Publications

Ultra-Reliable Low Latency Communication (URLLC) is a newly introduced service class targeting emerging Internet-of-Things (IoT) application scenarios. This paper assumes an interference-limited Fog Radio Access Network (F-RAN) setup composed of multiple Remote Radio Heads (RRHs) equipped with multiple antennas serving single-antenna users. F-RAN facilitates collaborative solutions while reducing delay by pushing the network capabilities beyond the edge. By leveraging diversity, RRHs may cooperate through silencing, reducing interference, or joint transmission strategies such as maximal ratio transmission. We derive closed-form outage probability expressions and attain their diversity gain. We validate the derived analytical results through extensive numerical simulations. Furthermore, we propose a mini-slots-based scheduling framework to serve URLLC users within their fixed latency budget. In an interference-limited regime with the proposed scheduling framework, we show that a performance gain is superior when RRHs cooperate compared to when they do not. We briefly discuss the cost of reliability, i.e., the impact on the system's average sum throughput under cooperation. Moreover, numerical results verify that cooperating transmission schemes boost transmission reliability with a significantly improved latency performance at the cost of reduced system's average sum throughput.

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

Wireless energy transfer (WET) is a green enabler of low-power Internet of Things (IoT). Therein, traditional optimization schemes relying on full channel state information (CSI) are often too costly to implement due to excessive energy consumption and high processing complexity. This letter proposes a simple, yet effective, energy beamforming scheme that allows a multi-antenna power beacon (PB) to fairly power a set of IoT devices by only relying on the first-order statistics of the channels. In addition to low complexity, the proposed scheme performs favorably as compared to benchmarking schemes and its performance improves as the number of PB's antennas increases. Finally, it is shown that further performance improvement can be achieved through proper angular rotations of the PB.

In this letter, we study an attack that leverages a reconfigurable intelligent surface (RIS) to induce harmful interference toward multiple users in massive multiple-input multiple-output (mMIMO) systems during the data transmission phase. We propose an efficient and flexible weighted-sum projected gradient-based algorithm for the attacker to optimize the RIS reflection coefficients without knowing legitimate user channels. Simulation results demonstrate that our malicious algorithm outperforms baseline strategies while offering adaptability for targeting specific users. To counter such a threat, we propose two reception strategies, which we show to be effective even if only an imperfect estimate of the cascade RIS channel is available.

No abstract is provided for this article.

The problem of max-min signal-to-interference plus noise ratio (SINR) for uplink transmission of cell-free massive multiple-input multiple-output (MIMO) system is considered. We assume that the system is employed with local minimum mean square error (L-MMSE) detection. The objective is to preserve user fairness by solving max-min rate optimization problem, by optimizing transmit power of each user equipment (UE) and weighting coefficients at central processing unit (CPU), subject to a transmit power constraint at each UE. This problem is not jointly convex. Hence, we decompose original problem into two subproblems, particularly for optimizing power allocation and weight coefficients. Then, we propose an iterative algorithm to solve these two subproblems alternately. Weight coefficient subproblem is solved in the form of generalized eigen value problem while power allocation subproblem is solved by approximating as geometric programming (GP) problem.

In this paper, the average achievable rate of a re-configurable intelligent surface (RIS) aided factory automation is investigated in finite blocklength (FBL) regime. First, the composite channel containing the direct path plus the product of reflected paths through the RIS is characterized. Then, the distribution of the received signal-to-noise ratio (SNR) is matched to a Gamma random variable whose parameters depend on the total number of RIS elements as well as the channel pathloss. Next, by assuming FBL model, the achievable rate expression is identified and the corresponding average rate is elaborated based on the proposed SNR distribution. The phase error due to quantizing the phase shifts is considered in the simulation. The numerical results show that Monte Carlo simulations conform to the matched Gamma distribution for the received SNR for large number of RIS elements. In addition, the system reliability indicated by the tightness of the SNR distribution increases when RIS is leveraged particularly when only the reflected channel exists. This highlights the advantages of RIS-aided communications for ultra-reliable low-latency communications (URLLC) systems. The reduction of average achievable rate due to working in FBL regime with respect to Shannon capacity is also investigated as a function of total RIS elements.

We consider a single-cell multiple-input multiple-output (MIMO) downlink channel where linear transmission and reception strategy is employed. The base station (BS) transmitter is equipped with a scheduler using a simple opportunistic beamforming strategy, which associates an intended user for each of the transmitted data streams. For the case when the channel of the scheduled users is available at the BS, we propose a general method for joint design of the transmit and the receive beamformers according to different optimization criteria, including weighted sum rate maximization, weighted sum mean square error minimization, minimum signal-to-interference-plus-noise ratio (SINR) maximization and sum power minimization under a minimum SINR constraint. The proposed method can handle multiple antennas at the BS and at the mobile user with single and/or multiple data streams per scheduled user. The optimization problems encountered in the beamformer design (e.g., covariance rank constraint) are not convex in general. Therefore, the problem of finding the global optimum is intrinsically nontractable. However, by exploiting the uplink-downlink SINR duality, we decompose the original optimization problem as a series of simpler optimization problems which can be efficiently solved by using standard convex optimization tools. Even though each subproblem is optimally solved, there is no guarantee that the global optimum has been found due to the nonconvexity of the problem. However, the simulations show that the algorithms converge fast to a solution, which can be a local optimum, but is still efficient.

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.

While the robustness of rate-splitting multiple access (RSMA) to imperfect channel state information (CSI) is well-documented, its susceptibility to attacks launched with malicious reconfigurable intelligent surfaces (RISs) remains unexplored. This paper fills this gap by investigating three potential RIS-induced attacks against RSMA in a multi-user multiple-input multiple-output (MIMO) network: random interference, aligned interference, and mitigation attack. The random interference attack employs random RIS coefficients to disrupt RSMA. The other two attacks are triggered by optimizing the RIS through weighted-sum strategies based on the projected gradient method. Simulation results reveal significant degradation caused by all the attacks under perfect CSI conditions. Remarkably, when imperfect CSI is considered, RSMA, owing to its flexible power allocation strategy designed to counter CSI-related interference, can be robust to the attacks even when the base station is blind to them. It is also shown that RSMA can significantly outperform conventional space-division multiple access (SDMA).

No abstract is provided for this article.

No abstract is provided for this article.

No abstract is provided for this article.

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.

Radio frequency wireless energy transfer (WET) is a promising solution for powering autonomous Internet of Things (IoT) deployments. In this work, we leverage energy beamforming for powering multiple user equipments (UEs) with stringent energy harvesting (EH) demands in an indoor distributed massive multiple-input multiple-output system. Based on semi-definite programming, successive convex approximation (SCA), and maximum ratio transmission (MRT) techniques, we derive optimal and sub-optimal precoders aimed at minimizing the radio stripes' transmit power while exploiting information of the power transfer efficiency of the EH circuits at the UEs. Moreover, we propose an analytical framework to assess and control the electromagnetic field (EMF) radiation exposure in the considered indoor scenario. Numerical results show that i) the EMF radiation exposure can be more easily controlled at higher frequencies at the cost of a higher transmit power consumption, ii) training is not a very critical factor for the considered indoor system, iii) MRT/SCA-based precoders are particularly appealing when serving a small number of UEs, thus, especially suitable for implementation in a time domain multiple access (TDMA) scheduling framework, and iv) TDMA is more efficient than spatial domain multiple access (SDMA) when serving a relatively small number of UEs. Results suggest that additional boosting performance strategies are needed to increase the overall system efficiency, thus making the technology viable in practice.