This work investigates a multi-user, multi-antenna uplink wireless system, in which multiple users transmit signals to a base station. Prior research has explored the potential for linear growth in spectral efficiency by employing multiple transmit and receive antennas. This gain depends heavily on the quality of channel state information and the number of uncorrelated antennas. However, spatial correlations, arising from closely-spaced antennas and channel aging effects, stemming from the difference between the channel state at pilot and data time instances, can substantially counteract these benefits, and degrade the transmission rate, especially in non-stationary environments. To address these challenges, this work introduces a real-time beamforming framework to compensate for the spatial correlation and channel aging effects. First, a channel estimation scheme leveraging temporal channel correlations and considering mobile device velocity and antenna spacing is developed. Subsequently, an expression approximating the average spectral efficiency, which depends on pilot spacing, pilot and data powers, and beamforming vectors, is obtained. By maximizing this expression, optimal parameters are identified. Numerical results demonstrate the effectiveness of the proposed approach compared to prior works. Interestingly, the optimal pilot spacing remains unaffected by large-scale channel parameters and the velocities of interfering users. The impact of interference components also diminishes with an increasing number of transmit antennas.

The transition toward 6G is pushing wireless communication into a regime where the classical plane-wave assumption no longer holds. Millimeter-wave and sub-THz frequencies shrink wavelengths to millimeters, while meter-scale arrays featuring hundreds of antenna elements dramatically enlarge the aperture. Together, these trends collapse the classical Rayleigh far-field boundary from kilometers to mere single-digit meters. Consequently, most practical 6G indoor, vehicular, and industrial deployments will inherently operate within the radiating near-field, where reliance on the plane-wave approximation leads to severe array-gain losses, degraded localization accuracy, and excessive pilot overhead. This paper re-examines the fundamental question: "Where does the far-field truly begin?" Rather than adopting purely geometric definitions, we introduce an application-oriented approach based on user-defined error budgets and a rigorous Fresnel-zone analysis that fully accounts for both amplitude and phase curvature. We propose three practical mismatch metrics: worst-case element mismatch, worst-case normalized mean square error, and spectral efficiency loss. For each metric, we derive a provably optimal transition distance–the minimal range beyond which mismatch permanently remains below a given tolerance–and provide closed-form solutions. Extensive numerical evaluations across diverse frequencies and antenna-array dimensions show that our proposed thresholds can exceed the Rayleigh distance by more than an order of magnitude. By transforming the near-field from a design nuisance into a precise, quantifiable tool, our results provide a clear roadmap for enabling reliable and resource-efficient near-field communications and sensing in emerging 6G systems.

Integrated sensing and communications (ISAC) is a promising component of 6G networks, fusing communication and radar technologies to facilitate new services. Additionally, the use of extremely large-scale antenna arrays (ELAA) at the ISAC common receiver not only facilitates terahertz-rate communication links but also significantly enhances the accuracy of target detection in radar applications. In practical scenarios, communication scatterers and radar targets often reside in close proximity to the ISAC receiver. This, combined with the use of ELAA, fundamentally alters the electromagnetic characteristics of wireless and radar channels, shifting from far-field planar-wave propagation to near-field spherical wave propagation. Under the far-field planar-wave model, the phase of the array response vector varies linearly with the antenna index. In contrast, in the near-field spherical wave model, this phase relationship becomes nonlinear. This shift presents a fundamental challenge: the widely-used Fourier analysis can no longer be directly applied for target detection and communication channel estimation at the ISAC common receiver. In this work, we propose a feasible solution to address this fundamental issue. Specifically, we demonstrate that there exists a high-dimensional space in which the phase nonlinearity can be expressed as linear. Leveraging this insight, we develop a lifted super-resolution framework that simultaneously performs communication channel estimation and extracts target parameters with high precision.

We propose a novel pilot-free multi-user uplink framework for integrated sensing and communication (ISAC) in mm-wave networks, where single-antenna users transmit orthogonal frequency division multiplexing signals without dedicated pilots. The base station exploits the spatial and velocity diversities of users to simultaneously decode messages and detect targets, transforming user transmissions into a powerful sensing tool. Each user's signal, structured by a known codebook, propagates through a sparse multi-path channel with shared moving targets and user-specific scatterers. Notably, common targets induce distinct delay-Doppler-angle signatures, while stationary scatterers cluster in parameter space. We formulate the joint multi-path parameter estimation and data decoding as a 3D super-resolution problem, extracting delays, Doppler shifts, and angles-of-arrival via atomic norm minimization, efficiently solved using semidefinite programming. A core innovation is multi-user fusion, where diverse user observations are collaboratively combined to enhance sensing and decoding. This approach improves robustness and integrates multi-user perspectives into a unified estimation framework, enabling high-resolution sensing and reliable communication. Numerical results show that the proposed framework significantly enhances both target estimation and communication performance, highlighting its potential for next-generation ISAC systems.

Distributed multiple-input and multiple-output (D-MIMO) technology is a promising candidate to be integrated in beyond fifth generation networks offering uniform quality of service along the network coverage and higher overall system capacity. It leverages the cooperation of many antenna arrays spread over the coverage area that jointly and coherently serve user equipment devices. While the spectrum efficiency benefits of D-MIMO (sometimes also referred to as cell-free technology) are well documented, the corresponding energy efficiency (EE) of such networks has received more attention in recent years as concerns about sustainability become central in future 6 G systems. In this context, this paper proposes and investigates the combined effect of intelligent access point clustering and power control to enhance D-MIMO's EE. While many works on D-MIMO assume that the whole network serves every user, we consider scalability and massive MIMO constraints to address the practical issues of a fully connected network in terms of high computational complexity, elevated signaling bandwidth, and limitations of MIMO's spatial degrees of freedom. To this end, we propose a resource-aware graph-based clustering method combined with a deep-learning-based power control. Comprehensive computer simulations demonstrate that the proposed strategy significantly enhances the network's EE, while producing comparable spectral efficiency performance to a fully connected scenario, while also outperforming a state-of-the-art existing clustering approach.

The multibeam technique, which enables antenna arrays to produce several beams or lobes in different directions, has been proposed to enable integrated sensing and communication for the upcoming sixth-generation of mobile communication systems. In this work, while adopting the multibeam technique, we optimize the power distribution between communication and sensing beams. Imposing constraints on the communication to ensure a minimum performance level, we look for the power control coefficient value that optimizes one of three suitable objective functions for sensing: the sensing signal-to-interference-plus-noise ratio, the Cramér-Rao lower bound of the angle-of-arrival estimator, and the mutual information between the sensing channel and the echo signals from the targets. We find that multibeam modeling leads to convex optimization problems, which are therefore suitable for real-time implementation. Our scheme manages the inherent trade-off involved in balancing the power between communication and sensing by using the optimum power coefficient. The numerical results show an improvement of the beam shape and the relevant performance metrics compared with the radiation pattern obtained by previously proposed multibeam schemes.

This paper proposes a pilot decoupling-based two-dimensional channel parameter estimation method for intelligent reflecting surface (IRS)-assisted networks. We exploit the combined effect of Terahertz sparse propagation and the geometrical structure of arrays deployed at the base station, the IRS, and the user equipment to develop a low-complexity channel parameter estimation method. By means of a new pilot design along the horizontal and vertical directions, the overall channel parameter estimation problem is decoupled into different domains. Furthermore, with this decoupling, it is possible to simultaneously sense/estimate the channel parameters and to communicate with the sensed node. Specifically, we formulate two estimators by decoupling the global problem into sub-problems and exploiting the built-in tensor structure of the sensing/estimation problem by means of multiple rank-one approximations for rank-one and low-rank channels. The Cramér-Rao lower bound is derived to assess the performance of the proposed estimators. We show that our two proposed methods yield accurate parameter estimates and outperform state-of-the-art methods in terms of complexity. The tradeoffs between performance and complexity offered by the proposed methods are discussed and numerically assessed.

Several previous works have addressed the inherent trade-off between allocating resources in the power and time domains to pilot and data signals in multiple input multiple output systems over block-fading channels. In particular, when the channel changes rapidly in time, channel aging degrades the performance in terms of spectral efficiency without proper pilot spacing and power control. Despite recognizing non-stationary stochastic processes as more accurate models for time-varying wireless channels, the problem of pilot spacing and power control in multi-antenna systems operating over non-stationary channels is not addressed in the literature. In this paper, we address this gap by introducing a refined first-order autoregressive model that exploits the inherent temporal correlations over non-stationary Rician aging channels. We design a multi-frame structure for data transmission that better reflects the non-stationary fading environment than previously developed single-frame structures. Subsequently, to determine the optimal pilot spacing and power control within this multi-frame structure, we develop an optimization framework and an efficient algorithm based on maximizing a deterministic equivalent expression for the spectral efficiency, demonstrating its generality by encompassing previous channel aging results. Our numerical results indicate the efficacy of the proposed method in terms of spectral efficiency gains over the single frame structure.

Previous works in array processing have proposed two types of snapshot models for the angle of arrival (AoA) estimation problem in multi-antenna systems. The deterministic model assumes that the source waveforms are non-random, while the random sensor noise is white Gaussian with a known covariance matrix. The stochastic model assumes that both the waveforms and the noise are zero-mean Gaussian. Interestingly, the performance of these two models have rarely been compared in integrated sensing and communication (ISAC) systems. Therefore, in this paper, we consider the uplink of a bistatic ISAC system that uses unitary constant envelope signaling and pilot-based channel estimation while transmitting a sensing signal simultaneously with the communication signals. The base station uses both the pilot and data signals to estimate the angle of a passive source and the transmitted data symbol by an active (connected) user equipment device. For this system, we derive the classical Cramér-Rao bound for unbiased estimators of the AoA and the transmitted symbol, along with the Bayesian Cramér-Rao bound, which bounds the error of all estimators. We also derive the ISAC-aware minimum mean squared error receiver for both the deterministic and stochastic models. We study the trade-off between sensing and communication under the deterministic and stochastic waveform assumptions. Specifically, we show that the fundamental trade-off between sensing and communication power allocations is expressed differently in the deterministic and stochastic models and argue that the results serve as basic considerations when designing pilot and sensing signals for ISAC systems.

The Internet of Things interconnects billions of devices and forms a vast network where users sporadically transmit short messages through multi-path wireless channels. These channels are characterized by the superposition of a small number of scaled and delayed copies of Dirac spikes. At the receiver, the observed signal is a sum of these convolved signals, and the task is to find the amplitudes, continuous-indexed delays, and transmitted messages from a single signal. This task is inherently ill-posed without additional assumptions on the channel or messages. In this work, we assume the channel exhibits sparsity in the delay domain and that independent and identically distributed random linear encoding is applied to the messages at the devices. Leveraging these assumptions, we propose a semidefinite programming optimization capable of simultaneously recovering both messages and the delay parameters of the channels from only a single received signal. Our theoretical analysis establishes that the required number of samples at the receiver scales proportionally to the sum-product of sparsity and message length of all users, aligning with the degrees of freedom in the lifting-type optimization frameworks. Numerical experiments confirm the efficacy of the proposed method in accurately estimating closely-spaced delay parameters and recovering messages.

Driven by the expansion of enhanced mobile broadband and massive machine type communication use cases – including high-accuracy sensing services and radar sensing for autonomous vehicles – integrated sensing and communication using orthogonal frequency division multiplexing has been proposed for sixth generation systems. In this context, studying the possible system arrangements for cell-free networks is important to meet the upcoming requirements imposed on integrated sensing and communication systems. In this work, we evaluate the uplink communication of cell-free systems supporting integrated sensing and communications, i.e., when users transmit simultaneously sensing and communication signals while the access points act as receivers for both. To this end, multi-antenna users employ a multibeam solution for transmitting independent sensing and communication symbols, while adopting either singular value decomposition or angular beamforming. By means of Monte Carlo simulations, we show that the performance of the proposed multibeam solution yields high performance in terms of signal-to-interference-plus-noise ratio for both communication and sensing as well as a low spatial frequency error obtained via angular estimation using the multiple signal classification algorithm.

We consider a bistatic and a multistatic sensing scenario, in which both the sensing transmitter and receiver(s) are equipped with multiple antennas. Assuming perfect synchronization among the transmitter and receiver(s), we address the problem of estimating the Doppler frequency and the angle of arrival of a passive object. The input to this estimation problem is the received noisy sensing signals at multiple time instance(s), while the output is the estimated angle of arrival and Doppler frequency either at individual sensing nodes or at a hypothetical centralized entity. In this rather general setting, we evaluate how the estimation performance is affected by the system parameters such as the number of receive access points and the available time samples. Our numerical results show that the Cramér-Rao lower bound of the estimated parameters inversely depends on the number of time samples of the received echoes. We also observe an even stronger inverse dependency on the number and locations of the receiver access points within the region of interest, indicating the advantages of adopting a distributed multiple-input multiple-output architecture for sensing purposes.

In this paper, we study a new kind of pilot contamination appearing in multi-operator reconfigurable intelligent surfaces (RIS) assisted networks, where multiple operators provide services to their respective served users. The operators use dedicated frequency bands, but each RIS inadvertently reflects the transmitted uplink signals of the user equipment devices in multiple bands. Consequently, the concurrent reflection of pilot signals during the channel estimation phase introduces a new inter-operator pilot contamination effect. We investigate the implications of this effect in systems with either deterministic or correlated Rayleigh fading channels, specifically focusing on its impact on channel estimation quality, signal equalization, and channel capacity. The numerical results demonstrate the substantial degradation in system performance caused by this phenomenon and highlight the pressing need to address inter-operator pilot contamination in multi-operator RIS deployments. To combat the negative effect of this new type of pilot contamination, we propose to use orthogonal RIS configurations during uplink pilot transmission, which can mitigate or eliminate the negative effect of inter-operator pilot contamination at the expense of some inter-operator information exchange and orchestration.

We consider the problem of gridless blind deconvolution and demixing (GB2D) in scenarios where multiple users communicate messages through multiple unknown channels, and a single base station (BS) collects their contributions. This scenario arises in various communication fields, including wireless communications, the Internet of Things, over-the-air computation, and integrated sensing and communications. In this setup, each user's message is convolved with a multi-path channel formed by several scaled and delayed copies of Dirac spikes. The BS receives a linear combination of the convolved signals, and the goal is to recover the unknown amplitudes, continuous-indexed delays, and transmitted waveforms from a compressed vector of measurements at the BS. However, without prior knowledge of the transmitted messages and channels, GB2D is highly challenging and intractable in general. To address this issue, we assume that each user's message follows a distinct modulation scheme living in a known low-dimensional subspace. By exploiting these subspace assumptions and the sparsity of the multipath channels for different users, we transform the nonlinear GB2D problem into a matrix tuple recovery problem from a few linear measurements. To achieve this, we propose a semidefinite programming optimization that exploits the specific low-dimensional structure of the matrix tuple to recover the messages and continuous delays of different communication paths from a single received signal at the BS. Finally, our numerical experiments show that our proposed method effectively recovers all transmitted messages and the continuous delay parameters of the channels with sufficient samples.

Existing works on cell-free systems consider either coherent or non-coherent downlink data transmission and a network deployment with a single central processing unit (CPU). While it is known that coherent transmission outperforms non-coherent transmission when assuming unlimited fronthaul links, the former requires a perfect timing synchronization, which is practically not viable over a large network. Furthermore, relying on a single CPU for geographically large cell-free networks is not scalable. Thus, to realize the expected gains of cell-free systems in practice, alternative transmission strategies for realistic multi-CPU cell-free systems are required. Therefore, this paper proposes a novel downlink data transmission scheme that combines and generalizes the existing coherent and non-coherent transmissions. The proposed transmission scheme, named mixed transmission, works based on the realistic assumption that only the access points (APs) controlled by a same CPU are synchronized, and thus transmit in a coherent fashion, while APs from different CPUs require no synchronism and transmit in a non-coherent manner. We also propose extensions of existing clustering algorithms for multi-CPU cell-free systems with mixed transmission. Simulation results show that the combination of the proposed clustering algorithms with mixed transmission have the potential to perform close to the ideal coherent transmission.

Resource allocation and multiple access schemes are instrumental for the success of communication networks, which facilitate seamless wireless connectivity among a growing population of uncoordinated and non-synchronized users. In this paper, we present a novel random access scheme that addresses one of the most severe barriers of current strategies to achieve massive connectivity and ultra reliable and low latency communications for 6G. The proposed scheme utilizes wireless channels' angular continuous group-sparsity feature to provide low latency, high reliability, and massive access features in the face of limited time-bandwidth resources, asynchronous transmissions, and preamble errors. Specifically, a reconstruction-free goal oriented optimization problem is proposed which preserves the angular information of active devices and is then complemented by a clustering algorithm to assign active users to specific groups. This allows to identify active stationary devices according to their line of sight angles. Additionally, for mobile devices, an alternating minimization algorithm is proposed to recover their preamble, data, and channel gains simultaneously, enabling the identification of active mobile users. Simulation results show that the proposed algorithm provides excellent performance and supports a massive number of devices. Moreover, the performance of the proposed scheme is independent of the total number of devices, distinguishing it from other random access schemes. The proposed method provides a unified solution to meet the requirements of machine-type communications and ultra reliable and low latency communications, making it an important contribution to the emerging 6G networks.

The capability of nodes to transmit and receive data simultaneously within the same frequency band, referred to as in-band FD, disrupts the conventional assumptions underlying wireless network design. This new feature enhances spectral efficiency and reduces latency, which are essential drivers in advancing next-generation networks. In the past few years, full-duplex (FD) has evolved from being a laboratory idea to being incorporated into telecommunications standards and proof of concepts. From 2010 to 2020, considerable research and development efforts were devoted to advancing FD wireless communications. By 2015, the cable modem industry had already implemented in-band FD technology to establish the DOCSIS 4.0 standard, enabling next-generation cable modems to operate in FD mode. By 2020, FD wireless products started to emerge in the market.

The use of in-band full-duplex (FD) enables nodes to simultaneously transmit and receive on the same frequency band, which challenges the traditional assumption in wireless network design. The full-duplex capability enhances spectral efficiency and decreases latency, which are two key drivers pushing the performance expectations of next-generation mobile networks. In less than ten years, in-band FD has advanced from being demonstrated in research labs to being implemented in standards, presenting new opportunities to utilize its foundational concepts. Some of the most significant opportunities include using FD to enable wireless networks to sense the physical environment, integrate sensing and communication applications, develop integrated access and backhaul solutions, and work with smart signal propagation environments powered by reconfigurable intelligent surfaces. However, these new opportunities also come with new challenges for large-scale commercial deployment of FD technology, such as managing self-interference, combating cross-link interference in multi-cell networks, and coexistence of dynamic time division duplex, subband FD and FD networks.

In the uplink of multiuser multiple input multiple output (MU-MIMO) systems operating over aging channels, pilot spacing is crucial for acquiring channel state information and achieving high signal-to-interference-plus-noise ratio (SINR). Somewhat surprisingly, very few works examine the impact of pilot spacing on the correlation structure of subsequent channel estimates and the resulting quality of channel state information considering channel aging. In this paper, we consider a fast-fading environment characterized by its exponentially decaying autocorrelation function, and model pilot spacing as a sampling problem to capture the inherent trade-off between the quality of channel state information and the number of symbols available for information carrying data symbols. We first establish a quasi-closed form for the achievable deterministic equivalent SINR when the channel estimation algorithm utilizes multiple pilot signals. Next, we establish upper bounds on the achievable SINR and spectral efficiency, as a function of pilot spacing, which helps to find the optimum pilot spacing within a limited search space. Our key insight is that to maximize the achievable SINR and the spectral efficiency of MU-MIMO systems, proper pilot spacing must be applied to control the impact of the aging channel and to tune the trade-off between pilot and data symbols.

Intelligent reconfigurable surface IRS are becoming an attractive component of cellular networks due to their ability to shape the propagation environment and thereby improve coverage. While IRS nodes incorporate a great number of phase-shifting elements and a controller entity, the phase shifts are typically determined by the cellular base station (BS) due to its computational capability. Since controlling a large number of phase shifts may become prohibitive in practice, it is important to reduce the control overhead between the BS and the IRS controller. To this end, in this paper, we propose a low-rank modeling approach for the IRS phase shifts. The key idea is to represent the IRS phase shift vector using a low-rank tensor approximation model, where each rank-one component is modeled as the Kronecker product of a predefined number of factors of smaller sizes, obtained via tensor decomposition algorithms. We show that the proposed low-rank models drastically reduce the required feedback requirements associated with the BS-IRS control links. Our simulation results indicate that the proposed method is especially attractive in scenarios with a strong line of sight component, in which case nearly the same spectral efficiency is reached as in the cases with near-optimal phase shifts, but with significantly lower feedback overhead.

In this paper, we propose a rank-one tensor modeling approach that yields a compact representation of the optimum intelligent reconfigurable surface (IRS) phase-shift vector for reducing the feedback overhead. The main idea consists of factorizing the IRS phase-shift vector as a Kronecker product of smaller vectors, namely factors. The proposed phase-shift model allows the network to trade-off between achievable data rate and feedback reduction by controling the factorization parameters. Our simulations show that the proposed phase-shift factorization drastically reduces the feedback overhead, while improving the data rate in some scenarios, compared to the state-of-the-art schemes.

Cell-free multi-user multiple input multiple output networks are a promising alternative to classical cellular architectures, since they have the potential to provide uniform service quality and high resource utilisation over the entire coverage area of the network. To realise this potential, previous works have developed radio resource management mechanisms using various optimisation engines. In this work, we consider the problem of overall ergodic spectral efficiency maximisation in the context of uplink-downlink data power control in cell-free networks. To solve this problem in large networks, and to address convergence-time limitations, we apply scalable multi-objective Bayesian optimisation. Furthermore, we discuss how an intersection of multi-fidelity emulation and Bayesian optimisation can improve radio resource management in cell-free networks.

As several previous works have pointed out, the evolution of the wireless channels in multiple input multiple output systems can be advantageously modeled as an autoregressive process. Therefore, estimating the coefficients, and, in particular, the state transition matrix of this autoregressive process is a key to accurate channel estimation, tracking, and prediction in fast fading environments. In this paper we assume the time varying spatially uncorrelated channel which is approximately the case with proper antenna spacing at the base station in rich scattering environments. We propose a method for autoregressive parameter estimation for a single input multiple output (SIMO) channel. We show an almost sure convergence of the estimated coefficients to the true autoregressive coefficients in large dimensions. We apply the proposed method to the SIMO channel tracking.

We consider the downlink transmission in a single cell multiple-input multiple-output system, in which the user equipment correspond to a vehicle moving along a given trajectory. This system utilizes millimeter wave channels characterized by multiple non-line-of-sight (NLoS) components. As it has been pointed out in several related works, in such systems radio access network (RAN)-based positioning can effectively improve the positioning accuracy achieved by Global Navigation Satellite Systems. However, the RAN-based positioning accuracy is highly dependent on the quality of the channel estimates, especially if multipath propagation is exploited. Recognizing that the communication channels between the serving base station and the vehicle as well as the geographical position of the vehicle can be advantageously modeled as inter-related autoregressive processes, we propose a two-stage Kalman filter algorithm employing two intertwined filters for channel tracking, position tracking and abrupt channel change detection. The first Kalman filter tracks angles-of-departure and angles-of-arrival associated with the communication channels, which are used to make a coarse position estimation. The second Kalman filter tracks the position of the vehicle utilizing the kinematic parameters of the vehicle. Simulation results clearly show the advantages of using the proposed scheme, which exploits the memoryful property of both the communication channels and the geographical positions, as compared to employing previously proposed single-stage or not properly combined filters in NLoS environments.

Minimizing the symbol error in the uplink of multi-user multiple input multiple output systems is important, because the symbol error affects the achieved signal-to-interference-plus-noise ratio (SINR) and thereby the spectral efficiency of the system. Despite the vast literature available on minimum mean squared error (MMSE) receivers, previously proposed receivers for block fading channels do not minimize the symbol error in time-varying Rayleigh fading channels. Specifically, we show that the true MMSE receiver structure does not only depend on the statistics of the CSI error, but also on the autocorrelation coefficient of the time-variant channel. It turns out that calculating the average SINR when using the proposed receiver is highly non-trivial. In this paper, we employ a random matrix theoretical approach, which allows us to derive a quasi-closed form for the average SINR, which allows to obtain analytical exact results that give valuable insights into how the SINR depends on the number of antennas, employed pilot and data power and the covariance of the time-varying channel. We benchmark the performance of the proposed receiver against recently proposed receivers and find that the proposed MMSE receiver achieves higher SINR than the previously proposed ones, and this benefit increases with increasing autoregressive coefficient.

In the above article[1], the title of the article appears incorrectly. The full title should read “On the Achievable SINR in MU-MIMO Systems Operating in Time-Varying Rayleigh Fading.”

Existing works concluded that coherent transmission outperforms non-coherent transmission in the downlink of cell-free systems when the fronthaul links have unlimited capacity. Since the capacity of the fronthaul links of cell-free networks is typically limited, in this paper we ask the question whether this conclusion holds under more realistic assumptions on the fronthaul capacity. To answer this question, we study and compare the performance of these transmission strategies by formulating novel energy efficiency (EE) maximization problems for both strategies, where we explicitly consider realistic fronthaul capacity and power consumption constraints. Despite the non-convexity of these problems, we derive closed-form equations to find suboptimal solutions of both problems using a unified framework that combines successive convex approximation and the Dinkelbach algorithm. Numerical results show that the performance of coherent transmission is severely impacted by limited fronthaul capacities, power consumption on the fronthaul links, user-centric cluster size and the number of antennas at the access points, such that in many cases non-coherent transmission achieves higher EE than coherent transmission. Based on these results, we provide deployment guidelines on when to use coherent or non-coherent transmission to maximize the EE of cell-free systems with limited fronthauls.

Although signal distortion-based peak-to-average power ratio (PAPR) reduction is a feasible candidate for orthogonal frequency division multiplexing (OFDM) to meet standard/regulatory requirements, the error vector magnitude (EVM) stemming from the PAPR reduction has a deleterious impact on the performance of high data-rate achieving multiple-input multiple-output (MIMO) systems. Moreover, these systems must constrain the adjacent channel leakage ratio (ACLR) to comply with regulatory requirements. Several recent works have investigated the mitigation of the EVM seen at the receivers by capitalizing on the excess spatial dimensions inherent in the large-scale MIMO that assume the availability of perfect channel state information (CSI) with spatially uncorrelated wireless channels. Unfortunately, practical systems operate with erroneous CSI and spatially correlated channels. Additionally, most standards support user-specific/CSI-aware beamformed and cell-specific/non-CSI-aware broadcasting channels. Hence, we formulate a robust EVM mitigation problem under channel uncertainty with nonconvex PAPR and ACLR constraints catering to beamforming/broadcasting. To solve this formidable problem, we develop an efficient scheme using our recently proposed three-operator alternating direction method of multipliers (TOP-ADMM) algorithm and benchmark it against two three-operator algorithms previously presented for machine learning purposes. Numerical results show the efficacy of the proposed algorithm under imperfect CSI and spatially correlated channels.

High-accuracy angle of arrival (AoA) and angle of departure (AoD) estimation is critical for cell search, stable communications and positioning in millimeter wave (mmWave) cellular systems. Moreover, the design of low-complexity AoA/AoD estimation algorithms is also of major importance in the deployment of practical systems to enable a fast and resource-efficient computation of beamforming weights. Parametric mmWave channel estimation allows to describe the channel matrix as a combination of direction-dependent signal paths, exploiting the sparse characteristics of mmWave channels. In this context, a fast Transformed Spatial Domain Channel Estimation (TSDCE) algorithm was recently proposed to perform parametric channel estimation with low complexity, which in turn results in a full characterization of the transmitting and receiving angles for dominant signal paths. In this paper, we analyze the AoA/AoD estimation capability and accuracy of the TSDCE algorithm in detail. We find that the TSDCE algorithm has a significant performance advantage with respect to the traditional approach, which is based on frequency domain processing, in complexity-constrained environments, especially at high signal-to-noise ratios.

Due to the continuous advancements of orthogonal frequency division multiplexing (OFDM) and multiple antenna techniques, multiuser multiple input multiple output (MU-MIMO) OFDM is a key enabler of both fourth and fifth generation networks. In this paper, we consider the problem of weighted sum-rate maximization under latency constraints in finite buffer multicell MU-MIMO OFDM systems. Unlike previous works, the optimization variables include the transceiver beamforming vectors, the scheduled packet size and the resources in the frequency and power domains. This problem is motivated by the observation that multicell MU-MIMO OFDM systems serve multiple quality of service classes and the system performance depends critically on both the transceiver design and the scheduling algorithm. Since this problem is non-convex, we resort to the max-plus queuing method and successive convex approximation. We propose both centralized and decentralized solutions, in which practical design aspects, such as signaling overhead, are considered. Finally, we compare the proposed framework with state-of-the-art algorithms in relevant scenarios, assuming a realistic channel model with space, frequency and time correlations. Numerical results indicate that our design provides significant gains over designs based on the wide-spread saturated buffers assumption, while also outperforming algorithms that consider a finite-buffer model. Due to the continuous advancements of orthogonal frequency division multiplexing (OFDM) and multiple antenna techniques, multiuser multiple input multiple output (MU-MIMO) OFDM is a key enabler of both fourth and fifth generation networks. In this paper, we consider the problem of weighted sum-rate maximization under latency constraints in finite buffer multicell MU-MIMO OFDM systems. Unlike previous works, the optimization variables include the transceiver beamforming vectors, the scheduled packet size and the resources in the frequency and power domains. This problem is motivated by the observation that multicell MU-MIMO OFDM systems serve multiple quality of service classes and the system performance depends critically on both the transceiver design and the scheduling algorithm. Since this problem is non-convex, we resort to the max-plus queuing method and successive convex approximation. We propose both centralized and decentralized solutions, in which practical design aspects, such as signaling overhead, are considered. Finally, we compare the proposed framework with state-of-the-art algorithms in relevant scenarios, assuming a realistic channel model with space, frequency and time correlations. Numerical results indicate that our design provides significant gains over designs based on the wide-spread saturated buffers assumption, while also outperforming algorithms that consider a finite-buffer model.

The performance of the uplink of single and multiuser multiple input multiple output (MIMO) systems depends crucially on the receiver architecture and the quality of channel state information at the receiver. Therefore, several previous works have developed minimum mean squared error (MMSE) receivers and proposed balancing the resources spent on acquiring channel state information and transmitting the payload of data packets. Somewhat surprisingly, the most popular MIMO linear MMSE receivers do not exploit the correlation structure that is present in autoregressive Rayleigh fading environments. Therefore, in this article we first develop a new linear receiver that not only takes channel state information errors into account in minimizing the MSE of the received data symbols, but it also utilizes that the subsequent noisy channel coefficients are correlated. For this new linear MMSE receiver, we derive the achieved MSE as a function of the number of receive antennas and the pilot-to-data power ratio. Interestingly, we find that the pilot power that minimizes the MSE of the data symbols does not depend on the number of antennas and that the new linear MMSE receiver outperforms previously proposed MIMO receivers when the autocorrelation coefficient of the channel is high.

Full-duplex communications have the potential to almost double the spectral efficiency. To realize such a potentiality, the signal separation at base station’s antennas plays an essential role. This article addresses the fundamentals of such separation by proposing a new smart antenna architecture that allows every antenna to be either shared or separated between uplink and downlink transmissions. The benefits of such architecture are investigated by an assignment problem to optimally assign antennas, beamforming and power to maximize the weighted sum spectral efficiency. We propose a near-to-optimal solution using block coordinate descent that divides the problem into assignment problems, which are NP-hard, a beamforming and power allocation problems. The optimal solutions for the beamforming and power allocation are established while near-to-optimal solutions to the assignment problems are derived by semidefinite relaxation. Numerical results indicate that the proposed solution is close to the optimum, and it maintains a similar performance for high and low residual self-interference powers. With respect to the usually assumed antenna separation technique and half-duplex transmission, the sum spectral efficiency gains increase with the number of antennas. We conclude that our proposed smart antenna assignment for signal separation is essential to realize the benefits of multiple antenna full-duplex communications.

Spectral precoding is a promising technique to suppress out-of-band emissions and comply with leakage constraints over adjacent frequency channels and with mask requirements on the unwanted emissions. However, spectral precoding may distort the original data vector, which is formally expressed as the error vector magnitude (EVM) between the precoded and original data vectors. Notably, EVM has a deleterious impact on the performance of multiple-input multiple-output orthogonal frequency division multiplexing-based systems. In this paper we propose a novel spectral precoding approach which constrains the EVM while complying with the mask requirements. We first formulate and solve the EVM-unconstrained mask-compliant spectral precoding problem, which serves as a springboard to the design of two EVM-constrained spectral precoding schemes. The first scheme takes into account a wideband EVM-constraint which limits the average in-band distortion. The second scheme takes into account frequency-selective EVM-constraints, and consequently, limits the signal distortion at the subcarrier level. Numerical examples illustrate that both proposed schemes outperform previously developed schemes in terms of important performance indicators such as block error rate and system-wide throughput while complying with spectral mask and EVM constraints.

Although spectral precoding is a propitious technique to suppress out-of-band emissions, it has a detrimental impact on the system-wide throughput performance, notably, in high data-rate multiple-input multiple-output (MIMO) systems with orthogonal frequency division multiplexing (OFDM), because of (spatially-coloured) transmit error vector magnitude (TxEVM) emanating from spectral precoding. The first contribution of this paper is to propose two mask-compliant spectral precoding schemes, which mitigate the resulting TxEVM seen at the receiver by capitalizing on the immanent degrees-of-freedom in (massive) MIMO systems and consequently improve the system-wide throughput. Our second contribution is an introduction to a new and simple three-operator consensus alternating direction method of multipliers (ADMM) algorithm, referred to as TOP-ADMM, which decomposes a large-scale problem into easy-to-solve subproblems. We employ the proposed TOP-ADMM-based algorithm to solve the spectral precoding problems, which offer computational efficiency. Our third contribution presents substantial numerical results by using an NR release 15 compliant simulator. In case of perfect channel knowledge at the transmitter, the proposed methods render similar block error rate and throughput performance as without spectral precoding yet meeting out-of-band emission (OOBE) requirements at the transmitter. Further, no loss on the OOBE performance with a graceful degradation on the throughput is observed under channel uncertainty.

Fast channel estimation in millimeter-wave (mmWave) systems is a fundamental enabler of high-gain beamforming, which boosts coverage and capacity. The channel estimation stage typically involves an initial beam training process where a subset of the possible beam directions at the transmitter and receiver is scanned along a predefined codebook. Unfortunately, the high number of transmit and receive antennas deployed in mmWave systems increase the complexity of the beam selection and channel estimation tasks. In this work, we tackle the channel estimation problem in analog systems from a different perspective than used by previous works. In particular, we propose to move the channel estimation problem from the angular domain into the transformed spatial domain, in which estimating the angles of arrivals and departures corresponds to estimating the angular frequencies of paths constituting the mmWave channel. The proposed approach, referred to as transformed spatial domain channel estimation (TSDCE) algorithm, exhibits robustness to additive white Gaussian noise by combining low-rank approximations and sample autocorrelation functions for each path in the transformed spatial domain. Numerical results evaluate the mean square error of the channel estimation and the direction of arrival estimation capability. TSDCE significantly reduces the first, while exhibiting a remarkably low computational complexity compared with well-known benchmarking schemes.

The performance of Global Navigation Satellite System (GNSS)-based positioning techniques degrades in tunnels, urban canyons and other areas, in which GNSS coverage is poor. Recent advances indicate that radio-based positioning techniques have the potential of complementing GNSS-based positioning services in such problematic areas. In this work, we propose to combine range and angle measurements routinely exercised by fifth generation of mobile communication (5G) base stations with acceleration measurements by vehicles to generate position estimates. Specifically, we propose to utilize an extended Kalman filter to combine the range, angle and acceleration measurements. The accuracy of this positioning system is studied in millimeter-wave 5G cellular networks. Specifically, the proposed positioning system using 5G and sensor fusion is tested in a highway scenario in which 5G base stations provide cellular coverage. In this setting, we study the impact of certain parameters of the 5G network such as the available bandwidth and the propagation environment on the positioning accuracy. We find that the positioning accuracy is largely affected by the number of antennas in the base station and that the proposed scheme outperforms GNSS based schemes in the problematic areas.

While the problems of sum-rate maximization and sum-power minimization subject to quality of service (QoS) constraints in the multiple input multiple output interference broadcast channel (MIMO IBC) have been widely studied, most of the proposed solutions have neglected the user scheduling aspect assuming that a feasible set of users has been previously selected. However, ensuring QoS for each user in the MIMO IBC involves the joint optimization of transmit/receive beamforming vectors, transmit powers, and user scheduling variables. To address the full problem, we propose a novel formulation of a rate-constrained sum-utility maximization problem which allows to either deactivate users or minimize the QoS degradation for some scheduled users in infeasible scenarios. Remarkably, this is achieved avoiding the complexity of traditional combinatorial formulations, but rather by introducing a novel expression of the QoS constraints that allows to solve the problem in a continuous domain. We propose centralized and decentralized solutions, where the decentralized solutions focus on practical design and low signaling overhead. The proposed solutions are then compared with benchmarking algorithms, where we show the effectiveness of the joint scheduling and transceiver design as well as the flexibility of the proposed solution performing advantageously in several MIMO IBC scenarios.

Millimeter-wave using large-antenna arrays is a key technological component for the future cellular systems, where it is expected that hybrid beamforming along with quantized phase shifters will be used due to their implementation and cost efficiency. In this paper, we investigate the efficacy of full-duplex mmWave communication with hybrid beamforming using low-resolution phase shifters. We assume that the self-interference can be sufficiently cancelled by a combination of propagation domain and digital self-interference techniques, without any analog self-interference cancellation. We formulate the problem of joint self-interference suppression and downlink beamforming as a mixed-integer nonconvex joint optimization problem. We propose LowRes, a near-to-optimal solution using penalty dual decomposition. Numerical results indicate that LowRes using low-resolution phase shifters perform within 3% of the optimal solution that uses infinite phase shifter resolution. Moreover, even a single quantization bit outperforms half-duplex transmissions, respectively by 29% and 10% for both low and high residual self-interference scenarios, and for a wide range of practical antenna to radio-chain ratios. Thus, we conclude that 1-bit phase shifters suffice for full-duplex millimeter-wave communications, without requiring any additional new analog hardware.

While the current generation of mobile and fixed communication networks has been standardized for mobile broadband services, the next generation is driven by the vision of the Internet of Things and mission-critical communication services requiring latency in the order of milliseconds or submilliseconds. However, these new stringent requirements have a large technical impact on the design of all layers of the communication protocol stack. The cross-layer interactions are complex due to the multiple design principles and technologies that contribute to the layers' design and fundamental performance limitations. We will be able to develop low-latency networks only if we address the problem of these complex interactions from the new point of view of submilliseconds latency. In this paper, we propose a holistic analysis and classification of the main design principles and enabling technologies that will make it possible to deploy low-latency wireless communication networks. We argue that these design principles and enabling technologies must be carefully orchestrated to meet the stringent requirements and to manage the inherent tradeoffs between low latency and traditional performance metrics. We also review currently ongoing standardization activities in prominent standards associations, and discuss open problems for future research.

Future mobile networks will provide support for real-time control applications. The tight real-time and reliability constraints of these applications introduce novel challenges for mobility management. Legacy individual handover schemes do not sufficiently address these issues, as they do not consider physical interactions between mobile nodes. A novel group handover scheme is proposed which allows for the simultaneous handover of a group of nodes. Both the individual and the group handover are modeled as discrete-time Markov chains. Based on these models expressions for the stochastic handover delay are derived. The results are numerically evaluated in a vehicle platooning scenario. The group handover is shown to significantly reduce the handover delay in comparison to the individual handover. Furthermore, the group handover is shown to scale well when the number of vehicles increases. These improvements are shown to come at the cost of an increased messaging overhead.

We consider the uplink of a single cell multi-user multiple input multiple output (MU-MIMO) system, in which the base station acquires channel state information at the receiver by means of uplink pilot signals. Since each mobile station has a sum power budget that is used to transmit pilot and data symbols, the pilot power ratio (PPR) has a large impact on the system performance in terms of spectral and energy efficiency. We formulate the problem of PPR setting as a non-cooperative game, in which each mobile station aims at minimizing the mean squared error of the uplink received data symbols at the base station. We show that in this game a unique Nash equilibrium exists, and propose an iterative decentralized algorithm-termed best PPR algorithm (BPA)-that is guaranteed to converge to that Nash equilibrium. Since BPA dynamically responds to the measured interference, it outperforms widely used schemes that use a predetermined PPR. BPA also performs close to the global optimum, especially when mobile stations with similar path loss values are co-scheduled in the MU-MIMO system. Based on these insights, we propose a practical signaling mechanism for implementing BPA in MU-MIMO systems.

Multiple-input multiple-output (MIMO) millimeter-wave (mm-wave) systems are vulnerable to hardware impairments due to operating at high frequencies and employing a large number of radio-frequency hardware components. In particular, nonlinear power amplifiers (PAs) employed at the transmitter distort the signal when operated close to saturation due to energy efficiency considerations. In this paper, we study the performance of an MIMO mm-wave hybrid beamforming scheme in the presence of nonlinear PAs. First, we develop a statistical model for the transmitted signal in such systems and show that the spatial direction of the inband distortion is shaped by the beamforming filter. This suggests that even in the large antenna regime, where narrow beams can be steered toward the receiver, the impact of nonlinear PAs should not be ignored. Then, by employing a realistic power consumption model for the PAs, we investigate the tradeoff between spectral and energy efficiency in such systems. Our results show that increasing the transmit power level when the number of transmit antennas grows large can be counter-effective in terms of energy efficiency. Furthermore, using numerical simulation, we show that when the transmit power is large, analog beamforming leads to higher spectral and energy efficiency compared to digital and hybrid beamforming schemes.

Pilot contamination, defined as the interference during the channel estimation process due to reusing the same pilot sequences in neighboring cells, can severely degrade the performance of massive multiple-input multiple-output systems. In this paper, we propose a location-based approach to mitigating the pilot contamination problem for uplink multiple-input multiple-output systems. Our approach makes use of the approximate locations of mobile devices to provide good estimates of the channel statistics between the mobile devices and their corresponding base stations. Specifically, we aim at avoiding pilot contamination even when the number of base station antennas is not very large, and when multiple users from different cells, or even in the same cell, are assigned the same pilot sequence. First, we characterize a desired angular region of the target user at the serving base station based on the number of base station antennas and the location of the target user, and make the observation that in this region the interference is close to zero due to the spatial separability. Second, based on this observation, we propose pilot coordination methods for multi-user multi-cell scenarios to avoid pilot contamination. The numerical results indicate that the proposed pilot contamination avoidance schemes enhance the quality of the channel estimation and thereby improve the per-cell sum rate offered by target base stations.

Device‐to‐device (D2D) communications in cellular spectrum have the potential of increasing the spectral and energy efficiency by taking advantage of the proximity and reuse gains. Although several resource allocation (RA) and power control (PC) schemes have been proposed in the literature, a comparison of the performance of such algorithms as a function of the available channel state information has not been reported. In this paper, we examine which large scale channel gain knowledge is needed by practically viable RA and PC schemes for network assisted D2D communications. To this end, we propose a novel near‐optimal and low‐complexity RA scheme that can be advantageously used in tandem with the optimal binary power control scheme and compare its performance with three heuristics‐based RA schemes that are combined either with the well‐known 3GPP Long‐Term Evolution open‐loop path loss compensating PC or with an iterative utility optimal PC scheme. When channel gain knowledge about the useful as well as interfering (cross) channels is available at the cellular base station, the near‐optimal RA scheme, termed Matching, combined with the binary PC scheme is superior. Ultimately, we find that the proposed low‐complexity RA + PC tandem that uses some cross‐channel gain knowledge provides superior performance.

Although the benefits of precoding and combining of data streams are widely recognized, the potential of precoding the pilot signals at the user equipment (UE) side and combining them at the base station (BS) side has not received adequate attention. This paper considers a multiuser multiple input multiple output (MU-MIMO) cellular system in which the BS acquires channel state information (CSI) by means of uplink pilot signals and proposes pilot precoding and combining to improve the CSI quality. We first evaluate the channel estimation performance of a baseline scenario in which CSI is acquired with no pilot precoding. Next, we characterize the channel estimation error when the pilot signals are precoded by spatial filters that asymptotically maximize the channel estimation quality. Finally, we study the case when, in addition to pilot precoding at the UE side, the BS utilizes the second order statistics of the channels to further improve the channel estimation performance. The analytical and numerical results show that, specially in scenarios with large number of antennas at the BS and UEs, pilot precoding and combining has a great potential to improve the channel estimation quality in MU-MIMO systems.

To increase the spectral efficiency of wireless networks without requiring full-duplex capability of user devices, a potential solution is the recently proposed three-node full-duplex mode. To realize this potential, networks employing three-node full-duplex transmissions must deal with self-interference and user-to-user interference, which can be managed by frequency channel and power allocation techniques. Whereas previous works investigated either spectral efficient or fair mechanisms, a scheme that balances these two metrics among users is investigated in this paper. This balancing scheme is based on a new solution method of the multi-objective optimization problem to maximize the weighted sum of the per-user spectral efficiency and the minimum spectral efficiency among users. The mixed integer nonlinear nature of this problem is dealt by Lagrangian duality. Based on the proposed solution approach, a low-complexity centralized algorithm is developed, which relies on large scale fading measurements that can be advantageously implemented at the base station. Numerical results indicate that the proposed algorithm increases the spectral efficiency and fairness among users without the need of weighting the spectral efficiency. An important conclusion is that managing user-to-user interference by resource assignment and power control is crucial for ensuring spectral efficient and fair operation of full-duplex networks.

Although the benefits of precoding and combining data signals are widely recognized, the potential of these techniques for pilot transmission is not fully understood. This is particularly relevant for multiuser multiple-input multiple-output (MU-MIMO) cellular systems using millimeter-wave (mmWave) communications, where multiple antennas have to be used both at the transmitter and the receiver to overcome the severe path loss. In this paper, we characterize the gains of pilot precoding and combining in terms of channel estimation quality and achievable data rate. Specifically, we consider three uplink pilot transmission scenarios in an mmWave MU-MIMO cellular system: 1) non-precoded and uncombined; 2) precoded but uncombined; and 3) precoded and combined. We show that a simple precoder that utilizes only the second-order statistics of the channel reduces the variance of the channel estimation error by a factor that is proportional to the number of user equipment (UE) antennas. We also show that using a linear combiner design based on the second-order statistics of the channel significantly reduces multiuser interference and provides the possibility of reusing some pilots. Specifically, in the large antenna regime, pilot precoding and combining help to accommodate a large number of UEs in one cell, significantly improve channel estimation quality, boost the signal-to-noise ratio of the UEs located close to the cell edges, alleviate pilot contamination, and address the imbalanced coverage of pilot and data signals.

In cellular networks, the three-node full-duplex transmission mode has the potential to increase spectral efficiency without requiring full-duplex capability of users. Consequently, three-node full-duplex in cellular networks must deal with self-interference and user-to-user interference, which can be managed by power control and user-frequency assignment techniques. This paper investigates the problem of maximizing the sum spectral efficiency by jointly determining the transmit powers in a distributed fashion, and assigning users to frequency channels. The problem is formulated as a mixed-integer nonlinear problem, which is shown to be non-deterministic polynomial-time hard. We investigate a close-to-optimal solution approach by dividing the joint problem into a power control problem and an assignment problem. The power control problem is solved by Fast-Lipschitz optimization, while a greedy solution with guaranteed performance is developed for the assignment problem. Numerical results indicate that compared with the half-duplex mode, both spectral and energy efficiencies of the system are increased by the proposed algorithm. Moreover, results show that the power control and assignment solutions have important, but opposite roles in scenarios with low or high self-interference cancellation. When the self-interference cancellation is high, user-frequency assignment is more important than power control, while power control is essential at low self-interference cancellation.

This paper investigates the extent to which spectrum sharing in millimeter-wave (mmWave) networks with multiple cellular operators is a viable alternative to traditional dedicated spectrum allocation. Specifically, we develop a general mathematical framework to characterize the performance gain that can be obtained when spectrum sharing is used, as a function of the underlying beamforming, operator coordination, bandwidth, and infrastructure sharing scenarios. The framework is based on joint beamforming and cell association optimization, with the objective of maximizing the long-term throughput of the users. Our asymptotic and non-asymptotic performance analyses reveal five key points: 1) spectrum sharing with light on-demand intra- and inter-operator coordination is feasible, especially at higher mmWave frequencies (for example, 73 GHz); 2) directional communications at the user equipment substantially alleviate the potential disadvantages of spectrum sharing (such as higher multiuser interference); 3) large numbers of antenna elements can reduce the need for coordination and simplify the implementation of spectrum sharing; 4) while inter-operator coordination can be neglected in the large-antenna regime, intra-operator coordination can still bring gains by balancing the network load; and 5) critical control signals among base stations, operators, and user equipment should be protected from the adverse effects of spectrum sharing, for example by means of exclusive resource allocation. The results of this paper, and their extensions obtained by relaxing some ideal assumptions, can provide important insights for future standardization and spectrum policy.

In systems employing pilot-symbol aided channel estimation, the pilot-to-data power ratio is known to have a large impact on performance. Therefore, previous works proposed methods setting the pilot power such that either the weighted sum of the mean squared error (MSE) of the estimated data symbols is minimized or the overall spectral efficiency (SE) is maximized. However, previous works did not take into account the impact of correlated antennas and channel state information (CSI) errors on the optimal pilot power setting. In this paper, we consider the uplink of a multiuser multiple-input multiple-output (MU MIMO) system employing a receiver that minimizes the MSE of the received data symbols in the presence of CSI errors and derive closed-form expressions for the MSE and the achievable SE. These expressions take into account the impact of antenna correlation and CSI errors, and are a function of pilot power and the number of receive antennas. The analytical and numerical results can help set the pilot power, minimizing the MSE in multiple antenna systems.

A promising new transmission mode in cellular networks is the three-node full-duplex (FD) mode, which involves a base station with FD capability and two half-duplex user transmissions on the same frequency channel for uplink and downlink. The three-node FD mode can increase spectral efficiency, especially in the low transmit power regime, without requiring FD capability at user devices. However, when a large set of users are scheduled in this mode, self-interference at the base station and user-to-user interference can substantially hinder the potential gains of FD communications. This paper investigates the problem of grouping users to pairs and assigning frequency channels to each pair in a spectral efficient and fair manner. Specifically, the joint problem of user uplink/downlink frequency channel pairing and power allocation is formulated as a mixed integer nonlinear problem that is solved by a novel joint fairness assignment maximization algorithm. Realistic system-level simulations indicate that the spectral efficiency of the users having the lowest spectral efficiency is increased by the proposed algorithm, while a high ratio of connected users in different loads and self-interference levels is maintained.

Massive MIMO systems, while being a promising technology for 5G systems, face a number of practical challenges. Among those, pilot contamination stands out as a key bottleneck to design high-capacity beamforming methods. We propose and analyze a location-aided approach to reduce the pilot contamination effect in uplink channel estimation for massive MIMO systems. The proposed method exploits the location of user terminals, scatterers, and base stations. The approach removes the need for direct estimation of large covariance matrices and provides good channel estimation performance in the large antenna regime.

We consider the uplink of a single cell single input multiple output (SIMO) system, in which the mobile stations use intra-cell orthogonal pilots to facilitate uplink channel estimation. In such systems, the problem of transmission power balancing between pilot and data is known to have a large impact on the mean square error (MSE) for the received signal and, consequently, on the achievable uplink data rate. In this paper, we derive a closed form expression of the MSE for the received signal as a function of the pilot and data power levels under a per-user sum pilot-data power constraint. As a major contribution, our model is developed for arbitrary channel covariance matrices and it enables us to study the impact of the number of antennas and antenna correlation structures, including the popular 3GPP spatial channel model. Numerical results suggest that the effect of the antenna spacing is limited, but the angle of arrival and angular spread have a strong and articulated impact on the MSE performance. Moreover, as the number of antennas at the base station grows large, we show that a higher percentage of the power budget should be allocated to pilot signals than with a lower number of antennas.

The performance of massive multiple input multiple output systems may be limited by inter-cell pilot contamination (PC) unless appropriate PC mitigation or avoidance schemes are employed. In this paper we develop techniques based on existing long term evolution (LTE) measurements - open loop power control (OLPC) and pilot sequence reuse schemes, that avoid PC within a group of cells. We compare the performance of simple least-squares channel estimator with the higher-complexity minimum mean square error estimator, and evaluate the performance of the recently proposed coordinated pilot allocation (CPA) technique (which is appropriate in cooperative systems). The performance measures of interest include the normalized mean square error of channel estimation, the downlink signal-to-interference-plus-noise and spectral efficiency when employing maximum ratio transmission or zero forcing precoding at the base station. We find that for terminals moving at vehicular speeds, PC can be effectively mitigated in an operation and maintenance node using both the OLPC and the pilot reuse schemes. Additionally, greedy CPA provides performance gains only for a fraction of terminals, at the cost of degradation for the rest of the terminals and higher complexity. These results indicate that in practice, PC may be effectively mitigated without the need for second-order channel statistics or inter-cell cooperation.

The millimeter-wave (mmWave) frequency band is seen as a key enabler of multigigabit wireless access in future cellular networks. In order to overcome the propagation challenges, mmWave systems use a large number of antenna elements both at the base station and at the user equipment, which leads to high directivity gains, fully directional communications, and possible noise-limited operations. The fundamental differences between mmWave networks and traditional ones challenge the classical design constraints, objectives, and available degrees of freedom. This paper addresses the implications that highly directional communication has on the design of an efficient medium access control (MAC) layer. The paper discusses key MAC layer issues, such as synchronization, random access, handover, channelization, interference management, scheduling, and association. This paper provides an integrated view on MAC layer issues for cellular networks, identifies new challenges and tradeoffs, and provides novel insights and solution approaches.

In the current literature considering multi-cell multi-user massive multiple-input multiple-output (MU-Massive-MIMO) systems, equal uplink power allocation among users is typically assumed, which does not exploit the potential of peruser power control. By contrast, in this paper we apply multi-cell uplink power control, assuming the minimum mean-square-error receiver based on the pilot contaminated channel estimation and a very large but finite number of antennas at the base station. We derive the lower bound on the average post-processing uplink signal to interference-plus-noise ratio (SINR) with individual power assignment between pilot and data transmissions for each user, which facilitates a joint iterative uplink pilot and data power control strategy that minimizes the sum transmit power of all users subject to the per-user SINR and per-user power constraints. The convergence of the proposed algorithm to a unique fixed point optimal solution is discussed for both single- and multi-user scenarios. Numerical results indicate the significance of uplink power control which further improves the energy efficiency in MU-Massive-MIMO systems.

Device-to-device (D2D) communications as an underlay to cellular networks can not only increase the system capacity and energy efficiency, but it can also enable national security and public safety (NSPS) services. A key requirement is to provide access to cellular services when the network is available and maintain local communication in the (partial) absence of the infrastructure. In this paper we propose and compare clustering schemes that are applicable for integrating D2D communications into cellular networks such that communication services can be maintained when the cellular infrastructure becomes partially dysfunctional. Our results show that there is a trade-off between coverage, energy efficiency and cluster formation delay and this trade-off can be handled by our proposed threshold-based clustering scheme.

Device-to-device (D2D) communications integrated into cellular networks is a means to take advantage of the proximity of devices and thereby to increase the user bitrates and system capacity. D2D communications has recently been proposed for the 3GPP Long Term Evolution (LTE) system as a method to increase the spectrum- and energy-efficiency. Such systems support a wide range of power control schemes based on a combination of open-loop and closed-loop components and there is a need to set the associated control parameters such that spectrum- and energy-efficiency targets are met. In this paper we study the performance of various power control strategies applicable to D2D communications in LTE networks and compare them with a utility function maximization approach that balances spectrum efficiency and the total transmission power. Our reference scheme is based on a fully distributed algorithm that iteratively sets the signal-to-interference-plus-noise (SINR) targets and corresponding transmit power levels. We find that the LTE-based power control approach performs close to the optimal scheme provided that the associated parameters are properly set1.

The wireless-access networks of today will have to evolve in several dimensions in order to address the challenges and expectations of the future. New technology components will be introduced as part of the evolution of current wireless-access technologies, such as HSPA and LTE. However, additional components may also constitute future new wireless-access technologies, which may complement the evolved technologies. Examples of such new technology components are new ways of accessing spectrum and substantially higher frequency ranges, the introduction of massive antenna configurations, direct device-to-device communication, and ultra-dense deployments.

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

Device-to-device (D2D) communications underlaying a cellular infrastructure has recently been proposed as a means of increasing the resource utilization, improving the user throughput and extending the battery lifetime of user equipments. In this paper we propose a new distributed power control algorithm that iteratively determines the signal- to-noise-and- interference-ratio (SINR) targets in a mixed cellular and D2D environment and allocates transmit powers such that the overall power consumption is minimized subject to a sum-rate constraint. The performance of the distributed power control algorithm is benchmarked with respect to the optimal SINR target setting that we obtain using the Augmented Lagrangian Penalty Function (ALPF) method. The proposed scheme shows consistently near optimum performance both in a single-input-multiple-output (SIMO) and a multiple-input-multiple-output (MIMO) setting.

Network multiple input, multiple output (MIMO) systems are built around a broadband backbone network that allows for the fast communication of channel state information (CSI) as well as user data between different base stations. Previous works have shown that multicell channel adaptive (opportunistic) power control can minimize the sum power or maximize the sum rate when the backbone is used for the exchange of CSI in network MIMO systems. In this work we investigate the gains of multicell opportunistic power control under per user fairness constraints when both CSI and user data are shared between multiple sites. We find that multicell opportunistic power control working in concert with uplink joint signal detection is an efficient means both for the capacity and the power control problems that not only minimizes sum power or maximizes overall capacity, but is also able to provide arbitrary level of fairness.

We consider the problem of setting the uplink signal-to-noise-and-interference (SINR) target and allocating transmit powers for mobile stations in multicell spatial multiplexing wireless systems. Our aim is twofold: to evaluate the potential of such mechanisms in coordinated multipoint transmission (CoMP) systems, and to develop scalable numerical schemes that allow real-time near-optimal resource allocation across multiple sites. We formulate two versions of the SINR target and power allocation problem: one for maximizing the sum rate subject to power constraints, and one for minimizing the total power needed to meet a sum-rate target. To evaluate the potential of our approach, we perform a semi-analytical study in Mathematica using the augmented Lagrangian penalty function method. We find that the gain of the joint optimum SINR setting and power allocation may be significant depending on the degree of fairness that we impose. We develop a numerical technique, based on successive convexification, for real-time optimization of SINR targets and transmit powers. We benchmark our procedure against the globally optimal solution, and demonstrate consistently strong performance in realistic CoMP scenarios.

Recently, tight network coordination in cellular systems has been demonstrated to improve the spectrum efficiency by means of signal processing methods. However, the performance of signal processing based multi-cell coordination is sensitive to backhaul delays, channel estimation errors and imperfections in fast link control. In this paper we consider tight network coordination for fast radio resource management (RRM) including packet scheduling, power control and modulation and coding scheme selection. We use a system level simulator to analyze the uplink performance of a multi-cell coordinated system that is built around a fast backhaul transport infrastructure for the purpose of enabling coordinated RRM rather than coordinated signal processing. We find that coordinated RRM alone can provide significant performance gains, up to 50% for cell edge and cell capacity as compared to traditional single-cell configurations and that multi-cell fast power control and modulation and coding scheme selection can significantly improve the accuracy of link adaptation in terms of signal-to-interference-and-noise (SINR) distribution, while imposing lower demands on the capacities of backhaul links compared to coordinated signal processing. Therefore RRM coordination can be an efficient complement to coordinated signal processing in multi-cell coordinated clusters.

This paper presents a mathematical modeling framework for studying the capacity of multi-cell orthogonal frequency division multiplexing networks in single- and multi-service scenarios with various quality of service (QoS) constraints. The framework is built around a feasible network load concept which relates cell resource utilization and interference-dependent resource demand generated by traffic. The feasible load problem is formulated for a general irregular network with non-uniform traffic distribution. Unlike in earlier works, the proposed model explicitly takes into account interference which is load- and service-dependent and models the service constraints on a time scale longer than the typical scheduling interval. A computationally efficient stochastic model is proposed for a regular network deployment with a user distribution pattern repeated over cells and then enhanced with QoS constraints. As an example application, the capacity region problem is studied and numerical results are presented for a realistic network setup. The framework can further be used, for example, for studying radio resource management algorithms or optimizing QoS parameters with respect to an operator QoS policy.

Recently, several works have pointed out the negative impact of intercell interference variation on the performance of link adaptation in multi-cell single input single output (SISO) systems. However, the performance of adaptive precoder selection in multiple input multiple output (MIMO) cellular networks in the presence of interference variation is much less understood. In this paper we develop a simulation model for MIMO orthogonal frequency division multiple access (OFDMA) networks that allows us to study the impact of popular scheduling and power control algorithms on the time variation of the uplink interference power. We find that employing a time persistent proportional fair scheduler (which we term proportional fair in frequency, PFF, scheduler) together with an open loop power control scheme significantly reduces intercell interference variation and thereby it provides higher throughput than traditional schedulers that are primarily optimized for a single cell setting without taking into account the variation of the intercell interference power.

We consider the uplink of a multi-cell orthogonal frequency division multiple access (OFDMA) system that employs multiple (M > 1) receive antennas and maximum ratio combining at the base station. We propose a model that captures the impact of intercell interference coordination on the modulation and coding scheme dependent bit error rate (BER) performance. We analyze this model in two steps. First, we find a closed form formula to calculate the BER under Rayleigh fading for a single subcarrier. Second, we calculate the subcarrier collision probabilities under random and coordinated subcarrier allocation. Combining these two steps allows us to derive the average BER performance (over all OFDM subcarriers) without/with intercell interference coordination (ICIC). We perform extensive Monte Carlo simulations in order to compare these two schemes when mobile stations are positioned randomly in multi-cell cell system. We find that at low and medium load coordinated allocation is superior (for various values of M), while at higher loads the two schemes perform similarly. We also find that at asymmetric load, it is beneficial to employ "asymmetric ICIC" as opposed to "symmetric ICIC". Finally, we explain a somewhat counterintuitive result that shows that the ICIC gain can be larger with multiple antennas at the base station than when there is only one antenna, although this gain is in the very low BER regime.

While intercell interference coordination (ICIC) for the downlink of multi-cell systems in general and orthogonal frequency division multiple access (OFDMA) networks in particular has been extensively studied, the uplink has received less attention. For the uplink, the impact of ICIC on the overall system throughput ("the ICIC gain") must be analyzed in a system model that captures specific constraints such as power limitation and the behavior of other radio resource management functions including scheduling, fast packet retransmissions by means of hybrid automatic repeat requests (HARQ), power control and adaptive modulation and code rate selection. In this paper we investigate the ICIC gain for the uplink of the 3GPP long term evolution (LTE) system and find that this gain much depends on the employed traffic model. Specifically, for non greedy (sometimes termed "non full buffer") traffic sources, HARQ and link adaptation are able to compensate the effect of intercell collisions and therefore the real ICIC gains are typically smaller than those reported based on full buffer models assuming greedy traffic. We expect that our findings provide useful insights for the system design of ICIC schemes.

In orthogonal frequency division multiple access networks, link adaptation (LA) selects the appropriate modulation and coding scheme for the upcoming transmission time interval. Since LA relies on measurements of the interference level and assumes that it remains similar during the next scheduling instance, the performance of LA depends on the variation (rather than simply the level) of intercell interference (ICI). In this paper we propose a minimum variance scheduler that allows for opportunistic scheduling - and thereby to take advantage of multi-user frequency diversity - but keeps the variation of intercell interference at a low level. We build on previously proposed collision models (detailed in own work and related other papers) and study the interplay between this scheduler and two intercell interference coordination (ICIC) methods that we call random and coordinated subcarrier allocation. We find that the proposed low variance scheduling scheme together with coordinated ICIC reduces ICI and its variance and is superior to other scheduling techniques in terms of the overall system throughput.

While several previous works have considered the problem of resource (including subcarrier and power) allocation in multicell orthogonal frequency division multiple access networks, only a few contributions have explicitly taken into account the elastic nature of data applications. In this work, each user is associated with a minimum and maximum resource block requirement and the resource allocation problem consists of maximizing the overall throughput such that these requirements are met. We propose a hybrid method that partitions this problem into a centralized and a distributed algorithm that balances between maximizing the overall throughput and being feasible in real systems. By means of simulations we compare four resource allocation strategies that represent various degrees of multi- cell coordination and taking advantage of multi-user diversity. We find that a feasible dynamic coordination combined with intra-cell multi-user diversity provides large throughput gains compared with non-coordinated schemes or schemes that would limit the degree of freedom of multi-user diversity.

The problem of access selection (AS) for multi-access networks has for long been addressed by both the standardization and research communities. As a result, a number of papers have proposed efficient AS algorithms that can take into account radio resource efficiency, overall capacity and quality of service (QoS) requirements in a multi-service environment. However, only a few works have developed on-line AS algorithms that do not require a priori knowledge of the traffic mix when delay sensitive (e.g. voice) and best effort (data) applications are supported. In this work, we present an online AS algorithm that performs well in a multiaccess network supporting two service classes and specifically takes into account the elastic nature of data applications. We model AS as a binpacking problem and realize that the problem is NP-complete. Therefore, we develop a heuristic algorithm called LessDamage that calculates a damage parameter and uses it as a metric for the allocation strategy. Simulation results show that LessDamage performs better in terms of blocking probability and elastic data throughput than available online binpacking heuristics, independently of the number of the available access technologies.

It has for long been recognized that in multi-cell wide-band code division multiple access (WCDMA) networks the admission of a new session into the system can have undesirable impacts on the neighboring cells. Although admission control algorithms that take into account such multi-cell impacts have been studied in the past, little attention has been paid to multi-cell admission and rate control algorithms when traffic is elastic. In this work, we propose a model for multi-cell WCDMA networks to study the impact of admission and rate control algorithms on key performance measures such as the class-wise blocking and dropping probabilities, block error rates and the overall throughput. By means of simulation we compare the performance of multi-cell algorithms with that of a single cell algorithm. For voice traffic, we find that multi-cell algorithms improve the session drop and block error probabilities in both of the examined heterogenous (termed "Hotspot" and "Hotround") scenarios. When traffic is elastic, the multicell algorithms perform better in the examined homogeneous case.

The current 3GPP working assumption on the evolved universal terrestrial radio access (E-UTRA) physical layer is that it will be based on single carrier frequency division multiple access (SC-FDMA) for the uplink and orthogonal frequency division multiple access (OFDMA) for the downlink. According to the concept specification, inter-cell interference mitigation techniques applicable to SC-FDMA and OFDMA systems are expected to be the key radio resource management techniques for E-UTRA. In this paper we propose and analyze a simple reuse partitioning technique (assuming coordinated sub-carrier allocation in the cells) that is able to minimize inter-cell interference. We propose a model that is able to take into account that sessions dynamically enter and leave the system. Rigid sessions require a class-specific fixed number of sub-carriers, while elastic sessions can enter the system if a minimum number of sub-carriers is allocated to them. In this rather general setting we analyze the system performance in terms of the expected number of sub-carrier collisions, the session blocking probabilities and the signal-to-noise-and-interference ratio performance. We present numerical results on the various trade-offs between these measures that provide insight into the behavior of OFDM based cellular systems and help dimension the parameters of a reuse partitioned system

It is expected that future wireless systems will consist of several distinct radio access technologies (including WCDMA/HSDPA, GSM/EDGE/GPRS, WLAN and others) forming a multi-access system that offers advanced voice and multimedia services. Previous works have shown that the combined capacity region of such systems depend on the service allocation policy that assigns user sessions to the available subsystems. The currently available service allocation policies typically operate off-line implying that the actual service mix is assumed to be known prior to the service allocation taking place. In this paper we consider the on-line problem according to which sessions arrive one after the other and no assumptions on the service mix can be made. We adopt four on-line bin-packing algorithms to the multi-access environment and study their performance by means of simulation in terms of the class-wise blocking probability and throughput. We find that the algorithm termed Less Voice provides the best performance in terms of the blocking probabilities and imposes the least slow down for elastic sessions

In the paper, we consider the problem of routing and bandwidth allocation in networks that support elastic traffic. We assume that the bandwidth demand between each source-destination (S-D) pair is specified in terms of a minimum and maximum value, and a set of flows between each S-D pair is allowed to realize these demands. (We say that a set of flows realizes the demand associated with an S-D pair, if the sum of the bandwidths allocated to these flows is greater than the minimum value assumed for the demand of that S-D pair). In this setting, we show that routing and bandwidth allocation can be formulated as an optimization problem, where network utilization is to be maximized under capacity and the widely used max-min fairness constraints. We describe three different algorithms to solve variants of this problem. The most important one, an efficient, original algorithm assuming multipath routing is studied in detail and illustrated with a numerical example.

We consider the problem of link capacity dimensioning and routing optimization in networks that support elastic flows and maintain proportional fairness among these flows. We assume that each demand between the origin-destination (O-D) pairs is associated with a minimum and a maximum bandwidth requirement and that a certain allocated bandwidth to a user demand (which must be between these minimum and maximum values) generates revenue for the network operator. On the other hand, the operator is incurred a capacity dependent cost for each link in the network. We then formulate the problem of bandwidth allocation, routing optimization and link capacity dimensioning as an optimization problem where the operator's objective is to maximize profit (under the fairness constraint). We propose computationally efficient algorithms to solve some important variants of this problem.

We develop a call level model of UMTS core networks where calls belonging to one of the four UMTS service classes arrive randomly. Arriving calls are granted service depending on the call's service class, the required maximum and minimum-bandwidth, and the available network resources at the arrival instance. We use a Markov model of transmission links to derive GoS (blocking probability) and QoS (throughput) measures under two reasonable and technologically feasible bandwidth sharing policies. We conclude that one of these policies is able to provide GoS/QoS guarantees for a wide range of traffic mixes. We argue that the results are applicable to the all IP/MPLS based new UMTS architecture.

As the all-IP architecture for W-CDMA based 3rd generation cellular networks matures within the ITU and 3GPP, there is a growing interest in devising resource reservation schemes that allocate both radio and IP resources in the access part of the network. We consider both the control- and the user plane of an end-to-end scenario with both IP and radio level QoS mechanisms and propose the addition of new parameters to the RSVP/IntServ model. Simulations indicate that these new parameters improve the efficiency of the radio resource allocation.

In this paper we consider an output port of an ATM switch, where cell streams belonging to different TCP and UDP sessions arrive. During congestion the switch applies early packet discard (EDP) in order to reduce bandwidth waste. In order to guarantee fairness in the sense that only misbehaving sources get affected by the packet drop mechanism and to ensure high server utilization we propose an algorithm which also features simplicity. A salient feature of this algorithm is that it allows a predefined share /spl alpha//sub i/ to be associated with stream i. The algorithm (which we call the weighted fair EPD, WFEPD) attempts to provide this weighted share of the bandwidth for the streams in the long time average. A sliding window implementation of WFEPD allows us to demonstrate its efficiency in terms of fairness and bandwidth utilization.

This paper presents a general traffic control framework for Asynchronous Transfer Mode (ATM) networks with its performance evaluation. The proposed traffic control scheme can incorporate all the recently considered ATM service classes including Constant Bit Rate (CBR), real time Variable Bit Rate (rtVBR), non-real time Variable Bit Rate (nrVBR), Available Bit Rate (ABR) and Unspecified Bit Rate (UBR) services. The control is based on a complete buffer partitioning architecture and on the associated buffer scheduling rule with adaptive weighting functions. We present, the formulation of the traffic control as an optimization problem in a 3-dimensional Quality of Service (QoS) state space. A solution approach based on dynamic programming is also suggested. A comprehensive performance evaluation of the method has been performed based on simulations and results are presented with several examples. The QoS dependence on CBR load, VBR load, VBR burstiness, UBR load are investigated and results are demonstrated with explanations.

While several papers and standards promote ATM in combination with the ATM Adaptation Layer Type 2 (AAL2) as the basic switching and multiplexing technology for 3/sup rd/ generation mobile access networks, very little work addresses the issue of AAL2 call admission control (CAC). The hardship of AAL2 CAC comes from the fact that AAL2, unlike ATM, supports variable packet size traffic. In addition, when applied in the cellular environment, the AAL2 CAC needs to co-operate with the radio interface (RI) resource management. In this paper we develop and compare AAL2 CAC algorithms that operate on the AAL2 traffic parameters currently considered by the ITU-T. Based on this comparison we find that one of these algorithms, employing the well-known Hoeffding inequality provides a reasonable trade-off between complexity and precision.

We analyze voice quality and system performance in third-generation mobile communication systems. We argue that in these networks that are expected to integrate voice, data, and multimedia services, the transport network can no longer be considered as a lossless transparent traffic channel. Rather, proper dimensioning is needed even for plain voice services. Furthermore, the efficient utilization of the network resources shared by voice and data traffic requires sophisticated traffic management in the statistical multiplexing environment. The contribution of the paper is twofold: first, voice quality in the cellular transport network is analyzed and network dimensioning criteria are derived. Second, building on advances reported in the literature and taking into consideration current standardization activities, a joint performance model is established for the air interface (AI) and the transmission network (TN) allowing for the analysis of end-to-end service performance within a uniform framework. To demonstrate the applicability of this model, we point to some performance problems in the multiservice environment and suggest and evaluate traffic management actions to overcome these.

This paper is the sequel of two previous papers describing and analyzing partially blocking-queueing systems. In those papers Blaabjerg et al. (see Proc. of the IEEE International Conference on Communication Systems, ICCS '96, Singapore, 1996 and Proc. of the 5th IFIP International Conference on Telecommunications Systems Performance Modelling, Nashville, TN, USA, 1997) proposed the extension of the classical multirate circuit switched loss model in order to model and analyze on the call level ATM systems supporting best effort type service classes, such as the ABR or UBR service classes. After a brief review of the basic modelling concepts, in this paper we are concerned with optimal call admission control, (CAC) of CBR/VBR and ABR/UBR calls in the sense that we want to maximize the revenue on a single ATM link when both QoS guaranteed and best effort service classes are present. We formulate the problem as a Markov decision problem and find that the concept of intelligent blocking is useful in this context. We solve this optimization task by policy iteration to find the blocking states of the link. This modelling framework (in its present form and by extensions) allows one to study the impact of pricing policies on CAC and fairness.