Integrated Access and Backhaul (IAB) is a cost-effective and adaptable solution for deploying ultra-dense next-generation (5G and 6G) cellular networks to increase the likelihood of Line-of-Sight (LOS) coverage. This technology allows wireless backhaul connections to be established using the same technology and specifications as available in the access links. However, the absence of a physical testbed or a dataset that can be used for simulation in the millimeter wave (mmWave) band prevents researchers’ validation of the proposed algorithms in the IAB scenario. In this paper, we propose a novel data generator based on a Generative Adversarial Network (GAN), trained on a real dataset from a mobile network that operates in Europe, and maintains a significant market share that returns accurate traffic data for an IAB network. Also, we introduce IAB-CNPos, an intelligent IAB node positioning framework using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) that indicates IAB node positions to increase the coverage network with minimal deployment cost. Furthermore, we integrate this data generator with the SeBaSi simulator (an IAB simulator based on Sionna), which obtains accurate, data-consistent, and realistic end-to-end IAB simulation results. The performance results indicate that the data generator successfully passes the Kolmogorov–Smirnov (KS) criterion, so, it could operate as a verified data generator. Furthermore, we use the SeBaSi simulator, integrated with the data generator, to evaluate the performance of an IAB network in the London City scenario.

5G and beyond technologies are designed to convey multiple QoS requirements coming from highly heterogeneous services. In this regard, 5G extends the QoS definition of previous technologies. In order to fulfill the requirements of new services, it is necessary to develop resource management solutions that effectively and efficiently translate them to usage of resources. Therefore, MAC schedulers are to be designed to accommodate the demands of emerging applications with stringent QoS requirements, such as XR. In this work, we introduce a Lyapunov-based MAC scheduler designed to appropriately tackle coexisting heterogeneous QoS flows. The proposed approach allows the implementation of policies that explicitly consider the QoS requirements, as well as an efficient use of allocated resources. The results obtained through extensive experiments over the ns-3 5G-LENA simulator demonstrate that our scheduler guarantees the required time-average bit rate for every QoS flow at every time window, while ensuring the stability of traffic queues for all users.

XR applications place increasing pressure on mobile networks to meet their stringent Quality of Service (QoS) demands. In particular, XR services require high throughput and low latency, with traffic patterns that differ greatly from traditional applications. To address these challenges, we propose a novel Medium Access Control (MAC) scheduling solution that takes into account the segmentation of XR media units into Packet Data Units (PDUs), referred to as PDU sets in 5G-Advanced networks. These PDU sets correspond to the core traffic unit for XR services and must be delivered according to their specific QoS requirements, which are dynamically established in real-time by the XR application. The proposed scheduling solution leverages Lyapunov drift-plus-penalty optimization to jointly optimize resource allocation and stabilize traffic queues while ensuring the timely delivery of PDU sets. Our solution is implemented within the ns-3 5G-LENA framework, and the paper features a comparative analysis through extensive simulations, evaluating the performance of our proposal against existing MAC scheduling algorithms. Results evince that the proposed scheduler enhances resource utilization, ensures QoS compliance, and improves network performance.

In 5th Generation (5G) virtualized Radio Access Networks (vRAN), where the protocol stack is split across three different elements (Radio Unit (RU), Distributed Unit (DU), and Centralized Unit (CU)), and data is transmitted across the transport network (fronthaul (FH) and midhaul) between these units, the capacity of the fronthaul pipe can become a limiting factor for packet transmission. To address this challenge, state-of-the-art approaches have introduced several fronthaul control methods, designed to limit the sending rate, to ensure that it does not exceed the available capacity. However, it is important to avoid potential conflicts between the decisions of such fronthaul control methods and those of the Medium Access Control (MAC) scheduling schemes, as they might impact one another. In this context, we provide a thorough analysis of the trade-off between the fronthaul and the air interface limitations and we propose a new FH Control method to limit packet transmissions without altering the decisions made by the MAC scheduler. An extensive simulation campaign in a 3GPP defined scenario with Virtual Reality (VR) and Cloud Gaming (CG) services is conducted, for air-limited and air/fronthaul capacity-limited conditions. The results demonstrate that the proposed fronthaul control method efficiently coordinates the MAC scheduler decisions with the constraints imposed by the fronthaul capacity.

This paper presents an in-depth analysis of the Listen-Before-Talk (LBT) procedures required for cellular technologies operating in the unlicensed spectrum. We specifically investigate the 5G New Radio Unlicensed Standalone (NR-U SA) design under the most recent 3GPP standard-compliant Type 1 Channel Access Procedure (CAP). We highlight and address key challenges in adapting the asynchronous Type 1 CAP (i.e., load-based CAP) to NR-U’s synchronous slot-based framework and propose the Scheduled Type 1 CAP, which introduces a novel scheduling mechanism and implementation of additional sensing to facilitate seamless integration. We carry out our performance evaluation using a full-stack end-to-end network simulator and construct a realistic 3GPP-compliant coexistence scenario in which 5G NR-U SA and Wi-Fi networks operate indoors in unlicensed sub-7 GHz bands. The users of the 5G NR-U SA and Wi-Fi networks demand eXtended Reality applications. Through an extensive evaluation study, we analyse the interplay between the NR-U numerologies and the CAPs used in shared channel access and evaluate their impact on the end-to-end performance of both technologies by considering various quality of service metrics. The results reveal some of the main pitfalls of the LBT procedure defined by 3GPP in TS 37.213 and demonstrate the clear advantages of the proposed Scheduled Type 1 CAP. Our work provides valuable insights into CAPs for shared spectrum, proposes standard-compliant solutions, and lays the groundwork for future research by introducing a pioneering open-source network simulation platform for evaluating NR-U SA coexistence scenarios with the Scheduled Type 1 CAP.

5G networks are designed to support a variety of services with highly demanding Quality-of-Service (QoS) requirements. This opened the door for novel extended reality (XR) media applications to emerge with 5G. However, recent 5G field tests and system-level simulation studies show that further XR enhancements are required to support a massive adoption of XR services in 5G networks. Such enhancements are expected to come into play with 5G-Advanced. In this line, we propose and study an XR loopback mechanism that adapts the XR traffic to the instantaneous 5G network conditions by exploiting an XR application feedback. We propose various XR loopback algorithms, strategies, and parameters’ configurations and study their impact on the 5G end-to-end performance. We conduct extensive simulation campaigns by building realistic end-to-end 5G network scenarios with 3GPP mixed XR traffic setups. Results show that the proposed XR loopback mechanism can boost XR performance in 5G networks by adapting to 5G network conditions, while keeping the XR QoS requirements under control. We provide various insights and practical directions on XR loopback design that allow us to take full advantage of the 5G network capabilities and progress toward 5G-Advanced network design.

Next generation Radio Access Networks (RANs) consider virtualized architectures in which base station functions are distributed in different logical nodes, connected through fronthaul (FH) links. To reduce the FH deployment costs and the required FH capacity, operators may install a single FH link shared among multiple cells and exploit key enabling techniques, such as modulation compression, to reduce FH data. In shared FH capacity scenarios, it is essential to provide efficient methods to control and optimize the FH resources’ utilization with limited impact on the air interface performance. In this paper, a multi-cell multi-user scenario with a shared FH link across multiple cells is considered. We focus on optimizing the resource allocation and modulation compression of each user, in a centralized and dynamic manner, aiming to maximize the air interface performance subject to a shared FH capacity constraint. The problem is formulated as a convex optimization problem, which allows deriving the optimal resource allocation and modulation compression per user. Then, we evaluate the proposed FH-aware scheduling methods against baseline holistic strategies over an end-to-end dynamic 5G NR system-level simulator based on ns-3. Under a tight available FH capacity, results show gains that vary from 16% to 567% in different percentile statistics of the user-perceived throughput.

When users exchange data with Unmanned Aerial Vehicles - (UAVs) over Air-to-Ground - (A2G) wireless communication networks, they expose the link to attacks that could increase packet loss and might disrupt connectivity. For example, in emergency deliveries, losing control information (i.e., data related to the UAV control communication) might result in accidents that cause UAV destruction and damage to buildings or other elements. To prevent these problems, these issues must be addressed in 5G and 6G scenarios. This research offers a Deep Learning (DL) approach for detecting attacks on UAVs equipped with Orthogonal Frequency Division Multiplexing - (OFDM) receivers on Clustered Delay Line (CDL) channels in highly complex scenarios involving authenticated terrestrial users, as well as attackers in unknown locations. We use the two observable parameters available in 5G UAV connections: the Received Signal Strength Indicator (RSSI) and the Signal to Interference plus Noise Ratio (SINR). The developed algorithm is generalizable regarding attack identification, which does not occur during training. Further, it can identify all the attackers in the environment with 20 terrestrial users. A deeper investigation into the timing requirements for recognizing attacks shows that after training, the minimum time necessary after the attack begins is 100 ms, and the minimum attack power is 2 dBm, which is the same power that the authenticated UAV uses. The developed algorithm also detects moving attackers from a distance of 500 m.

Next generation Radio Access Networks (RANs) consider virtualized architectures in which base station functions are distributed in different logical nodes, connected through fronthaul (FH) links. To reduce the FH deployment costs and the required FH capacity, operators may install a single FH link shared among multiple cells and exploit key enabling techniques, such as modulation compression, to decrease the data rates over the FH. In shared FH capacity scenarios, it is essential to provide efficient methods to control and optimize the FH resources' utilization with limited impact on the air interface performance. In this paper, we propose and analyze different fronthaul-aware scheduling strategies, leveraging on modulation compression, for multi-cell multi-user scenarios with a shared FH link across multiple cells. We consider holistic approaches based on packet dropping at the PHY layer and postponing of MAC scheduling decisions, combined with the reduction of the modulation order per cell. Additionally, we propose optimization methods for resource allocation and dynamic modulation compression, in which the modulation order and resource block assignment is dynamically optimized per user and slot. We finally evaluate the proposed FH-aware scheduling methods over an end-to-end dynamic 5G NR system-level simulator based on ns-3.

Next generation Radio Access Networks (RANs) consider virtualized architectures in which base station functions are distributed in different logical nodes that are connected through fronthaul links. To reduce the required fronthaul capacity, modulation compression is considered as a key enabler. Modulation compression achieves fronthaul capacity reduction at the cost of reducing the maximum modulation order that can be used over the air interface, thus creating a cell-fronthaul trade-off. The trade-off is further accentuated and needs to be optimized appropriately when multiple cells share the same fronthaul link. In this paper, a multi-cell scenario with a shared fronthaul link across multiple cells is considered, and we focus on optimizing the modulation compression of each cell. We propose semi-static optimization procedures that aim at maximizing the air interface performance subject to a shared fronthaul capacity constraint, by taking into account the average traffic load and system configuration of every cell. The problem is formulated as a convex optimization problem, which allows deriving the optimal maximum modulation order that is permitted per cell, under two different optimization criteria. Then, we use a dynamic multi-cell 5G NR system-level simulator based on ns-3 to evaluate the proposed optimized solutions.

A physical layer (PHY) abstraction model estimates the PHY performance in system-level simulators to speed up the simulations. This paper presents a PHY abstraction model for 5G New Radio (NR) and its integration into an open-source ns-3 based NR system-level simulator. The model capitalizes on the exponential effective signal-to-interference-plus-noise ratio (SINR) mapping (EESM) and considers the latest NR specification. To generate it, we used an NR-compliant link-level simulator to calibrate the EESM method as well as to obtain SINR-block error rate (BLER) lookup tables for various NR configurations. We also illustrate the usability of the developed model through end-to-end simulations in ns-3, under different NR settings of modulation and coding schemes, hybrid automatic repeat request combining methods, and link adaptation approaches.

Recently, wake-up scheme has been proposed to enhance the energy-efficiency of 5G mobile devices and prolong its battery lifetime while reducing the buffering delay. The existing wake-up optimization mechanisms use off-line methods and are tied to specific traffic models. In this paper, a novel concept of wake-up scheduling is introduced to further improve the energy-efficiency of mobile devices and to deal with realistic traffic. The main idea is to use a fixed configuration of the wake-up scheme and adjust the scheduling of the wake-up signals dynamically. For this, a proactive wake-up scheduler is proposed to take online decisions based on traffic prediction. Towards this end, a framework to predict packet arrivals based on recurrent neural networks is developed. Numerical results show that for given delay requirements of video, audio streaming, and mixed traffic flow, the proactive wake-up scheduler reduces the power consumption of the baseline wake-up scheme without scheduler by up to 36%, 28% and 9%, respectively.

Recently, the concept of wake-up radio based access has been considered as an effective power saving mechanism for 5G mobile devices. In this article, the average power consumption of a wake-up radio enabled mobile device is analyzed and modeled by using a semi-Markov process. Building on this, a delay-constrained optimization problem is then formulated, to maximize the device energy-efficiency under given latency requirements, allowing the optimal parameters of the wake-up scheme to be obtained in closed form. The provided numerical results show that, for a given delay requirement, the proposed solution is able to reduce the power consumption by up to 40% compared with an optimized discontinuous reception (DRX) based reference scheme.

Recently, discontinuous reception mechanisms (DRX) and wake-up schemes (WuS) have been proposed to enhance the energy efficiency of 5G mobile devices and prolong the battery lifetime. The existing DRX and WuS use commonly pre-configured parameters that cannot be adjusted dynamically. In this paper, a novel wake-up scheduling (WuSched) concept is introduced to further improve the energy efficiency of WuS-enabled mobile devices while controlling the buffering delay in a dynamic manner. The main idea of WuSched is to use a fixed configuration of the wake-up scheme and adjust the scheduling of the wake-up signals dynamically based on actual traffic arrivals. For this purpose, two different optimization approaches of the wake-up scheduling concept are proposed, analyzed, and compared, namely offline and online wake-up schedulers (WuSched-Offline and WuSched-Online). First, the WuSched-Offline is analyzed analytically for Poisson traffic arrivals and optimized (offline) to balance the average delay and power consumption. Second, the WuSched-Online is proposed to take online decisions based on traffic prediction, which is able to deal with general and more complex traffic models. Towards this end, we develop a framework for the prediction of packet arrivals based on recurrent neural networks. Numerical results show that both wake-up schedulers outperform the ordinary WuS-based system where wake-up scheduler is not deployed. In particular, for predefined delay requirements of video streaming, audio streaming, and mixed traffic flow, the WuSched-Online reduces the power consumption of the baseline WuS by up to 36%, 28% and 9%, respectively. Results also show that the WuSched-Offline has slightly better energy efficiency than the WuSched-Online in the case of Poisson packet arrivals, as it is optimized for that, while its power consumption is slightly higher than that of the WuSched-Online scheduler for realistic traffic scenarios.

One of the main design targets of New Radio (NR) is to support multiple applications, including low-latency data transmissions. To achieve that, multiple features have been introduced, among which dynamic scheduling timings (denoted by K0, K1, K2) is the one which determines the delay between the different paired control and data transmissions. For example, K2 is the delay (in unit of slots) between an uplink grant reception and the corresponding uplink data transmission. The End-to-End (E2E) latency would be highly impacted by these scheduling timings. Based on the common observation, lower scheduling timings would lead to low E2E latency. However, in this paper, especially for uplink traffic, we show that this is not always true, and there are cases in which increasing the scheduling timings provides better E2E delay performance. This is due to the interplay between traffic patterns, gNB-UE process, and the NR numerology. To show such impact of scheduling timings on E2E latency with different uplink traffic patterns and NR numerologies, we implement an E2E ns-3 based NR simulator.

The limitations of state-of-the-art cellular modems prevent achieving low-power and low-latency Machine Type Communications (MTC) based on current power saving mechanisms alone. Recently, the concept of wake-up scheme has been proposed to enhance battery lifetime of 5G devices, while reducing the buffering delay. The existing wake-up algorithms use static operational parameters that are determined by the radio access network at the start of the user's session. In this paper, the average power consumption of the wake-up enabled MTC TIE is modeled by using a semi-Markov process and then optimized through a delay-constrained optimization problem, by which the optimal wake-up cycle is obtained in closed form. Numerical results show that the proposed solution reduces the power consumption of an optimized Discontinuous Reception (DRX) scheme by up to 40% for a given delay requirement.

The 5G New Radio (NR) access technology defines multiple numerologies to support a wide range of carrier frequencies, deployment scenarios, and variety of use cases. In this paper, we consider a resource allocation problem to efficiently support multiple numerologies simultaneously. We assume frequency division multiplexing (FDM) of numerologies in a time division duplex (TDD) system with a self-contained slot format. We focus on optimizing the numerology subband (SB) configuration, as well as the duplexing ratio between downlink (DL) and uplink (UL) directions within each SB. The optimization problem minimizes the weighted sum of the normalized load (NL) for each SB in each direction. We prove that our optimization problem is convex and, furthermore, we derive the optimal closed-form expressions for the numerology SB configuration and the DL-UL duplexing ratio per SB. The effectiveness of the proposed resource allocation is validated through an end-to-end ns-3 based simulator, which shows how the optimization of the NLs is translated into an improved throughput and delay performance.

Listen-Before-Talk (LBT) has been adopted as the spectrum sharing technique that guarantees a fair LTE/Wi-Fi coexistence in the unlicensed spectrum at the 5 GHz band. Differently, at mmWave bands, where beamforming is a must to overcome propagation limits, LBT scope becomes limited because the interference layout changes due to the directionality of transmissions. In this regard, this paper proposes a Listen-Before-Receive (LBR) technique for shared spectrum access and analyzes its potentials to promote a fair coexistence of multiple Radio Access Technologies (RATs) in unlicensed mmWave bands, as, e.g., 5G New Radio (NR) access technology and Wireless Gigabit (WiGig) devices using IEEE 802.11ad/ay standard. Since the less likely but still harmful interference situations with directional transmissions can no longer be detected easily at the transmitter, we believe that the receiver has useful information to be used. The main idea of LBR is that we provide to the receiver a say when it comes to allowing/preventing the access to the channel. In this line, we propose potential implementations of LBR, in conjunction with LBT and the self-contained slot, for NR-based access to unlicensed mmWave bands.

Orthogonal Frequency Division Multiplexing (OFDM)-based networks rely on time synchronization to obtain their best performance. Time synchronization with neighboring nodes can be satisfied by increasing the cyclic prefix (CP) length (at the expenses of spectral efficiency reduction) We show that by optimizing the transmit pre-compensation and receive post-compensation we can meet the time synchronization constraints and keep the CP at its minimum value. This concept is applied to paired-bands Frequency Division Duplexing (FDD) systems which tend to show inefficient occupancy of the uplink (UL)-band due to the traffic asymmetry. We consider the possibility of deploying multiple Time Division Duplexing (TDD) small eNBs (SeNBs) in the unused UL spectrum. In this scenario, time synchronization with macro eNB (MeNB) and neighboring SeNBs becomes essential. Two algorithms are proposed in order to ensure orthogonality of OFDM transmissions network-wide.

New short-length single-direction frame structures are proposed for 5G time division duplex (TDD) systems, where the transmit direction [i.e., either downlink (DL) or uplink (UL)] can be independently chosen at each cell in every frame. Accordingly, high flexibility is provided to match the per-cell DL/UL traffic asymmetries and full exploitation of dynamic TDD is allowed. As a downside, interference management becomes crucial. In this regard, this paper proposes a procedure for dynamic TDD in dense multiple-input multiple-output small cell networks, where the transmit direction selected per small cell (SC) is dynamically optimized together with the user scheduling and transmit precoding. We focus on the maximization of a general utility function that takes into account the DL/UL traffic asymmetries of each user and the interference conditions in the network. Although the problem is non-convex, it is decomposed thanks to the interference-cost concept and then efficiently solved in parallel. Simulation results show gains in DL and UL average rates for different traffic asymmetries and SC/user densities as compared to existing dynamic TDD schemes thanks to the proposed joint optimization. The gains become more significant when there is high interference and limited number of antennas.

This paper proposes a joint user scheduling and transmit direction selection procedure for dynamic TDD in 5G dense small cell networks, where the transmit direction (i.e. downlink (DL) or uplink (UL)) selected per small cell is dynamically optimized together with the user scheduling at every frame. We focus on the maximization of a general utility function that takes into account the DL/UL traffic asymmetries of each user and the interference conditions in the network. After relaxation of the integer variables that subsume the user scheduling and transmit direction selection, the problem is decomposed and efficiently solved in parallel at every frame. Simulation results show significant gains in DL and UL average rates for different traffic asymmetries, network densities, and user densities as compared to existing schemes for dynamic TDD.

This paper presents long-term resource provisioning schemes for dense multi-cell OFDMA-based networks, where multiple cells with possibly overlapping coverage areas compete for the same set of resources. The optimization is done over the long term, being independent of the specific users connected to each cell but dynamic enough to follow significant variations of the traffic load. In this sense, we focus on the average resource utilization (RU) and propose schemes to minimize the maximum RU of all cells. Firstly, we consider orthogonal resource usage among cells. Optimal closed-form expressions for the long-term resource provisioning are derived for this case. Secondly, we assume that resources can be reused at non-overlapping cells. In this case, the resource provisioning is solved in two steps: i) the number of resources required per cell is obtained by discretizing the optimal solution of a convex problem, and then ii) the specific resources to be utilized by each cell are determined by using graph coloring. In contrast to previous works, graph coloring can be applied to get an implementable solution for any condition of the cell loads. Simulation results show a significant reduction of the maximum RU, which translates into an increase of the served traffic and a reduction of the packet delay, as compared to static resource provisioning schemes.

Recent results have elucidated the benefits of using improper Gaussian signaling (IGS) as compared with conventional proper Gaussian signaling (PGS) in terms of achievable rate for interference-limited conditions. This paper exploits majorization theory tools to formally quantify the gains of IGS along with widely linear transceivers for multiple-input multiple-output (MIMO) systems in interference-limited scenarios. The MIMO point-to-point channel with interference is analyzed, assuming that received interference can be either proper or improper, and we demonstrate that the use of the optimal IGS when received interference is improper strictly outperforms (in terms of achievable rate and mean square error) the use of the optimal PGS when interference is proper. Then, these results are extended to two practical situations. First, the MIMO Z-interference channel (Z-IC) is investigated, where a trade-off arises: with IGS, we could increase the achievable rate of the interfered user while gracefully degrading the rate of the non-interfered user. Second, these concepts are applied to a two-tier heterogeneous cellular network where macrocells and small cells coexist and multiple MIMO Z-ICs appear.

This paper addresses distributed interference management in downlink of multi-cell multiple- input multiple-output (MIMO) time division duplex (TDD) networks. Each user is associated with a user-centric cluster of base stations (BSs), which cooperatively serve the user through CoMP joint transmission. Clusters of different users possibly share some BSs such that they may overlap, being coupled by interference and transmit power constraints at each BS. Our objective is the design of BSs clustering and precoding matrices per-user in order to maximize the weighted sum-rate of the system by controlling the interference and the power spent at BSs. In contrast to previous works where all channel matrices in the system are needed, we propose a distributed procedure whereby only channel matrices towards a limited number of candidate BSs per user are required while interference is still controlled by using the signal received from an uplink transmission. For an LTE-compliant dense deployment of 2×2 MIMO BSs/users, results show gains of 6-16% in terms of sum-rate and 49-84% in terms of 5%-tile per-user rate (depending on the maximum cluster size) as compared to distributed BS-disjoint clustering schemes.

Self-organizing network is a new capability envisioned for the near-future wireless cellular networks. Among the different use cases, here we tackle the coverage optimization with the objective of adjusting the power devoted for the pilot signal as a function of the load of the different small Node Bs (SNBs). The optimization is done with the objective of minimizing the total transmitted power for the pilot signals in the network, leading to a switch on/off SNBs solution as a particular case. In this paper we present a decentralized algorithm that is executed at UEs and SNBs, exploiting the messages reported by UEs.

Cellular networks need the densification of small eNBs (SeNBs) to face the tremendous data traffic demand growth, implying an interference increase and making transmit coordination a key enabler. This article proposes a decentralized coordinated precoding (D-CoP) for downlink (DL) weighted sum-rate maximization in dense MIMO TDD small cell networks (SCNs). Each SeNB designs its own precoding matrices based on channel state information (CSI) of the served users and knowledge of the interference-cost matrix that allows managing interference towards unintended users. A protocol is proposed to acquire the interference-cost matrix by processing the uplink (UL) received signal provided that: (1) channel reciprocity can be assumed and (2) all users participating in DL can transmit in UL with an adequate transmit filter. In contrast to existing transmit coordination techniques, D-CoP is fully scalable, avoids estimation of the interfering channels, and does not require information exchange between SeNBs. In case all parameters are perfectly acquired, an iterative algorithm is presented with demonstrated monotonic convergence when all SeNBs update its transmit precoders simultaneously. Further, the problem is reformulated in order to derive a robust D-CoP under imperfect CSI conditions. Finally, simulations in 3GPP LTE-Advanced SCNs show significant user packet throughput gains, without increasing the complexity associated to transmit coordination. Robustness to imperfect CSI and non-ideal channel reciprocity is shown through simulations.

A precise knowledge of the MIMO channel between the serving node and the user equipment (UE) is important for attaining good data rates in downlink transmissions (DL) in cellular systems. The interfering point-to-multipoint (I-P2MP) channel, consisting of multiple transmitters coexisting in the same area, where each transmitter is intended to serve multiple users, is a model that subsumes most of the scenarios that can be found in wireless cellular networks with a dense deployment of small cells (SCs). In conventional channel estimation procedures, resources allocated to each SC for training tend to be orthogonal, negatively impacting in the efficiency of the whole system. Reusing resources for training allows releasing resources for data transmission, but at the cost of degrading the channel estimation due to interference. We propose a decentralized algorithm for interference management that enforces the coordination among SCs in the design of the training sequences for de DL. Results in this work elucidate how to reuse resources for training and significantly improve the throughput of the system.

The optimal transmission scheme for a multiple-input multiple-output point-to-point channel (MIMO-P2P) is dependent on the type of received interference, which can be either proper or improper. We will show that the presence of improper interference is beneficial in terms of improving the achievable rate compared to a case with proper interference. These benefits are due to using widely linear precoder and receiver. Additionally, we investigate a cellular scenario where two types of users are coexisting, ones that are receiving proper interference while others which might be receiving improper interference, trying to devise the best transmission scheme to be adopted. We propose an improper Gaussian signaling-based scheme that allows controlling system performance and fairness in terms of bitrate through a single parameter.

This paper proposes distributed inter-cluster interference management procedures for 1 Coordinated Multi-Point (CoMP) Time Division Duplexing (TDD) cellular networks, in which a set of base stations (BSs), that form a cluster, cooperate in the downlink (DL) and uplink (UL) transmissions to communicate with a group of users while causing interference to users in neighbor clusters. In order to reduce the complications associated to the estimation of the interfering channels, we describe new iterative procedures based on channel reciprocity that avoid such estimation by exploiting the received signal in the UL, provided that transmit and receive filters in DL and UL are properly designed. Simulations show that proposed techniques attain significant DL and UL spectral efficiency gains. Furthermore, an analysis of imperfect channel state information (CSI) at both ends is included, evidencing the robustness of the proposed procedures. Considerations on convergence are included.

A relay station (RS)-based cellular system deployment is considered, where multiple base stations (BS) cooperate in the BS-RS in-band transmission for the downlink. With the joint optimization of the resources allocated to the wireless backhaul and to the RS-MS access, it is possible to exploit the benefits of networked-MIMO along with combating the pathloss and shadowing effects, and thus obtain significantly enhanced spectral efficiency and coverage homogeneity. The problem is shown to be convex under conventional QoS objective functions. Suboptimal low complexity solutions are also proposed and evaluated.