Semantic communications focus on prioritizing the understanding of the meaning behind transmitted data and ensuring the successful completion of tasks that motivate the exchange of information. However, when devices rely on different languages, logic, or internal representations, semantic mismatches may occur, potentially hindering mutual understanding. This paper introduces a novel approach to addressing latent space misalignment in semantic communications, exploiting multiple-input multiple-output (MIMO) communications. Specifically, our method learns a MIMO precoder/decoder pair that jointly performs latent space compression and semantic channel equalization, mitigating both semantic mismatches and physical channel impairments. We explore two solutions: (i) a linear model, optimized by solving a biconvex optimization problem via the alternating direction method of multipliers (ADMM); (ii) a neural network-based model, which learns semantic MIMO precoder/decoder under transmission power budget and complexity constraints. Numerical results demonstrate the effectiveness of the proposed approach in a goal-oriented semantic communication scenario, illustrating the main trade-offs between accuracy, communication burden, and complexity of the solutions.

Semantic communication (SC) is a promising approach for enabling reliable communication with minimal data transfer while maintaining seamless connectivity for wireless users. Unlocking the advantages of multi-user SC systems requires revisiting the communication and computation resource allocation problem focusing on the users' reasoning abilities. Reasoning in SC allows end-users to infer missing information or anticipate future events more effectively. Yet, state-of-the-art SC systems primarily focus on resource allocation through compression based on semantic relevance, while overlooking the underlying data generation mechanisms and the tradeoff between communications and computing. Thus, they cannot help prevent a disruption in connectivity. In contrast, in this paper, a novel framework for computing and communication resource allocation is proposed that seeks to demonstrate how SC systems with reasoning capabilities at the users can improve reliability in an end-to-end multi-user wireless system with intermittent communication links. Towards this end, a novel reasoning-aware SC system is proposed for enabling users to utilize their local computing resources to reason the representations when the communication links are unavailable. To optimize communication and computing resource allocation in this system, a noncooperative game is formulated to maximize the effective semantic information (computed as a product of reliability and semantic information) while controlling the number of semantically relevant links that are disrupted. To find a Nash equilibrium of the game, an algorithm based on best response is proposed. Simulation results show that the proposed reasoning-aware SC system results in at least a 16.6% enhancement in throughput and a significant improvement in reliability compared to classical communications systems that do not incorporate reasoning.

In forthcoming AI-assisted 6G networks, integrating semantic, pragmatic, and goal-oriented communication strategies becomes imperative. This integration will enable sensing, transmission, and processing of exclusively pertinent task data, ensuring conveyed information possesses understandable, pragmatic semantic significance, aligning with destination needs and goals. Without doubt, no communication is error free. Within this context, besides errors stemming from typical wireless communication dynamics, potential distortions between transmitter-intended and receiver-interpreted meanings can emerge due to limitations in semantic processing capabilities, as well as language and knowledge representation disparities between transmitters and receivers. The main contribution of this paper is two-fold. First, it proposes and details a novel mathematical modeling of errors stemming from language mismatches at both semantic and effectiveness levels. Second, it provides a novel algorithmic solution to counteract these types of errors which leverages optimal transport theory. Our numerical results show the potential of the proposed mechanism to compensate for language mismatches, thereby enhancing the attainability of reliable communication under noisy communication environments.

In this paper, a pragmatic semantic communication framework that enables effective goal-oriented information sharing between two-intelligent agents is proposed. In particular, semantics is defined as the causal state that encapsulates the fundamental causal relationships and dependencies among different features extracted from data. The proposed framework leverages the emerging concept in machine learning (ML) called theory of mind (ToM). It employs a dynamic two-level (wireless and semantic) feedback mechanism to continuously fine-tune neural network components at the transmitter. Thanks to the ToM, the transmitter mimics the actual mental state of the receiver's reasoning neural network operating semantic interpretation. Then, the estimated mental state at the receiver is dynamically updated thanks to the proposed dynamic two-level feedback mechanism. At the lower level, conventional channel quality metrics are used to optimize the channel encoding process based on the wireless communication channel's quality, ensuring an efficient mapping of semantic representations to a finite constellation. Additionally, a semantic feedback level is introduced, providing information on the receiver's perceived semantic effectiveness with minimal overhead. Numerical evaluations demonstrate the framework's ability to achieve efficient communication with a reduced amount of bits while maintaining the same semantics, outperforming conventional systems that do not exploit the ToM-based reasoning.

We consider a multi-user semantic communications system in which agents (transmitters and receivers) interact through the exchange of semantic messages to convey meanings. In this context, languages are instrumental in structuring the construction and consolidation of knowledge, influencing conceptual representation and semantic extraction and interpretation. Yet, the crucial role of languages in semantic communications is often overlooked. When this is not the case, agent languages are assumed compatible and unambiguously interoperable, ignoring practical limitations that may arise due to language mismatching. This is the focus of this work. When agents use distinct languages, message interpretation is prone to semantic noise resulting from critical distortion introduced by semantic channels. To address this problem, this paper proposes a new semantic channel equalizer to counteract and limit the critical ambiguity in message interpretation. Our proposed solution models the mismatch of languages with measurable transformations over semantic representation spaces. We achieve this using optimal transport theory, where we model such transformations as transportation maps. Then, to recover at the receiver the meaning intended by the teacher we operate semantic equalization to compensate for the transformation introduced by the semantic channel, either before transmission and/or after the reception of semantic messages. We implement the proposed approach as an operation over a codebook of transformations specifically designed for successful communication. Numerical results show that the proposed semantic channel equalizer outperforms traditional approaches in terms of operational complexity and transmission accuracy.

Learning and inference at the edge is all about distilling, exchanging, and processing data in a cooperative and distributed way, to achieve challenging trade-offs involving energy, delay, and accuracy. This calls for a joint orchestration of radio and computing resources. We propose an online adaptive resource allocation algorithm to choose where to compute, and how to offload computations, exploiting the concept of Deep Neural Network (DNN) splitting. The latter allows a device to locally execute part of an inference related processing, and delegate the other portion to a nearby Mobile Edge Host (MEH), which receives intermediate results from the device via a time varying wireless communication channel. Our method deals with dynamic parameters involving wireless channels, data arrivals, and MEH's CPU availability, by taking online control actions including the best splitting point, and the uplink data rate to transfer raw data or intermediate results (e.g., extracted features). The decision is taken only based on instantaneous observations of context parameters, to minimize the long-term device energy consumption, while guaranteeing the end-to-end delay not to exceed a predefined threshold, on average and probabilistic sense. Besides a theoretical analysis, numerical simulations show the effectiveness of our adaptive method in selecting the best partial offloading decision (DNN splitting) under different network conditions. Differently from previous works on edge inference, we exploit recently developed empirical models for the energy consumption of NVIDIA®edge boards, to evaluate the performance of DNN splitting at the edge, when exploring the typical offloading trade-off between energy and delay, both entailing communication and computing.

Targeting ultra-reliable and scalable connectivity of extremely high data rates, in the 100 Gbps to Tbps range, at almost “zero-latency” in 6G systems would require taking advantage of breakthrough novel technology concepts, including THz wireless links, broadband and spectrally efficient RF-frontends for a variety of different bands, the employment of intelligent materials (e.g., reconfigurable intelligent surfaces) and the design of machine learning-based models, protocols, and management techniques. To materialize the 6G vision, novel system techniques will need to be devised, including channel modeling and estimation, waveforms, beamforming, and multiple-access schemes, all tailored to the particularities of the adopted breakthrough technologies. As challenging Tbit/s usage scenarios are becoming ever more relevant for 6G systems, including non-line of sight connectivity based on intelligent surfaces and ad hoc connectivity in fast-moving network topologies, e.g., based on drones or V2X links, performance targets need to be reassessed. In such scenarios, apart from the high data rates in the order of Tbit/s other critical parameters may arise as more relevant: range, reliability, adaptability, reconfigurability, and agility, to name just a few.

In this paper, we study a meta-learning based neural network (NN) model to enhance the energy-efficiency related to power amplification in wireless communication systems. Specifically, we introduce a low-complexity adaptive solution to perform digital pre-distortion (DPD) for power amplifiers (PAs) using neural networks. Thus, we design a dedicated NN architecture to derive pre-distortion functions using few neurons. Moreover, we develop a meta-learning training approach that allows better generalization and faster adaptation, over time-varying PAs, compared to classical DPD schemes. Thereby, we propose a new approach to realize an adaptive digital predisorter based on meta-learning. A dedicated architecture allows to achieve lowcomplexity while meta-learning permits adapting to most parameters change in the system using few data. Through the simulation results, we have shown that this approach can offer a metatrained DPD function that can provide satisfying performance for different PA models using only 3000 IQ symbols during training phase. Contrary to fixed DPD architectures, the performance of our meta-trained DPD can be improved through only few gradient steps and over few samples during online calibration, achieving excellent performance with moderate complexity.

Recently, semantic communications are envisioned as a key enabler of future 6G networks. Back to Shannon’s information theory, the goal of communication has long been to guarantee the correct reception of transmitted messages irrespective of their meaning. However, in general, whenever communication occurs to convey a meaning, what matters is the receiver’s understanding of the transmitted message and not necessarily its correct reconstruction. Hence, semantic communications introduce a new paradigm: transmitting only relevant information sufficient for the receiver to capture the meaning intended can save significant communication bandwidth. Thus, this work explores the opportunity offered by semantic communications for beyond 5G networks. In particular, we focus on the benefit of semantic compression. We refer to semantic message as a sequence of well-formed symbols learned from the "meaning" underlying data, which have to be interpreted at the receiver. This requires a reasoning unit, here artificial, on a knowledge base: a symbolic knowledge representation of the specific application. Therefore, we present and detail a novel architecture that enables representation learning of semantic symbols for effective semantic communications. We first discuss theoretical aspects and successfully design objective functions, which help learn effective semantic encoders and decoders. Eventually, we show promising numerical results for the scenario of text transmission, especially when the sender and receiver speak different languages.

Wireless traffic is exploding, due to the myriad of new connections and the exchange of capillary data at the edge of the networks to operate real-time processing and decision making. The latter especially affects the uplink traffic, which will grow in 6G and beyond networks, calling for new optimization metrics that include energy, service delay, and electromagnetic field (EMF) exposure (EMFE). To this end, reconfigurable intelligent surfaces (RISs) represent a promising solution to mitigate the EMFE, thanks to their ability of shaping and manipulating the impinging electromagnetic waves. In line with this vision, this paper proposes an online adaptive method to mitigate the EMFE under end-to-end delay constraints of a computation offloading service, in the context of RIS and multi-access edge computing (ME C)-aided wireless networks. The goal is to minimize the long-term average of the EMF human exposure under such constraints, investigating the advantages of RISs towards blue (i.e. low EMFE) communications. A multiple-input multiple-output (MIMO) system is investigated as part of the visions towards 6G. Focusing on a typical scenario of computation offloading, the method jointly and adaptively optimizes user precoding, transmit power, RIS reflectivity parameters, and receiver combiner, with theoretical guarantees on the desired long-term performance. Besides the theoretical results, numerical simulations assess the performance of the proposed algorithm, when exploiting accurate antenna patterns, thus showing the advantage of the RIS and that of our method, compared to benchmark solutions.

Effective resource use in Ultra-Reliable and Low-Latency Communications (URLLC) is one of the main challenges for 5G and beyond systems. In this paper, we propose a novel scheduling methodology (combining reactive and proactive resource allocation strategies) specifically devised for URLLC services. Our ultimate objective is to characterize the level of proactivity required to cope with various scenarios. Specifically, we propose to operate at the scheduling level, addressing the trade-off between reliability, latency and resource efficiency. We offer an evaluation of the proposed methodology in the case of the well-known Hybrid Automatic Repeat reQuest (HARQ) protocol in which the proactive strategy allows a number of parallel retransmissions instead of the ‘'send-wait-react’' mode. To this end, we propose some deviations from the HARQ procedure and benchmark the performance in terms of latency, reliability outage and resource efficiency as a function of the level of proactivity. Afterwards, we highlight the critical importance of proactive adaptation in dynamic scenarios (i.e. with changing traffic rates and channel conditions).

Neural networks (NNs) based digital predistortion (DPD) have been shown to be a very promising technique to enhance power amplifier linearity. However, studies consider high level of quantization NNs (32-bits) whose hardware implementation is not feasible in practice. This paper addresses the challenge of low level quantization of NNs based DPD for PA linearization in OFDM communication systems. The goal is to improve the overall inference time, resources and energy efficiency. To perform quantization, we first operate a post training quantization to assess the impact of quantization on the NN. Second, we perform a quantization aware training to cope with the quantization noise. Thus, we truly believe that the proposed quantization approach puts forward an efficient transmission chain using NN DPD for OFDM based wireless communication systems. Indeed, numerical simulations show that using 4-bits quantization, resource usage is reduced by 62% compared to the 32-bits quantization. Moreover, the error vector magnitude is still lower than −35dB which is a slight degradation compared to 32-bits quantization.

The concept of Internet-of-Vehicles (IoV) has emerged to support the future Intelligent Transportation System (ITS). As an enabling technology for the IoV, Multi-access Edge Computing (MEC) provides cloud computing capabilities at the edge of the radio access network and supports vehicular task offloading of low-latency services. However, vehicles move across radio cells and, computing services offloaded to the Mobile Edge Hosts (MEH)s might be interrupted. The key to ensuring service continuity is to preemptively anticipate the service migration process to a well-identified subset of available MEHs, at the optimal time. In this paper, we develop a new algorithm that combines the exponential-weight algorithm for exploration and exploitation (EXP3) and the Lyapunov optimization to jointly and proactively decide when to trigger migration and where to migrate the service before the handover. Our algorithm leads to significant cost saving while meeting any given risk and availability targets which are new metrics introduced to assess service continuity.

We study the problem of user association, namely finding the optimal assignment of user equipment to base stations to achieve a targeted network performance. In this paper, we focus on the knowledge transferability of association policies. Indeed, traditional non-trivial user association schemes are often scenario-specific or deployment-specific and require a policy re-design or re-learning when the number or the position of the users change. In contrast, transferability allows to apply a single user association policy, devised for a specific scenario, to other distinct user deployments, without needing a substantial re-learning or re-design phase and considerably reducing its computational and management complexity. To achieve transferability, we first cast user association as a multi-agent reinforcement learning problem. Then, based on a neural attention mechanism that we specifically conceived for this context, we propose a novel distributed policy network architecture, which is transferable among users with zero-shot generalization capability i.e., without requiring additional training. Numerical results show the effectiveness of our solution in terms of overall network communication rate, outperforming centralized benchmarks even when the number of users doubles with respect to the initial training point.

The goal of this paper is to promote the idea that including semantic and goal-oriented aspects in future 6G networks can produce a significant leap forward in terms of system effectiveness and sustainability.Semantic communication goes beyond the common Shannon paradigm of guaranteeing the correct reception of each single transmitted bit, irrespective of the meaning conveyed by the transmitted bits.The idea is that, whenever communication occurs to convey meaning or to accomplish a goal, what really matters is the impact that the received bits have on the interpretation of the meaning intended by the transmitter or on the accomplishment of a common goal.Focusing on semantic and goal-oriented aspects, and possibly combining them, helps to identify the relevant information, i.e. the information strictly necessary to recover the meaning intended by the transmitter or to accomplish a goal.Combining knowledge representation and reasoning tools with machine learning algorithms paves the way to build semantic learning strategies enabling current machine learning algorithms to achieve better interpretation capabilities and contrast adversarial attacks.6G semantic networks can bring semantic learning mechanisms at the edge of the network and, at the same time, semantic learning can help 6G networks to improve their efficiency and sustainability.

Vehicles are becoming connected entities, and with the advent of online gaming, on demand streaming and assisted driving services, are expected to turn into data hubs with abundant computing needs. In this article, we show the value of estimating vehicular mobility as 5G users move across radio cells, and of using such estimates in combination with an online algorithm that assesses when and where the computing services (virtual machines, VM) that are run on the mobile edge nodes are to be migrated to ensure service continuity at the vehicles. This problem is tackled via a Lyapunov-based approach, which is here solved in closed form, leading to a low-complexity and distributed algorithm, whose performance is numerically assessed in a real-life scenario, featuring thousands of vehicles and densely deployed 5G base stations. Our numerical results demonstrate a reduction of more than 50% in the energy expenditure with respect to previous strategies (full migration). Also, our scheme self-adapts to meet any given risk target, which is posed as an optimization constraint and represents the probability that the computing service is interrupted during a handover. Through it, we can effectively control the trade-off between seamless computation and energy consumption when migrating VMs.

The Multi-Access Edge Computing (MEC) constitutes computing over virtualized resources distributed at the edge of mobile network. For mobile users, an optimal allocation of communication and computing resources changes over time and space, and the resource allocation becomes a complex problem. Moreover, for delay constrained applications, the resource allocation to mobile users cannot be solved by approaches designed for static users, as a solution would not be obtained within a desired time. Thus, in this paper, we propose a low-complexity computing and communication resource allocation for offloading of real-time computing tasks generated with a high arrival rate by the mobile users. We exploit probabilistic modeling of the users’ movement to pre-allocate the computing resources at base stations and to select suitable communication paths between the users and the base station with the pre-allocated computing resources. The simulations show that the proposed algorithm keeps the offloading delay below 100 ms for the small tasks even with the arrival rate of five tasks per second per user, while the state-of-the-art algorithms can handle only up to 0.5 tasks per second per user. Thus, the proposal enables an exploitation of the MEC for various real-time applications even if the users are moving.

The dense deployment of millimeter wave small cells combined with directional beamforming is a promising solution to enhance the network capacity of the current generation of wireless communications. However, the reliability of millimeter wave communication links can be affected by severe pathloss, blockage, and deafness. As a result, mobile users are subject to frequent handoffs, which deteriorate the user throughput and the battery lifetime of mobile terminals. To tackle this problem, our paper proposes a deep multi-agent reinforcement learning framework for distributed handover management called RHando (Reinforced Handover). We model users as agents that learn how to perform handover to optimize the network throughput while taking into account the associated cost. The proposed solution is fully distributed, thus limiting signaling and computation overhead. Numerical results show that the proposed solution can provide higher throughput compared to conventional schemes while considerably limiting the frequency of the handovers.

Network densification and millimeter-wave technologies are key enablers to fulfill the capacity and data rate requirements of the fifth generation (5G) of mobile networks. In this context, designing low-complexity policies with local observations, yet able to adapt the user association with respect to the global network state and to the network dynamics is a challenge. In fact, the frameworks proposed in literature require continuous access to global network information and to recompute the association when the radio environment changes. With the complexity associated to such an approach, these solutions are not well suited to dense 5G networks. In this paper, we address this issue by designing a scalable and flexible algorithm for user association based on multi-agent reinforcement learning. In this approach, users act as independent agents that, based on their local observations only, learn to autonomously coordinate their actions in order to optimize the network sum-rate. Since there is no direct information exchange among the agents, we also limit the signaling overhead. Simulation results show that the proposed algorithm is able to adapt to (fast) changes of radio environment, thus providing large sum-rate gain in comparison to state-of-the-art solutions.

This paper proposes a novel algorithmic solution for dynamic computation offloading, aimed at reducing the energy consumption of a mobile network endowed with multi-access edge computing. The dynamic evolution of the system is modeled through three queues: a local queue at the user side, a computation queue at the edge server, and a queue of results at the network access point. The optimization problem is cast as the minimization of the long-term average energy consumption of the whole system, comprising user devices, servers, and access points. Quality of service constraints for end users are imposed in terms of probability that the \textit{sum of the queues} exceeds a given threshold. A suitable weighting parameter can be tuned to drive the system toward a user-centric, a network-centric, or a hybrid solution. Exploiting stochastic optimization tools, the problem is solved thanks to a dynamic optimization algorithm, based on the solution of deterministic convex problems in each time slot. The algorithm does not assume any knowledge on the task input and output random sizes and the radio channel statistics. Several numerical results illustrate the advantages of the proposed method.

Finding the optimal association between users and base stations that maximizes the network sum-rate is a complex task. This problem is combinatorial and non-convex, and is even more challenging in millimeter-wave networks due to beamforming, blockages, and severe path loss. Despite the interest that this problem has gained over the last years, the various solutions proposed so far in the literature still fail at being flexible, computationally effective, and suitable to the dynamic nature of mobile networks. This paper addresses these issues with a novel distributed algorithm based on multi-agent reinforcement learning. More specifically, we model each user as an agent, which, at each time step, maps its observations to an action corresponding to an association request to a base station in its coverage range. Our numerical results show that the proposed solution offers near optimal performance and thanks to its flexibility, provides large sum-rate gain with respect to the state-of-art approaches.

5G mobile networks encompass the capabilities of hosting a variety of services such as mobile social networks, multimedia delivery, healthcare, transportation, and public safety. Therefore, the major challenge in designing the 5G networks is how to support different types of users and applications with different quality-of-service requirements under a single physical network infrastructure. Recently, network slicing has been introduced as a promising solution to address this challenge. Network slicing allows programmable network instances which match the service requirements by using network virtualization technologies. However, how to efficiently allocate resources across network slices has not been well studied in the literature. Therefore, in this paper, we first introduce a model for orchestrating network slices based on the service requirements and available resources. Then, we propose a Markov decision process framework to formulate and determine the optimal policy that manages cross-slice admission control and resource allocation for the 5G networks. Through simulation results, we show that the proposed solution is efficient not only in providing slice-as-a-service based on service requirements, but also in maximizing the provider's revenue.

This paper focuses on analyzing some key business aspects that arise during the deployment of key novel enabling technologies for 5G systems. Results are taken out of two EU-funded ongoing research projects, namely 5G-MiEdge and Superfluidity, which largely exploit mmWave communications and softwarization concepts for 5G networks. We initially provide a stakeholder analysis of the 5G ecosystem, as well as a Strengths Weaknesses Opportunities Threats (SWOT) analysis of a couple of key and most promising 5G use cases. For one use case also a preliminary business model is provided. Then we detail an economic 5G cost model, which is able to provide indications on the profitability of 5G networks. Finally, we highlight the planned future works.

Edge computing through local mobile cloud computing platforms is a key enabler for coping with the ever increasing data traffic requirements. A key enabler for this technology is the awaited ultra-dense deployment of radio access points for future 5G networks. Local cloud platforms allow maintaining a scalable network design by jointly managing local radio and computational resources. The Fog, a platform with rich services, introduces distributed intelligence at the edge of the network where entities such as radio access points form a local computing resources pool. In this paper, we address the problem of radio access points clustering for fog computing applications. We focus on the multi-user case where the local cloud resources are to be shared by several devices. We propose a novel clustering algorithm in which management functionalities are split into two layers: centralized and decentralized. The proposed strategy compromises centralized optimality with decentralized distribution intelligence for faster and less complex decision making. We compare, through simulations, the performance of the proposed algorithm to centralized and decentralized strategies, and show how it can achieve good quality of experience.

In 5G mobile networks, computing and communication converge into a single concept. This convergence leads to introduction of Mobile Edge Computing, where computing resources are distributed at the edge of mobile network, i.e., in base stations. This approach significantly reduces delay for computation of tasks offloaded from users' devices to cloud and reduces load of backhaul. However, due to users' mobility, optimal allocation of the computational resources at the base stations might change over time. The computational resources are allocated in a form of Virtual Machines (VM), which emulate a given computer system. User's mobility can be solved by VM migration, i.e., transfer of VM from one base station to another. Another option is to find a new communication path for exchange of data between the VM and the user. In this paper we propose an algorithm enabling flexible selection of communication path together with VM placement. To handle dynamicity of the system, we exploit prediction of users' movement. The prediction is used for dynamic VM placement and to find the most suitable communication path according to expected users' movement. Comparing to state of the art approaches, the proposal leads to reduction of the task offloading delay between 10% and 66% while energy consumed by user's equipment is kept at similar level. The proposed algorithm also enables higher arrival rate of the offloading requirements.

In 5G future wireless networks, the (ultra)-dense deployment of radio access points is a key drive for satisfying the increase of traffic demand and improving perceived users' quality. (Ultra)-dense deployment combined with capillary edge cloud, the fog, leads the way for optimization of users' Quality of Experience (QoE) and network performance. In this paper, we focus on improving users' QoE by addressing the issue of load balancing in fog computing. In this paper, we consider the challenging case of multiple users requiring computation offloading, where all requests should be processed by local computation clusters resources. We propose a low complexity small cell clusters establishment and resources management customizable algorithm for fog clustering. Our simulation results show that the proposed algorithm yields high users' satisfaction percentage of a minimum of 90% for up to 4 users per small cell, moderate power consumption, and/or high latency gain.

Ultra-dense deployment of radio access points is a key enabler for future 5G networks. It allows the network to cope with the ever increasing mobile data traffic. In addition, these radio access points can serve as an infrastructure for a local mobile cloud computing platform referred to as fog computing. The fog is a capillary edge cloud that enables joint optimization of communication and computational resources for maintaining an efficient and scalable network design. In this paper, we address the problem of radio access points clustering for fog computing applications. We focus on the case where multiple users require fog computing services. We formulate the distributed clustering problem as a joint optimization of the computation and communication resources. We transform the non-convex original problem into an equivalent convex one. Our simulation results show that the clustering solution derived from this problem yields high users' satisfaction ratio while keeping low the communication power consumption of the computation cluster.

Following Mobile Cloud Computing, Mobile Edge Computing and Network Functions Virtualisation tendencies, we envisage the utilization of computationally-enhanced base stations as computing nodes in which Virtual Machines can be deployed to perform computing tasks, leveraging the closeness of computing resources to end-users. This paper presents a seamless approach for the deployment of computationally-enhanced Small- Cells, also applicable to macro base stations, with no impact on the LTE-A architecture. To that end, the conventional mobile traffic and the traffic generated and consumed by the new computing resources are segregated and handled independently at the access point, with the latter being transmitted through the radio channel making use of the pre-established Data Radio Bearers. Assuming a general-purpose hardware configuration for the Small-Cells, we describe the functionality of the different physical and logical components along with the new protocol stacks and interfaces. Finally, we evaluate the additional delay and amount of signaling overhead introduced by the system to benchmark the proposed solution.

Introduction of Internet of Things and Machine Type Communication to future mobile networks will cause significant increase in the number of connected devices. At the same time, the connected devices can change traffic patterns as frequent transmission of small volumes of data is expected from sensors and machines. Transmission of such data is very inefficient due to redundancy of signaling information. In this paper, we analyze limits for the number of devices and machines communicating in current 4G mobile network. Then, we propose a novel solution, which shifts the current limits of the number of communicating devices towards requirements on 5G mobile networks. The proposed solution exploits cross-layer approach considering buffering of data and clustering of nearby users in order to minimize overhead and improve transmission efficiency. This way, we can increase the number of devices served by a single cell up to 24 times comparing to the state of the art solution.

To avoid call drops after handover due to unavailability of radio resources at a target handover cell, call admission control procedure reserves a specific amount of resources for users performing handover to this cell. If a high amount of resources is reserved, the available capacity for users served by the cell is lowered. Contrary, if a low amount of resources is booked for users entering the new cell, handover cannot be performed and user's connection is dropped. To optimize the amount of reserved resources, we propose an algorithm for prediction of channel quality between the user and the target cell after completing handover to the target cell. The algorithm is based on the knowledge of handover hysteresis and on decomposition of overall interference caused by other cells in the network. The prediction accuracy is tuned by correction parameter, which is dynamically set based on Q-learning approach. As the results show, the proposed algorithm with learning improves the efficiency of channel quality prediction up to twice comparing to conventional solution.

Due to large numbers of interferers, in-band interference is a bottleneck that future wireless heterogeneous networks have to deal with. Recent advances in information theory have shown that interference does not have to be avoided or strongly limited, so that reliable transmissions can occur. In this paper, we propose a novel Radio Resource Management (RRM) approach, capable to exploit those recent advances, by methodically coupling and processing in-band interferers. To do so, we first reuse the design of a previous two interference regime classifier. Then, we propose an algorithm which forms coalitions of interferers over the available spectral resources, in such a way that the spectral efficiency (SE) of the system is maximized. Simulations results show that our proposed RRM algorithm strongly limits the undesired effects of the traditional tradeoff between in-band interference and overall system performance (SE).

Femto-cloud is a novel networking architecture that joins femtocells networks and computation offloading to the cloud in a single framework. This allows to form server farms of femtocell access points providing cloud services. However, femtocells cannot offer the same computation and storage capacities as traditional cloud servers. In the femto-cloud platform, femtocells cooperate together through cluster formation. Effective cooperation between femtocells through clustering has to take into account many challenging limitations such as radio resources availability, base stations deployment scenarios, delay constraints and power consumption limitations. These parameters affect the choice of the femtocells cluster size and the computation load distribution. In this paper, we evaluate different strategies of clustering in the femto-cloud framework and show their effect on cluster characteristics in terms of size, latency, and power consumption.

Performance of heterogeneous network is strongly limited by the interference due to multiple access points operating in the same geographical area, with overlapping service coverage. The common understanding is that interference, classically processed as additive noise, compromises the transmission and therefore must be ideally avoided or at least strongly limited. However, recent investigations in the domain of information theory and successive interference cancellation (SIC) techniques have proved that interference may not necessarily be treated as an opponent, but may become an ally. In this paper, we propose a novel interference aware resource management algorithm, where the system may only control its interference perception. In a system consisting of a couple of downlink users and access points with overlapping coverage, we aim to define the most spectral-efficient way to process interference at each receiver. Based on a 3-regimes interference classifier, both users in the system may either treat interference as noise, orthogonalize transmissions so that interference may be avoided, or cancel interference out of the received signal via SIC-based techniques. Our study shows that, when aiming at maximizing total spectral efficiency, ignoring or avoiding interference is not always the best option. Based on our theoritical study, we propose an interference classification algorithm, with only 2 admissible regimes for each user. Finally, we assess its notable performance improvement by simulation results.

Today, mobile handsets are more and more capable to run complex applications. Mobile computation offloading offers the potential of extending mobile devices capabilities and battery lifetime. Traditional mobile computation offloading decision algorithms are mainly based on the offloading energy trade-off between locally consumed energy and offloading energy. Femtocloud is a novel paradigm introduced by the European project TROPIC [1] that merges cloud computing services and the benefits of femtocell networks. In this paper we present a novel offloading algorithm that takes decision about offloading mobile computation to a femtocloud. The proposed offloading algorithm incorporates a multitude of parameters in the offloading decision process while reducing the mobile handset energy consumption and keeping a good user quality of experience. Simulations results show that our proposed algorithm is able to extend the mobile battery life and to assure the computation of all the applications while respecting latency and memory constraints.

Predictive small cells networks and proactive resource allocation are considered as one of the key mechanisms for increasing the long-term energy-efficiency of communication networks. Learning techniques exploit repetitive patterns in human behavior to predict some future transmission contexts of the network. In this paper, we target to improve the energy efficiency of delay-tolerant transmissions by enabling flexibility in resource allocation with prediction-based strategies. We study the performance, in terms of energy efficiency of several scenarios of future knowledge ranging from zero to perfect knowledge of the future context, but also partial knowledge scenarios (short-term predictions, long-term statistics or partial knowledge). An iterative process, approaching the optimal strategies in each scenario, is described. In some cases, closed-form expressions of the optimal strategies to be implemented can be obtained and the performance in each scenario is computed. Our analytical and numerical results assess the potential benefit of exploiting the knowledge of the future in the case of a delay-tolerant transmission and show how the system may benefit from a provided piece of information about the future transmission context.

The paper presents a simple probabilistic analysis of the energy consumption in preamble sampling MAC protocols. We validate the analytic results with simulations. We compare the classical MAC protocols (B-MAC and X-MAC) with LA-MAC, a method proposed in a companion paper. Our analysis highlights the energy savings achievable with LA-MAC with respect to B-MAC and X-MAC. It also shows that LA-MAC provides the best performance in the considered case of high density networks under traffic congestion.

The paper presents LA-MAC, a low-latency asynchronous access method for efficient forwarding in wireless sensor networks. It is suitable for current and future sensor networks that increasingly provide support for multiple applications, handle heterogeneous traffic, and become organized according to some complex structure (tree, DAG, partial mesh). It takes advantage of the network structure so that a parent of some nodes becomes a coordinator that schedules transmissions in a localized region. Allowing burst transmissions improves the network capacity so that the network can handle load fluctuations. At the same time, the method reduces energy consumption by decreasing the overhead of node coordination per frame. The paper reports on the results of extensive simulations that compare LA-MAC with B-MAC and X-MAC, two representative methods based on preamble sampling. They show excellent performance of LA-MAC with respect to latency, delivery ratio, and consumed energy.

The exponential increase in high rate traffic driven by a new generation of wireless devices is expected to overload cellular network capacity in the near future. Femtocells have recently been proposed as an efficient and cost-effective approach to enhance cellular network capacity and coverage. However, dense and unplanned deployment of additional Base Stations and their uncoordinated operation may increase the system power consumption. Thus, efficient schemes are essential for managing femtocells activity and improving the system performance. In this paper, we investigate the effect of femtocell deployment on the cellular network energy efficiency. The goal is twofold: first, we aim to analyse how classic femtocell access schemes affect the system energy consumption; second, we propose a novel cell selection scheme for Open Access femtocells that allows the effective deployment of femtocells in the cellular network reducing power consumption and limiting the effect of interference.

The exponential increase in high rate traffic driven by the new generation of wireless services is expected to overload cellular network capacity in the near future. Femtocell networks have recently been proposed as an efficient and cost-effective solution to enhance cellular network capacity and coverage. However, dense and unplanned deployment of new Base Stations (BSs) and their uncoordinated operation may increase the system power consumption and rise co-channel interference. Thus, efficient schemes are essential for managing femtocell activity and improving the system performance. Classical cell switch off and discontinuous transmission (DTX) algorithms aim at improving the network Energy Efficiency (EE) in lightly loaded scenarios. In this paper, we propose a novel multi-cell architecture for Open Access femtocell networks, which also enables energy saving at medium and high loads without compromising the end-user performance.

In this paper, we propose the Base-Station (BS) Discontinuous Transmission (DTX) as the means for saving energy. Traditional proposals utilize DTX at the BS to enable sleep modes under lightly loaded scenarios. However, we propose to shape the traffic to enable more frequent sleep modes at the BS, while ensuring the QoS requirements in terms of application delay are successfully met. The results show that our DTX proposal has a significant potential even under moderately loaded scenarios. The system Energy Efficiency (EE) gain is achieved in our proposal by exploiting the classical Energy-delay tradeoff. We show the performance of our scheme in the presence of realistic system power models proposed within the framework of EARTH project.

The paper presents DA-MAC, a density aware access method for efficient forwarding in multi-hop dense wireless sensor networks. Its principle is to offer a configurable channel sensing phase during which nodes request transmission opportunity in a way that avoids collisions. The receiver can thus schedule transmissions so that nodes may return to sleep and only wake up at their scheduled transmission instants. Allowing burst transmissions improves network capacity and the network can handle load fluctuations. The paper presents simulation results on the performance of DA-MAC compared with two other adaptive access methods: B-MAC with Contention Window and SCP-MAC. They show excellent performance of DA-MAC with respect to latency, packet delivery ratio, and power consumption.

Packet scheduling algorithms are viewed as one of the key mechanisms for increasing the diversity order, robustness and effectiveness of a wireless multi-user communication systems. Traditional packet scheduling algorithms are designed to save energy at the Base-station(BS) in downlink by exploiting tradeoffs between spectral efficiency, delay and energy while at the same time meeting the QoS requirements of the system. However, these algorithms ignore the User-Equipment(UE) circuit power consumption to receive and process downlink traffic. In this paper, we show that the optimization of only BS transmit power consumption in downlink can lead to significant drain of UE battery power. Hence, we propose sub-optimal algorithms that exploit Discontinuous Transmission(DTX) at the base-station to tradeoff delay with energy consumption to improve Energy Efficiency (EE) of the UE circuit power and BS transmit power, while at the same time meeting the application QoS requirements in terms of throughput and service delay. The proposed algorithm is also shown to achieve good performance in saving transmit power at the base-station and also the UE circuit power consumption over traditional scheduling algorithms.

Dynamic spectrum access (DSA) is a challenging issue among the cognitive radio (CR) users. In this paper, we investigate an interference-aware local cartography-based DSA (LC-DSA) technique for OFDM-based CR system. In particular, a joint power and frequency resource block (RB) allocation technique has been proposed for resource allocation among secondary users (SUE) exploiting radio environmental map (REM) of the local surrounding environment. With REM, we produce an interference database for each RB at each SUE's location. Having knowledge of these measured interference values, LC-DSA technique elaborates an interference cartography (IC) diagram of CR coverage area with the help of spatial interpolation algorithm. Using IC diagram, CR applies interference classification algorithm to classify RBs at target location and uses them for transmission with appropriate transmit power without producing interference above the threshold value to the primary user (PUE). Our simulation results show how the proposed LC-DSA technique significantly improves the system throughput. Simultaneously, the interference introduced to the PUE remains within a tolerable limit.

The femtocell deployment in 3GPP/LTE sets new challenges to interference mitigation techniques and Radio Resource Management (RRM). Traditional schemes are mainly designed for classical cellular networks while the ad hoc nature of femtocells notably limits the complexity of possible algorithms. Thus, efficient RRM schemes are essential for limiting the interference impact on end-user performance. The goal of this paper is to achieve effective spectral reuse between macrocells and femtocells while guaranteeing the QoS of users served by both macro and femto base stations. We propose a novel resource management scheme that limits the overall interference per chunk generated outside the coverage range of a femtocell while reducing the transmission power in each Resource Block (RB). Our simulation results show that the proposed RRM scheme enhances the energy efficiency of femtocells and improves both macrocell and femtocell throughput.

The goal of our work is to limit in-band interference in wireless communication systems. Based on a three-regime interference classifier, we propose a novel distributed inter-cell power allocation algorithm where each cell computes by an iterative process its minimum power budget to meet its local quality of service (QoS) constraints. Analytical results show how our proposal permits to notably reduce both transmission power and harmful effects of in-band interference, while meeting QoS constraints of users in each cell.

Packet scheduling algorithms are viewed as one of the key mechanisms for increasing the diversity order, robustness and effectiveness of a wireless multi-user communication system. Traditional packet scheduling are mainly designed to increase the system capacity. In this paper we present a novel scheduling algorithm that improves the scheduling energy efficiency in OFDMA based wireless cellular networks. Our goal is to reduce the overall downlink energy consumption while adapting the target of spectral efficiency to the actual load of the system and meeting the Quality of Service (QoS). Our analysis reveals how the proposed approach permits to achieve notable energy gain over traditional scheduling algorithm especially in not saturated scenarios.

The goal of our work is to limit inter-cell interference in two hops cooperative communication systems.We propose to exploit the standard half-duplex limitation of relays so as to coordinate in time and frequency the resource allocation of a cluster of neighbor cells, and then to adapt resource allocation to changes in the communication context. Simulation results show how our proposed allocation modes permit to outperform classical modes in terms of cooperation effectiveness, power consumption and perceived Quality of Service (QoS).

The goal of our work is to limit in-band inter-cell interference in wireless communication cellular systems. Based on interference classification techniques, we propose a novel centralized inter-cell power allocation algorithm which computes the minimum power budget required in each cell to meet its local quality of service (QoS) constraints. Both analytical and numerical results applied to cellular networks show how our algorithm permits to notably reduce both power budget and harmful effects of in-band inter-cell interference, while meeting QoS constraints of users in each cell.

The challenge of our work is to enhance the effectiveness of Channel Quality Indicator (CQI)reporting schemes in multi-user OFDMA-based communication systems. Based on information theory, we propose a novel CQI reporting scheme that exploits instantaneous mutual information at the User Equipment (UE) in order to choose the best Physical Resource Blocks (PRBs) / CQI pairs to be fed back. While predicting the channel outage instances, the CQI feedback signalling is improved and is still compatible with any classical reporting schemes. As can be seen in our simulation results, the proposed CQI reporting scheme can significantly enhance the overall cell capacity, UE throughput, packet latency and Packet Error Rate PER) while reducing multi-user scheduling complexity at the Base Station (BS).

EARTH is a major new European research project starting in 2010 with 15 partners from 10 countries. Its main technical objective is to achieve a reduction of the overall energy consumption of mobile broadband networks by 50%. In contrast to previous efforts, EARTH regards both network aspects and individual radio components from a holistic point of view. Considering that the signal strength strongly decreases with the distance to the base station, small cells are more energy efficient than large cells. EARTH will develop corresponding deployment strategies as well as management algorithms and protocols on the network level. On the component level, the project focuses on base station optimizations as power amplifiers consume the most energy in the system. A power efficient transceiver will be developed that adapts to changing traffic load for an energy efficient operation in mobile radio systems. With these results EARTH will reduce energy costs and carbon dioxide emissions and will thus enable a sustainable increase of mobile data rates.

This paper deals with decoding latency and power consumption reduction techniques, with a special focus on packet transmission over non-ergodic block fading channels. Based on information theory, we propose a decoder activation criterion that forecasts unsuccessful decoding attempts before activating the decoding process. This solution permits to avoid useless decoder activations and consequent inefficient decoder computations. Simulation results show that our proposal permits to substantially reduce the average packet decoding cost without degrading performance.

Packet scheduling algorithms are viewed as one of the key mechanisms for increasing the diversity order, robustness and effectiveness of a wireless multi-user communication system. Traditional packet scheduling are mainly designed for homogeneous single traffic scenarios. In this paper we present a novel scheduling algorithm that support real-time (RT) and non real-time (NRT) traffics at the same time. Our goal is to maximize the achievable cell traffic load while keeping the active users satisfied. Simulation results show that in an OFDMA air interface based communication system our proposed HYGIENE scheduler is able to give better performance than existing packet scheduling algorithms such as maximum channel to interference ratio (MCI), proportional fair (PF), Earliest Deadline First (EDF) and Modified Largest Weighted Deadline First (MLWDF).

The challenge of our work is to enhance the effectiveness of packet retransmission in multi-user OFDMA based communication systems. Based on information theory, we propose a retransmission scheduler (re-scheduler) that recomputes the allocation of chunks for negatively acknowledged packets only when correct packet decoding is impossible or it requires a too large number of retransmissions. Our proposal is compatible with any classical priority packet scheduling algorithms. Simulation results show that our solution permits to avoid useless retransmission scheduler activations and to enhance the overall resource allocation performance in terms of both residual PER and maximum achievable cell capacity.

This paper deals with power allocation techniques for cooperative protocols where terminals are constrained by half-duplex assumption and average total network power. We propose a cooperation scheme based on the combination of hybrid cooperation strategies with an effective power allocation algorithm. Simulation results show that our proposal improves the outage performance while reducing the overall cooperation complexity.

Efficient cooperative communication protocols are viewed as one of the mechanisms for increasing the diversity order, robustness and energy efficiency of a wireless communication system. Innovative concepts such as opportunistic relay cooperative transmission coupled with adaptive transmission may drive to low power consumption while keeping advantage of the diversity gain offered by the adaptive mechanisms. The goal of this paper is to investigate the effectiveness of the hybrid cooperation protocols proposed in (Strinati et al., 2007) for wide-band wireless mobile communication systems, such for instance IEEE 802.16e. We modify the hybrid cooperation protocol to take into account the non ideal behavior of the real system in the hybrid cooperation controller criterion. Simulation results show that our proposal improves the average system performance and reduces the average cooperation cost.

This paper deals with throughput oriented adaptive modulation and coding (AMC) techniques combined with cooperative protocols where terminals are constrained by half-duplex assumption and average total network power. We propose a novel hybrid cooperation protocol that switches from cooperative to non-cooperative transmission based on the momentary direct source-destination link quality. Then, we propose anhybridcooperativeAMCmechanism, which combines AMC with hybrid cooperation. Simulation results show that our proposal improves the average system performance and reduces the average cooperation signaling cost.

In this paper, we investigate reliable error rate estimation techniques for MAC layer adaptive mechanisms. In particular, we propose and analyze a novel error rate estimator based on soft output information available as output of receivers with turbo principle. Contrary to previous works on the subject, we relax the assumption of perfect knowledge of signal-noise-to-ratio (SNR) at the receiver, and we analyze the impact of a SNR estimation error on the error rate estimate. We show that, differently to previous techniques, the proposed estimation method is insensitive to such SNR estimation error. Our analytical and simulation results validate the conclusion.

This paper deals with packet error rate (PER) prediction and its impact on adaptive modulation and coding (AMC) for an OFDM turbo coded transmission on a multipath channel. We compare four link quality metrics (LQM), including the signal to noise ratio (SNR), the capacity, and two LQM which we designed to exploit the soft outputs of the turbo-decoder. Since error prediction is used to perform AMC, we assess the throughput performance of a realistic system employing each of the four LQM. The comparison shows that while SNR performs poorly and capacity remains suboptimum, the two metrics exploiting soft outputs approach the throughput which would be achieved by perfect PER prediction