and spatial correlations in realistic MIMO channels. The proposed predictor uses a vector transmit spatial signature model and two-dimensional ESPRIT for the estimation of the channel parameters. The proposed scheme outperforms existing algorithms and is well suited to both two dimensional azimuth only and three dimensional MIMO spatial channel models.