Abstracts:Broadcasting is a fundamental problem in the information dissemination area. In classical broadcasting, a message must be sent from one network member to all other members as rapidly as feasible. Although this problem is NP-hard for arbitrary graphs, it has several applications in various fields. As a result, the universal lists model, which replicates some real-world restrictions like the memory limits of nodes in large networks, is introduced as a branch of this problem in the literature. In the universal lists model, each node is equipped with a fixed list and has to follow the list regardless of the originator. As opposed to various applications for the problem of broadcasting with universal lists, the literature lacks any heuristic or approximation algorithm. In this regard, we suggest HUB-GA: A heuristic for universal lists broadcasting with genetic algorithm, as the first heuristic for this problem. HUB-GA works toward minimizing the universal lists broadcast time of a given graph with the aid of genetic algorithm. We undertake various numerical experiments on frequently used interconnection networks in the literature, graphs with clique-like structures, and synthetic instances with small-world model in order to cover many possibilities of industrial topologies. We also compare our results with state-of-the-art methods for classical broadcasting, which is proved to be the fastest model among all. Nevertheless of the substantial memory reduction in the universal list model compared to the classical model, our algorithm finds the same broadcast time as the classical model in diverse situations.

Abstracts:Deep learning (DL), a branch of artificial intelligence (AI) techniques, has shown great promise in various disciplines such as image classification and segmentation, speech recognition, language translation, among others. This remarkable success of DL has stimulated increasing interest in applying this paradigm to wireless channel estimation in recent years. Since DL principles are inductive in nature and distinct from the conventional rule-based algorithms, when one tries to use DL technique to the channel estimation, one might easily get stuck and confused by so many knobs to control and small details to be aware of. The primary purpose of this paper is to discuss key issues and possible solutions in DL-based wireless channel estimation and channel state information (CSI) feedback including the DL model selection, training data acquisition, and neural network design for 6G. Specifically, we present several case studies together with the numerical experiments to demonstrate the effectiveness of the DL-based wireless channel estimation framework.

Abstracts:Massive multiple-input multiple-output (MIMO) can provide higher spectral efficiency and energy efficiency compared to conventional MIMO systems. Unfortunately, as the numbers of modulation orders and antennas increase, the computational complexity of conventional symbol detection algorithms becomes unaffordable and their performance deteriorates. However, deep learning (DL) techniques can provide flexibility, nonlinearity and computational parallelism for massive MIMO detection to address these challenges. We propose an efficient data-driven detection network, i.e., accelerated multiuser interference cancellation network (AMIC-Net), for uplink massive MIMO systems. Specifically, we first introduce an extrapolation factor regarded as a learnable parameter into the multiuser interference cancellation (MIC) algorithm for iterative sequential detection (ISD) detector through extrapolation technique to enhance the convergence performance. Then we unfold the above accelerated iterative algorithm and adopt a sparsely connected approach, instead of fully connected, to obtain a relatively simple deep neural network (DNN) structure to enhance the detection performance through the data-driven DL approach. Furthermore, in order to accommodate communication scenarios with higher-order modulation, a novel activation function is proposed, which is composed of multiple softsign activation functions with additional learnable parameters to implement a multi-segment mapping of the set of constellation points with different modulations. Numerical results show that the proposed DL network can bring significant performance gain to ISD detector with various massive antenna settings and outperform the existing detectors with the same or lower computational complexity, especially in high-order QAM modulation scenarios.

Abstracts:In this paper, we propose a user selection scheme based on location-aided interference prediction to reduce the training overhead in a non-orthogonal multiple access (NOMA) system. First, we cluster the users based on their location information, enabling the use of non-orthogonal pilot sequence within a cluster and orthogonal pilot sequence between different clusters to reduce the uplink pilot training length. Secondly, we exploit the location information in the computation of the covariance matrices, enabling the prediction of the interference between users. The predicted interference is employed to select the set of users with minimum interference for uplink channel estimation and downlink NOMA data transmission. Finally, the achievable sum-rate of the massive multiple-input multiple-output millimeter wave NOMA system is analyzed. The analytical and numerical results reveal that the location information can be exploited for user selection to reduce the effect of pilot contamination, enhancing the uplink channel estimation and downlink achievable sum-rate.

Abstracts:Rapid deployment of wireless devices with 5G and beyond enabled a connected world. However, an immediate demand increase right after a disaster paralyzes network infrastructure temporarily. The continuous flow of information is crucial during disaster times to coordinate rescue operations and identify the survivors. Communication infrastructures built for users of disaster areas should satisfy rapid deployment, increased coverage, and availability. Unmanned air vehicles (UAV) provide a potential solution for rapid deployment as they are not affected by traffic jams and physical road damage during a disaster. In addition, ad-hoc WiFi communication allows the generation of broadcast domains within a clear channel which eases one-to-many communications. Moreover, using reinforcement learning (RL) helps reduce the computational cost and increases the accuracy of the NP-hard problem of aerial network deployment. To this end, a novel flying WiFi ad-hoc network management model is proposed in this paper. The model utilizes deep-Q-learning to maintain quality-of-service (QoS), increase user equipment (UE) coverage, and optimize power efficiency. Furthermore, a testbed is deployed on Istanbul Technical University (ITU) campus to train the developed model. Training results of the model using testbed accumulates over 90% packet delivery ratio as QoS, over 97% coverage for the users in flow tables, and 0.28 KJ/Bit average power consumption.

Abstracts:Intelligent reflecting surface (IRS) has been regarded as promising technique to improve system performance for wireless communications. In this paper, we propose a rate-splitting (RS) scheme for an IRS-assisted cognitive radio-inspired non-orthogonal multiple access (CR-NOMA) system, where the primary user's (PU's) quality of service (QoS) requirements must be guaranteed to be same as in orthogonal multiple access. Assisted by IRS, the threshold for the PU's tolerable interference power is improved, which in turn makes it possible to increase the achievable rate for the secondary user (SU). The optimal transmit power allocation, target rate allocation, and successive interference cancellation (SIC) decoding order are jointly designed for the proposed RS scheme. Taking into account the statistics of the direct link and IRS reflecting channels, closed-form expression for the PU's and SU's outage probabilities are respectively derived. Various simulation results are presented to clarify the enhanced outage performance achieved by the proposed RS scheme over the existing CR-NOMA and IRS-assisted CR-NOMA schemes.