Publications

Effective Capacity defines the maximum communication rate subject to a specific delay constraint, while effective energy efficiency (EEE) indicates the ratio between effective capacity and power consumption. We analyze the EEE of ultra-reliable networks operating in the finite blocklength regime. We obtain a closed form approximation for the EEE in quasi-static Nakagami-$m$ (and Rayleigh as sub-case) fading channels as a function of power, error probability, and latency. Furthermore, we characterize the QoS constrained EEE maximization problem for different power consumption models, which shows a significant difference between finite and infinite blocklength coding with respect to EEE and optimal power allocation strategy. As asserted in the literature, achieving ultra-reliability using one transmission consumes huge amount of power, which is not applicable for energy limited IoT devices. In this context, accounting for empty buffer probability in machine type communication (MTC) and extending the maximum delay tolerance jointly enhances the EEE and allows for adaptive retransmission of faulty packets. Our analysis reveals that obtaining the optimum error probability for each transmission by minimizing the non-empty buffer probability approaches EEE optimality, while being analytically tractable via Dinkelbach's algorithm. Furthermore, the results illustrate the power saving and the significant EEE gain attained by applying adaptive retransmission protocols, while sacrificing a limited increase in latency.

No abstract is provided for this article.

Wireless networks are inherently graph-structured, which can be utilized in graph representation learning to solve complex wireless network optimization problems. In graph representation learning, feature vectors for each entity in the network are calculated such that they capture spatial and temporal dependencies in their local and global neighbourhoods. Graph neural networks (GNNs) are powerful tools to solve these complex problems because of their expressive representation and reasoning power. In this paper, the potential of graph representation learning and GNNs in wireless networks is presented. An overview of graph learning is provided which covers the fundamentals and concepts such as feature design over graphs, GNNs, and their design principles. Potential of graph representation learning in wireless networks is presented via few exemplary use cases and some initial results on the GNN-based access point selection for cell-free massive MIMO systems.

We model the problem of infrastructure sharing among mobile network operators (MNOs) as a multiple-seller single-buyer market where the MNOs are able to share their own base stations (BSs) with each other. First, we use techniques from stochastic geometry to find the coverage probability of the infrastructure sharing system and analyze the trade-off between increasing the transmit power of a BS and the BS intensity of a buyer MNO required to achieve a given quality-of-service (QoS) in terms of the coverage probability. We show that when the transmit power of the BSs and/or the BS intensity of a network increases, the system becomes interference limited and the coverage probability tends to saturate at a certain value. As such, when the required QoS is set above this bound, an MNO can improve its coverage by buying infrastructure from other MNOs. Subsequently, we analyze the strategy of a buyer MNO on choosing how many MNOs and which MNOs to buy the infrastructure from. The optimal strategy of the buyer is given by greedy fractional knapsack algorithm. On the sellers' side, the pricing problem and the problem of determining the fraction of infrastructure to be sold are formulated using a Cournot oligopoly game.

Compressive sensing theory asserts that, under certain conditions, a high dimensional but compressible signal can be recovered from a small number of random linear projections by utilizing computationally efficient algorithms. The a priori knowledge of the basis in which the signal of interest is sparse is the key assumption utilized by such algorithms. However, the basis in which the signal is the sparsest is unknown for many natural signals of interest. Instead there may exist multiple bases which lead to a compressible representation of the signal: e.g., an image is compressible in different wavelet transforms. We show that a significant performance improvement can be achieved by utilizing multiple estimates of the signal using sparsifying bases in the context of signal reconstruction from compressive samples. Further, we derive a customized interior-point method to jointly obtain multiple estimates of a 2-D signal (image) from compressive measurements utilizing multiple sparsifying bases as well as the fact that the images usually have a sparse gradient.

We investigate the problem of multi-hop scheduling in self-backhauled millimeter wave (mmWave) networks. Owing to the high path loss and blockage of mmWave links, multi-hop paths between the macro base station and the intended users via full-duplex small cells need to be carefully selected. This paper addresses the fundamental question: how to select the best paths and how to allocate rates over these paths subject to latency constraints. To answer this question, we propose a new system design, which factors in mmWave-specific channel variations and network dynamics. The problem is cast as a network utility maximization subject to a bounded delay constraint and network stability. The studied problem is decoupled into: (i) a path selection and (ii) rate allocation, whereby learning the best paths is done by means of a reinforcement learning algorithm, and the rate allocation is solved by applying the successive convex approximation method. Via numerical results, our approach ensures reliable communication with a guaranteed probability of 99.9999%, and reduces latency by 50.64% and 92.9% as compared to baselines.

Stacked intelligent metasurface (SIM) is an emerging technology that uses multiple reconfigurable surface layers to enable flexible wave-based beamforming. In this paper, we focus on an SIM-assisted multi-user multiple-input single-output system, where it is essential to ensure that all users receive a fair and reliable service level. To this end, we develop two max-min fairness algorithms based on instantaneous channel state information (CSI) and statistical CSI. For the instantaneous CSI case, we propose an alternating optimization algorithm that jointly optimizes power allocation using geometric programming and wave-based beamforming coefficients using the gradient descent-ascent method. For the statistical CSI case, since deriving an exact expression for the average minimum achievable rate is analytically intractable, we derive a tight upper bound and thereby formulate a stochastic optimization problem. This problem is then solved, capitalizing on an alternating approach combining geometric programming and gradient descent algorithms, to obtain the optimal policies. Our numerical results show significant improvements in the minimum achievable rate compared to the benchmark schemes. In particular, for the instantaneous CSI scenario, the individual impact of the optimal wave-based beamforming is significantly higher than that of the power allocation strategy. Moreover, the proposed upper bound is shown to be tight in the low signal-to-noise ratio regime under the statistical CSI.

A lattice code construction that employs two separate lattices, a high dimension lattice for coding gain and a low-dimension lattice for shaping gain, is described. Systematic lattice encoding is a method to encode an integer sequence to a lattice point that is nearby that integer sequence. We describe the "Voronoi integers" ℤ <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">m</sup> /Λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> , the set of integers inside the fundamental Voronoi region of a shaping lattice Λ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</sub> , and a concrete scheme to label these integers. By first shaping the information using the Voronoi integers in low dimension, and then performing systematic lattice encoding using a high-dimension lattice, good shaping and coding gains can be simultaneously obtained. We concentrate on the case of using the E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sub> lattice for shaping and low-density lattice codes (LDLC) with dimension ~ 10,000 for coding. While optimal shaping provides a well-known 1.53 dB gain, previously reported shaping gains with LDLC lattices are on the order of 0.4 dB. The proposed method preserves the shaping gain of the E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">8</sub> lattice, that is, as much as 0.65 dB. This shaping operation can be implemented with lower complexity than previous LDLC approaches.

The growing demand in indoor small cell networks has given rise to the concept of micro-operators (MOs) for local service delivery. We model and analyze a spectrum sharing system involving such MOs where a buyer MO buys multiple licensed subbands provided by the regulator. Also, all small cell base stations (SBSs) owned by a buyer MO can utilize multiple licensed subbands at the same time which are also used by other MOs. A deterministic model in which the location of the SBSs are known can lead to unwieldy problem formulation, when the number of SBSs is large. Subsequently, we adopt a stochastic geometric model of the SBS deployment instead of a deterministic model. Assuming that the locations of the SBSs can be modeled as a homogeneous Poisson point process, we find the downlink signal-to-interference-plus-noise ratio (SINR) coverage probability and average data rate for a typical user (UE) served by the buyer MO in a spectrum sharing environment. In order to satisfy the QoS constraint, we provide a greedy algorithm to find how many licensed subbands and which subband for the buyer MO to purchase from the regulator. We also derive the coverage probability of the buyer MO for interference the limited system.

In this paper, a novel method is proposed to study the tradeoff between energy efficiency (EE) of small-cell users and unmanned aerial vehicles (UAV) users in multi-cell orthogonal frequency division multiple access (OFDMA)-based networks. Contrary to the prior works that only maximize the EE of the UAV network subject to some constraints on transmit power of UAV users, we formulate a multi-objective optimization problem (MOOP) that jointly maximize the EE of small-cell and UAV users while guaranteeing the minimum rate for UAV users as well as maximum transmit powers for the corresponding small-cell and UAV BSs. The proposed MOOP is transformed into a single optimization problem (SOOP) by the weighted Tchebycheff approach. Then, an iterative technique is used to optimize alternatively subchannels and transmission powers of small-cell and UAV networks at each step. Numerical results show that a substantial performance gain can be obtained over the existing solutions.

A deep learning-based resource allocation algorithm that maximizes the sum rate of a limited fronthaul cell-free massive MIMO network with transceiver hardware impairments is proposed in this paper. The sum rate maximization problem with user power constraints and total fronthaul capacity constraints for channel state information (CSI) and data transmission is considered. The deep neural network (DNN) PowerNet is proposed to learn solutions to the joint power control and capacity allocation problem in a low-complex, flexible, and scalable way. An unsupervised learning approach is used which eliminates the need of knowing the optimal resource allocation vectors during model training, hence having a simpler and more flexible model training stage. Numerical simulations show that PowerNet achieves close sum rate performance compared to the existing optimization-based approach, with a significantly lower time complexity which does not exponentially scale with the number of users and access points (APs) in the network. Furthermore, the addition of the online learning stage resulted in a better sum rate than the optimization-based method.

In this paper, the problem of content-aware user clustering and content caching in wireless small cell networks is studied. In particular, a service delay minimization problem is formulated, aiming at optimally caching contents at the small cell base stations (SCBSs). To solve the optimization problem, we decouple it into two interrelated subproblems. First, a clustering algorithm is proposed grouping users with similar content popularity to associate similar users to the same SCBS, when possible. Second, a reinforcement learning algorithm is proposed to enable each SCBS to learn the popularity distribution of contents requested by its group of users and optimize its caching strategy accordingly. Simulation results show that by correlating the different popularity patterns of different users, the proposed scheme is able to minimize the service delay by %42 and 27%, while achieving a higher offloading gain of up to 280% and 90%, respectively, compared to random caching and unclustered learning schemes.

We propose a multiple-input multiple-output (MIMO) two-way relaying based underlay device-to-device (D2D) communication system, in which relay uses physical layer network coding (PNC). Both cellular and D2D communication take place in the same spectrum. D2D communication is based on PNC, and a joint transceiver design is required to facilitate PNC mapping with MIMO channels. A joint precoder-decoder scheme is also considered in the cellular communication to mitigate interference, and improve the error performance. Mean square errors at nodes are considered as the objective function, and interference threshold limits and power constraints are also used in joint precoder-decoder design problems. These turn out to be non-convex optimization problems, and we propose two algorithms in both phases of D2D and cellular communication. Distributed optimization methods are used in the proposed algorithms. We analyze the convergence of proposed algorithms, and the system performances are investigated with different D2D pair locations, interference constraint thresholds, and maximum available power at nodes.

Ultra reliable low latency communications (URLLC) is a new service class introduced in 5G which is characterized by strict reliability $(1-10^{-5})$ and low latency requirements (1 ms). To meet these requisites, several strategies like overprovisioning of resources and channel-predictive algorithms have been developed. This paper describes the application of a Nonlinear Autoregressive Neural Network (NARNN) as a novel approach to forecast interference levels in a wireless system for the purpose of efficient resource allocation. Accurate interference forecasts also grant the possibility of meeting specific outage probability requirements in URLLC scenarios. Performance of this proposal is evaluated upon the basis of NARNN predictions accuracy and system resource usage. Our proposed approach achieved a promising mean absolute percentage error of 7.8 % on interference predictions and also reduced the resource usage in up to 15 % when compared to a recently proposed interference prediction algorithm.