Publications

This paper presents a secure beamforming design to prevent eavesdropping on multiple-input multiple-output (MIMO) device-to-device (D2D) communication. The devices communicate via a trusted relay which performs physical layer network coding (PNC), and multiple eavesdroppers are trying to intercept the device information. The beamforming design is based on minimizing mean square error of the D2D communication while employing signal-to-interference-plus-noise ratio (SINR) threshold constraints to prevent possible eavesdropping. The channel state information of the device-to-eavesdropper and relay-to-eavesdropper channels is imperfect at the devices and relay. The channel estimation errors are assumed with Gaussian Markov uncertainty model. Consequently, robust optimization problems are formulated considering the multiple access and broadcasting stages of the D2D communication. These problems are non-convex, and two algorithms are proposed to solve them. In the numerical analysis, we discuss the convergence of the proposed algorithms, impact of the number of eavesdroppers on the performance, and the SINR distributions at eavesdroppers.

We study the practical phase shift design in a non-ideal reconfigurable intelligent surface (RIS)-aided ultra-reliable and low-latency communication (URLLC) system under finite blocklength (FBL) regime by leveraging a novel deep reinforcement learning (DRL) algorithm named as twin-delayed deep deterministic policy gradient (TD3). First, assuming industrial automation system with multiple actuators, the signal-to-interference-plus-noise ratio (SINR) and achievable rate in FBL regime are identified for each actuator in terms of the phase shift configuration matrix at the RIS. The channel state information (CSI) variations due to feedback delay are also considered that result in channel coefficients' obsolescence. Then, the problem framework is proposed where the objective is to maximize the total achievable FBL rate in all ACs, subject to the practical phase shift constraint at the RIS elements. Since the problem is intractable to solve using conventional optimization methods, we resort to employing an actor-critic policy gradient DRL algorithm based on TD3, which relies on interacting RIS with FA environment by taking actions which are the phase shifts at the RIS elements, to maximize the expected observed reward, which is defined as the total FBL rate. The numerical results show that optimizing the practical phase shifts in the RIS via the proposed TD3 method is highly beneficial to improve the network total FBL rate in comparison with typical DRL methods.

We study the problem of how autonomous cognitive nodes (CNs) can arrive at an efficient and fair opportunistic channel access policy in scenarios where channels may be non-homogeneous in terms of primary user (PU) occupancy. In our model, a CN that is able to adapt to the environment is limited in two ways. First, CNs have imperfect observations (such as due to sensing and channel errors) of their environment. Second, CNs have imperfect memory due to limitations in computational capabilities. For efficient opportunistic channel access, we propose a simple adaptive win-shift lose-randomize (WSLR) strategy that can be executed by a twostate machine (automaton). Using the framework of repeated games (with imperfect observations and limited memory), we show that the proposed strategy enables the CNs (without any explicit coordination) to reach an outcome that: 1) maximizes the total network payoff and also ensures fairness among the CNs; 2) reduces the likelihood of collisions among CNs; and 3) requires a small number of sensing steps (attempts) to find a channel free of PU activity. We compare the performance of the proposed autonomous strategy with a centralized strategy and also test it with real spectrum data collected at RWTH Aachen.

Radio frequency (RF) wireless power transfer (WPT) is a promising charging technology for future wireless systems. However, low end-to-end power transfer efficiency (PTE) is a critical challenge for practical implementations. One of the main inefficiency sources is the power consumption and loss of key components such as the high-power amplifier (HPA) and rectenna, which must be considered for PTE optimization. Herein, we investigate the power consumption of an RF-WPT system considering the emerging dynamic metasurface antenna (DMA) as the transmitter. Moreover, we incorporate the HPA and rectenna non-linearities and consider the Doherty HPA to reduce power consumption. We provide a mathematical framework to calculate each user's harvested power from multi-tone signal transmissions and the system power consumption. Then, the waveform and beamforming are designed using swarm-based intelligence to minimize power consumption while satisfying the users' energy harvesting (EH) requirements. Numerical results manifest that increasing the number of transmit tones enhances the performance in terms of coverage probability and power consumption since the HPAs operate below the saturation region in the simulation setup and the EH non-linearity is the dominant factor. Finally, our findings demonstrate that a properly shaped DMA may outperform a fully-digital antenna of the same size.

Grant-free random access and uplink non-orthogonal multiple access (NOMA) have been introduced to reduce transmission latency and signaling overhead in massive machine-type communication (mMTC). In this paper, we propose two novel group-based deep neural network active user detection (AUD) schemes for the grant-free sparse code multiple access (SCMA) system in mMTC uplink framework. The proposed AUD schemes learn the nonlinear mapping, i.e., multi-dimensional codebook structure and the channel characteristic. This is accomplished through the received signal which incorporates the sparse structure of device activity with the training dataset. Moreover, the offline pre-trained model is able to detect the active devices without any channel state information and prior knowledge of the device sparsity level. Simulation results show that with several active devices, the proposed schemes obtain more than twice the probability of detection compared to the conventional AUD schemes over the signal to noise ratio range of interest.

This paper investigates solutions that combine relay selection algorithms and opportunistic geographic routing strategies for autonomous wireless sensor networks, wherein transmissions often occur over multiple hops. In order to design and assess such contention-based geographic forwarding strategies, we establish an analytical framework that incorporates concepts from probability generating functions (through power series expansion), spatial point processes, and auction theory, as well as Monte Carlo simulations to corroborate analytical results. The investigated strategies are built upon three constituent parts: 1) relay selection algorithm; 2) contention resolution mechanism; and 3) geographic forwarding strategy. We then propose an auction-based algorithm for selecting the relay node that relies on the network topology as side-information. Our results show that geographic solutions that iteratively exploit the local knowledge of the topology to ponder the algorithm operation outperforms established random approaches. Although, there is an intrinsic tradeoff between the time needed to identify a suitable next hop relay and the respective progress that is achieved toward the final destination.

Recent advances on wireless energy transfer (WET) make it a promising solution for powering future Internet of Things (IoT) devices enabled by the upcoming sixth generation (6G) era. The main architectures, challenges and techniques for efficient and scalable wireless powering are overviewed in this paper. Candidates enablers such as energy beamforming (EB), distributed antenna systems (DAS), advances on devices' hardware and programmable medium, new spectrum opportunities, resource scheduling and distributed ledger technology are outlined. Special emphasis is placed on discussing the suitability of channel state information (CSI)-limited/free strategies when powering simultaneously a massive number of devices. The benefits from combining DAS and EB, and from using average CSI whenever available, are numerically illustrated. The pros and cons of the state-of-the-art CSI-free WET techniques in ultra-low power setups are thoroughly revised, and some possible future enhancements are outlined. Finally, key research directions towards realizing WET-enabled massive IoT networks in the 6G era are identified and discussed in detail.

This paper analyzes the throughput of industrial communication networks under a secrecy constraint. The proposed scenario is composed by sensors that measure some relevant information of the plant that is first processed by aggregator node and then sent to the control unit. The sensor measurements, their communication with the aggregetor and the information processing are all assumed perfect. To reach the control unit, the message may travel through relay nodes, forming a multi-hop, wireless link. At every hop, eavesdropper nodes attempt to acquire the messages transmitted through the legitimate link. The communication design problem posed here is how to maximize the multi-hop throughput from the aggregator to the control unit by finding the best combination of relay positions (i.e. hop length: short or long) and coding rates (i.e. high or low spectral efficiency) so that the secrecy constraint is satisfied. Using a stochastic-geometry approach, we show that the optimal choice of coding rate depends only on the path- loss exponent and is normally high while greater number of shorter hops are preferable to smaller number of longer hops. For the scenarios of interest, we prove that the optimal throughput subject to the secrecy constraint achieves the unconstrained optimal performance if a feasible solution exists.

In URLLC the performance of short message communications highly depends on the training sequence length due to the stringent latency and reliability requirements. In this paper, we study the performance of cooperative and non-cooperative transmissions under imperfect channel estimation and Rayleigh fading for URLLC. We assume a peak power constraint on pilot symbols in addition to the average power constraint which is used for comparison purposes. We obtain the optimal training length as a function of blocklength and power constraint factor to meet the URLLC requirements. Moreover, the simulation results show the impact of pilot overhead on reliability, latency, and goodput of cooperative communications compared to point-to-point transmission.

The rapidly increasing demand in indoor small cell networks has given rise to the concept of local 5G operator (OP) for local service delivery. In this regard, we develop a novel game-theoretic framework with geometric programming to model and analyze cache-enabled small cell base stations (SBSs) with infrastructure sharing for local 5G OP networks. In such a network, the local 5G OP provides wireless network in indoor area and rent out the infrastructure which are RAN and cache storage to multiple mobile network operators (MNOs) while guarantee the quality-of-experience (QoE) at the users (UEs) of MNOs. We formulate a Stackelberg game model where the local 5G OP is the leader and the MNOs are the followers. The local 5G OP aims to maximize its profit by optimizing its infrastructure rental fee, and the MNOs aim to minimize their renting cost of infrastructure by minimizing the cache intensity subject to latency constraint at each UE. Here, the cache intensity is defined as the product of the number of SBSs per unit area and the number of popular files stored in each SBS. The optimization problems of the local 5G OP and the MNOs are transformed into geometric programming. Accordingly, the subgame perfect equilibrium of Stackelberg game is obtained through the succesive geometric programming (SGP) method. Since the MNOs share their rented infrastructure, for cost sharing, we apply the concept of Shapley value to divide the cost among the MNOs. We show that the cost sharing problem can be mapped into a simplified “airport runway cost sharing problem”, in which the Shapley value can be computed efficiently. Finally, we present an extensive performance evaluation that reveals interesting insights into designing resource sharing with edge caching in local 5G OP networks.

Multi-user shared access (MUSA) is introduced as advanced code domain non-orthogonal complex spreading sequences to support a massive number of machine-type communications (MTC) devices. In this paper, we propose a novel deep neural network (DNN)-based multiple user detection (MUD) for grant-free MUSA systems. The DNN-based MUD model determines the structure of the sensing matrix, randomly distributed noise, and inter-device interference during the training phase of the model by several hidden nodes, neuron activation units, and a fit loss function. The thoroughly learned DNN model is capable of distinguishing the active devices of the received signal without any a priori knowledge of the device sparsity level and the channel state information. Our numerical evaluation shows that with a higher percentage of active devices, the DNN-MUD achieves a significantly increased probability of detection compared to the conventional approaches.

We analyze the performance of the type-I automatic repeat request (ARQ) protocol with ultra-reliability constraints. First, we show that achieving a very low packet outage probability by using an open loop setup is a difficult task. Thus, we introduce the ARQ protocol as a solution for achieving the required low outage probabilities for ultra reliable communication. For this protocol, we present an optimal power allocation scheme that would allow us to reach any outage probability target in the finite block-length regime. We formulate the power allocation problem as minimization of the average transmitted power under a given outage probability and maximum transmit power constraint. By utilizing the Karush-Kuhn-Tucker (KKT) conditions, we solve the optimal power allocation problem and provide a closed form solution. Next, we analyze the effect of implementing the ARQ protocol on the throughput. We show that by using the proposed power allocation scheme we can minimize the loss of throughput that is caused from the retransmissions. Furthermore, we analyze the effect of the feedback delay length in our scheme.