Stelios Stefanatos

UltraDense Networks: The New Wireless Frontier for Enabling 5G Access

The extreme traffic load that future wireless networks are expected to accommodate requires a rethinking of the system design. The initial estimations indicate that, unlike the evolutionary path of previous cellular generations that was based on spectral efficiency improvements, the most substantial amount of future system performance gains will be obtained by means of network infrastructure densification. By increasing the density of operator-deployed infrastructure elements along with incorporation of user-deployed access nodes (ANs) and mobile user devices acting as infrastructure prosumers, having one or more ANs exclusively dedicated to each user is expected to become feasible, introducing the ultradense network (UDN) paradigm. Although it is clear that UDNs are able to take advantage of the significant benefits provided by proximal transmissions and increased spatial reuse of system resources, large node density and irregular deployment introduce new challenges, mainly due to the interference environment characteristics that are vastly different from previous cellular deployments. This article attempts to provide insights on fundamental issues related to UDN deployment, such as determining the infrastructure density required to support the given traffic load requirements and the benefits of network-wise coordination, demonstrating the potential of UDNs for fifth-generation (5G) wireless networks.

Downlink OFDMA Resource Allocation Under Partial Channel State Information

The problem of resource allocation (RA) in a downlink OFDMA system is examined under the realistic assumption of imperfect channel state information (CSI) at the base station. In this case, there is a nonzero outage probability that the assigned rate at a particular subcarrier/user pair will not be supported by the true channel realization, which may lead to a significant waste of the systems available resources. It is therefore necessary to obtain RA algorithms that take into account the effect of imperfect CSI. By exploiting the statistical description of the imperfect CSI, various algorithms are proposed that tradeoff complexity versus performance, aiming at maximizing the systems successfully transmitted rate.

Joint Data Detection and Channel Tracking for OFDM Systems With Phase Noise

This paper addresses the problem of data detection in orthogonal frequency division multiplexing (OFDM) systems operating under a time-varying multipath fading channel. Optimal detection in such a scenario is infeasible, which makes the introduction of approximations necessary. The typical joint data-channel estimators are decision directed, that is, assume perfect past data decisions. However, their performance is subject to error propagation phenomena. The variational Bayes method is employed here, which approximates the joint data and channel distribution as a separable one, greatly simplifying the problem. The data detection part of the resulting algorithm provides soft data estimates that are used for channel tracking. The channel itself is modeled as an autoregressive process allowing for a Kalman-like tracking algorithm. According to the developed algorithm, both data and channel estimates are exchanged and updated in an iterative manner. The performance of the proposed algorithm is evaluated by simulations. Furthermore, since OFDM is extremely sensitive to the presence of phase noise, the algorithm is extended to operate under severe phase noise conditions, with moderate performance degradation.

Spatial coordination strategies in future ultra-dense wireless networks

Ultra network densification is considered a major trend in the evolution of cellular networks, due to its ability to bring the network closer to the user side and reuse resources to the maximum extent. In this paper we explore spatial resources coordination as a key empowering technology for next generation (5G) ultra-dense networks. We propose an optimization framework for flexibly associating system users with access nodes in a densely deployed network, opting for the exploitation of densification and the control of overhead signaling. Combined with spatial precoding strategies, network resources management approaches are designed, reflecting various features, namely local vs. global channel state information knowledge exploitation, centralized vs. distributed implementation, and non-cooperative vs. joint multi-node data processing. We apply these strategies to future dense network setups, and explore the impact of critical network parameters, that is, the densification levels of access nodes, the density of users requesting service, and the power budget constraints. We demonstrate that spatial resources coordination is a key factor for capitalizing on the gains of ultra dense network deployments.

Optimal user association for Massive MIMO empowered ultra-dense wireless networks

Ultra network densification and Massive MIMO are considered major 5G enablers since they promise huge capacity gains by exploiting proximity, spectral and spatial reuse benefits. Both approaches rely on increasing the number of access elements per user, either through deploying more access nodes over an area or increasing the number of antenna elements per access node. At the network-level, optimal user-association for a densely and randomly deployed network of Massive MIMO empowered access nodes must account for both channel and load conditions. In this paper we formulate this complex problem, report its computationally intractability and reformulate it to a plausible form, amenable to acquire a global optimal solution with reasonable complexity. We apply the proposed optimization model to typical ultra-dense outdoor small-cell setups and demonstrate: (i) the significant impact of optimal user-association to the achieved rate levels compared to a baseline strategy, and (ii) the optimality of alternative network access element deployment strategies.

Operational Region of D2D Communications for Enhancing Cellular Network Performance

An important enabler towards the successful deployment of any new element/feature to the cellular network is the investigation and characterization of the operational conditions where its introduction will enhance performance. Although there has been significant research activity on the potential of device-to-device (D2D) communications, there are currently no clear indications of whether D2D communications are actually able to provide benefits for a wide range of operational conditions, thus justifying their introduction to the system. This paper attempts to fill this gap by taking a stochastic geometry approach on characterizing the set (region) of operational conditions for which D2D communications enhance performance in terms of average user rate. For the practically interesting case of a heavy-loaded network, the operational region is provided in closed form as a function of a variety of parameters such as maximum D2D link distances and user densities, reflecting a wide range of operational conditions (points). It is shown that, under the appropriate deployment scheme, D2D communications can indeed be beneficial not only for the usually considered regime of “proximal communications” but to a wide range of operational conditions that include D2D link distances comparable to the distance to the cellular access point and considerably large user densities.

Downlink Mobile OFDMA Resource Allocation with Minimum User Rate Requests

The problem of resource allocation (RA) in a downlink OFDMA system with minimum user rate requests is examined under the realistic scenario of partial (imperfect) channel state information (CSI) at the base station. The challenge in this setting is the non-zero probability of outage events which may lead to significant performance degradation if algorithms assuming perfect CSI are utilized. In this paper the statistical description of the partial CSI is incorporated for deriving an (optimal) RA algorithm as the solution of an appropriate constrained optimization problem. Simplifications of the algorithm are utilized for significant complexity reduction with the additional property of allowing the system to adapt on-the-fly to time-varying minimum user rate requests.

Operational region of overlay D2D communications

An important enabler towards the successful introduction of any new element/feature to the cellular network, such as the ability to allow for direct, device-to-device (D2D) communications, is the investigation and characterization of the operational conditions, e.g., density of users, where its deployment will enhance performance. This paper attempts to provide a mathematically rigorous characterization of the set (region) of operational conditions, for which overlay D2D communications enhance performance in terms of average user rate. Using tools from stochastic geometry, and for the practically interesting case of a heavy loaded network, the operational region is identified in closed form as a function of a variety of parameters reflecting a wide range of operational conditions. It is shown that, under the appropriate deployment scheme, D2D communications can be beneficial not only in the typically assumed regime of “proximal communications” but to a wide range of operational conditions that include D2D link distances comparable to the distance from the cellular access point(s) as well as significantly large user densities.

Decision-aided compensation of severe phase-impairment-induced inter-carrier interference in frequency-selective OFDM

A new, reduced complexity algorithm is proposed for compensating the Inter-Carrier Interference (ICI) caused by severe PHase Noise (PHN) and Residual Frequency Offset (RFO) in OFDM systems. The algorithm estimates and compensates the most significant terms of the frequency domain ICI process, which are optimally selected via a Minimum Mean Squared Error (MMSE) criterion. The algorithm requires minimal knowledge of the phase process statistics, the estimation of which is also considered. The scheme outperforms previously proposed compensation methods of similar complexity, when severe phase impairments are present.

Efficient Two-Dimensional Filters for Doubly-Dispersive Channel Estimation in Time-Frequency Signal Processing

We consider minimum mean squared error (MSE) channel estimators operating on the basis of time-frequency signal observations comprising mixtures of known pilot and unknown data signals. These estimators are useful for a general class of receivers resorting to a time-frequency signal representation of the received signal - obtained by, e.g., a filter bank - which have recently been considered in the context of reconfigurable radio. Taking advantage of the Kronecker product properties we derive two-dimensional filter formulations for doubly-dispersive channel estimation, which afford implementations with moderate complexities. Rank reduction and the use of fast Fourier transform methods are shown to offer further complexity reductions. The different estimator variants are compared in terms of MSE performance and complexity.