This work investigates the following large-scale parameters: delay spread (DS), azimuth angle spread of departure (ASD), and zenith angle spread of departure (ZSD), from over-the-air (OTA) up-link channel measurements in an urban macro-cell (UMa) [email protected] Hz, considering different antenna array positions within a virtual large antenna array. Results indicate differences between extracted parameters and the 3rd generation partnership project (3GPP) model. The largest difference is perceived for horizontal angle spread. Additionally, the difference between user equipment (UE) ports parameters is notable, indicating room for improvement of the UMa scenario parameters of the current standard.

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.

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.

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.

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.

This work studies the application of a reconfigurable intelligent surface (RIS) in a cloud radio access network (C-RAN) targeting the reduction of resource usage while providing adequate capacity. We investigate if an RIS can contribute to improve the trade-off between the downlink system spectral efficiency (SE) and energy consumption of a multi-base-station (BS) multi-user single-RIS setup by means of link activation, radiated power control, and operational power mode decisions that can benefit from RIS-enhanced radio channels. For this purpose, we optimize the activations jointly with BS and RIS beamforming for maximum energy efficiency (EE) under a centralized approach and subject to SE, power, fronthaul capacity, and RIS phase-shift constraints. The associated mixed-boolean non-linear problem is solved using monotonic and semidefinite relaxation methods integrated in a Branch-Reduce-and-Bound procedure. Simulations show that the RIS helps to increase the EE of a C-RAN w.r.t. its non-RIS-aided and fully-connected versions by 30% and 80%, respectively.

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 spectrum scarcity problem can be tackled by promoting a more efficient use of the radio spectrum. Spectrum sharing techniques like TV White Spaces (TVWS), Licensed Shared Access (LSA), and Spectrum Access System (SAS), are adequate solutions for this problem and there exist already regulation and standardization efforts worldwide. To evaluate the coexistence between primary and secondary spectrum users, a Long Term Evolution (LTE) multi-cell simulator has been designed, which employs a generic spectrum sharing technique, Dynamic Spectrum Access (DSA), in order to obtain additional network capacity. As a way to bring attention and confidence of the telecommunications sector regarding the benefits that this technology may bring, the analysis is done in terms of interference suffered by incumbents (primary users) and capacity by secondary users. Results have confirmed the unfeasibility of co-channel coexistence for moderate distances between the cordless camera service (incumbent) and LTE network (secondary user) due to high interference. However, for adjacent bands, a gain of 40% in the secondary users’ capacity is possible following more restrictive interference protection requirements and 30% gain with the smaller frequency separation between primary and secondary operating frequencies.

Dynamic-time division duplex (TDD) networks have been considered as a promising solution to handle the asymmetry of traffic demand between downlink (DL) and uplink (UL) in dense networks. However, this solution creates a new kind of interference among base stations (BS-to-BS) and among user equipments (UE-to-UE), which is called cross-channel interference. This article presents some insights on how to deal with the cross-channel interference in dynamic-TDD systems. A new space division multiple access (SDMA) grouping problem is formulated in this work for dynamic-TDD scenarios, which aims at selecting the best set of user equipments (UEs) and is solved through low complexity greedy algorithms. Results are presented for zero-forcing (ZF) beamforming methods and SDMA grouping strategies for a given frequency resource. Some different trade-off parameters defined in the optimization problem are also analyzed, which have a different impact on the downlink and uplink capacity.

In this paper, a multi-group multi-way relaying network is considered. Multiple half-duplex nodes form a group and each node wants to share its message with all other nodes in its group via an intermediate half-duplex relay. The nodes of the whole network are equally distributed over the groups. In this paper, we propose a multicast interference alignment algorithm for a multi-group multi-way relay network, in which the minimum required number of antennas at the relay is independent of the number of nodes per group. This is an important property, because physical antenna resources are limited in general. In order to achieve this, we consider a transmission scheme with several multiple access phases and several multicast phases. In each of the multicast phases we create a MIMO interference multicast channel, by separating the antennas of the relay into clusters. Each of these clusters serves a specific group of nodes and transmits in such a way that the signals transmitted from different clusters are aligned at the non-intended multicast groups. It is shown that the proposed multicast algorithm outperforms a reference algorithm from the literature for a broad range of Signal-to-Noise Ratio (SNR) values, while still requiring less antennas at the relay.

Finite rate feedback model based on Random Vector Quantization (RVQ) is efficient in terms of number of feedback bits. However, the required scaling of feedback bits (codebook size) to achieve full multiplexing gain becomes extremely large with high Signal-to-Noise Ratio (SNR) and might not be practical. This paper considers the use of a Random Element Quantization (REQ) approach, which quantizes the channel vector element-wise. A simple statistics is calculated and the presented simulation results validate our calculations. The main primary result is that, the required codebook size per receiver to achieve full multiplexing gain is kept very small, which would avoid high computational complexity and memory usage.

The paper builds up on the idea of separating, in the time dimension, between conventional uncoordinated MIMO and IA transmission. The former usually utilizes all available dimensions (antennas) to send useful information, while the latter mitigates inter-cell interference. Due to the multitude of users in cellular networks, the transmission schemes are suitable for different groups of users. Therefore, in order to take advantage of both schemes, the users are classified into two groups: one with those users that supposedly benefit from cooperation and the other with those that should not cooperate. The proposed method for grouping the users has the advantage of being based on a simple decision metric, which avoids having to calculate precoders for all different possible combinations. Since each group must apply the proper transmission scheme, a fraction of the temporal resources is dedicated to each type of transmission. An automatic reservation of the temporal resources and an adaptive adjustment of the grouping threshold for each user, which avoids having to empirically determine a single threshold, are also proposed. These proposed mechanisms are shown to provide performance gains of up to 30% over the conventional transmissions.

In this work we propose a variation to the Block Diagonalization (BD) algorithm described that mitigates the interference from an external interference source which does not participate in the joint transmission inherent to the BD algorithm. The idea consists of precoding the signal at the transmission side so that the received signal at each receiver lies in a subspace orthogonal to the subspace containing the external interference. It is similar to how interference alignment algorithms work, but here instead of limiting the caused interference at the unintended receivers to a specific subspace, we are limiting the desired signal at the intended receivers. Since this “limiting” aspect reduces the dimensions available for the useful signal, a trade-off is involved between how many useful streams can be transmitted and how much of the external interference energy can be canceled. The proposed algorithm is analyzed and we show different metrics that can be used to determine the “good point” in this trade-off.

More and more Coordinated Multi-Point (CoMP) systems are attracting interest as a means to improve the performance of conventional cellular networks, particularly thanks to the joint processing approach. However, the large amount of signaling and computer operations required deserves special attention regarding both backhaul and radio resources constraints. Partitioning the set of transmission points into clusters is likely to reduce this coordination cost, but the search for the best formations of clusters may lead to expressive further computational efforts. Herein we introduce a novel approach, the Clustering-based Assignment Algorithm (CbAA), to cluster dynamically the transmission points on the downlink in order to transmit coherently to multiple users. When compared to static clustering, the proposed CbAA provided expressive gains in terms of system spectral efficiency. Compared to state-of-the-art dynamic clustering approaches, CbAA can waive the exhaustive search over all possible formations as well as the repetitive precoder calculations during the clustering procedure.

In multiuser MIMO systems, the transmitter can select a subset of antennas and/or users which have good channel conditions to maximize the system throughput using various selection criteria. Furthermore, the Interference Alignment (IA) method, which is based on the concept of precoding, can provide interference free dimensions. It offers different trade-offs between complexity and performance. The basic idea of Interference Alignment consists in precoding the transmitted signals such that they are aligned at the receiver where they constitute interference, while at the same time disjointed from the desired signal. We analyze the performance of antenna selection and multiuser diversity together in order to allow opportunistic IA using chordal and Fubini-Study distances. Analyses and simulations verify the behavior of the proposed scheme.

Space Division Multiple Access (SDMA) grouping algorithms are usually employed in order to find a suitable set of User Equipments (UEs) for spatial multiplexing. The largest SDMA group is not always the best SDMA group in a given transmission such that higher gains might be achieved by dynamically adjusting the SDMA group size. Besides, algorithms that balance the Signal to Interference-plus-Noise Ratio (SINR) among different links might ensure a certain level of link quality and so provide a more reliable communication for the scheduled UEs. This paper provides system-level analyses for strategies of Radio Resource Allocation (RRA) in Coordinated Multi-Point (CoMP) systems, which consider dynamic SDMA grouping, joint precoding and power allocation for SINR balancing. Our results show that Sequential Removal Algorithms (SRAs) and SINR balancing provide gains of system spectral efficiency.

This paper evaluates the performance of four algorithms for wireless network multicast transmission in a single cell. An efficient retransmission technique for multicast services based on network coding with a buffer is proposed and compared to other traditional schemes, such as the case with no retransmission, a simple retransmission and retransmission with multiple coded packets scheme. It is shown that the proposed algorithm outperforms other techniques and quality of the multicast transmission is improved.

Since the provision of both unicast and multicast services is expected for the next generation of multi-carrier wireless systems, the allocation of resources for this combination of services is a relevant topic, which may have a significant impact on the system performance. The focus of this work lies on the allocation of power to the different downlink subchannels of a multi-carrier system containing both unicast and multicast users. The power allocation problem is analyzed considering on the one hand the maximization of the sum throughput and on the other hand the maximization of the minimum SNR. The solution of the former is presented, which depends on numerical optimization, and an algorithm similar to the waterfilling hypothesis testing is proposed for reducing the processing time, while for the latter a closed-form solution is demonstrated. A simplified power allocation algorithm based on multicast group quality criteria is also evaluated and shown to approximate the maximal achievable performance under certain circumstances.

The provision of multicast services is a relevant feature in the context of the further evolution of cellular communication systems. The scope of this paper lies on the analysis and comparison of linear and non-linear downlink precoding techniques for separating users of both unicast and multicast services in space. The paper investigates and derives a non-linear algorithm based on Tomlinson-Harashima precoding (THP) for this unicast/multicast scenario. It is seen that, differently from the linear case, the application of a non-linear multicast-aware algorithm is not capable of providing significant gains. Additionally, a hybrid linear/non-linear algorithm is proposed, which is shown to achieve a good trade-off between performance and complexity.

This paper evaluates and compares different adaptive antenna techniques applied in the context of multicast services and presents a methodology for performing the spatial multiplexing of both unicast and multicast users. It is seen that adaptive beamforming is able to provide good results even for large groups of multicast users. A fair multicast beamforming algorithm is proposed, which focuses on the performance of the worst multicast user and particularly benefits from scenarios with strong line-of-sight. Additionally, grouping strategies that allow the allocation of the same resources to unicast and multicast users, and which make use of the proposed spatial multiplexing procedure, are shown to be more efficient than allocating separate resources to unicast and multicast users.

The capacity of GERAN is expected to be limited by interference, particularly in urban areas, and by the quality of service (QoS) requirements demanded by the users. Smart antenna arrays employing beamforming techniques, commonly known as adaptive antennas (AA), are an efficient technology in mitigating interference in urban areas with low azimuth spread. In EGPRS, the packet data service of GERAN, resource scheduling is responsible for multiplexing users on shared physical channels in order to best fulfill QoS requirements. By using more efficient scheduling algorithms, the resulting QoS is enhanced and, ultimately, it may be exchanged for capacity. In this work, the QoS and capacity gains provided by an AA system and two scheduling algorithms in several EGPRS system configurations are shown. Furthermore, it is demonstrated that, when combined, a tighter reuse scheme, a base station AA system, and an enhanced scheduling algorithm can result in a capacity gain of up to approximately 450% over the reference scenario.