In this article, we establish a comprehensive theoretical framework for remote estimation in a networked system composed of a source that is observed by a sensor, a remote monitor that needs to estimate the state of the source in real time, and a communication channel that connects the source to the monitor. The source is a partially observable dynamical process, and the communication channel is a packet-erasure channel with feedback. We consider a novel communication model that captures implicit information. Our main objective is to identify the optimal strategies and the fundamental performance limits of the underlying system in the sense of a causal tradeoff between the packet rate and the mean square error when both forward and backward channels are unreliable. We characterise an optimal coding policy profile consisting of a scheduling policy for an encoder and an estimation policy for a decoder, collocated with the source and the monitor, respectively. We derive the recursive equations that must be solved online by the encoder and the decoder. In addition, we prove that the value function, originally defined over an expanding information set, admits a lower-dimensional representation depending only on two variables. We discuss the structural properties of the optimal policies, and analyse the computational complexity of an algorithm proposed for their computation. We then examine a range of special cases derived from our main theoretical results. We complement the theoretical results with a numerical analysis, and compare the performance of different remote estimation tasks in various operating regimes.

The problem of minimizing the Age of Information has been extensively studied in real-time monitoring applications. In this paper, we consider a scheduling problem in which a central monitor decides, at each time slot, whether to schedule a Markovian source to receive new status updates, with the goal of minimizing the Mean Age of Incorrect Information (MAoII). When the source parameters are known, we show that the optimal solution is a threshold-based policy and propose a low-complexity algorithm to determine this optimal threshold. When the source parameters are unknown, the main challenge lies in balancing exploration and exploitation. Specifically, the scheduler must estimate the unknown parameters (exploration) while simultaneously minimizing the MAoII (exploitation). In our model, the monitor can only explore a source by scheduling it. Using a naive greedy approach risks prematurely stopping exploration if, at a given time, the estimated parameters suggest that the optimal action is never to schedule the source. To address this, we develop a novel learning algorithm that overcomes this issue and prove that its regret, compared to a genie-aided solution, grows logarithmically with the time horizon. Finally, we present numerical results to demonstrate the performance of our proposed policy against baseline approaches.

This paper proposes a dual-aspect semantic metric in the context of a cyber-physical system and investigates a scheduling problem where a sensor and a controller communicate over an unreliable channel. In this setting, the sensor observes the state of a source at each time, and according to a scheduling policy should determine whether to transmit a compressed sampled state, transmit the uncompressed sampled state, or remain idle. Upon receiving the transmitted information, the controller executes a control action aimed at stabilizing the system, such that the effectiveness of stabilization depends on the quality of the received sensory information. Our primary objective is to derive an optimal scheduling policy that optimizes system performance subject to resource constraints, when the performance is measured by a dual-aspect metric penalizing both the frequency of transitioning to undesirable states and the continuous duration of remaining in those states. We formulate this problem as a Markov decision process, and prove that the optimal solution is a multi-threshold scheduling policy.

Federated Learning (FL) has emerged as a promising paradigm for enabling collaborative machine learning while pre-serving data privacy, making it particularly suitable for Internet of Things (IoT) environments. However, resource-constrained IoT devices face significant challenges due to limited energy, unreliable communication channels, and the impracticality of assuming infinite blocklength transmission. This paper proposes a federated learning framework for IoT networks that integrates finite blocklength transmission, model quantization, and an error-aware aggregation mechanism to enhance energy efficiency and communication reliability. The framework also optimizes up-link transmission power to balance energy savings and model performance. Simulation results demonstrate that the proposed approach significantly reduces energy consumption by up to 75% compared to a standard FL model, while maintaining robust model accuracy, making it a viable solution for FL in real-world IoT scenarios with constrained resources. This work paves the way for efficient and reliable FL implementations in practical IoT deployments.

Existing performance analyses and transmission policy designs under Time-Sensitive Networking (TSN) typically assume that all data flows are periodic and deterministic. However, in real-world scenarios, various sources of uncertainty - such as device failures, environmental variations, or upstream congestion - can lead to unexpected packet arrivals or losses, introducing non-deterministic behaviors into the network. This work develops a stochastic model based on Markov Decision Process (MDP) to capture the dynamics of TSN switch transmission under such conditions. To address the inefficiency caused by stale packets blocking fresher and more valuable ones, we propose a stale packet skipping policy to enhance the transmission efficiency. Specifically, a stale packet is discarded if a newer packet of the same flow is injected into the queue or if its deadline can no longer be met. This paper presents a detailed model description of the stochastic packet behaviors, the transmission system, and the stale packet skipping policy.

We study a monitoring system in which a single source sends status updates to a monitor through a communication channel. The communication channel is modeled as a queueing system, and we assume that attacks occur following a random process. When an attack occurs, all packets in the queueing system are discarded. While one might expect attacks to always negatively impact system performance, we demonstrate in this paper that, from the perspective of Age of Information (AoI), attacks can in some cases reduce the AoI. Our objective is to identify the conditions under which AoI is reduced and to determine the attack rate that minimizes or reduces AoI. First, we analyze single and tandem M/M/1/1 queues with preemption and show that attacks cannot reduce AoI in these cases. Next, we examine a single M/M/1/1 queue without preemption and establish necessary and sufficient conditions for the existence of an attack rate that minimizes AoI. For this scenario, we also derive an upper bound for the optimal attack rate and prove that it becomes tight when the arrival rate of updates is very high. Through numerical experiments, we observe that attacks can reduce AoI in tandem M/M/1/1 queues without preemption, as well as in preemptive M/M/1/2 and M/M/1/3 queues. Furthermore, we show that the benefit of attacks on AoI increases with the buffer size.

This paper investigates denial-of-service attacks on status updates in a binary networked control system. The physical system is abstracted as a Markovian source with two states: stable and unstable. The source sends status updates over an unreliable wireless channel to a remote controller in the presence of a malicious agent. Upon receiving a new status update, the controller sends back a control signal to stabilize the source if it is in the unstable state. The vulnerability of this system is measured by theage of physical instability—the duration the system remains in the unstable state. Our objective is to design jamming policies for the malicious agent that maximize this measure subject to energy constraints. We first present our results in a single-source scenario, and then extend them to a multi-source scenario. More specifically, we explore: (i) the optimal jamming policy that makes a balance between the system performance degradation and the adversary energy expenditure in a single-source scenario; and (ii) the optimal jamming policy that maximizes the aggregate system performance degradation by selectively jamming a subset of target channels in a multi-source scenario.

In this paper, we investigate denial-of-service attacks against status updating. The target system is modeled by a Markov chain along with an unreliable wireless channel, and the performance of status updating in this system is measured based on two metrics: age of information and age of incorrect information. Our objective is to devise optimal jamming policies that strike a balance between the system's performance deterioration and the adversary's energy expenditure. We model the optimal problem as a Markov decision process, and derive the optimal jamming policy. We prove rigorously that the optimal jamming policy is a threshold policy under both metrics. In addition, we provide a low-complexity algorithm for fining the optimal threshold value of the jamming policy. Our numerical results show that the networked system with the age-of-incorrect-information metric is less sensitive to jamming attacks than with the age-of-information metric.

Federated learning (FL) is a creative technique that enables multiple edge devices to train a model without revealing raw data. However, several issues hinder the practical implementation of FL, especially in wireless environments. These issues comprise the limited capacity of the upload transmission link between the edge devices and the aggregator, as well as the wireless disturbances. To address these challenges, we develop a zero-order (ZO) communication-efficient framework for FL. While in standard FL, each device must upload a long vector containing the gradient or the model per communication round, our novel ZO method incorporates a two-point gradient estimator, which requires uploading only two scalars. What also sets our approach apart is that it directly incorporates wireless perturbations into the learning, eliminating the need for additional computational resources to remove their impact. In this work, we overcome the technical and analytical challenges associated with FL problems and ZO methods, comprehensively study our algorithm, and prove it converges almost surely under different conditions, convexity and non-convexity, noise-free and noisy environments. We then find theoretical bounds on the convergence rate when the objective is strongly convex, non-convex, and $\kappa$-gradient-dominated that compete with first-order (FO) or centralized methods under the same settings. Finally, we provide experimental results demonstrating the effectiveness of our algorithm, considering relevant examples. We provide an example illustrating the amount of communication saved due to its efficiency compared to its FO counterpart.

In the literature, machine learning (ML) has been implemented at the base station (BS) and user equipment (UE) to improve the precision of downlink channel state information (CSI). However, ML implementation at the UE can be infeasible for various reasons, such as UE power consumption. Motivated by this issue, we propose a CSI learning mechanism at BS, called CSILaBS, to avoid ML at UE. To this end, by exploiting channel predictor (CP) at BS, a light-weight predictor function (PF) is considered for feedback evaluation at the UE. CSILaBS reduces over-the-air (OTA) feedback overhead, improves CSI quality, and lowers the computation cost of UE. Besides, in a multiuser environment, we propose various mechanisms to select the feedback by exploiting PF while aiming to improve CSI accuracy. We also address various ML-based CPs, such as NeuralProphet (NP), an ML-inspired statistical algorithm. Furthermore, inspired to use a statistical model and ML together, we propose a novel hybrid framework composed of a recurrent neural network and NP, which yields better prediction accuracy than individual models. The performance of CSILaBS is evaluated through an empirical dataset recorded at Nokia Bell-Labs. The outcomes show that ML elimination at UE can retain performance gains, for example, precoding quality.

Zero-order (ZO) optimization is a powerful tool for dealing with realistic constraints. On the other hand, the gradient-tracking (GT) technique proved to be an efficient method for distributed optimization aiming to achieve consensus. However, it is a first-order (FO) method that requires knowledge of the gradient, which is not always possible in practice. In this work, we introduce a zero-order distributed optimization method based on a one-point estimate of the gradient tracking technique. We prove that this new technique converges with a single noisy function query at a time in the non-convex setting. We then establish a convergence rate of $O(\frac{1}{\sqrt[3]{K}})$ after a number of iterations K, which competes with that of $O(\frac{1}{\sqrt[4]{K}})$ of its centralized counterparts. Finally, a numerical example validates our theoretical results.

In this paper, we investigate the performance of a distributed Reconfigurable Intelligent Surface (RIS) system as a solution to RIS-aided Joint Spatial Division and Multiplexing (JSDM) in Multi-User Multiple Input Single Output (MU-MISO) scenarios. JSDM is a well-studied massive MIMO (mMIMO) scheme in the literature, where two-stage beamforming at the base station (BS) is used to decrease the channel estimation overhead and mitigate interference. We consider a situation where several users with obstructed direct BS channels are being served through RIS-created links, giving rise to larger channel dimensions than conventional mMIMO systems. JSDM comes in handy in this case to overcome the increased estimation overhead. However, RIS-aided links suffer from rank deficiency which will be prohibitive in serving multiple users solely through these channels. The downlink of a single-cell MU-MISO system employing JSDM is investigated, where a distributed RISs set-up installed between the BS and the users is proposed as a solution to overcome the rank deficiency of the provided alternative link, making it reliable to serve several users through spatial multiplexing. Phase shift configuration of the RISs is optimized through a projected gradient ascent algorithm maximizing the deterministic equivalent of the sum rate expression, providing a low-overhead algorithm dependent on statistical channel state information (CSI) only. Monte Carlo simulations and analytical deterministic equivalent results show that despite the higher double reflection path-loss, distributed RISs systems with increased rank outperform the one RIS rank-deficient system, along with reduced CSI overhead and complexity as a consequence of JSDM.

The age of Information (AoI) has been introduced to capture the notion of freshness in real-time monitoring applications. However, this metric falls short in many scenarios, especially when quantifying the mismatch between the current and the estimated states. To circumvent this issue, in this paper, we adopt the age of incorrect information metric (AoII) that considers the quantified mismatch between the source and the knowledge at the destination while tracking the impact of freshness. We consider for that a problem where a central entity pulls the information from remote sources that evolve according to a Markovian Process. It selects at each time slot which sources should send their updates. As the scheduler does not know the actual state of the remote sources, it estimates at each time the value of AoII based on the Markovian sources' parameters. Its goal is to keep the time average of the AoII function as small as possible. For that purpose, we develop a scheduling scheme based on Whittle's index policy. To that extent, we use the Lagrangian Relaxation Approach and establish that the dual problem has an optimal threshold policy. Building on that, we compute the expressions of Whittle's indices. Finally, we provide some numerical results to highlight the performance of our derived policy compared to the classical AoI metric.

In this paper, we introduce the Age of Incorrect Information (AoII) as an enabler for semantics-empowered communication, a newly advocated communication paradigm centered around data’s role and its usefulness to the communication’s goal. First, we shed light on how the traditional communication paradigm, with its role-blind approach to data, is vulnerable to performance bottlenecks. Next, we highlight the shortcomings of several proposed performance measures destined to deal with the traditional communication paradigm’s limitations, namely the Age of Information (AoI) and the error-based metrics. We also show how the AoII addresses these shortcomings and captures more meaningfully the purpose of data. Afterward, we consider the problem of minimizing the average AoII in a transmitter-receiver pair scenario. We prove that the optimal transmission strategy is a randomized threshold policy, and we propose an algorithm that finds the optimal parameters. Furthermore, we provide a theoretical comparison between the AoII framework and the standard error-based metrics counterpart. Interestingly, we show that the AoII-optimal policy is also error-optimal for the adopted information source model. Concurrently, the converse is not necessarily true. Finally, we implement our policy in various applications, and we showcase its performance advantages compared to both the error-optimal and the AoI-optimal policies.

Channel state information (CSI) is of pivotal importance as it enables wireless systems to adapt transmission parameters more accurately, thus improving the system's overall performance. However, it becomes challenging to acquire accurate CSI in a highly dynamic environment, mainly due to multi-path fading. Inaccurate CSI can deteriorate the performance, particularly of a massive multiple-input multiple-output system. This paper adapts machine learning for CSI prediction. Specifically, we exploit time-series models of deep learning (DL) such as recurrent neural network (RNN) and bidirectional long-short term memory. Further, we use NeuralProphet (NP), a recently introduced time-series model, composed of statistical components, e.g., autoregressive and Fourier terms, for CSI prediction. Inspired by statistical models, we also develop a novel hybrid framework comprising RNN and NP to achieve better prediction accuracy. The proposed channel predictors performance is evaluated on a real-time dataset recorded at the Nokia Bell-Labs campus in Stuttgart, Germany. Numerical results show that DL brings performance gain when used with statistical models and showcases robustness.

Joint Spatial Division and Multiplexing (JSDM) is a massive MIMO (mMIMO) scheme in which the beamforming is split into two stages, decreasing the channel estimation overhead and mitigating the interference. Moreover, Reconfigurable Intelligent Surfaces (RIS) have emerged as a hot research topic in recent years, being widely advocated as a candidate technology for next-generation wireless communications. These surfaces passively alter the behavior of propagation environments ameliorating the performance of wireless communication systems. However, this gives rise to even larger channel dimensions than conventional mMIMO systems, causing a surge in the estimation overhead. In this paper, we investigate the downlink of a single-cell Multi-User MIMO (MU-MIMO) system employing JSDM with the aid of a RIS installed between the base station (BS) and the users having a weak direct BS link. The RIS provides an alternative communication link, and JSDM reduces the channel estimation overhead. We briefly explain the concepts of JSDM and spatial signatures. Then, we introduce a novel similarity measure suitable for clustering the users in the presence of a RIS. Furthermore, we propose to add a precoding stage to the overall JSDM precoder to remove undesired effects induced by the use of a RIS in the considered MU-MIMO scheme. Simulation results show the improved performance of RIS-aided JSDM in comparison to the conventional scheme when the RIS is large enough for a given transmit power with a gain in the average sum rate, reduced effective channel dimensions and increased cell coverage.

In this paper, we introduce the Age of Incorrect Information (AoII) as an enabler for semantics-empowered communication, a newly advocated communication paradigm centered around data’s role and its usefulness to the communication’s goal. First, we shed light on how the traditional communication paradigm, with its role-blind approach to data, is vulnerable to performance bottlenecks. Next, we consider the problem of minimizing the average AoII in a transmitter-receiver pair scenario. We prove that the optimal transmission strategy is a randomized threshold policy, and we propose an algorithm that finds the optimal parameters. Finally, we implement our policy in a real-life application, and we showcase its performance advantages compared to both the error-optimal and the AoI-optimal policies.

In this paper, we provide an analysis of a status update system modeled through the Stochastic Hybrid Systems (SHSs) tool. Contrary to previous works, which assumed constant transition rates, we allow the system’s transition dynamics to be functions of the Age of Information (AoI). This dependence allows us to encapsulate many applications and opens the door for more sophisticated systems to be studied. However, this same dependence on the AoI engenders technical and analytical difficulties. Our paper provides a first step in addressing these difficulties. Specifically, we first showcase the regularity and other critical characteristics of the age process in our system of interest. Then, we provide a framework to establish the Lagrange stability and positive recurrence of the process. Building on these results, we provide an approach, dubbed as the moment closure technique, to compute the m-th moment of the age process for any m≥1. Interestingly, this technique allows us to approximate the average age of various systems by solving a simple set of linear equations.

In this paper, we tackle the problem of the signal-to-interference-plus-noise ratio (SINR) distribution estimation under flat Rayleigh fading with a single interferer. Assuming that we collect a set of SINRs’ values from the receiver, the distribution is parametrized by the direct and interfering channel magnitude that need to be estimated. To that purpose, we propose here two new estimators: the maximum likelihood estimator (MLE) and a method of moments (MoM) estimator. For both estimators we derive the equations and propose a numerical implementation scheme based on Newton’s algorithm. The MoM estimator is less complex but also less accurate, whereas the MLE is more accurate but requires careful initialization. Thus, we propose to use the MoM’s estimates to initialize the MLE algorithm. In addition, we derive the Cramer-Rao lower bound (CRLB) associated to this estimation problem and propose an efficient computation of it. We provide simulations to assess the performance of our proposed estimators and verify that the mean square error of the MLE is close to the CRLB.

In this paper, we consider a scheduling problem, in which several streams of status update packets with different priority levels are sent through a shared channel to their destinations. We introduce a notion oflexicographic age optimality, or simplylex-age-optimality, to evaluate the performance of multi-class status update policies. In particular, a lex-age-optimal scheduling policy first minimizes the Age of Information (AoI) metrics for high-priority streams, and then, within the set of optimal policies for high-priority streams, achieves the minimum AoI metrics for low-priority streams. We propose a new scheduling policy named Preemptive Priority, Maximum Age First, Last-Generated, First-Served (PP-MAF-LGFS), and prove that the PP-MAF-LGFS scheduling policy is lex-age-optimal. This result holds (i) for minimizing any time-dependent, symmetric, and non-decreasing age penalty function; (ii) for minimizing any non-decreasing functional of the stochastic process formed by the age penalty function; and (iii) for the cases where different priority classes have distinct arrival traffic patterns, age penalty functions, and age penalty functionals. For example, the PP-MAF-LGFS scheduling policy is lex-age-optimal for minimizing the probability of age violation of a high-priority stream and the time-average age of a low-priority stream. Numerical results are provided to illustrate our theoretical findings.

This paper examines the average age minimization problem where only a fraction of the network users can transmit simultaneously over unreliable channels. Finding the optimal scheduling scheme, in this case, is known to be challenging. Accordingly, the Whittle’s index policy was proposed in the literature as a low-complexity heuristic to the problem. Although simple to implement, characterizing this policy’s performance is recognized to be a notoriously tricky task. In the sequel, we provide a new mathematical approach to establish its optimality in the many-users regime for specific network settings. Contrary to previous works in the literature that use restrictive mathematical assumptions, which can be challenging to verify, our novel approach is based on intricate techniques and it is free of any strong mathematical assumptions. These findings showcase that the Whittle’s index policy has analytically provable asymptotic optimality for the AoI minimization problem. Finally, we lay out numerical results that corroborate our theoretical findings and demonstrate the policy’s notable performance in the many-users regime.

Unmanned aerial vehicles (UAVs) evolution has gained an unabated interest for the use in several applications, such as agriculture, aerial surveillance, goods delivery, disaster recovery, intelligent transportation. The main features of this technology are high coverage, strong line-of-sight (LoS) links, promising throughput, cost-effective and flexible deployment. Currently, the Third Generation Partnership Project (3GPP) is working on the specification of release-17 (R-17) new radio (NR) for non-terrestrial networks (NTN). Therefore, owing to the drastic increase of UAV technology, in this paper, we propose channel state information (CSI) compression and its recovery with the aid of machine learning (ML)-based twin channel predictors. Due to the characteristic of gaining higher LoS communication paths in UAV network, the proposed strategy can bring potential benefits such as over-the-air (OTA)-overhead reduction, minimizing mean-squared-error (MSE) of a channel and maximizing precoding gain. Simulation-based results corroborate the validity of the proposed strategy, which can reap benefits in multiple factors.

In wireless communication, accurate channel state information (CSI) is of pivotal importance. In practice, due to processing and feedback delays, estimated CSI can be outdated, which can severely deteriorate the performance of the communication system. Besides, to feedback estimated CSI, a strong compression of the CSI, evaluated at the user equipment (UE), is performed to reduce the over-the-air (OTA) overhead. Such compression strongly reduces the precision of the estimated CSI, which ultimately impacts the performance of multipleinput multiple-output (MIMO) precoding. Motivated by such issues, we present a novel scalable idea of reporting CSI in wireless networks, which is applicable to both time-division duplex (TDD) and frequency-division duplex (FDD) systems. In particular, the novel approach introduces the use of a channel predictor function, e.g., Kalman filter (KF), at both ends of the communication system to predict CSI. Simulation-based results demonstrate that the novel approach reduces not only the channel mean-squared-error (MSE) but also the OTA overhead to feedback the estimated CSI when there is immense variation in the mobile radio channel. Besides, in the immobile radio channel, feedback can be eliminated, which brings the benefit of further reducing the OTA overhead. Additionally, the proposed method provides a significant signal-to-noise ratio (SNR) gain in both the channel conditions, i.e., highly mobile and immobile.

The age of Incorrect Information (AoII) has been introduced to address the shortcomings of the standard Age of information metric (AoI) in real-time monitoring applications. In this paper, we consider the problem of monitoring the states of remote sources that evolve according to a Markovian Process. A central scheduler selects at each time slot which sources should send their updates in such a way to minimize the Mean Age of Incorrect Information (MAoII). The difficulty of the problem lies in the fact that the scheduler cannot know if the information at side of the monitor is correct or not before receiving the updates and it has then to estimate it. We show that the problem can be modeled as a partially Observable Markov Decision Process Problem framework. We develop a new scheduling scheme based on Whittles index policy. The scheduling decision is made by updating a belief value of the states of the sources, which is to the best of our knowledge has not been considered before in the Age of Information area. To that extent, we proceed by using the Lagrangian Relaxation Approach, and prove that the dual problem has an optimal threshold policy. Building on that, we show that the problem is indexable and compute the expressions of the Whittles indices. Finally, we provide some numerical results to highlight the performance of our derived policy compared to the classical AoI metric.

Reconfigurable Intelligent Surfaces (RIS) have been a hot research topic in recent years, being widely advocated to represent a promising technology for beyond 5G cellular networks. In this paper, we propose a modulation scheme called RIS-IM in which Index Modulation (IM) is applied using RIS. However, to overcome the strong channel correlation caused by the small separation distance between the RIS elements a precoding stage based on the concept of signature constellations is added at the transmitter. The precoding stage is shown to counteract the degradations caused by these correlations providing interesting gains. We further improve the performance by passive beamforming, optimizing the phase shifts of the RIS elements using Semidefinite Programming (SDP) to improve the receiver SNR. We then provide performance analysis for this IM scheme using computer simulations. A comparison of RIS-IM with Spatial Modulation (SM), which is another member of the IM family, is also included, showing that RIS-IM provides substantial SNR gains.

The initial access in 5G New Radio (NR) standalone millimeter wave (mmWave) uses the techniques of beam sweeping to Figure out appropriate directions of transmission and reception. Beam sweeping refers to the transmission of cell specific signals by the base station (BS) while switching beam direction in a sequential manner in order to cover the whole cell. The conventional beam sweeping consists of transmitting sequentially and periodically the beams, according to a fixed pattern, which results in an initial access latency. The aim of this paper is to minimize the overall number of timeslots needed to connect all users to the BS by allowing using multiple beams in some slots and then activating the beams in a prioritized and controlled manner rather than sequentially and periodically as in the conventional scheme. The problem is formulated as a combinatorial optimization problem, for which a Semidefinite Relaxation-based (SDR-based) solving approach is proposed. Numerical results demonstrate that our scheme outperforms significantly the conventional beam sweeping.

Reconfigurable intelligent surfaces (RIS) technology, as the name implies, is a grid of many small intelligent surfaces that can be reconfigured. It is a new and promising concept in wireless communications, expected to help realize the requirements of the future cellular generations. Numerous studies have been done to prove its added advantages and to control these surfaces in beneficial ways. However, these control schemes come with difficulties related to efficient and practical implementation. In this paper, we propose to control multiple RISs in a multi-user scheme with an algorithm that leads to a simple implementation. We formulate a stochastic optimization problem and we propose a new method using a distributed stochastic algorithm that allows the optimization to be done locally at each RIS with low computational requirements and without the need for instantaneous channel knowledge at the RIS. Also, the signaling between the base station (BS) and each RIS is limited to the exchange of a scalar only. Simulation results prove the success and efficiency of this algorithm.

In this paper, we consider the downlink transmission of URLLC packets requiring very low latency and ultra-reliability. Because of the low latency constraint, the Base Station may not have enough time to acquire the instantaneous Channel State Information (CSI) of the corresponding device and has then to transmit urgent packets immediately in the absence of CSI. To enhance reliability, we explore frequency diversity where a packet can be simultaneously sent over multiple channels. Using a Markov Decision Process framework, we address the problem of dynamic channel allocation to the URLLC devices in absence of instantaneous CSI. More precisely, we define a multi-agent MDP wherein the state of each device is the packet loss rate experienced in the previous time slots and the decision variable is how to split the available orthogonal channels across the devices. We design a new low-complexity algorithm which avoids the exhaustive enumeration of all possible resource allocations and enables significant computational savings compared to the Value Iteration algorithm. We investigate the gap between our proposed low complexity algorithm and the Value Iteration policy. We provide numerical performance results and show that our algorithm can achieve more than 80% of the optimal reward with substantial computational complexity reduction.

We consider a distributed stochastic optimization problem in networks with finite number of nodes. Each node adjusts its action to optimize the global utility of the network, which is defined as the sum of local utilities of all nodes. While Gradient descent method is a common technique to solve such optimization problem, the computation of the gradient may require much information exchange. In this paper, we consider that each node can only have a noisy numerical observation of its local utility, of which the closed-form expression is not available. This assumption is quite realistic, especially when the system is either too complex or constantly changing. Nodes may exchange partially the observation of their local utilities to estimate the global utility at each timeslot. We propose a distributed algorithm based on stochastic perturbation, under the assumption that each node has only part of the local utilities of the other nodes. We use stochastic approximation tools to prove that our algorithm converges almost surely to the optimum, given that the objective function is smooth and strictly concave. The convergence rate is also derived, under the additional assumption of strongly concave objective function. It is shown that the convergence rate scales as O(K-0.5) after a sufficient number of iterations K > K0, which is the optimal rate order in terms of K for our problem. Although the proposed algorithm can be applied to general optimization problems, we perform simulations for a typical power control problem in wireless networks and present numerical results to corroborate our claims.

Cell-Free (CF) massive multiple-input multiple-output (MIMO) system is a distributed antenna system, wherein a large number of access points wish to simultaneously communicate with a relatively small number of users. Similar to co-located massive MIMO system, pilot contamination and multi-user interference are two major impediments to CF massive MIMO network performance improvement. One can mitigate the detrimental effect of pilot contamination and multi-user interference through judicious resource allocation. In this work, we formulate and investigate the problem of frequency assignment for a CF massive MIMO. The formulated optimization problem is proven to be generally NP-hard. Local optima can be found by approaches such as the Lagrangian method that however, has considerably slow rates of convergence. To circumvent this issue, we propose an alternative solution being of two-folds. Firstly, we reformulate the problem as a grouping strategy which enables to attenuate the effect of intra-group multi-user interference. In the second fold, frequencies are assigned in a non-overlapping fashion to each scheduled group to palliate the effect of inter-group interference and pilot contamination. To further improve the performance of the proposed approach, power coefficients are allocated to the users via a sequential convex approximation (SCA)-based framework. The effectiveness of the proposed algorithms is then verified through extensive numerical simulations which demonstrate a non-negligible improvement in the performance of the studied scenario.

This paper considers the problem of fully distributed channel allocation in clustered wireless networks when the propagation medium is random. We extend here the existing Trial and Error (TE) framework developed in the deterministic case and for which strong convergence properties hold. We prove that using directly this solution in the random context leads to unsatisfactory solutions. Then we propose an adaptation of the original Trial and Error Learning (TEL) algorithm, called Robust TEL (RTEL), assuming that the random channel effects translate into a bounded stochastic disturbance of the utility function. The solution consists in introducing thresholds in the transitions of the TEL’s Finite State Controller (FSC). We prove that this new solution restores the good convergence property inherited from the TEL. Furthermore, we provide analysis of the stochastic utilities in the Rayleigh fading case in order to check the bounded assumption. Finally, we develop an online algorithm that dynamically estimates the optimal threshold values to adapt to the instantaneous disturbance. Numerical results corroborate our theoretical claims.

In this article, we consider the average age minimization problem where a central entity schedules M users among the N available users for transmission over unreliable channels. It is well-known that obtaining the optimal policy, in this case, is a difficult task. Accordingly, the Whittle's index policy has been suggested in earlier works as a heuristic for this problem. However, the analysis of its performance remained elusive. In the sequel, we overcome these difficulties and provide rigorous results on its asymptotic optimality in the many-users regime. Specifically, we first establish its optimality in the neighborhood of a specific system's state. Next, we extend our proof to the global case under a recurrence assumption, which we verify numerically. These findings showcase that the Whittle's index policy has analytically provable optimality in the many-users regime for the AoI minimization problem. Finally, numerical results that showcase its performance and corroborate our theoretical findings are presented.

In this paper, we consider the average age minimization problem where a central entity schedules M users among the N available users for transmission over unreliable channels. It is well-known that obtaining the optimal policy, in this case, is out of reach. Accordingly, the Whittle's index policy has been suggested in earlier works as a heuristic for this problem. However, the analysis of its performance remained elusive. In the sequel, we overcome these difficulties and provide rigorous results on its asymptotic optimality in the many-users regime. Specifically, we first establish its optimality in the neighborhood of a specific system's state. Next, we extend our proof to the global case under a recurrence assumption, which we verify numerically. These findings showcase that the Whittle's index policy has analytically provable optimality in the many-users regime for the AoI minimization problem. Finally, numerical results that showcase its performance and corroborate our theoretical findings are presented.

In this article, we address the problem of long-term resource allocation for a 5G and beyond dense wireless network. Of particular interest within the considered framework are machine-type communications (MTCs). To establish an optimal operation for an energy-efficient MTC system, the power allocation policy should be designed by taking into consideration both channel and queue states of the devices. This problem formulation belongs to the category of complex stochastic optimization that can be recast as a Markov decision process (MDP) over high-dimensional state space. We circumvent the hurdle inherent to state space of a huge dimension by resorting to mean-field approximation on the MDP. More specifically, we demonstrate that the formulated MDP converges to a deterministic control problem provided that the number of devices approaches infinity. We develop a low-complexity power allocation scheme to efficiently solve the original high-dimensional state space stochastic optimization problem. Our proposed power allocation policy is drawn from the solution of the Hamilton-Jacobi-Bellman equation. It can be understood as a threshold-based policy and can be implemented in a decentralized manner that makes it very appealing for high scale networks. Finally, the simulations analyses are provided to establish the effectiveness of our proposed framework.

In this paper, we investigate a network where $N$ links contend for the channel using the well-known carrier sense multiple access scheme. By leveraging the notion of stochastic hybrid systems, we find: 1) a closed-form expression of the average age when links generate packets at will 2) an upperbound of the average age when packets arrive stochastically to each link. This upperbound is shown to be generally tight, and to be equal to the average age in certain scenarios. Armed with these expressions, we formulate the problem of minimizing the average age by calibrating the back-off time of each link. Interestingly, we show that the minimum average age is achieved for the same back-off time in both the sampling and stochastic arrivals scenarios. Then, by analyzing its structure, we convert the formulated optimization problem to an equivalent convex problem that we find its optimal solution. Insights on the interaction between links and numerical implementations of the optimized Carrier Sense Multiple Access (CSMA) scheme in an IEEE 802.11 environment are presented. Next, to further improve the performance of the optimized CSMA scheme, we propose a modification to it by giving each link the freedom to transition to SLEEP mode. The proposed approach provides a way to reduce the burden on the channel when possible. This leads, as will be shown in the paper, to an improvement in the performance of the network. Simulations results are then laid out to highlight the performance gain offered by our approach in comparison to the optimized standard CSMA scheme.

In this paper, we introduce a new performance metric in the framework of status updates that we will refer to as the Age of Incorrect Information (AoII). This new metric deals with the shortcomings of both the Age of Information (AoI) and the conventional error penalty functions as it neatly extends the notion of fresh updates to that of fresh “informative” updates. The word informative in this context refers to updates that bring new and correct information to the monitor side. After properly motivating the new metric, and with the aim of minimizing its average, we formulate a Markov Decision Process (MDP) in a transmitter-receiver pair scenario where packets are sent over an unreliable channel. We show that a simple “always update” policy minimizes the aforementioned average penalty along with the average age and prediction error. We then tackle the general, and more realistic case, where the transmitter cannot surpass a specific power budget. The problem is formulated as a Constrained Markov Decision Process (CMDP) for which we provide a Lagrangian approach to solve. After characterizing the optimal transmission policy of the Lagrangian problem, we provide a rigorous mathematical proof to showcase that a mixture of two Lagrange policies is optimal for the CMDP in question. Equipped with this, we provide a low complexity algorithm that finds the AoII-optimal operating point of the system in the constrained scenario. Lastly, simulation results are laid out to showcase the performance of the proposed policy and highlight the differences with the AoI framework.

Model-free decentralized optimizations and learning are receiving increasing attention from theoretical and practical perspectives. In particular, two fully decentralized learning algorithms, namely Trial and Error Learning (TEL) and Optimal Dynamical Learning (ODL), are very appealing for a broad class of games. Indeed, ODL has the property to spend a high proportion of time in an optimum state that maximizes the sum of the utilities of all players, whereas, TEL has the property to spend a high proportion of time in an optimum state that maximizes the sum of the utilities of all players if there is a pure Nash equilibrium, otherwise, it spends a high proportion of time in a state that maximizes a trade-off between the sum of the utilities of the players and a predefined stability function. On the other hand, estimating the mean fraction of time spent in the optimum state (as well as the mean time duration to reach it) is challenging due to the high complexity and dimension of the inherent Markov chains. In this article, under some specific system model, an evaluation of the above performance metrics is provided by proposing an approximation of the considered Markov chains, which allows overcoming the problem of high dimensionality. A comparison between the two algorithms is then performed which allows a better understanding of their performance.

In Device-to-Device (D2D) enabled cellular networks, user-to-network relaying can be handled for improving the performance of cellular networks. When relays are in mobility, a dynamic relay selection strategy is unavoidable. In this paper, we propose a dynamic policy of relay selection that maximizes the performance of cellular networks (e.g. throughput, reliability, coverage) under cost constraints (e.g. transmission power, power budget). We model the relays' dynamics as a Markov Decision Process (MDP). Since only the locations of the selected relays are observed, the sequential relay selection process is formulated as a Constrained Partially Observable Markov Decision Process (CPOMDP). The exact solution of such framework is intractable to find, therefore we prove the submodularity property of the reward and cost functions and deduce a greedy point based value iteration solution. Considering the throughput as reward metric and the energy as cost metric, numerical results are illustrated to endorse the proposed relay selection policy and to show how introducing D2D relaying can highly improve the performance of cellular networks.

In this paper, we propose a deep learning based approach to design online power control policies for large EH networks, which are often intractable stochastic control problems. In the proposed approach, for a given EH network, the optimal on-line power control rule is learned by training a deep neural network (DNN), using the solution of offline policy design problem. Under the proposed scheme, in a given time slot, the transmit power is obtained by feeding the current system state to the trained DNN. Our results illustrate that the DNN based online power control scheme outperforms a Markov decision process based policy. In general, the proposed deep learning based approach can be used to find solutions to large intractable stochastic control problems.

In this paper, we consider a queueing system with multiple channels (or servers) and multiple classes of users. We aim at allocating the available channels among the users in such a way to minimize the expected total average queue length of the system. This known scheduling problem falls in the framework of Restless Bandit Problems (RBP) for which an optimal solution is known to be out of reach for the general case. The contributions of this paper are as follows. We rely on the Lagrangian relaxation method to characterize the Whittle index values and to develop an index-based heuristic for the original scheduling problem. The main difficulty lies in the fact that, for some queue states, deriving the Whittle's index requires introducing a new approach which consists in introducing a new expected discounted cost function and deriving the Whittle's index values with respect to the discount parameter β. We then deduce the Whittle's indices for the original problem (i.e. with total average queue length minimization) by taking the limit β → 1. The numerical results provided in this paper show that this policy performs very well and is very close to the optimal solution for high number of users.

In this paper, we consider N information streams sharing a common service facility. The streams are supposed to have different priorities based on their sensitivity. A higher priority stream will always preempt the service of a lower priority packet. By leveraging the notion of Stochastic Hybrid Systems (SHS), we investigate the Age of Information (AoI) in the case where each stream has its own waiting room; when preempted by a higher priority stream, the packet is stored in the waiting room for future resume. Interestingly, it will be shown that a "no waiting room" scenario, and consequently discarding preempted packets, is better in terms of average AoI in some cases. The exact cases where this happen are discussed and numerical results that corroborate the theoretical findings and highlight this trade-off are provided.

In this paper, we study the problem of dynamic channel allocation for URLLC traffic in a multi-user multichannel wireless network where urgent packets have to be successfully received in a timely manner. We formulate the problem as a finite-horizon Markov Decision Process with a stochastic constraint related to the QoS requirement, defined as the packet loss rate for each user. We propose a novel weighted formulation that takes into account both the total expected reward (number of successfully decoded packets) and the risk which we define as the QoS requirement violation. First, we use the value iteration algorithm to find the optimal policy, which assumes a perfect knowledge of the controller of all the parameters, namely the channel statistics. We then propose a Q-learning algorithm where the controller learns the optimal policy without having knowledge of neither the CSI nor the channel statistics. We illustrate the performance of our algorithms with numerical studies.

While the topological interference management (TIM) problem was originally studied in a partially connected network, the novelity of this paper appears in exploring its performance in practical scenarios, where path losses exist. In this work, we also define the conditions under which TIM should be applied in order to save processing time when mobility is taken into account. Numerical results show the implication of these scenarios in constraining the degrees-of-freedom (DoF) achieved.

In this paper, we investigate a network of N interfering links contending for the channel to send their data by employing the well-known Carrier Sense Multiple Access (CSMA) scheme. By leveraging the notion of stochastic hybrid systems, we find a closed form of the total average age of the network in this setting. Armed with this expression, we formulate the optimization problem of minimizing the total average age of the network by calibrating the back-off time of each link. By analyzing its structure, the optimization problem is then converted to an equivalent convex problem that can be solved efficiently to find the optimal back-off time of each link. Insights on the interaction between the links are provided and numerical implementations of our optimized CSMA scheme in an IEEE 802.11 environment are presented to highlight its performance. We also show that, although optimized, the standard CSMA scheme still lacks behind other distributed schemes in terms of average age in some special cases. These results suggest the necessity to find new distributed schemes to further minimize the average age of any general network.

The goal in this work is to design online power control policies for large energy harvesting (EH) networks where, due to large energy overhead involved in the exchange of state information among the nodes, it is infeasible to use a centralized policy. Furthermore, typical applications of EH networks concern the scenario where the statistical information, about both the EH process and the wireless channel, is not available. In order to address these challenges, we propose a mean-field multiagent deep reinforcement learning framework. The proposed approach enables the nodes to learn online power control policies in a fully distributed fashion, i.e., it does not require the nodes to exchange the information about their states. Using the underlying structure of the problem, we analytically establish the convergence of the proposed scheme. In particular, we show that the policies obtained using the proposed approach converge to the `stationary' Nash equilibrium. Our simulation results illustrate the efficacy of the power control policies, learned through the proposed approach. In particular, the mean-field multi-agent reinforcement learning scheme achieves a performance close to the state-of-the-art centralized policies which operate using the information about the state of whole network.

In this paper, we develop a joint clustering and topological interference management (TIM) framework for a device-to-device (D2D) network. This scheme divides the whole network into multiple groups, each served on a different frequency, and the interference within each group is managed by TIM, based only on the connectivity pattern and not on the instantaneous channel state information (CSI). To this end, we model TIM as a low-rank-matrix-completion problem (LRMC) problem and solve it using a novel and low-complex scheme based on semidefinite programming (SDP). As for the clustering part, we develop a clustering algorithm that is suited for the LRMC approach to solve TIM while building on the SDP relaxation of the maximum-$k$-cut algorithm, and extending it to account for each cluster’s capacity. This clustering problem turns out to be a capacitated maximum-$k$-cut problem, for which we derive a relatively tight upper bound, that helps in determining the performance guarantee of many clustering algorithms. Simulation results show that the joint clustering-TIM can help, in some cases, improve the system degrees-of-freedom (DoF), especially in large D2D networks. Our proposed scheme also reduces the computation time of the LRMC-based TIM approach.

Carrier sense multiple access (CSMA) is widely used as a medium access control (MAC) in wireless networks due to its simplicity and distributed nature. This motivated researchers to find CSMA schemes that achieve throughput optimality. In 2008, it has been shown that a simple CSMA-type algorithm is able to achieve optimality in terms of throughput and has been given the name “adaptive” CSMA. Later, new technologies emerged where a prolonged battery life is crucial such as environment and industrial monitoring. This inspired the foundation of new CSMA-based MAC schemes, where links are allowed to transition into a sleep mode to reduce the power consumption. However, the throughput optimality of these schemes was not established. This paper, therefore, aims to find a new CSMA scheme that combines both throughput optimality and energy efficiency by adapting to the throughput and power consumption needs of each link. This is done by controlling operational parameters, such as back-off and sleeping timers, with the aim of optimizing a certain objective function. The resulting CSMA scheme is characterized by being asynchronous, completely distributed and being able to adapt to different power consumption profiles required by each link while still ensuring throughput optimality. The performance gain in terms of energy efficiency compared with the conventional adaptive CSMA scheme is demonstrated through computer simulations.

Non-Orthogonal Multiple Access (NOMA) has been a hot research topic over the past few years, particularly because it is widely recognized that this technique represents a promising technology for massive Machine-Type Communications (mMTC) in future 5G cellular networks. The NOMA literature today is heavily focused on the so-called Power-Domain NOMA, which requires a strong power imbalance at the receiver between user signals. In some recent papers ([1] and [2]), the present authors revived a NOMA concept introduced back in the year 2000 and completely overlooked in the recent NOMA literature. This NOMA concept, which uses two sets of orthogonal signal waveforms and iterative interference cancellation at the receiver, fully avoids the power imbalance requirements of power-domain NOMA and makes it possible to grant the same data rates and performance levels to different users. In this paper, we first shed further light on the limitations of today's power-domain NOMA and we give insight on the potential of superposing the signals of two user groups with different characteristics instead of superposing two user signals. Next, we propose a new variant of the NOMA technique proposed in [1] and [2], which avoids the use of a complex interference canceler. This scheme achieves a 25% channel overloading factor at a negligible degradation of the signal-to-noise ratio (SNR) using a very simple maximumlikelihood (ML) receiver.

Cell-edge caching emerges as an appealing approach to alleviate traffic load and to reduce transmission latency for future cellular networks in general and small cell networks (SCNs), in particular. In this paper, we investigate the design of a proactive caching policy that takes into account both time-correlation of user requests and the time duration users spent for a requested content; two important aspects in applications such as Video-on-Demand. The proposed caching policy is formulated as a combinatorial optimization problem. The inherently high computational complexity incurring by solving a combinatorial optimization problem is circumvented by leveraging the concept of semidefinite relaxation (SDR) method. In addition, we design a randomized procedure to efficiently find a rank one solution. Furthermore, extensive numerical results are provided in order to verify the validity of the theoretical findings and demonstrate the effectiveness of the proposed scheme.

Massive MIMO is considered as one of the key enablers of the next generation 5G networks.With a high number of antennas at the BS, both spectral and energy efficiencies can be improved. Unfortunately, the downlink channel estimation overhead scales linearly with the number of antenna. This does not create complications in Time Division Duplex (TDD) systems since the channel estimate of the uplink direction can be directly utilized for link adaptation in the downlink direction. However, this channel reciprocity is unfeasible for the Frequency Division Duplex (FDD) systems where different physical transmission channels are existent for the uplink and downlink. In the aim of reducing the amount of Channel State Information (CSI) feedback for FDD systems, the promising method of two stage beamforming transmission was introduced. The performance of this transmission scheme is however highly influenced by the users grouping and selection mechanisms. In this paper, we first introduce a new similarity measure coupled with a novel clustering technique to achieve the appropriate users partitioning. We also use graph theory to develop a low complexity groups scheduling scheme that outperforms currently existing methods in both sum-rate and throughput fairness. This performance gain is demonstrated through computer simulations.

We consider the problem of long term power allocation in dense wireless networks. The framework considered in this paper is of interest for machine-type communications (MTC). In order to guarantee an optimal operation of the system while being as power efficient as possible, the allocation policy must take into account both the channel and queue states of the devices. This is a complex stochastic optimization problem, that can be cast as a Markov Decision Process (MDP) over a huge state space. In order to tackle this state space explosion, we perform a mean-field approximation on the MDP. Letting the number of devices grow to infinity the MDP converges to a deterministic control problem. By solving the Hamilton-Jacobi-Bellman Equation, we obtain a well-performing power allocation policy for the original stochastic problem, which turns out to be a threshold-based policy and can then be easily implemented in practice.

In this paper, we examine a system where status updates are generated by a source and are forwarded in a First-Come-First-Served (FCFS) manner to the monitor. We consider the case where the server has other tasks to fulfill referred to as vacations, a simple example being relaying the packets of another non age-sensitive stream. Due to the server's necessity to go on vacations, the age process of the stream of interest becomes complicated to evaluate. By leveraging specific queuing theory tools, we provide a closed form of the average age of the stream which enables us to optimize its packet generation rate and achieve the minimum possible average age. Numerical results are provided to corroborate the theoretical findings and highlight the interaction between the stream and the vacations in question.

Device-to-device (D2D) communications is a promising technique for improving the efficiency of 5G networks. Employing channel adaptive resource allocation can yield to a large enhancement in almost any performance metric of D2D communications (e.g. Energy Efficiency). Centralized approaches require the knowledge of D2D links' Channel State Information (CSI) at the BS level. However, CSI reporting suffers from the limited number of resources available for feedback transmission. Alternately, we propose a distributed algorithm for resource allocation that benefits from the users' knowledge of their local CSI in order to minimize the users' transmission power while maintaining predefined throughput constraint. The key idea is that users compute their local performance metrics (e.g. energy efficiency) and then use a new signaling mechanism to share these values between each other. Under some condition, the performance of this distributed algorithm achieves that of theideal scheduling (i.e. with a global CSI knowledge of all the D2D links). We describe how this technique can be simply implemented by adapting existing CSI reporting (e.g. in Long-Term Evolution (LTE) systems). Furthermore, numerical results are presented to corroborate our claims and demonstrate the gain that the proposed distributed scheduling brings to cellular networks compared to the bestcentralized-limited feedback scheduling.

Non-orthogonal multiple access (NOMA) is recognized today as a most promising technology for future 5G cellular networks and a large number of papers have been published on the subject over the past few years. Interestingly, none of these authors seems to be aware that the foundation of NOMA actually dates back to the year 2000, when a series of papers introduced and investigated multiple access schemes using two sets of orthogonal signal waveforms and iterative interference cancellation at the receiver. The purpose of this paper is to shed light on that early literature and to describe a practical scheme based on that concept, which is particularly attractive for machine-type communications (MTC) in future 5G cellular networks. Using this approach, NOMA appears as a convenient extension of orthogonal multiple access rather than a strictly competing technology, and most important of all, the power imbalance between the transmitted user signals that is required to make the receiver work in other NOMA schemes is not required here.

This paper characterizes the performance in terms of queueing stability of a network composed of multiple MIMO transmitter-receiver pairs taking into account the dynamic traffic pattern and the probing/feedback cost. We adopt a centralized scheduling scheme that selects a number of active pairs in each time-slot. We consider that the transmitters apply interference alignment (IA) technique if two or more pairs are active, whereas in the special case where one pair is active point-to-point MIMO singular value decomposition (SVD) is used. We consider a time-division duplex (TDD) system where transmitters acquire their channel state information (CSI) by decoding the pilot sequences sent by the receivers. Since global CSI knowledge is required for IA, the transmitters have also to exchange their estimated CSIs over a backhaul of limited capacity (i.e. imperfect case). Under this setting, we characterize in this paper the stability region of the system under both the imperfect and perfect (i.e. unlimited backhaul) cases, then we examine the gap between these two resulting regions. Further, under each case we provide a centralized probing policy that achieves the max stability region. These stability regions and scheduling policies are given for the symmetric system, where all the path loss coefficients are equal to each other, as well as for the general system. For the symmetric system, we provide the conditions under which IA yields a queueing stability gain compared to SVD. Under the general system, the adopted scheduling policy is of a high computational complexity for moderate numbers of pairs, consequently we propose an approximate policy that has a reduced complexity but that achieves only a fraction of the system stability region. A characterization of this fraction is provided.

We investigate a pilot allocation problem in wireless networks over Markovian fading channels. In wireless systems, the channel state information (CSI) is collected at the base station, in particular, this paper considers a pilot-aided channel estimation method (TDD mode). Typically, there are less available pilots than users, hence at each slot the scheduler needs to decide an allocation of pilots to users with the goal of maximizing the long-term average throughput. There is an inherent tradeoff in how the limited pilots are used: assign a pilot to a user with up-to-date CSI and good channel condition for exploitation, or assign a pilot to a user with outdated CSI for exploration. As we show, the arising pilot allocation problem is a restless bandit problem and thus its optimal solution is out of reach. In this paper, we propose an approximation based on the Lagrangian relaxation method, which provides a low-complexity Whittle index policy. We prove this policy to be asymptotically optimal in the many users regime (when the number of users in the system and the available pilots for channel sensing grow large). We evaluate the performance of Whittle's index policy in various scenarios and illustrate its remarkably good performance for small number of users, where it is not guaranteed to be optimal.

This paper studies the problem of feedback allocation and scheduling for a multichannel downlink cellular network under limited and delayed feedback. We propose two efficient algorithms that select the link states that should be reported to the base station (BS). A novelty here is that these feedback allocation algorithms are performed at the users' side to take advantage of their local channel state information knowledge in order to achieve higher gains. The first algorithm is suitable for a continuous-time contention scheme and requires only one feedback per channel, whereas the second one is adapted for a discrete-time contention scheme and adopts a threshold-based concept. For this second algorithm, we study some implementation aspects related to the feedback period and investigate the tradeoff between knowing at the BS a small number of accurate link states and a larger but outdated number of link states. We show that these algorithms, combined with the Max-Weight scheduling, achieve good stability performance compared with the ideal system.

Using a network of cache enabled small cells, traffic during peak hours can be reduced by proactively fetching the content that is most likely to be requested. In this paper, we aim to explore the impact of proactive caching on an important metric for future generation networks, namely, energy efficiency (EE). We argue that, exploiting the spatial repartitions of users in addition to the correlation in their content popularity profiles, can result in considerable improvement of the achievable EE. In this paper, the optimization of EE is decoupled into two related subproblems. The first one addresses the issue of content popularity modeling. While most existing works assume similar popularity profiles for all users, we consider an alternative framework in which, users are clustered according to their popularity profiles. In order to showcase the utility of the proposed clustering, we use a statistical model selection criterion, namely, Akaike information criterion. Using stochastic geometry, we derive a closed-form expression of the achievable EE and we find the optimal active small cell density vector that maximizes it. The second subproblem investigates the impact of exploiting the spatial repartitions of users. After considering a snapshot of the network, we formulate a combinatorial problem that optimizes content placement in order to minimize the transmission power. Numerical results show that the clustering scheme considerably improves the cache hit probability and consequently the EE, compared with an unclustered approach. Simulations also show that the small base station allocation algorithm improves the energy efficiency and hit probability.

Machine Type Communication (MTC) is a key component of future 5G networks, and its realization is now possible due to the advances in technology and the market drivers. Several challenges however exist which hinder the realization of MTC, among which signaling overhead is considered the prominent challenge, particularly due to the massive number of devices in MTC networks. In this work, we consider an MTC system which utilizes massive MIMO and cell densification to support massive number of devices. In our scheme, devices within small cells can transmit their uplink data simultaneously on the same time-frequency resource with minimal interference due to the massive MIMO implementation, and random access is applied on a per-cell basis to limit the inter-cell interference. We derive a stochastic geometry model for our system and we use it in an optimization study. Our results can be used for optimal network planning and optimal resource allocation.

This paper considers an orthogonal frequency division multiplexing system where the Effective Exponential SNR Mapping (EESM) is applied. We first present an approximate model in which the subcarriers grouped into one block are assumed to have the same channel coefficient and, subcarriers in different blocks have independent and identically distributed channel coefficients. Then, we present an algorithm based on a goodness of fit test to estimate the number of subcarriers to be grouped into one block. Building on this approximation model, we provide an approach to compute the statistics of the effective SNR obtained from the EESM mapping. This method is based on convolution technique. The approach presented in the paper is then validated through simulations.

This work studies the problems of feedback allocation and scheduling for a multichannel downlink cellular network under limited and delayed feedback. We consider a realistic scenario where a fixed and small number F̅ of link states can be reported to the base-station (BS) per time-slot. We study the trade-off between knowing at the BS a small number of accurate link states (i.e. that can be reported within one time-slot) and a larger but outdated number of link states (i.e. number of link states > F̅ that requires more than one slot to be reported). We propose an efficient algorithm that selects the link states that should be reported to the base-station. A novelty here is that this feedback allocation algorithm is performed at the users side. We show that this algorithm combined with the MaxWeight scheduling achieves at least a fraction η of the stability region achieved under the ideal system (i.e. with full and perfect feedback at no cost). We then provide numerical results that show the best aforementioned trade-off under various system setups.

We investigate a pilot allocation problem in wireless networks over Markovian fading channels. In wireless systems, the Channel State Information (CSI) is collected at the Base Station (BS) through either a feedback channel (FDD mode) or a pilot-aided channel estimation method (TDD mode). This paper focuses on the latter. Typically, there are less available pilots than users, hence at each slot the scheduler needs to decide an allocation of pilots to users with the goal of maximizing the long-term average throughput. A trade-off emerges between exploiting users with up-to-date CSI for immediate gains or, exploring users with outdated CSI for a potential larger future gain. As we show, the arising pilot allocation problem is a restless bandit problem and thus its optimal solution is out of reach. In this paper, we propose a Lagrangian relaxation approach to obtain a Whittle index policy, which represents a low-complexity heuristic solution with remarkably good performance.

This paper considers the problem of energy efficient beamforming allocation in a cellular wireless network. Unlike most of the existing work that considers the problem at the physical layer, we include in our analysis the impact of the bursty traffic patterns of the users. We formulate a stochastic optimization problem that minimizes the average transmit power of the network such that the queues of the users are stable. We then develop a solution that combines Lyapunov optimization and mixed integer quadratic problem (MIQP). We provided a fundamental complexity analysis of the resulting optimization problem and show that it is NP-hard. We then provide a Semi-Definite Relaxation (SDR) of the problem and develop an algorithm with low complexity to get as close as possible to the desired optimal solution. Our claims are corroborated by simulation results.

In this paper, we consider last level cache (LLC) sharing problems in large-scale cloud networks with a fair payoff function. We formulate the problem as a strategic decision-making problem (i.e., a game). We examine the resource-sharing game with finite and infinite number of players. Exploiting the aggregate structure of the payoff functions, we show that the resource-sharing game has a Nash equilibrium in a wide range of return index. We show that the Nash equilibrium is not an evolutionarily stable strategy in the finite regime. Then, we introduce a myopic mean-field response where each player implements a mean-field-taking strategy. We show that such a mean-field-taking strategy is an evolutionarily stable strategy in both finite and infinite regime. We provide closed-form expression of the optimal pricing that gives an efficient resource-sharing policy. As the number of active players grows without bound, we show that the equilibrium strategy converges to a mean-field equilibrium, and the optimal prices for resources converge to the optimal price of the mean-field game. Then, we address the demand satisfaction problem for which a necessary and sufficient condition for satisfactory solutions is provided. In addition, a very fast mean-field learning algorithm is provided.

This paper considers a MIMO interference system where interference alignment (IA) technique is adopted to manage the problem of interference. We consider a time division duplex (TDD) system where each transmitter estimates its channel state information (CSI) by probing the receivers. In addition, the transmitters share their local CSI estimate between each other using a backhaul links of limited capacity. A quantization over the backhaul is therefore required to reduce the amount of information to exchange. We study in this paper the impact of this quantization on the system performance and determine the optimal number of transmitter-receiver pairs that maximizes the system throughput.

In this paper we study the downlink of a TDD (Time Division Duplex) single cell system where the Base Station (BS) employs multiple antennas to serve the users taking into account the traffic patterns. The BS chooses each slot the users to be active, and serves them using Zero Forcing (ZF) precoding. This requires the knowledge of the users' channels which is assumed to be performed e.g. via uplink training. Due to the channel acquisition overhead, only a subset of users must be active at each timeslot (depending on traffic patterns and channel states). In this paper, we develop an active user selection strategy where the base station sets a given threshold for the channel gain of the users. Then, only the users that have their channel gain higher than the threshold send their training sequence and are then considered to be served. The base station estimates the channel states of these users and, due to channel reciprocity, uses these channel states to transmit data via ZF precoding. With appropriate signaling and threshold selection, which adapt to the queuing behavior of the users, we prove that our proposed method achieves a larger stability region than the baseline centralized policy where the BS selects the users based on channel statistics and queue lengths. The performance of the threshold-based method is illustrated via simulations, where we can observe a tradeoff between the expansion of the stability region and delay performance.

This paper characterizes the performance of IA technique taking into account the dynamic traffic pattern and the probing/feedback cost. We consider a TDD system where transmitters acquire their CSI (Channel State Information) by decoding the pilot sequences sent by the receivers. Since global CSI knowledge is required for IA, the transmitters have also to exchange their estimated CSIs over a backhaul of limited capacity. Under this setting, we characterize in this paper the stability region of the system and provide a probing algorithm that achieves the max stability region. In addition, we compare the stability region of IA to the one achieved by a TDMA system where each transmitter applies a simple ZF (Zero Forcing technique).

We consider a small cell network (SCN) consisting of N cells, with the small cell base stations (SCBSs) equipped with Nt≥ 1 antennas each, serving K single antenna user terminals (UTs) per cell. Under this set up, we address the following question: given certain time average quality of service (QoS) targets for the UTs, what is the minimum transmit power expenditure with which they can be met? Our motivation to consider time average QoS constraint comes from the fact that modern wireless applications such as file sharing, multi-media etc. allow some flexibility in terms of their delay tolerance. Time average QoS constraints can lead to greater transmit power savings as compared to instantaneous QoS constraints since it provides the flexibility to dynamically allocate resources over the fading channel states. We formulate the problem as a stochastic optimization problem whose solution is the design of the downlink beamforming vectors during each time slot. We solve this problem using the approach of Lyapunov optimization and characterize the performance of the proposed algorithm. With this algorithm as the reference, we present two main contributions that incorporate practical design considerations in SCNs. First, we analyze the impact of delays incurred in information exchange between the SCBSs. Second, we impose channel state information (CSI) feedback constraints, and formulate a joint CSI feedback and beamforming strategy. In both cases, we provide performance bounds of the algorithm in terms of satisfying the QoS constraints and the time average power expenditure. Our simulation results show that solving the problem with time average QoS constraints provide greater savings in the transmit power as compared to the instantaneous QoS constraints.

In this paper, the problem of feedback and active user selection in multiple-input single-output (MISO) wireless systems such that the system's stability region is as big as possible is examined. The focus is on a system in a Rayleigh fading environment where zero forcing precoding is used to serve all active users in every slot. Acquisition of the channel states is done via uplink training in time division duplexing mode by the active users. Clearly, only a subset of users can perform uplink training and the selection of this subset is a challenging and interesting problem especially in MISO systems. The stability regions of a baseline centralized scheme and two novel decentralized policies are examined analytically. In the decentralized schemes, the transmitter broadcasts periodically the queue state information and the users contend for the channel in a carrier sense multiple access-based manner with parameters based on the outdated queue state information and real-time channel state information. We show that, using infrequent signaling between the base station and the users, the decentralized policies outperform the centralized policy. In addition, a threshold-based user selection and training scheme for discrete-time contention is proposed. The results of this paper imply that, as far as stability is concerned, the users must be involved in the active user selection and feedback/training decision. This should be leveraged in future communication systems.

We consider the problem of coordinated multicell downlink beamforming in massive multiple input multiple output (MIMO) systems consisting of$N$cells,$N_{t}$antennas per base station (BS) and$K$user terminals (UTs) per cell. In particular, we formulate a multicell beamforming algorithm for massive MIMO systems that requires limited amount of information exchange between the BSs. The design objective is to minimize the aggregate transmit power across all the BSs subject to satisfying the user signal-to-interference-noise ratio (SINR) constraints. The algorithm requires the BSs to exchange parameters which can be computed solely based on the channel statistics rather than the instantaneous channel state information (CSI). We make use of tools from random matrix theory to formulate the decentralized algorithm. We also characterize a lower bound on the set of target SINR values for which the decentralized multicell beamforming algorithm is feasible. We further show that the performance of our algorithm asymptotically matches the performance of the centralized algorithm with full CSI sharing. While the original result focuses on minimizing the aggregate transmit power across all the BSs, we formulate a heuristic extension of this algorithm to incorporate a practical constraint in multicell systems, namely the individual BS transmit power constraints. Finally, we investigate the impact of imperfect CSI and pilot contamination effect on the performance of the decentralized algorithm, and propose a heuristic extension of the algorithm to accommodate these issues. Simulation results illustrate that our algorithm closely satisfies the target SINR constraints and achieves minimum power in the regime of massive MIMO systems. In addition, it also provides substantial power savings as compared with zero-forcing beamforming when the number of antennas per BS is of the same orders of magnitude as the number of UTs per cell.

In this paper we study the stability region of the 2-user MISO broadcast channel where the transmitter employs Zero Forcing precoding when both users are scheduled, taking into account the time overheads needed for uplink channel training. We show that, with proper signalling design, combining a decentralized policy with the baseline centralized one for user selection can increase the stability region of the system.

In this work, we address the problem of power allocation for interfering transmitter-receiver pairs so that the probability that each queue length exceeds a specified threshold is fixed at a desired value. One application is satisfying QoS requirements in a dense cellular network. We deal with this problem using heavy traffic approximation techniques which lead to an asymptotic model of a (controlled) stochastic differential equation. The proposed power control strategy consists of allocating most of the power according to the states of the channel and a smaller fraction according to the queue lengths, for which we find a closed-form expression. We first consider a scenario where all channel realizations and queue lengths are known instantaneously to every transmitter. Then, the algorithm is extended to the case where only local SINR feedback is available and when queue length information is shared with delays among the transmitters. These models and results are also extended to the case where the transmitters are equipped with multiple antennas. Finally, the applicability in practical system settings are discussed and simulation results are provided to illustrate the performance of the proposed method.

In this work we address the problem of channel probing in a multicarrier downlink wireless network where in order to collect CSI feedback from each user at a channel, a fraction of the available time for transmission is used. This means that the time left to transmit is getting smaller. We study the aspect of stability of such a system and we find a randomized algorithm which can guarantee an expansion of the stability region with respect to full probing and prior works. In addition, we investigate a special case of a probing scheme that does not require knowledge of the statistics of the channels and can still enlarge the stability region of the system. Simulations show the performance of the proposed scheme.

In this work we study the problem of channel state feedback and user scheduling in a single cell downlink wireless network employing multiple orthogonal parallel channels. The aspect of the system we are focusing on is stability. For user scheduling for stability as a performance measure, both the queue and channel states need to be known by the base station. However channel states can be known only via feedback from the receivers. In order to collect CQI feedback from each user at one channel, a fraction of the available time for transmission is used. This means that the time left to transmit is getting smaller. We present a joint feedback and scheduling algorithm which can guarantee an expansion of the stability region with respect to prior works. We also provide expressions regarding the distribution of the time needed to be devoted for feedback at each channel in some special cases. The proposed algorithm does not need knowledge of the statistics of the channels and traffic patterns. Simulations illustrate the operation of the proposed scheme.

There has been substantial interest over the last decade in developing low complexity decentralized scheduling algorithms in wireless networks. In this context, the queue-length based Carrier Sense Multiple Access (CSMA) scheduling algorithms have attracted significant attention because of their attractive throughput guarantees. However, the CSMA results rely on the mixing of the underlying Markov chain and their performance under fading channel states is unknown. In this work, we formulate a partially decentralized randomized scheduling algorithm for a two transmitter receiver pair set up and investigate its stability properties. Our work is based on the Fast-CSMA (FCSMA) algorithm first developed in [1] and we extend its results to a signal to interference noise ratio (SINR) based interference model in which one or more transmitters can transmit simultaneously while causing interference to the other. In order to improve the performance of the system, we split the traffic arriving at the transmitter into schedule based queues and combine it with the FCSMA based scheduling algorithm. We theoretically examine the performance of our algorithm in both non-fading and fading environment and characterize the set of arrival rates which can be stabilized by our proposed algorithm.

In this paper, we study a weighted-sum rate maximization problem in SC-FDMA which is adopted as the multiple access scheme for the uplink in the 3GPP-LTE standard. Unlike OFDMA, in addition to the restriction of allocating a sub-channel to one user at most, the multiple sub-channels allocated to a user in SC-FDMA should be consecutive as well. This renders the resource allocation problem prohibitively difficult and the standard optimization tools (e.g., Lagrange dual approach widely used for OFDMA, etc.) can not help towards its optimal solution. We propose a novel polynomial-complexity optimization framework that is inspired from the recently developed canonical duality theory. We first formulate the resource allocation problem as a binary-integer programming (BIP) problem and then transform the BIP problem into a continuous space canonical dual problem which is a concave maximization problem. Based on the solution of the continuous space dual problem, we propose a resource allocation algorithm that has polynomial complexity. We provide conditions under which the proposed algorithm is optimal. We compare the proposed algorithm with the existing algorithms in the literature to assess its performance. Simulation results show that the proposed algorithm improves the system performance significantly.

In this paper, we consider the challenging problem of power allocation in interfering wireless networks with services having stringent delay constraints. The objective is to allocate optimally the power to each user, take advantage of the time diversity of the time varying channel and satisfy the delay constraints of the services. To this end, we model the bit rate dynamics of the users as a linear stochastic control scheme and formulate a risk-sensitive control framework that minimizes an exponential cost function of the deviation between the traffic arrivals and departures from the queues. We provide the optimal solution of the power control problem and provide simulation results to assess the superiority of our solution compared to some existing solutions.

We analyze the downlink of multiple input multiple output (MIMO) multicell systems in the presence of intercell interference (ICI), under different transmit channel state information (CSI) assumptions. We assume, for the first scenario, that the Base Stations (BSs) have only the statistical CSI of all the channels. For the second scenario, we assume that the BSs have perfect CSI of their User Terminals(UTs) but only the statistical CSI of channels of the interfering BSs. We consider the following receiver structures at the UTs a) Optimal Decoding b) Minimum Mean Square (MMSE) receiver. We derive analytical expressions to compute the optimal number of streams each BS must use in order to maximize the total spectral efficiency of the system. We perform our analysis in the large dimensional regime (assuming the number of antennas on the BSs and UTs approaching infinity, at the same rate) using results from random matrix theory (RMT). However, the asymptotic results provide close approximation in the finite dimensional scenario. Remarkably, in the asymptotic regime, the optimization parameters depend only on the channel statistics and not on the instantaneous CSI, thus enabling a decentralized resource allocation policy in a multicell scenario. Our results show that in an interference limited regime, it is optimal for the BSs to use only a small subset of its streams to maximize the total spectral efficiency of the system.

In this paper, we study sum-power minimization problem in SC-FDMA which is adopted as the multiple access scheme for uplink in the 3GPP-LTE standard. Unlike OFDMA, in addition to the restriction of allocating a sub-channel to one user at most, the multiple sub-channels allocated to a user in SC-FDMA should be consecutive as well. This renders the resource allocation problem prohibitively difficult and the standard optimization tools (e.g., Lagrange dual approach widely used for OFDMA, etc.) can not help towards its optimal solution. We propose a novel polynomial-complexity optimization framework that is inspired from the recently developed canonical duality theory. We first formulate the resource allocation problem as a binary-integer programming (BIP) problem and then transform the BIP problem into a continuous space canonical dual problem which is a concave maximization problem under certain conditions. Based on the solution of the canonical dual problem, we derive a joint power and sub-channels allocation algorithm that has polynomial complexity. We provide conditions under which the proposed algorithm is optimal. The proposed framework is illustrated through simulations where the results show that the proposed algorithm improves the system performance significantly.

In this work, we address the joint problem of traffic scheduling and interference management related to the deployment of Small Cell Networks (SCNs). The Base Stations of the SCNs (which we will refer to as Micro Base Stations, MBSs) are low power devices with limited buffer size. They are connected to a Central Scheduler (CS) with limited capacity backhaul links. In this scenario, traffic has to be scheduled from the network to the MBS queues in such a way that the queue-length at MBS remains as close as possible to a given target queue-length. The challenge is to design a scheduler which is oblivious to the wireless link between the MBSs and the User Terminals (UTs). For the traffic arriving at the MBS, we need to efficiently transmit it over the wireless channel to the UTs with minimum power in an interference limited environment. Additionally, real time centralized interference management techniques will not be feasible. In this paper, we decouple the joint scheduling and interference management into two separate parts. For the scheduling problem, we propose a H∞control based scheduler which regulates the arrival rates to the queues at the MBS. For the problem of power minimization and decentralized interference management over the wireless link, we propose a multi-cell beamforming technique in which MBSs need to exchange only the channel statistics of their UTs. We use tools from the field Random Matrix Theory to formulate our algorithm. Our simulation results show that the H∞based queue length control algorithm stabilizes the queue-lengths at the MBS and keeps the variation of the queue-length around the target to a minimum.

We consider the problem of resource allocation in downlink OFDMA systems for multi service and unknown environment. Due to users' mobility and intercell interference, the base station cannot predict neither the Signal to Noise Ratio (SNR) of each user in future time slots nor their probability distribution functions. In addition, the traffic is bursty in general with unknown arrival. The probability distribution functions of the SNR, channel state and traffic arrival/density are then unknown. Achieving a multi service Quality of Service (QoS) while optimizing the performance of the system (e.g. total throughput) is a hard and interesting task since it depends on the unknown future traffic and SNR values. In this paper we solve this problem by modeling the multiuser queuing system as a discrete time linear dynamic system. We develop a robust H∞controller to regulate the queues of different users. The queues and Packet Drop Rates (PDR) are controlled by proposing a minimum data rate according to the demanded service type of each user. The data rate vector proposed by the controller is then fed as a constraint to an instantaneous resource allocation framework. This instantaneous problem is formulated as a convex optimization problem for instantaneous subcarrier and power allocation decisions. Simulation results show small delays and better fairness among users.

In wireless systems, most of nowadays scheduler assumes perfect knowledge of the channel quality indicator (CQI) at the transmitter. Unfortunately, this is never the case since the receiver (e.g. mobile users) estimates the CQI at time t-τ and feed back the estimate to the transmitter (e.g. base station). The transmitter uses then this outdated CQI at time t to allocate the radio resources to the users. This paper analyzes this issue at various mobile velocities and proposes improvement of scheduling that reduces the impact of outdated CQI. By using a channel prediction model, we determine the probability distribution function (pdf) of CQI conditioned on the outdated CQIs. We use then this (pdf) in the evaluation of the Block Error Rate (BLER) of the users' transmission and we exploit the result in the development of a novel scheduling rule. Simulation results show improvement of the system performance without any fairness loss.

In this paper we develop a time Scheduling, Subcarrier and Power Allocation scheme for Multi-Service Downlink OFDMA Systems. We propose a two step solution where time scheduling is separated from subcarrier and power allocation decisions. The system is modeled as a linear dynamic system with the aim of minimizing a quadratic cost function. The proposed scheduler is a Linear-Quadratic-Regulator (LQR) which achieves fairness among users by proposing an instantaneous data rate for each user in each time slot. The data rate vector proposed by the regulator is then fed as a constraint to the subcarrier and power allocation problem. This problem is formulated as a constrained convex optimization problem and we develop algorithms for subcarrier and power allocation to achieve the data rates proposed by LQR. Simulation results show good performance and better fairness among users.

We consider the problem of multi-cell downlink beamforming with N cells and K terminals per cell. Cooperation among base stations (BSs) has been found to increase the system throughput in a multi-cell set up by mitigating inter-cell interference. Most of the previous works assume that the BSs can exchange the instantaneous channel state information (CSI) of all their user terminals (UTs) via high speed backhaul links. However, this approach quickly becomes impractical as N and K grow large. In this work, we formulate a distributed beamforming algorithm in a multi-cell scenario under the assumption that the system dimensions are large. The design objective is the minimize the total transmit power across all BSs subject to satisfying the user SINR constraints while implementing the beamformers in a distributed manner. In our algorithm, the BSs would only need to exchange the channel statistics rather than the instantaneous CSI. We make use of tools from random matrix theory to formulate the distributed algorithm. The simulation results illustrate that our algorithm closely satisfies the target SINR constraints when the number of UTs per cell grows large, while implementing the beamforming vectors in a distributed manner.

In this paper, we study resource allocation in a downlink OFDMA system assuming imperfect channel state information (CSI) at the transmitter. To achieve the individual QoS of the users in OFDMA system, adaptive resource allocation is very important, and has therefore been an active area of research. However, in most of the the previous work perfect CSI at the transmitter is assumed which is rarely possible due to channel estimation error and feedback delay. In this paper, we study the effect of channel estimation error on resource allocation in a downlink OFDMA system. We assume that each user terminal estimates its channel by using an MMSE estimator and sends its CSI back to the base station through a feedback channel. We approach the problem by using convex optimization framework, provide an explicit closed form expression for the users' transmit power and then develop an optimal margin adaptive resource allocation algorithm. Our proposed algorithm minimizes the total transmit power of the system subject to constraints on users' average data rate. The algorithm has polynomial complexity and solves the problem with zero optimality gaps. Simulation results show that our algorithm highly improves the system performance in the presence of imperfect channel estimation.

In this paper we study the problem of energy efficient packet scheduling in wireless fading channels with hard delay constraints. Each packet has a strict delay constraint of D time slots and it has to be delivered before its delay deadline. Optimal scheduling of each packet now depends on future Channel State Information (CSI) which is not available. We develop the upper and lower bounds on the optimal output rate and develop a two step solution. In the first step we solve the relaxed optimization problem without bounds by using Lagrange Optimization theory and obtain an initial output rate. In the second step this output rate is adjusted such that the deadlines are successfully achieved. Simulation results show good performance in terms of energy efficiency.

Most of existing works on resource allocation in TDMA and OFDMA systems assume the availability of perfect channel state information (CSI) at the transmitter. The availability of only a noisy estimate of the channel and the feedback delay are the problems which are faced more often. In this paper, we study the effect of feedback delay on resource allocation in a downlink OFDMA system. We approach the problem by using convex optimization framework and get the optimal solution with zero optimality gap. By considering the probability distribution function of the current CSI conditioned on outdated CSI in the resource allocation framework, we propose an algorithm that minimizes the total transmit power of the system subject to strict constraints on users' conditional expected capacities. Results show that the system performance is sensitive to feedback delay and provide guidelines for system dimensioning and design.

The objective of this paper is two fold: proposes a distributed resource optimization framework in the context of heterogeneous systems and optimizes allocation of discrete resources and bit rates to users within each system (network). In fact, most of existing wireless techniques use Adaptive Modulation and Coding technique where a finite set of Modulation and Coding schemes is available in the system. The bit rates to attribute to users are then selected among a finite set of available discrete rates. More specifically, the objective in this paper is to minimize to overall transmit power of the whole system while ensuring to each user a minimum data rate and fulfilling a maximum power constraint within each system. The use of centralized optimization frameworks to handle Radio Resource Management (RRM) problem in heterogeneous systems requires huge signaling overhead. In order to obtain distributed resource allocation, we approach the problem by using a convex optimization framework and thus split it into different subproblems each one performed by a given agent of the network(base station or user). We propose then a distributed algorithm based on successive iterations (negotiations) between the base stations and users. In each iteration, each network node (user or base station) has to handle a discrete optimization problem using an efficient (low complexity) algorithm described in the paper. By using in each iteration an appropriate update method, based on the subgradient of the dual Lagrangian objective function, our approach converges to the optimal solution with zero duality gap and after a finite number of iterations. Simulation results show good performance of the proposed algorithm and the convergence to the global optimal solution.

Most of the existing scheduling algorithms in OFDMA and TDMA systems assume the availability of perfect channel quality indicator (CQI) at the transmitter. Unfortunately, this is unrealistic since the receiver (e.g. mobile users) estimates the CQI at time t - ¿ and feed back this CQI estimate to the transmitter (e.g. base station). The scheduler uses then this outdated CQI at time t to make the allocation. In this paper, we analyze the impact of outdated CQI on the performance of MPF (multi carrier proportional fair) scheduler in the context of downlink OFDMA system. We propose thus improvement of this algorithm in order to reduce the impact of outdated CQI. The idea consists in analyzing the probability distribution function (pdf) of the CQI with respect to an estimated CQI and using this pdf in the scheduling rule. The improvement method proposed in this paper can be applied to other scheduling rules. Simulation results show improvement of the system performance.

This paper studies resource allocation problem in downlink multiuser orthogonal frequency division multiple access (OFDMA) system. The problem is formulated as sum-rate (cell throughput) maximization problem with strict data rate constraints imposed by the users and peak power constraint by the base station. We discuss the feasibility of the problem and propose two algorithms when the feasibility conditions are met. In the first algorithm subcarrier and power allocation decisions are made by jointly considering all the constraints. In the second algorithm subcarrier allocation decisions are based on data rate constraints and transmit power constraint is achieved on the allocated subcarrier set. The second algorithm although sub-optimal has almost identical performance compared to the first algorithm. The reduction in complexity is huge compared to the first algorithm.

We study the downlink multiuser Multiple Input Multiple Output-Orthogonal Frequency Division Multiple Access (MIMO-OFDMA) system for margin adaptive resource allocation where the base station (BS) has to satisfy individual quality of service (QoS) constraints of the users subject to transmit power minimization. Low complexity solutions involve beamforming techniques for multiuser inter-stream interference cancellation. However, when beamforming is introduced in the margin adaptive objective, it becomes a joint beamforming and resource allocation problem. We propose a sub-optimal two-step solution which decouples beamforming from subcarrier and power allocation. First a reduced number of user groups are formed and then the problem is formulated as a convex optimization problem. Finally an efficient algorithm is developed which allocates the best user group to each subcarrier. Simulation results reveal comparable performance with the hugely complex optimal solution.

This paper provides analysis of the inter cell interference coordination problem in multicell OFDMA systems. In order to reduce the interference without losing much frequency resources in each cell, cell users are partitioned into two classes, namely interior and exterior users. Frequency resources are universally used in all interior cells' areas whereas users of exterior zones have a frequency reuse factor strictly higher than 1. In this paper, we determine the optimal frequency reuse factor of the exterior users as well as the bandwidth to assign to both interior and exterior zones. The resource management and interference coordination are jointly formulated as an optimization problem. Results depicted in this paper allow determining the optimal configuration of the interior and exterior regions' dimensions as well as the optimal frequency reuse factor.

This paper proposes new frequency-time schedulers for 3GPP/LTE-like system in the downlink. 3GPP/LTE DL is OFDMA-based with adaptive modulation and coding (AMC) and frequency-time scheduling to enhance spectral efficiency and aggregate system throughput. Time-frequency scheduling and AMC can be implemented jointly or separately i.e. sequentially AMC after scheduling. Two novel schedulers are proposed in this paper with respectively joint and separate implementation of scheduling and AMC. Through system level performance evaluation, results show that the first scheduler with separate AMC and scheduling implementation outperforms the second scheduler (with joint AMC implementation) in terms of trade off between fairness and capacity and implementation complexity.

This paper provides a link and system level study of the downlink in a B3G system using MIMO OFDMA techniques. A 3GPP/LTE-like scenario, in terms of frame structure and system main parameters, is considered in this study. At the link level, the double Alamouti MIMO scheme is studied and the performances of various modulation and coding schemes (MCS) are provided. The impact of real MIMO channel estimation (including RF impairments, CFO compensation and automatic gain control AGC) on the MCSs link performance is also presented. System level study is thus carried out to assess the performance of the downlink OFDMA in terms of coverage and cell throughput. Several scheduling disciplines and MIMO schemes are investigated and the impact of the spatial diversity on the cell throughput is provided. This study has been carried out in the scope of the French RNRT OPUS project.

Streaming technique, widely developed and used in the internet to convey multimedia application (e.g., audio, video clip, etc.), is supposed to occupy a large share of the third generation system bandwidth. Recently, studies are focused on the transmission of streaming services over high speed downlink packet access (HSDPA) system using time shared channels. In this case, the stringent QoS constraints of these services may result in a loss of HSDPA cell capacity if an appropriate scheduler is not used. In this paper, we propose a new opportunistic scheduler that allows to transmit streaming traffic over HSDPA without losing much cell capacity. Results afforded by this scheduler are promising and show that our proposed scheduler outperforms the existing scheduling algorithms

This paper focuses on the interaction between the transport control protocol (TCP) layer and the radio interface in the high-speed downlink packet access (HSDPA) wireless system. In the literature, studies of the interaction between TCP and wireless networks are focused on the evaluation of user bit rate in the case of dedicated channels. In this paper, the interaction between TCP, hybrid automatic repeat request (HARQ), and scheduling techniques (especially, proportional fair scheduling) is conducted. Analytical models to evaluate HSDPA cell capacity, user bit rate, and interaction with TCP layer are presented. Even if as expected the bit rate per flow decreases strongly with the congestion frequency in the wired network, it is shown that the overall capacity achieved by HSDPA is not as affected by the TCP layer. Using this result, a method to reduce the effect of TCP on wireless network without losing much cell capacity is proposed. This method has the advantage of modifying the scheduling algorithm only and of not requiring any change to the TCP protocol.

This paper focuses on the effect of circuit switched (CS) services on high speed downlink packet access (HSDPA) packet services in the context of the UMTS FDD system. An objective of the analysis is to provide guidelines for the UMTS planning process to prioritize services when resources are dynamically shared between circuit and packet services. An analytical model, taking into account some of the features introduced in HSDPA (namely adaptive modulation and coding (AMC), hybrid automatic repeat request (HARQ) and fast cell selection) and the interaction between CS services and HSDPA, is proposed to approximate the HSDPA cell capacity. Simulation results to assess the efficiency of the analytical model are also reported.

This paper focuses on the study of scheduling techniques in HSDPA system. In this context, we propose to use the score based (SB) scheduling algorithm in HSDPA. This algorithm is compared to another scheduling algorithm, called proportional fair (PF), widely used and studied in the literature. To make this comparison, an analytical model allowing to evaluate the cell capacity and the user bit rate is proposed. The radio channel is assumed to he a dense multipath frequency selective channel with uncorrelated signal envelope following a Rayleigh distribution and wide-sense stationary uncorrelated scattering (WSSUS). Results show that SB seems to be a better trade-off between "fairness" and "efficiency" than PF

Abstract This paper focuses on the interaction between the transmission control protocol (TCP) layer and the radio interface in a 3G wireless system. An analytical model to evaluate the impact of TCP on the UMTS‐HSDPA capacity is presented. A method to minimize the effect of TCP on wireless networks using shared channels is also proposed. High speed downlink packet access (HSDPA) is an evolution of the UMTS standard over the air interface to achieve higher aggregate bit rates through the introduction of adaptive modulation and coding, hybrid automatic repeat request (ARQ), fast scheduling, fast cell selection, and multiple input multiple output (MIMO) (space time coding and blast) techniques. The proposed model is used to evaluate the effect of the TCP protocol on the bit rate of various data services (at 64 and 128 kbps). As expected, the bit rate per flow decreases strongly if the congestion frequency in the wired network increases. However, the overall capacity achieved by HSDPA is not as affected by the TCP layer. Using this result, a method to maintain the bit rate per TCP flow at a given value without loosing much cell capacity is proposed. The findings are supported by simulation results. Copyright © 2005 John Wiley & Sons, Ltd.

An analytical model to evaluate the impact of TCP on the UMTS-HSDPA capacity is presented. A method to minimize the effect of TCP on wireless networks using shared channels is also proposed. HSDPA (high speed downlink packet access) achieves higher aggregate bit rates through the introduction of adaptive modulation and coding, hybrid ARQ, fast scheduling, fast cell selection and MIMO (space time coding and BLAST) techniques. The proposed model is used to evaluate the effect of the TCP protocol on the bit rate of various data services (at 64 and 128 Kbps). As expected, the bit rate per flow decreases strongly if congestion in the wired network increases. However, the capacity achieved by HSDPA is not as affected by TCP. Using this result, a method to maintain the bit rate per TCP flow at a given value without losing much cell capacity is proposed. The findings are supported by simulation results.

A semi analytical model and framework for the estimation of MIMO/HSDPA cell capacity for UMTS is presented. The model includes the effect of fast fading, mobility and coverage on the capacity. An algorithm to select the number of HS-DSCH codes allocated to a given user and the associated modulation and coding schemes is proposed. The capacity model is used to compare MIMO systems and provide insight on expected performance and capacity. Results indicate that adaptive MIMO in the sense of number of activated antennas at the transmitter increases capacity and that adaptive space time coding outperforms adaptive BLAST techniques for the explored scenarios. The effect of coverage on MIMO/HSDPA cell capacity is also reported.

An analytical model for the evaluation of HSDPA capacity is proposed and used to carry out a comparison of several MIMO systems. HSDPA is an evolution of the UMTS standard over the air interface to achieve higher aggregate bit rates through the introduction of adaptive modulation and coding, hybrid ARQ, fast scheduling, fast cell selection and MIMO (space time coding and Blast) techniques. The model is used to compare MIMO systems, a typically difficult task to provide some insight. The results are supported by a simulation.