Taejoon Kim

The ever-increasing demand for high-speed wireless communication has generated significant interest in utilizing frequency bands that are adjacent to those occupied by legacy wireless systems. Since the legacy wireless systems were designed based on often decades-old assumptions about wireless interference, utilizing these new bands will result in interference with the existing legacy users. Many of these legacy wireless devices are used by critical infrastructure networks upon which society depends. There is an urgent need to develop schemes that can protect legacy users from such interference. For many applications, legacy users are located within geographically-constrained regions. Several studies have proposed mitigating interference through the implementation of exclusion zones near these geographically-constrained regions. In contrast to solutions based on geographic exclusion zones, this paper presents a communication theory-based solution. By leveraging knowledge of these geographically-constrained regions, we aim to reduce the interference impact on legacy users. We achieve this by incorporating received power constraints, termed as region constraints, in our massive multiple-input multiple-output (MIMO) system design. We perform a capacity analysis for single-user massive MIMO and a sum-rate analysis for the multi-user massive MIMO system with transmit power and region constraints. We present a precoding design method that allows for the utilization of new frequency bands while protecting legacy users.

A novel multibeam time-division (TD) multiple-input multiple-output (MIMO) integrated sensing and communications (ISAC) approach is proposed to achieve a balanced tradeoff between sensing and communication functionalities and accurate sensing parameter estimation with a wide field-of-view. Firstly, the TD strategy is introduced to address the simultaneous high demands for sensing performance and communication rate. By allocating time resources between sensing and communications, this approach can reach a desired balance between them while avoiding spectrum and spatial interference, as well as competition in power allocation. Next, a new multibeam method is developed to achieve wide-area sensing for TD MIMO ISAC. Conventional multibeam methods typically rely on beam scanning for direction estimation, suffering from limited accuracy. Inspired by Doppler division multiple access (DDMA) approach, the proposed method divides the Doppler spectrum into more subbands, generating more beams than the number of transmit antenna elements using only phase modulation. Beyond enabling flexible control over the sensing coverage location through beam selection, the proposed method also improves parameter estimation accuracy by fully leveraging the inter-beam relationships, particularly for targets located at null directions. Specifically, for such targets, the proposed method achieves a significantly higher maximum unambiguous velocity, mitigating the velocity ambiguity inherent in conventional DDMA. Simulation results validate the effectiveness of the proposed approach in enhancing both the performance tradeoff between sensing and communication, and the accuracy of sensing parameter estimation.

Optimization-based power control algorithms are predominantly iterative with high computational complexity, making them impractical for real-time applications in cell-free massive multiple-input multiple-output (CFmMIMO) systems. Learning-based methods have emerged as a promising alternative, and among them, graph neural networks (GNNs) have demonstrated their excellent performance in solving power control problems. However, all existing GNN-based approaches assume ideal orthogonality among pilot sequences for user equipments (UEs), which is unrealistic given that the number of UEs exceeds the available orthogonal pilot sequences in CFmMIMO schemes. Additionally, supervised training necessitates costly computational resources for computing the target power control solutions for a large volume of training samples. To address these issues, we propose a graph attention network for downlink power control in CFmMIMO systems that operates in a self-supervised manner while effectively handling pilot contamination and adapting to a dynamic number of UEs. Experimental results show its effectiveness, even in comparison to the optimal accelerated projected gradient method as a baseline.

Consider the Gaussian wiretap channel, where a transmitter wishes to send a confidential message to a legitimate receiver in the presence of an eavesdropper. It is well known that if the eavesdropper experiences less channel noise than the legitimate receiver, then it is impossible for the transmitter to achieve positive secrecy rates. A known solution to this issue consists in involving a second transmitter, referred to as a helper, to help the first transmitter to achieve security. While such a solution has been studied for the asymptotic blocklength regime and via non-constructive coding schemes, in this paper, for the first time, we design explicit and short blocklength codes using deep learning and cryptographic tools to demonstrate the benefit and practicality of cooperation between two transmitters over the wiretap channel. Specifically, our proposed codes show strict improvement in terms of information leakage compared to existing codes that do not consider a helper. Our code design approach relies on a reliability layer, implemented with an autoencoder architecture based on the successive interference cancellation method, and a security layer implemented with universal hash functions. We also propose an alternative autoencoder architecture that significantly reduces training time by allowing the decoders to independently estimate messages without successively canceling interference by the receiver during training. Additionally, we show that our code design is also applicable to the multiple access wiretap channel with helpers, where two transmitters send confidential messages to the legitimate receiver.

The reconstruction of sparse signals from a limited set of measurements poses a significant challenge as it necessitates a solution to an underdetermined system of linear equations. Compressed sensing (CS) deals with sparse signal reconstruction using techniques such as linear programming (LP) and iterative message passing schemes. The interval passing algorithm (IPA) is an attractive CS approach due to its low complexity when compared to LP. In this paper, we propose a sequential IPA that is inspired by sequential belief propagation decoding of low-density-parity-check (LDPC) codes used for forward error correction in channel coding. In the sequential setting, each check node (CN) in the Tanner graph of an LDPC measurement matrix is scheduled one at a time in every iteration, as opposed to the standard “flooding” interval passing approach in which all CNs are scheduled at once per iteration. The sequential scheme offers a significantly lower message passing complexity compared to flooding IPA on average, and for some measurement matrix and signal sparsity, a complexity reduction of approximately 36% is achieved. We show both analytically and numerically that the reconstruction accuracy of the IPA is not compromised by adopting our sequential scheduling approach.

Although user cooperation cannot improve the capacity of Gaussian two-way channels (GTWCs) with independent noises, it can improve communication reliability. In this work, we aim to enhance and balance the communication reliability in GTWCs by minimizing the sum of error probabilities via joint design of encoders and decoders at the users. We first formulate general encoding/decoding functions, where the user cooperation is captured by the coupling of user encoding processes. The coupling effect renders the encoder/decoder design non-trivial, requiring effective decoding to capture this effect, as well as efficient power management at the encoders within power constraints. To address these challenges, we propose two different twoway coding strategies: linear coding and learning-based coding. For linear coding, we propose optimal linear decoding and discuss new insights on encoding regarding user cooperation to balance reliability. We then propose an efficient algorithm for joint encoder/decoder design. For learning-based coding, we introduce a novel recurrent neural network (RNN)-based coding architecture, where we propose interactive RNNs and a power control layer for encoding, and we incorporate bi-directional RNNs with an attention mechanism for decoding. Through simulations, we show that our two-way coding methodologies outperform conventional channel coding schemes (that do not utilize user cooperation) significantly in sum-error performance. We also demonstrate that our linear coding excels at high signal-to-noise ratios (SNRs), while our RNN-based coding performs best at low SNRs. We further investigate our two-way coding strategies in terms of power distribution, two-way coding benefit, different coding rates, and block-length gain.

The distributed diversity reception with quantization and fusion has garnered considerable attention for improved reliability. In this paper, we introduce a novel buffer-aided distributed compressed transmission and fusion (DCTF) technique to cope with the fading effect and to improve the energy consumption of wireless sensor networks (WSNs). Exploiting the transmission flexibility enabled by data buffering, we design an optimal communication protocol by maximizing the average fusion rate at the FC subject to reliability, spectral efficiency, and energy consumption constraints. Unlike prior distributed reception schemes, our approach does not require any subjective quantization level setting; the compression rate in our approach is optimized by maximizing the average fusion rate. Based on the optimized compression rate, we develop two adaptive protocols, namely, adaptive multi-rate (AMR) and its simplified version, adaptive on-off (AOO). In contrast to the non-buffered scheme, the flexibility offered by buffering is exploited in our approach to lower the battery drainage in conjunction with opportunistic energy harvesting (EH). Through Monte-Carlo simulations we evaluate the performance of the proposed protocols under different scenarios and in comparison to the non-adaptive benchmark. Findings reveal that the proposed methodologies outperform conventional schemes in terms of reliability, energy consumption, and average fusion rate.

Recently, deep learning approaches have provided solutions to difficult problems in wireless positioning (WP). Although these WP algorithms have attained excellent and consistent performance against complex channel environments, the computational complexity coming from processing high-dimensional features can be prohibitive for mobile applications. In this work, we design a novel positioning neural network (P-NN) that utilizes the minimum description features to substantially reduce the complexity of deep learning-based WP. P-NN’s feature selection strategy is based on maximum power measurements and their temporal locations to convey information needed to conduct WP. We improve P-NN’s learning ability by intelligently processing two different types of inputs: sparse image and measurement matrices. Specifically, we implement a self-attention layer to reinforce the training ability of our network. We also develop a technique to adapt feature space size, optimizing over the expected information gain and the classification capability quantified with information-theoretic measures on signal bin selection. Numerical results show that P-NN achieves a significant advantage in performance-complexity tradeoff over deep learning baselines that leverage the full power delay profile (PDP). In particular, we find that P-NN achieves a large improvement in performance for low SNR, as unnecessary measurements are discarded in our minimum description features.