arXiv subjects

Ki Won Sung

Publications and source records attributed to Ki Won Sung.

At least 19 recordsLinked to original sources

arXiv2026

Green Cell-Free Massive MIMO for ISAC: Joint Cloud, Fronthaul and Radio Resource Allocation

In this paper, we develop a cross-layer end-to-end (E2E) resource orchestration framework for green CF-mMIMO ISAC systems with distributed multi-target detection. We propose a distributed sensing approach in which receive access points (RX-APs) compute local test statistics, which are aggregated at the cloud using weights based on sensing interference and channel quality. We derive the local maximum a posteriori ratio test (MAPRT) detectors under fully informed (FIS) and partially informed (PIS) operation, representing different levels of transmit-signal information at the RX-APs. We further characterize their processing and fronthaul requirements and derive a network power model incorporating radio transmission, AP and cloud processing, and fronthaul infrastructure. We formulate a mixed-integer non-convex problem that jointly optimizes transmit powers, AP modes, UE and sensing-area associations, RX-AP assignments, and active fronthaul and cloud resources subject to communication, sensing, power, processing-capacity, and fronthaul constraints. A two-stage iterative algorithm based on Big-M reformulation, convex--concave programming, penalty-based relaxation, and structured discrete recovery is developed. Numerical results show that the proposed E2E framework reduces total power by up to 50% compared with transmit-power-only optimization and by approximately 11-17% compared with joint radio optimization under full coordination, while maintaining detection probabilities above 0.95. The results also reveal a fundamental implementation trade-off: FIS provides lower detector-processing complexity and higher detection performance, whereas PIS substantially reduces fronthaul requirements.

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arXiv2026

Exploiting Pilot Contamination to Improve UL Sum Rate under Maximum Ratio Combining

In this work, we show that pilot contamination can be exploited to jointly increase the uplink sum rate and reduce pilot overhead. By leveraging the effect of pilot contamination on minimum mean-square-error (MMSE) channel estimates, we demonstrate that allowing users to share or use correlated pilots can, in certain regimes, yield better sum rate than with mutually orthogonal pilots or perfect channel state information (CSI) under maximum-ratio (MR) combining. To establish this result, we derive the use-and-then-forget (UATF) capacity bound for an arbitrary pilot design under correlated Rayleigh fading with MR combining. We show that, in the presence of pilot contamination, the key mechanism is the directional suppression of the channel estimate towards nearby interfering angular regions, enabling interference reduction. Numerical results confirm this behavior, demonstrating the sum-rate gain and showing that it grows with the number of antennas.

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arXiv2026

Energy Saving in Cell-Free Massive MIMO ISAC for Ultra-Reliable Target-Aware Actuation

Emerging 6G sensing-based applications rely on ultra-reliable target-aware actuation, where timely and accurate sensing information triggers critical actions. Achieving this requires tightly integrated sensing and communication (ISAC) under stringent reliability and latency constraints. This paper investigates ISAC in a downlink cell-free massive multiple-input multiple-output (CF-mMIMO) system supporting multi-static sensing and ultra-reliable low-latency communications (URLLC). We propose a joint power and blocklength allocation algorithm to minimize total energy consumption, accounting for both radio-site and cloud-side processing energy for communication and sensing, while meeting communication and sensing requirements. The non-convex optimization problem is solved using a combination of feasible point pursuit-successive convex approximation (FPP-SCA), concave-convex programming (CCP), and fractional programming techniques. We consider two types of target detectors: clutter-aware and clutter-unaware, each with distinct complexity and performance trade-offs. A computational complexity analysis based on giga-operations per second (GOPS) is conducted to quantify the processing requirements of communication and sensing tasks. We also introduce the refreshing rate for sensing information and derive a closed-form expression that accounts for both observation and processing delays. Simulation results show that the proposed algorithm achieves up to 34% energy reduction compared to schemes using the maximum allowable blocklength and enhances detection capability while consuming less total energy. Clutter-aware detectors, despite higher complexity, require fewer antennas and sensing receive APs, yielding up to 40% energy savings.

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arXiv2025

Detecting Multiple Targets with Distributed Sensing and Communication in Cell-Free Massive MIMO

This paper investigates multi-target detection in an integrated sensing and communication (ISAC) system within a cell-free massive MIMO (CF-mMIMO) framework. We adopt a user-centric approach for communication user equipments (UEs) and a distributed sensing approach for multi-target detection. A heuristic access point (AP) mode selection algorithm and a channel-aware distributed sensing scheme are proposed, where local measurements at receive APs (RX-APs) are weighted based on the received signals signal-to-interference ratio (SIR). A maximum a posteriori ratio test (MAPRT) detector is applied under two awareness levels at RX-APs. To balance the communication-sensing trade-off, we develop a power allocation algorithm to jointly maximize the minimum detection probability and communication signal-to-interference-plus-noise ratio (SINR) while satisfying power constraints. The proposed scheme outperforms non-weighted methods. Adding test statistics from more RX-APs can degrade sensing performance due to weaker channels, but this effect can be mitigated by optimizing the weighting exponent. Additionally, assigning more sensing RX-APs to a sensing area results in approximately 10 dB loss in minimum communication SINR due to limited communication resources.

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arXiv2025

Beam Misalignment in 3GPP mmWave NR

This paper presents an analytical framework for evaluating beam misalignment in 3GPP mmWave NR systems implementing analog beamforming. Our approach captures the interaction between user mobility, beam sweeping mechanisms, and deployment configurations, focusing on long-term average performance metrics. Specifically, we model the beam misalignment rates at both the base station (BS) and user equipment (UE) as Poisson processes and derive expressions for the expected misalignment duration, misalignment fraction, and overall beamforming gain. The framework accounts for practical constraints in NR such as Synchronization Signal Blocks (SSB) periodicity, TDD frame structures, and SSB overhead. Through numerical evaluation based on 3GPP mmWave parameters, we identify key trade-offs between beam counts, user mobility, and SSB timing, providing actionable design insights for robust and efficient beam management in future high-frequency networks.

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arXiv2024

Multi-Static Target Detection and Power Allocation for Integrated Sensing and Communication in Cell-Free Massive MIMO

This paper studies an integrated sensing and communication (ISAC) system within a centralized cell-free massive MIMO (multiple-input multiple-output) network for target detection. ISAC transmit access points serve the user equipments in the downlink and optionally steer a beam toward the target in a multi-static sensing framework. A maximum a posteriori ratio test detector is developed for target detection in the presence of clutter, so-called target-free signals. Additionally, sensing spectral efficiency (SE) is introduced as a key metric, capturing the impact of resource utilization in ISAC. A power allocation algorithm is proposed to maximize the sensing signal-to-interference-plus-noise ratio while ensuring minimum communication requirements. Two ISAC configurations are studied: utilizing existing communication beams for sensing and using additional sensing beams. The proposed algorithm's efficiency is investigated in realistic and idealistic scenarios, corresponding to the presence and absence of the target-free channels, respectively. Despite performance degradation in the presence of target-free channels, the proposed algorithm outperforms the interference-unaware benchmark, leveraging clutter statistics. Comparisons with a fully communication-centric algorithm reveal superior performance in both cluttered and clutter-free environments. The incorporation of an extra sensing beam enhances detection performance for lower radar cross-section variances. Moreover, the results demonstrate the effectiveness of the integrated operation of sensing and communication compared to an orthogonal resource-sharing approach.

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arXiv2024

Interplay between Sensing and Communication in Cell-Free Massive MIMO with URLLC Users

This paper studies integrated sensing and communication (ISAC) in the downlink of a cell-free massive multiple-input multiple-output (MIMO) system with multi-static sensing and ultra-reliable low-latency communication (URLLC) users. We propose a successive convex approximation-based power allocation algorithm that maximizes energy efficiency while satisfying the sensing and URLLC requirements. In addition, we provide a new definition for network availability, which accounts for both sensing and URLLC requirements. The impact of blocklength, sensing requirement, and required reliability as a function of decoding error probability on network availability and energy efficiency is investigated. The proposed power allocation algorithm is compared to a communication-centric approach where only the URLLC requirement is considered. It is shown that the URLLC-only approach is incapable of meeting sensing requirements, while the proposed ISAC algorithm fulfills both sensing and URLLC requirements, albeit with an associated increase in energy consumption. This increment can be reduced up to 75% by utilizing additional symbols for sensing. It is also demonstrated that larger blocklengths enhance network availability and offer greater robustness against stringent reliability requirements.

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arXiv2022

Power Allocation for Joint Communication and Sensing in Cell-Free Massive MIMO

This paper studies a joint communication and sensing (JCAS) system with downlink communication and multi-static sensing for single-target detection in a cloud radio access network architecture. A centralized operation of cell-free massive MIMO is considered for communication and sensing purposes. The JCAS transmit access points (APs) jointly serve the user equipments (UEs) and optionally steer a beam towards the target. A maximum a posteriori ratio test detector is derived to detect the target using signals received at distributed APs. We propose a power allocation algorithm to maximize the sensing signal-to-noise ratio under the condition that a minimal signal-to-interference-plus-noise ratio value for each UE is guaranteed. Numerical results show that, compared to the fully communication-centric power allocation, the detection probability under a certain false alarm probability can be increased significantly by the proposed algorithm for both JCAS setups: i) using additional sensing symbols or ii) using only existing communication symbols.

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arXiv2022

Low-Latency MAC Design for Pairwise Random Networks

Feasibility of using unlicensed spectrum for ultra reliable low latency communications (URLLC) is still a question for beyond 5G wireless networks. Low latency access to the channel and efficiently sharing spectrum among the multiple users are the main requirements for exploiting unlicensed spectrum for URLLC. Listen before talk and back-off procedures implemented to avoid the collisions in channel access hinder the low latency communication. In this paper, we propose a novel low-latency medium access control (MAC) scheme based on the collision resolution for a pairwise random wireless network. We use geometric sequence decomposition for collision resolution among the competing users. This enables the system to tackle collisions and thus removing the need for carrier sensing and back-off procedures. This saves time in obtaining access to the channel and improves the efficiency of the system. We implement our approach in the synchronized time slotted system and show that it yields significant improvement over existing MAC schemes.

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arXiv2021

Sparse Channel Estimation in Wideband Systems with Geometric Sequence Decomposition

The sparsity of multipaths in the wideband channel has motivated the use of compressed sensing for channel estimation. In this letter, we propose a different approach to sparse channel estimation. We exploit the fact that $L$ taps of channel impulse response in time domain constitute a non-orthogonal superposition of $L$ geometric sequences in frequency domain. This converts the channel estimation problem into the extraction of the parameters of geometric sequences. Numerical results show that the proposed scheme is superior to existing algorithms in high signal-to-noise ratio (SNR) and large bandwidth conditions.

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arXiv2021

Noise Learning Based Denoising Autoencoder

This letter introduces a new denoiser that modifies the structure of denoising autoencoder (DAE), namely noise learning based DAE (nlDAE). The proposed nlDAE learns the noise of the input data. Then, the denoising is performed by subtracting the regenerated noise from the noisy input. Hence, nlDAE is more effective than DAE when the noise is simpler to regenerate than the original data. To validate the performance of nlDAE, we provide three case studies: signal restoration, symbol demodulation, and precise localization. Numerical results suggest that nlDAE requires smaller latent space dimension and smaller training dataset compared to DAE.

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arXiv2020

Geometric Sequence Decomposition with $k$-simplexes Transform

This paper presents a computationally efficient technique for decomposing non-orthogonally superposed $k$ geometric sequences. The method, which is named as geometric sequence decomposition with $k$-simplexes transform (GSD-ST), is based on the concept of transforming an observed sequence to multiple $k$-simplexes in a virtual $k$-dimensional space and correlating the volumes of the transformed simplexes. Hence, GSD-ST turns the problem of decomposing $k$ geometric sequences into one of solving a $k$-th order polynomial equation. Our technique has significance for wireless communications because sampled points of a radio wave comprise a geometric sequence. This implies that GSD-ST is capable of demodulating randomly combined radio waves, thereby eliminating the effect of interference. To exemplify the potential of GSD-ST, we propose a new radio access scheme, namely non-orthogonal interference-free radio access (No-INFRA). Herein, GSD-ST enables the collision-free reception of uncoordinated access requests. Numerical results show that No-INFRA effectively resolves the colliding access requests when the interference is dominant.

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arXiv2018

Fast and Reliable Initial Access with Random Beamforming for mmWave Networks

Millimeter-wave (mmWave) communications rely on directional transmissions to overcome severe path loss. Nevertheless, the use of narrow beams complicates the initial access procedure and increase the latency as the transmitter and receiver beams should be aligned for a proper link establishment. In this paper, we investigate the feasibility of random beamforming for the cell-search phase of initial access. We develop a stochastic geometry framework to analyze the performance in terms of detection failure probability and expected latency of initial access as well as total data transmission. Meanwhile, we compare our scheme with the widely used exhaustive search and iterative search schemes, in both control plane and data plane. Our numerical results show that, compared to the other two schemes, random beamforming can substantially reduce the latency of initial access with comparable failure probability in dense networks. We show that the gain of the random beamforming is more prominent in light traffics and low-latency services. Our work demonstrates that developing complex cell-discovery algorithms may be unnecessary in dense mmWave networks and thus shed new lights on mmWave network design.

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arXiv2018

Reducing Initial Cell-search Latency in mmWave Networks

Millimeter-wave (mmWave) networks rely on directional transmissions, in both control plane and data plane, to overcome severe path-loss. Nevertheless, the use of narrow beams complicates the initial cell-search procedure where we lack sufficient information for beamforming. In this paper, we investigate the feasibility of random beamforming for cell-search. We develop a stochastic geometry framework to analyze the performance in terms of failure probability and expected latency of cell-search. Meanwhile, we compare our results with the naive, but heavily used, exhaustive search scheme. Numerical results show that, for a given discovery failure probability, random beamforming can substantially reduce the latency of exhaustive search, especially in dense networks. Our work demonstrates that developing complex cell-discovery algorithms may be unnecessary in dense mmWave networks and thus shed new lights on mmWave system design.

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arXiv2017

Millimeter-Wave Interference Avoidance via Building-Aware Associations

Signal occlusion by building blockages is a double-edged sword for the performance of millimeter-wave (mmW) communication networks. Buildings may dominantly attenuate the useful signals, especially when mmW base stations (BSs) are sparsely deployed compared to the building density. In the opposite BS deployment, buildings can block the undesired interference. To enjoy only the benefit, we propose a building-aware association scheme that adjusts the directional BS association bias of the user equipments (UEs), based on a given building density and the concentration of UE locations around the buildings. The association of each BS can thereby be biased: (i) toward the UEs located against buildings for avoiding interference to other UEs; or (ii) toward the UEs providing their maximum reference signal received powers (RSRPs). The proposed association scheme is optimized to maximize the downlink average data rate derived by stochastic geometry. Its effectiveness is validated by simulation using real building statistics.

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arXiv2017

Cooperative Transmissions in Ultra-Dense Networks under a Bounded Dual-Slope Path Loss Model

In an Ultra-dense network (UDN) where there are more base stations (BSs) than active users, it is possible that many BSs are instantaneously left idle. Thus, how to utilize these dormant BSs by means of cooperative transmission is an interesting question. In this paper, we investigate the performance of a UDN with two types of cooperation schemes: non-coherent joint transmission (JT) without channel state information (CSI) and coherent JT with full CSI knowledge. We consider a bounded dual-slope path loss model to describe UDN environments where a user has several BSs in the near-field and the rest in the far-field. Numerical results show that non-coherent JT cannot improve the user spectral efficiency (SE) due to the simultaneous increment in signal and interference powers. For coherent JT, the achievable SE gain depends on the range of near-field, the relative densities of BSs and users, and the CSI accuracy. Finally, we assess the energy efficiency (EE) of cooperation in UDN. Despite costing extra energy consumption, cooperation can still improve EE under certain conditions.

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arXiv2017

Joint Transmission with Dummy Symbols for Dynamic TDD in Ultra-Dense Deployments

Dynamic time-division duplexing (TDD) is considered a promising solution to deal with fast-varying traffic often found in ultra-densely deployed networks. At the same time, it generates more interference which may degrade the performance of some user equipment (UE). When base station (BS) utilization is low, some BSs may not have an UE to serve. Rather than going into sleep mode, the idle BSs can help nearby UEs using joint transmission. To deal with BS-to-BS interference, we propose using joint transmission with dummy symbols where uplink BSs serving uplink UEs participate in the precoding. Since BSs are not aware of the uplink symbols beforehand, any symbols with zero power can be transmitted instead to null the BS-to-BS interference. Numerical results show significant performance gains for uplink and downlink at low and medium utilization. By varying the number of participating uplink BSs in the precoding, we also show that it is possible to successfully trade performance in the two directions.

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arXiv2017

On the Asymptotic Behavior of Ultra-Densification under a Bounded Dual-Slope Path Loss Model

In this paper, we investigate the impact of network densification on the performance in terms of downlink signal-to-interference (SIR) coverage probability and network area spectral efficiency (ASE). A sophisticated bounded dual-slope path loss model and practical user equipment (UE) densities are incorporated in the analysis, which have never been jointly considered before. By using stochastic geometry, we derive an integral expression along with closed-form bounds of the coverage probability and ASE, validated by simulation results. Through these, we provide the asymptotic behavior of ultra-densification. The coverage probability and ASE have non-zero convergence in asymptotic regions unless UE density goes to infinity (full load). Meanwhile, the effect of UE density on the coverage probability is analyzed. The coverage probability will reveal an U-shape for large UE densities due to interference fall into the near-field, but it will keep increasing for low UE densites. Furthermore, our results indicate that the performance is overestimated without applying the bounded dual-slope path loss model. The derived expressions and results in this work pave the way for future network provisioning.

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