Novel networking paradigms based on cloudification and open interfaces are making mobile networks more agile to adapt and meet the requirements of emerging use cases. Based on an automated end-to-end cloud-native open-source deployment of a 5G mobile network with over-the-air capabilities, this demonstration features the inclusion of a cloud-native monitoring system to follow the radio access network (RAN) performance evolution. By just defining the number of gNBs, this monitoring system automatically configures independent data sources and dashboards representing the evolution of received information while such gNBs are progressively created on-demand in a distributed environment.

The architecture and management of nextgeneration mobile networks are integrating novel networking paradigms to support challenging scenarios. This paper presents the design, implementation, and evaluation of a fully cloud-native, end-to-end mobile network integrating O-RAN functionality. Built on top of open-source software, our proposed framework develops a set of cloud-native deployment artefacts-such as Helm Charts and Open Source MANO packages-also released as open-source. This framework supports highly disaggregated and distributed deployments of key network functions, including the$\mathbf{5 G}$core, gNodeB (with decoupled Centralised and Distributed Unit entities), near-RT RIC, and xApps, enabling agile, portable, and flexible instantiations. Experiments show that the full cloudnative mobile network can be instantiated in less than five minutes, achieving a throughput comparable to bare-metal setups. This work highlights significant improvements in deployment efficiency and adaptability, contributing towards automated and autonomous cloud-native Beyond 5G/6G networks.

Integrating edge computing with 5G/6G, bolstered by standards such as ETSI Multi-access Edge Computing (MEC), becomes indispensable, particularly in addressing end-to-end latency-sensitive vertical applications. Thus, it is imperative to delve into infrastructure and network considerations, including the relocation of server workloads and optimization of user data planes. Notably, 3GPP facilitates third-party vertical providers’ access to user equipment (UE) metrics and data plane capacities through the Network Exposure Function (NEF). This paper showcases a demonstration that focuses on the seamless coordination of cloud-native workload relocation and user plane function (UPF) selection within the 5G network. Here, the interaction between a vertical provider and the 5G core enables informed decisions aimed at ensuring minimal latency by triggering UPF selection and relocation of containerized servers to the cloud or the edge, illustrating the anticipated trend of increased automation within 6G networks.

Novel networking paradigms, such as virtualization, are transforming mobile networks to support challenging use cases and scenarios. This demonstration presents an on-demand, distributed, and fully cloud-native deployment of a 5G mobile network (including core and RAN) with over-the-air capabilities using open-source software. This deployment strategy provides the flexibility and dynamicity required to manage and orchestrate forthcoming B5G/6G networks, thereby empowering mobile networks to adapt and scale effectively.

Novel networking paradigms are enabling the introduction of innovative vertical use cases and new business relations in the B5G/6G mobile ecosystem. For instance, a use case may require the coordination of domains owned by different operators to provide service continuity and keep offering a vertical network service (NS) in similar conditions after a cross-border situation. This demonstration presents a procedure to perform a proactive service relocation in such a multi-administrative domain scenario considering an automotive use case. This demonstration proposes a cloud-native solution combining multiple enablers to manage the life-cycle of virtualised automotive NSs. During run-time, and upon registration, an AI/ML-based enabler decides proactively on the service relocation moment based on the collected vehicle’s positions and triggers an Integration Fabric enabler following ETSI ZSM guidelines to start a new instance of such automotive NS at the associated ETSI NFV management and orchestration stack present in the neighbouring administrative domain.

Effective and real-time management of data planes becomes of paramount importance to support the ever-increasing challenging use cases and scenarios foreseen in 5G and beyond. In this demonstration, we present a comprehensive showcase of a cloud-native open-source 5G core deployment, realised as a disaggregated non-public network (NPN) spanning multiple locations. Ranging from cloud to edge points of presence, the demonstration establishes an end-to-end experimental platform with over-the-air transmission capabilities, specifically highlighting the on-demand creation and deletion of User-Plane Functions (UPFs). This dynamic deployment approach harnesses the advantages offered by edge locations, empowering the mobile network to adapt and scale as per its specific requirements. Furthermore, our demo elucidates how to use Open Source MANO (OSM) for easing the management operations of network administrators.

The automation and flexibility introduced in the management and orchestration of B5G/6G mobile networks are allowing the creation of innovative vertical use cases considering the coordination of multiple provider domains owned by different operators and/or service providers. The first step to enable this automation is the onboarding of network service artefacts prior to enabling its instantiation in such challenging scenarios. This demonstration focuses on the automatic onboarding operation in a multi-administrative domain scenario. We propose a cloud-native solution deployed at each administrative domain made of an artefact registry management system coupled with an Integration Fabric element following ETSI ZSM guidelines. These elements synchronize between them autonomously and with the associated ETSI NFV management and orchestration stacks ensuring a consistent catalogue of vertical services at the different administrative domains.

The virtualization of mobile network functions constitutes one of the main blocks for addressing the high flexibility requirements of fifth generation (5G) communication systems. Reconfigurable hotspots are expected to be massively deployed to enable on-demand services and dynamically adapt the network capacity according to traffic requirements. In this paper, we present the extensions and modifications of the long term evolution (LTE) module of the ns-3 simulator (LENA) to include a software defined radio (SDR) physical layer implementation. These extensions combine the native flexibility of the simulator with the SDR features of a real-time prototype. Moreover, the framework was designed to distribute the communication functions across different elements of the network with the possibility of adjusting several transmission parameters as in a network function virtualization (NFV) paradigm. Thanks to an emulated full network protocol stack, the prototype allows the experimentation of novel 5G solutions and the evaluation of relevant key performance indicators (KPIs) from the lower layer protocols up to application level. To this aim, we present the experimental evaluation of the KPIs of energy, latency, throughput and reconfiguration time in relevant scenarios.

The paper elaborates on the technological and architectural innovations researched and developed by 5G-PPP Phase 1 projects and covering innovation areas such as 5G system design and evaluation, novel air interfaces, network management and security as well as virtualization and service deployment aspects.

This paper considers a network of energy harvesting wireless nodes transmitting simultaneously in a Gaussian interference channel and investigates a distributed power allocation algorithm that maximizes the sum-rate. The power consumption model is based on a series of step functions that allow to model, among others, radio frequency circuits being on/off and the startup power consumption of the transmitter. After showing that the sum-rate maximization problem is nonsmooth, nonconvex, and NP-hard, the Iterative Smooth and Convex approximation Algorithm (ISCA) is proposed, which successively approximates the step functions by proper smooth functions to obtain a sequence of smooth nonconvex problems that can be solved by means of the successive convex approximation method. It is demonstrated that the ISCA distributedly converges to a stationary solution of the sum-rate maximization problem. For the particular case of point to point communications, the numerical results show that the ISCA is able to avoid bad stationary solutions, performing close to the globally optimal solution. The performance of the ISCA is also evaluated in the interference channel and with real solar energy harvesting data.

In this paper, an experimental performance evaluation is carried out within a communication scenario that features two of the key enablers of 5G: (i) the use of post-OFDM modulations and (ii) an efficient use of the spectrum via spectrum sharing. The experimental lab set-up has been assembled so as to operate in conditions as realistic as possible via the actual realtime implementation of the involved transceivers and also via the utilization of propagation channels which have been recorded in a field measurement campaign and which are loaded in a channel emulator that also operates in real-time. The experimental results show that co-existence in a shared spectrum scenario is possible and that the performance degradation is kept at a low level as long as one of the two users is making use of spectrally agile post-OFDM modulations such as filterbank multicarrier (FBMC).

This paper considers a communication system composed of multiple transmitter-receiver pairs (users) that must share the same frequency band. As battery replacement may be a tedious task, transmitters are solely powered by energy harvesters, specifically, by solar and electromagnetic energies. In previous works for non-harvesting nodes, transmissions of other nodes limit the achievable rate of a certain user due to interference. In contrast, when a certain transmitter harvests electromagnetic energy, transmissions of other nodes may be beneficial as part of the transmitted energy can be harvested and later used to increase the transmission power and, accordingly, the achievable rate. In this context, this paper formulates a Nash Equilibrium (NE) problem with prices to assign the transmission powers of the different nodes by taking into account constraints in the transmission power and the Energy Causality Constraints (ECCs), which intrinsically couple the different user transmission strategies. The existence and uniqueness of the equilibrium are characterized by using the theory of finite dimensional Variational Inequalities (VIs).

In this paper, an energy harvesting transmitter operating in a point-to-point link through a discrete-time fading channel is considered, where symbols can be transmitted through several parallel independent streams. Taking into account the different sources of energy consumption at the transmitter, the resource allocation (in terms of power allocation and selection of the active streams and channel accesses) that asymptotically maximizes the mutual information is derived by assuming that the transmitter has non-causal knowledge of the harvested energy and channel state (offline approach). The Boxed Water-Flowing graphical interpretation is presented, which intuitively depicts the asymptotically optimal offline resource allocation. Moreover, an online algorithm is proposed for the case in which the transmitter only has causal (past and present) knowledge of the harvested energy and channel state. Finally, the performance of the proposed offline and online solutions is numerically evaluated and the associated computational complexities are assessed and compared.

This paper presents the FPGA design of an interference-aware digital front end tailored for heterogeneous multi-cell LTE-based systems. A resource-optimized RTL architecture has been implemented and validated under realistic operating conditions using the GEDOMIS®testbed. The parallelization and concurrent resource utilization of the joint synchronization and interference detection processing blocks is covered with low-level details.

An energy harvesting transmitter operating in a point-to-point link through a discrete time fading channel is considered in this paper. Assuming non-causal knowledge of the harvested energy and channel state, the resource allocation that maximizes the mutual information is investigated by considering both the radiated power and the circuitry power consumption. A resource allocation strategy that asymptotically maximizes the mutual information along N independent channel accesses is derived. The Boxed Water-Flowing graphical interpretation is presented, which intuitively depicts the asymptotically optimal resource allocation.

A Wireless Energy Harvesting Node (WEHN) operating in linear vector Gaussian channels with arbitrarily distributed input symbols is considered in this paper. The precoding strategy that maximizes the mutual information along N independent channel accesses is studied under non-causal knowledge of the channel state and harvested energy (commonly known as offline approach). It is shown that, at each channel use, the left singular vectors of the precoder are equal to the eigenvectors of the Gram channel matrix. Additionally, an expression that relates the optimal singular values of the precoder with the energy harvesting profile through the Minimum Mean-Square Error (MMSE) matrix is obtained. Then, the specific situation in which the right singular vectors of the precoder are set to the identity matrix is considered. In this scenario, the optimal offline power allocation, named Mercury Water-Flowing, is derived and an intuitive graphical representation is presented. Two optimal offline algorithms to compute the Mercury Water-Flowing solution are proposed and an exhaustive study of their computational complexity is performed. Moreover, an online algorithm is designed, which only uses causal knowledge of the harvested energy and channel state. Finally, the achieved mutual information is evaluated through simulation.

The scalar additive Gaussian noise channel has the “single crossing point” property between the minimum mean square error (MMSE) in the estimation of the input given the channel output, assuming a Gaussian input to the channel, and the MMSE assuming an arbitrary input. This paper extends the result to the parallel vector additive Gaussian channel in three phases. 1) The channel matrix is the identity matrix, and we limit the Gaussian input to a vector of Gaussian i.i.d. elements. The “single crossing point” property is with respect to the signal-to-noise ratio (as in the scalar case). 2) The channel matrix is arbitrary, and the Gaussian input is limited to an independent Gaussian input. A “single crossing point” property is derived for each diagonal element of the MMSE matrix. 3) The Gaussian input is allowed to be an arbitrary Gaussian random vector. A “single crossing point” property is derived for each eigenvalue of the difference matrix between the two MMSE matrices. These three extensions are then translated to new information theoretic properties on the mutual information, using the I-MMSE relationship, a fundamental relationship between estimation theory and information theory revealed by Guo and coworkers. The results of the last phase are also translated to a new property of Fisher information. Finally, the applicability of all three extensions on information theoretic problems is demonstrated through a proof of a special case of Shannon's vector entropy power inequality, a converse proof of the capacity region of the parallel degraded broadcast channel (BC) under an input per-antenna power constraint and under an input covariance constraint, and a converse proof of the capacity region of the compound parallel degraded BC under an input covariance constraint.

Energy harvesting is increasingly gaining importance as a means to charge battery powered devices such as sensor nodes. Efficient transmission strategies must be developed for Wireless Energy Harvesting Nodes (WEHNs) that take into account both the availability of energy and data in the node. We consider a scenario where data and energy packets arrive to the node where the time instants and amounts of the packets are known (offline approach). In this paper, the best data transmission strategy is found for a finite battery capacity WEHN that has to fulfill some Quality of Service (QoS) constraints, as well as the energy and data causality constraints. As a result of our analysis, we can state that losing energy due to overflows of the battery is inefficient unless there is no more data to transmit and that the problem may not have a feasible solution. Finally, an algorithm that computes the data transmission curve minimizing the total transmission time that satisfies the aforementioned constraints has been developed.

The lifetime of autonomous wireless nodes can be extended by using energy harvesters in the node. This additional source of energy implies a loss of optimality of the traditional transmission strategies such as waterfilling. In this paper, the precoding strategy along N independent channel uses that maximizes the mutual information in linear vector Gaussian channels for arbitrarily distributed inputs is studied, under non-causal knowledge of the harvested energy over time. Precisely, the optimal right singular vectors and singular values of each precoder are derived. The derivation of the left singular vectors is a computationally hard problem, similarly as in the case of non-harvesting nodes, and is left as an open problem. At each channel use, the observed channel is diagonalized and power is allocated following the proposed MIMO Mercury Water-Flowing solution.

The autonomy of wireless nodes is dropping year after year as the battery capacity increase is not able to follow the increasing energy consumption of the nodes, which is produced due to the high demand of data traffic and transmitter processing complexity of the users. Energy harvesting and energy efficient communications, e.g., energy efficient network topologies, are promising solutions to overcome this problem. In this context, this paper studies the power allocation and slot selection strategies that maximize the total throughput of a wireless energy harvesting node by taking into account both the transmission power and circuitry power consumption spent along the radio frequency chain of the transmitter.

This work considers a multiple-input multiple-output (MIMO) orthogonal frequency division multiplexing based multiuser broadcast system with precoding at the transmitter and feedback of channel state information. A general framework is presented for the transceiver design, and also for the design of the feedback link based on the quantization of the users' MIMO channel Gram matrices. The proposed design of the feedback link exploits the correlation of the channel response in the frequency domain due to the finite length of the channel time impulse responses to outperform other schemes based on feedback of the per carrier frequency responses. The transceiver design framework is based on a unitary linear transformation applied at the receivers which allows the computation of equivalent triangular channel response matrices at the transmitter. An analytic study of the error propagation due to the channel quantization in the feedback link and the computation of the equivalent triangular channel matrices is also performed. Based on the previous concepts, all the usual transceiver design criteria can be applied within this framework, and the particular case of a space-frequency precoder for robust mean square error minimization is derived as an example. Finally, the benefits of the proposed strategy are evaluated by means of numerical simulations and compared to other existing techniques.

Energy harvesting is increasingly gaining importance as a means to charge battery powered devices such as sensor nodes. Efficient transmission strategies must be developed taking into account both the availability of energy and data in the node. In this paper, the best data transmission strategy is found by taking into account that the node has a finite battery capacity and that the data and energy arrivals occur at known time instants. Losing energy due to overflows of the battery is proved to be inefficient unless there is no more data to transmit. Finally, an algorithm that computes the data transmission curve minimizing the total transmission time while spending the minimum energy and that satisfies energy and data constraints has been developed.

Transmit beamforming is a simple strategy that uses only one spatial direction in a multiple-input multiple-output (MIMO) channel. Due to its simplicity, beamforming is usually seen sensitive to inaccuracies of the channel state information at the transmitter (CSIT). In this paper, we investigate the robustness of beamforming with respect to the worst-case CSIT. Our result shows that beamforming is actually robust, in the sense that it achieves the maximum received signal-to-noise ratio (SNR) in the worst channel within an elliptical uncertainty region defined by the weighted spectral norm. This result implies that beamforming has the ability to combat against the imperfectness of CSIT, especially when channel dimensions or channel uncertainty are small.

This paper addresses the problem of resource allocation in a multiple access channel where several mobile terminals (MT's) are transmitting simultaneously to a single receiving base station, all of them with multiple antennas and exploiting beamforming. A multi-carrier modulation is assumed, either orthogonal frequency division multiplexing (OFDM) or filter bank multi-carrier (FBMC). The main advantage of FBMC is that the spectrum of the sub-channels is much more concentrated than for OFDM. This property is explicitly exploited in this paper in order to evaluate an asynchronous network where the transmissions from different MT's are not aligned in time. This implies that a frequency guard between groups of carriers assigned to different users has to be included, being larger for OFDM than for FBMC. The improvement in performance due to this fact is evaluated by means of the rate that can be achieved by dynamically allocating carriers to users and by calculating the power and bit loading to be applied.

This paper extends the “single crossing point” property of the scalar MMSE function, derived by Guo, Shamai and Verdú (first presented in ISIT 2008), to the parallel degraded MIMO scenario. It is shown that the matrix Q(t), which is the difference between the MMSE assuming a Gaussian input and the MMSE assuming an arbitrary input, has, at most, a single crossing point for each of its eigenvalues. Together with the I-MMSE relationship, a fundamental connection between Information Theory and Estimation Theory, this new property is employed to derive results in Information Theory. As a simple application of this property we provide an alternative converse proof for the broadcast channel (BC) capacity region under covariance constraint in this specific setting.

A two-way relay channel with independent parallel Gaussian channels between the relay and the two terminals is considered. Focusing on the decode-and-forward protocol, the second phase of the communication, in which the relay broadcasts the two messages to their respective receivers, is studied. Precisely, the problem of computing the power allocation among the parallel channels that maximizes the weighted sum rate assuming arbitrarily distributed channel inputs (such as m-QAM) is stated and shown to be convex. A numerical algorithm is provided to solve the problem for the general case and, for the particular cases of high and low power regimes, expressions for the optimal power allocation are derived in closed form.

The design of the precoder the maximizes the mutual information in linear vector Gaussian channels with an arbitrary input distribution is studied. Precisely, the precoder optimal left singular vectors and singular values are derived. The characterization of the right singular vectors is left, in general, as an open problem whose computational complexity is then studied in three cases: Gaussian signaling, low SNR, and high SNR. For the Gaussian signaling case and the low SNR regime, the dependence of the mutual information on the right singular vectors vanishes, making the optimal precoder design problem easy to solve. In the high SNR regime, however, the dependence on the right singular vectors cannot be avoided and we show the difficulty of computing the optimal precoder through an NP-hardness analysis.

Within the framework of linear vector Gaussian channels with arbitrary signaling, the Jacobian of the minimum mean square error and Fisher information matrices with respect to arbitrary parameters of the system are calculated in this paper. Capitalizing on prior research where the minimum mean square error and Fisher information matrices were linked to information-theoretic quantities through differentiation, the Hessian of the mutual information and the entropy are derived. These expressions are then used to assess the concavity properties of mutual information and entropy under different channel conditions and also to derive a multivariate version of an entropy power inequality due to Costa.

A simple multivariate version of Costapsilas entropy power inequality is proved. In particular, it is shown that if independent white Gaussian noise is added to an arbitrary multivariate signal, the entropy power of the resulting random variable is a multidimensional concave function of the individual variances of the components of the signal. As a side result, we also give an expression for the Hessian matrix of the entropy and entropy power functions with respect to the variances of the signal components, which is an interesting result in its own right.

In this paper, we study the design of the transmitter in the downlink of a multiuser and multiantenna wireless communications system, considering the realistic scenario where only an imperfect estimate of the actual channel is available at both communication ends. Precisely, the actual channel is assumed to be inside an uncertainty region around the channel estimate, which models the imperfections of the channel knowledge that may arise from, e.g., estimation Gaussian errors, quantization effects, or combinations of both sources of errors. In this context, our objective is to design a robust power allocation among the information symbols that are to be sent to the users such that the total transmitted power is minimized, while maintaining the necessary quality of service to obtain reliable communication links between the base station and the users for any possible realization of the actual channel inside the uncertainty region. This robust power allocation is obtained as the solution to a convex optimization problem, which, in general, can be numerically solved in a very efficient way, and even for a particular case of the uncertainty region, a quasi-closed form solution can be found. Finally, the goodness of the robust proposed transmission scheme is presented through numerical results. Robust designs, imperfect CSI, multiantenna systems, broadcast channel, convex optimization.

In this letter, we study spatial Tomlinson-Harashima precoding (STHP) from an information theoretic point of view encompassing both the single and the multiuser scenarios. Since the performance of any communications preceding scheme relies on the quality of the channel state information (CSI) that is made available at the transmitter side, in this work we focus our attention on the analysis of the robustness of STHP in terms of rate loss when the CSI is imperfect. Precisely, we present a robust design of the set of moduli used in STHP and, consequently, of the corresponding power allocation, that maximizes the achievable rates for the worst-case errors in the CSI in the small errors regime.

We are interested in studying different capacity formulations in linear vector Gaussian channels where the transmitter is only informed with the magnitude of the complex channel matrix coefficients and the receiver has perfect channel knowledge. Initially, we give the general expressions for the ergodic, compound, and outage capacities for our particular model of channel state information. Next, focusing on the compound formulation, we find that the optimal transmitter strategy consists in independent signaling through the transmit dimensions. Finally, we present a new result on the power allocation for the maximization of the outage mutual information.

We present the optimum design of a linear transmitter for a multi-input multi-output communication system when the input data consists of two QPSK streams and the receiver performs maximum likelihood detection. The transmitter design is optimal in the sense that it minimizes the worst-case pairwise error probability. We prove that the resulting linear transmitter constructs, as a function of the two input QPSK streams, a new signal constellation. Moreover, we show that this new signal constellation is such that the minimum distance among the points of the received constellation is maximized and, indirectly, the number of neighbors at minimum distance is also maximized.

The performance of any communications precoding scheme relies on the quality of the channel state information that is made available at the transmitter side. In this paper, we study the robustness of spatial Tomlinson-Harashima precoding (STHP) in terms of rate loss when errors are considered in the feedback link. We will evaluate the performance degradation of the uniform power allocation with antenna selection strategy for STHP (which is quasioptimal when there are no feedback errors) and compare it with a robust power allocation designed to maximize the achievable rates for the worst-case feedback noise. It is shown that the performance difference between both approaches is very small. Consequently, uniform power allocation with antenna selection arises as a good solution for STHP even in the presence of feedback errors.

In this paper, we study the ergodic capacity of a multiple input multiple output (MIMO) uncorrelated flat fading channel with perfect channel state information at the receiver and partial channel state information at the transmitter. We focus our attention on the case where the transmitter is informed only with the modulus of the channel matrix coefficients. First, we prove that a simple power allocation strategy among transmitting antennas is the optimal scheme, in the sense that is a capacity achieving architecture. Next, for the particular case where only two antennas are used at each communication end, we derive closed form expressions for the ergodic capacity and the optimal power assigned to each antenna.