arXiv subjects

Mohamad Assaad

Publications and source records attributed to Mohamad Assaad.

At least 73 records · Page 4Linked to original sources

arXiv2018

Risk-Sensitive Reinforcement Learning for URLLC Traffic in Wireless Networks

In this paper, we study the problem of dynamic channel allocation for URLLC traffic in a multi-user multi-channel wireless network where urgent packets have to be successfully transmitted 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 transmitted 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.

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arXiv2018

Distributed Stochastic Optimization in Networks with Low Informational Exchange

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. Gradient descent method is a common technique to solve the optimization problem, while 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 too complicated or constantly changing. Nodes may exchange the observation of their local utilities to estimate the global utility at each timeslot. We propose stochastic perturbation based distributed algorithms under the assumptions whether each node has collected local utilities of all or only part of the other nodes. We use tools from stochastic approximation to prove that both algorithms converge to the optimum. The convergence rate of the algorithms is also derived. Although the proposed algorithms can be applied to general optimization problems, we perform simulations considering power control in wireless networks and present numerical results to corroborate our claim.

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arXiv2018

Enhancing Favorable Propagation in Cell-Free Massive MIMO Through Spatial User Grouping

Cell-Free (CF) Massive multiple-input multiple-output(MIMO) is a distributed antenna system, wherein a large number of back-haul linked access points randomly distributed over a coverage area serve simultaneously a smaller number of users. CF Massive MIMO inherits favorable propagation of Massive MIMO systems. However, the level of favorable propagation which highly depends on the network topology and environment may be hindered by user' spatial correlation. In this paper, we investigate the impact of the network configuration on the level of favorable propagation for a CF Massive MIMO network. We formulate a user grouping and scheduling optimization problem that leverages users' spatial diversity. The formulated design optimization problem is proved to be NP-hard in general. To circumvent the prohibitively high computational cost, we adopt the semidefinite relaxation method to find a sub-optimal solution. The effectiveness of the proposed strategies is then verified through numerical results which demonstrate a non-negligible improvement in the performance of the studied scenario.

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arXiv2018

The Age of Updates in a Simple Relay Network

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, a simple example being relaying the packets of another 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 for both streams which enables us to optimize the generation rate of packets belonging to each stream to achieve the minimum possible average age. The tools used can be further adopted to provide insights on more general multi-hop scenarios. Numerical results are provided to corroborate the theoretical findings and highlight the interaction between the two streams.

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arXiv2018

A Spatial Basis Coverage Approach For Uplink Training And Scheduling In Massive MIMO Systems

Massive multiple-input multiple-output (massive MIMO) can provide large spectral and energy efficiency gains. Nevertheless, its potential is conditioned on acquiring accurate channel state information (CSI). In time division duplexing (TDD) systems, CSI is obtained through uplink training which is hindered by pilot contamination. The impact of this phenomenon can be relieved using spatial division multiplexing, which refers to partitioning users based on their spatial information and processing their signals accordingly. The performance of such schemes depend primarily on the implemented grouping method. In this paper, we propose a novel spatial grouping scheme that aims at managing pilot contamination while reducing the required training overhead in TDD massive MIMO. Herein, user specific decoding matrices are derived based on the columns of the discrete Fourier transform matrix (DFT), taken as a spatial basis. Copilot user groups are then formed in order to obtain the best coverage of the spatial basis with minimum overlapping between decoding matrices. We provide two algorithms that achieve the desired grouping and derive their respective performance guarantees. We also address inter-cell copilot interference through efficient pilot sequence allocation, leveraging the formed copilot groups. Various numerical results are provided to showcase the efficiency of the proposed algorithms.

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arXiv2018

Heterogeneous Doppler Spread-based CSI Estimation Planning for TDD Massive MIMO

Massive multi-input multi-output (Massive MIMO) has been recognized as a key technology to meet the demand for higher data capacity and massive connectivity. Nevertheless, the number of active users is restricted due to training overhead and the limited coherence time. Current wireless systems assume the same coherence slot duration for all users, regardless of their heterogeneous Doppler spreads. In this paper, we exploit this neglected degree of freedom in addressing the training overhead bottleneck. We propose a new uplink training scheme where the periodicity of pilot transmission differs among users based on their actual channel coherence times. Since the changes in the wireless channel are, primarily, due to movement, uplink training decisions are optimized, over long time periods, while considering the evolution of the users channels and locations. Owing to the different rates of the wireless channel and location evolution, a two time scale control problem is formulated. In the fast time scale, an optimal training policy is derived by choosing which users are requested to send their pilots. In the slow time scale, location estimation decisions are optimized. Simulation results show that the derived training policies provide a considerable improvement of the cumulative average spectral efficiency even with partial location knowledge.

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arXiv2018

Queue-aware Energy Efficient Control for Dense Wireless Networks

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.

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arXiv2017

Graph Theory Based Approach to Users Grouping and Downlink Scheduling in FDD Massive MIMO

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.

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arXiv2017

Energy Efficiency in Cache Enabled Small Cell Networks With Adaptive User Clustering

Using a network of cache enabled small cells, traffic during peak hours can be reduced considerably through proactively fetching the content that is most probable to be requested. In this paper, we aim at exploring the impact of proactive caching on an important metric for future generation networks, namely, energy efficiency (EE). We argue that, exploiting the correlation in user content popularity profiles in addition to the spatial repartitions of users with comparable request patterns, can result in considerably improving the achievable energy efficiency of the network. In this paper, the problem of optimizing 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 in the network, we consider an alternative caching framework in which, users are clustered according to their content popularity profiles. In order to showcase the utility of the proposed clustering scheme, we use a statistical model selection criterion, namely Akaike information criterion (AIC). 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 with comparable request patterns. After considering a snapshot of the network, we formulate a combinatorial optimization problem that enables to optimize content placement such that the used transmission power is minimized. Numerical results show that the clustering scheme enable to considerably improve the cache hit probability and consequently the EE compared with an unclustered approach. Simulations also show that the small base station allocation algorithm results in improving the energy efficiency and hit probability.

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arXiv2017

Stability Analysis of TDD Networks Revisited: A trade-off between Complexity and Precision

In this paper, we revisit the stability region of a cellular time division duplex (TDD) network. We characterize the queuing stability region of a network model that consists of two types of communications: (i) users communicating with the base station and (ii) users communicating with each other by passing through the base station. When a communication passes through the base station (BS) then a packet cannot be delivered to the destination UE until it is first received by the BS queue from the source UE. Due to the relaying functionality at the BS level, a coupling is created between the queues of the source users and the BS queues. In addition, contrarily to the majority of the existing works where an ON/OFF model of transmission is considered, we assume a link adaptation model (i.e. multiple rate model) where the bit rate of a link depends on its radio conditions. The coupling between the queues as well as the multiple rate model are the main challenges that highly increase the complexity of the stability region characterization. In this paper, we propose a simple approach that permits to overcome these challenges and to provide a full characterization of the exact stability region as a convex polytope with a finite number of vertices. An approximated model is proposed for reducing the computational complexity of the exact stability region. For the multi-user scenario, a trade-off is established between the complexity and the preciseness of the approximated stability region compared to the exact one. Furthermore, numerical results are presented to corroborate our claims.

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arXiv2017

Performance Analysis of Trial and Error Algorithms

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 (TEL) and Optimal Dynamical Learning (ODL), are very appealing for a broad class of games. In fact, ODL has the property to spend a high proportion of time in an optimum state that maximizes the sum of utility of all players. And the TEL has the property to spend a high proportion of time in an optimum state that maximizes the sum of utility of all players if there is a Pure Nash Equilibrium (PNE), otherwise, it spends a high proportion of time in an optimum state that maximizes a tradeoff between the sum of utility of all 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 paper, 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 performances.

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arXiv2017

On the Foundation of NOMA and its Application to 5G Cellular Networks

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.

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arXiv2017

Enhancing massive MIMO: A new approach for Uplink training based on heterogeneous coherence time

Massive multiple-input multiple-output (MIMO) is one of the key technologies in future generation networks. Owing to their considerable spectral and energy efficiency gains, massive MIMO systems provide the needed performance to cope with the ever increasing wireless capacity demand. Nevertheless, the number of scheduled users stays limited in massive MIMO both in time division duplexing (TDD) and frequency division duplexing (FDD) systems. This is due to the limited coherence time, in TDD systems, and to limited feedback capacity, in FDD mode. In current systems, the time slot duration in TDD mode is the same for all users. This is a suboptimal approach since users are subject to heterogeneous Doppler spreads and, consequently, different coherence times. In this paper, we investigate a massive MIMO system operating in TDD mode in which, the frequency of uplink training differs among users based on their actual channel coherence times. We argue that optimizing uplink training by exploiting this diversity can lead to considerable spectral efficiency gain. We then provide a user scheduling algorithm that exploits a coherence interval based grouping in order to maximize the achievable weighted sum rate.

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arXiv2017

Power Control in Massive MIMO with Dynamic User Population

This paper considers the problem of power control in Massive MIMO systems taking into account the pilot contamination issue and the arrivals and departures of users in the network. Contrary to most of existing work in MIMO systems that focuses on the physical layer with fixed number of users, we consider in this work that the users arrive dynamically and leave the network once they are served. We provide a power control strategy, having a polynomial complexity, and prove that this policy stabilizes the network whenever possible. We then provide a distributed implementation of the power control policy requiring low information exchange between the BSs and show that it achieves the same stability region as the centralized policy.

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arXiv2016

Asymptotically optimal pilot allocation over Markovian fading channels

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), 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 that, through the Lagrangian relaxation approach, provides a low-complexity heuristic, the 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.

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arXiv2016

Caching Improvement Using Adaptive User Clustering

In this article we explore one of the most promising technologies for 5G wireless networks using an underlay small cell network, namely proactive caching. Using the increase in storage technologies and through studying the users behavior, peak traffic can be reduced through proactive caching of the content that is most probable to be requested. We propose a new method, in which, instead of caching the most popular content, the users within the network are clustered according to their content popularity and the caching is done accordingly. We present also a method for estimating the number of clusters within the network based on the Akaike information criterion. We analytically derive a closed form expression of the hit probability and we propose an optimization problem in which the small base stations association with clusters is optimized.

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arXiv2016

Queueing Stability and CSI Probing of a TDD Wireless Network with Interference Alignment

This paper characterizes the performance of interference alignment (IA) technique taking into account the dynamic traffic pattern and the probing/feedback cost. 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 algorithm (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 compare the stability region of IA with the one achieved by a time division multiple access (TDMA) system where each transmitter applies a simple singular value decomposition technique (SVD). We then propose a scheduling policy that consists in switching between these two techniques, leading the system, under some conditions, to achieve a bigger stability region. Under the general system, the adopted scheduling policy is of a high computational complexity for moderate number 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.

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arXiv2015

Transmit Power Minimization in Small Cell Networks Under Time Average QoS Constraints

We consider a small cell network (SCN) consisting of N cells, with the small cell base stations (SCBSs) equipped with Nt \geq 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.

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