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.

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.

In this letter, we present two closed-form semiblind receivers for a two-way amplify-and-forward relaying system. The proposed receivers jointly estimate the symbol and channel matrices involved in the two-way relaying system by exploiting tensor structures of the received signals at the relay and the destination, and without using training sequences, in contrast to previous works. Differently from competing receivers, one of the proposed receivers does not require channel reciprocity between uplink and downlink phases, which can be of interest in frequency division duplexing relaying systems. Parameter identifiability and computational complexity are analyzed, and simulation results are provided to corroborate the effectiveness of the proposed semiblind receivers in scenarios with and without channel reciprocity.

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 letter, we present a sequential closed-form semiblind receiver for a one-way multihop amplify-and-forward relaying system. Assuming Khatri-Rao space-time coding at each relay, it is shown that the system with K relays can be modeled by means of a generalized nested PARAFAC model. Decomposing this model into K + 1 third-order PARAFAC models, we develop a closed-form semiblind receiver for jointly estimating the information symbols and the individual channels, at the destination node. Each step consists of a Khatri-Rao factorization. Parameter identifiability conditions are given, and simulation results are provided to illustrate the effectiveness of the proposed semiblind receiver.

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.

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.

In this paper we propose a modified successive interference cancellation algorithm and its performance is evaluated considering hybrid diversity/spatial multiplexing MIMO schemes. As consequence of this modification, the diversity layer of hybrid schemes is now able to achieve the full diversity and thereby improves performance considering bit error rate, while offering greater efficiency in bits per channel use (pcu) when compared with traditional STBC using the same number of transmit antennas.

This paper aims to propose and evaluate the performance of hybrid cooperative transceiver schemes on mobile wireless networks. In such scenario with a relay node, the hybrid scheme is readily applicable as its structure is well divided into parallel layers. Thus, an efficient cooperative coding can occur by those following factors: use of layers previously defined in the hybrid scheme, known protocols of cooperation in a relay channel and techniques of pre- and post-processing at source and destination nodes, respectively.

This work formulates a new receiver for the blind decoding of multi-layered space-frequency codes (MLSFC) in multiple input multiple output (MIMO) systems based on orthogonal frequency division multiplexing (OFDM). We introduce a slight modification on the standard MLSFC transmit structure with linear constellation precoding, which consists in extending the constellation rotation across multiple OFDM symbols. Thanks to this added feature, we formulate the received signal as a three-way array following a parallel factor (PARAFAC) model exploiting the powerful uniqueness property of this tensor model, blind MLSFC decoding is guaranteed under mild conditions using an alternating least squares estimation algorithm. Our preliminary numerical results illustrate the bit error rate (BER) performance of the proposed receiver in comparison with the non-blind zero-forcing based MLSFC receiver that assumes perfect channel knowledge.

In this work we deal with Congestion Control (CC) strategies to protect the Quality of Service (QoS) of Real Time (RT) services in Orthogonal Frequency Division Multiple Access (OFDMA)-based and packet-switched networks such as Long Term Evolution (LTE) and Worldwide Interoperability for Microwave Access (WiMAX). Specifically, the first contribution of this paper is the adaptation of a CC strategy previously proposed for High-Speed Downlink Packet Access (HSDPA) to these modern systems. This CC strategy comprises in a coordinated manner the functionalities scheduling, Admission Control (AC) and Load Control (LC). The second contribution is the proposal of a new feature to be added to this CC strategy in order to allow for an early detection of overload situations based on the packet delay of RT flows. In the results we show that our proposed overload prediction based on delay can efficiently prevent QoS degradation of RT flows.

The International Telecommunications Union established through the International Mobile Telecommunications-Advanced a set of requirements for high performance 4rd Generation communication systems and, with the aim of meeting such requirements, 3rd Generation Partnership Project Long Term Evolution (LTE) is considering a set of enhancements, referred to as LTE-Advanced. In LTE-Advanced context, Coordinated Multi-Point (CoMP) communication appears as a promissing technology to boost system throughput and to allow for an efficient Resource Allocation (RA). In this paper, we study RA algorithms that exploit coordination in the Downlink (DL) of CoMP systems to implement adaptive frequency reuse and so improve system throughput. We show through system-level analyzes that quite high throughput gains are achieved through intelligent RA.

In this paper, we consider a system based on Orthogonal Frequency Division Multiple Access (OFDMA) and investigate the benefits of combining the Satisfaction Oriented Resource Allocation (SORA) algorithm, which aims at maximizing the number of satisfied users in a system, and Orthogonal Random Beamforming (ORB), which provides spatial multiplexing gains and has low computacional complexity, low signaling demands, and high transparency to the User Equipment. Our investigations have shown that considerable gains in terms of throughput and user satisfaction can be achieved by combining SORA and ORB.

In this paper, we propose a switching between hybrid Multiple-Input Multiple-Output (MIMO) structures based on a distortion model per Group of Pictures (GOP) to improve the transmission of video streaming over wireless systems. The proposed algorithm uses measurements of the application and physical layers. In the application layer, a distortion model for GOP is used based on the packet loss rate and media data units. In the physical layer, the switching between hybrid MIMO structures is realized by simultaneously taking advantage of multiplexing and diversity gains provided by hybrid MIMO structure. The simulation results presented in this article show that the switching between hybrid MIMO structures based in the distortion model per GOPs is capable of improving the objective and subjective video quality at the end user.

In this paper, we propose a suboptimal algorithm to assign MIMO transmit structures subject a required quality of service (QoS). In particular, we use the error probability of MIMO structures designed to achieve diversity and multiplexing gains as the QoS target. This suboptimal algorithm provides two MIMO structures that are hybrids between diversity and multiplexing.

In this paper a radio resource allocation (RRA) and scheduling algorithm to maximize the satisfaction of users served by an orthogonal frequency division multiple access (OFDMA) system is proposed. The satisfaction oriented resource allocation (SORA) algorithm, based on low complexity heuristics, divides the users into two sets: satisfied and unsatisfied. Then, it performs resource allocation taking advantage of channel frequency selectivity and the characteristics of each set. The main advantage of the proposed algorithm is the capacity of keeping a high number of users satisfied by resources from users that would be unsatisfied in any case.

Hybrid multiple-input multiple-output (MIMO) transceiver scheme (HMTS) combines transmit diversity and spatial multiplexing, thus achieving at the same time the two possible spatial gains offered by MIMO channels. In the design of HMTS spatial diversity and spatial multiplexing branches are disposed in parallel in order to achieve diversity and multiplexing gains at the same time. Since the spatial multiplexing branches have no protection, they are more susceptible to the fading effect becoming the bottleneck in the performance of the whole transceiver. In this paper, we propose a solution to this bottleneck in the hybrid MIMO transceiver scheme using a partial channel state information at the transmitter side. The idea is to perform an antenna allocation. Thus, the most powerful subchannels are allocated to the most susceptible layers (spatial multiplexing branches). Through this solution we decrease the performance imbalance between the two layers of the HMTS G3+1, increasing the whole transceiver performance with low complexity feedback requirements.

The use of antenna arrays at both ends of the link has attracted significant attention of the researches on space-time equalization and coding techniques for so called multiple-input-multiple-output (MIMO) channels. The presence of strong co-channel interference (CCI) in addition to inter-symbol interference (ISI) in current wireless (mobile) communication systems places a significant challenge to MIMO space-time equalizers. We assess the performance of BLAST/MIMO on frequency-selective MIMO channel model in the presence of CCI. Space-time processing is used in order to deal with the frequency selectivity and to provide more degrees of freedom to deal with cochannel interference. We consider two non-linear space-time processing-based receivers. The first one is a MIMO space-time decision feedback equalizer (MIMO ST-DFE) and the second one is a space-time delayed decision feedback sequence estimator (MIMO ST-DDFSE) with actual adaptive algorithms for channel acquisition. Noise-limited as well as interference-limited situations are evaluated.

Transmission diversity schemes have recently emerged in wireless systems as an attractive solution in order to mitigate fading effects. Space-time block codes (STBC) with two antennas can provide similar order diversity as maximal-ratio receiver combining (MARC). In this paper a transmit diversity scheme using STBC with two and four antennas is applied for the EDGE/EGPRS system and its results are evaluated in a interference-limited scenario. We jointly compare this strategy of multiple antennas with incremental redundancy (IR), a technique for link quality control (LQC) in EDGE.