Integrated sensing and communication (ISAC) systems demand precise and efficient target localization, a task challenged by rich multipath propagation in complex wireless environments. This paper introduces MARBLE-Net (Multipath-Aware Rainbow Beam Learning Network), a deep learning framework that jointly optimizes the analog beamforming parameters of a frequency-dependent rainbow beam and a neural localization network for high-accuracy position estimation. By treating the phase-shifter (PS) and true-time-delay (TTD) parameters as learnable weights, the system adaptively refines its sensing beam to exploit environment-specific multipath characteristics. A structured multi-stage training strategy is proposed to ensure stable convergence and effective end-to-end optimization. Simulation results show that MARBLE-Net outperforms both a fixed-beam deep learning baseline (RaiNet) and a traditional k-nearest neighbors (k-NN) method, reducing localization error by more than 50\% in a multipath-rich scene. Moreover, the results reveal a nuanced interaction with multipath propagation: while confined uni-directional multipath degrades accuracy, structured and directional multipath can be effectively exploited to achieve performance surpassing even line-of-sight (LoS) conditions.

Phase-time arrays (PTAs), integrating phase shifters and true-time delays, emerge as a cost-effective and energy-efficient architecture for frequency-dependent beamforming in wideband communications. In this work, we investigate a wideband system in which a base station equipped with a PTA and single RF chain serves multiple near-field and far-field users. The goal is to jointly optimize PTA-based beamforming, subband allocation, and power allocation to maximize overall system performance. To this end, we formulate a system utility maximization problem, which includes sum-rate and geometric mean rate maximization as special cases and is highly non-convex. We first develop a three-step alternating optimization (AO) algorithm that iteratively optimizes the beamforming and resource allocations. To further enhance efficiency, we propose an unsupervised learning-based approach that combines a convolutional neural network, a graph attention network (GAT), and a normalization module with a utility-driven loss and a learnable adjacency initialized from hardware couplings. Simulation results confirm that PTAs strike a superior balance between energy efficiency and spectral efficiency compared with fully-digital and phased array architectures. The proposed GAT achieves AO-level performance with orders-of-magnitude lower computational complexity, i.e., only about 0.1% in our simulations.

High-frequency wideband cellular communications over mmWave and sub-THz offer the opportunity for high data rates. However, it also presents high path loss, resulting in limited coverage. High-gain beamforming from the antenna array is essential to mitigate the coverage limitations. The conventional phased antenna arrays (PAA) cause high scheduling latency owing to analog beam constraints, i.e., only one frequency-flat beam is generated. Recently introduced joint phase-time array (JPTA) architecture, which utilizes both true-time-delay (TTD) units and phase shifters (PSs), alleviates analog beam constraints by creating multiple frequency-dependent beams for scheduling multiple users at different directions in a frequency-division manner. One class of previous studies offered solutions with "rainbow" beams, which tend to allocate a small bandwidth per beam direction. Another class focused on uniform linear array (ULA) antenna architecture, whose frequency-dependent beams were designed along a single axis of either azimuth or elevation direction. This paper presents a novel 3D beamforming design that maximizes beamforming gain toward desired azimuth and elevation directions and across sub-bands partitioned according to scheduled users’ bandwidth requirements. We provide analytical solutions and iterative algorithms to design the PSs and TTD units for a desired subband beam pattern. Through simulations of the beamforming gain, we observe that our proposed solutions outperform the state-of-the-art solutions reported elsewhere.

Beam alignment – the process of finding an optimal directional beam pair – is a challenging procedure crucial to millimeter wave (mmWave) communication systems. We propose a novel beam alignment method that learns a site-specific probing codebook and uses the probing codebook measurements to predict the optimal narrow beam. An end-to-end neural network (NN) architecture is designed to jointly learn the probing codebook and the beam predictor. The learned codebook consists of site-specific probing beams that can capture particular characteristics of the propagation environment. The proposed method relies on beam sweeping of the learned probing codebook, does not require additional context information, and is compatible with the beam sweeping-based beam alignment framework in 5G. Using realistic ray-tracing datasets, we demonstrate that the proposed method can achieve high beam alignment accuracy and signal-to-noise ratio (SNR) while significantly – by roughly a factor of 3 in our setting – reducing the beam sweeping complexity and latency.

Beam alignment - the process of finding an optimal directional beam pair - is a challenging procedure crucial to millimeter wave (mmWave) communication systems. In this work, we propose a beam alignment method that learns a site-specific probing codebook and uses the probing codebook measurements to predict the optimal narrow beam. A novel neural network (NN) architecture is designed to jointly learn the probing codebook and the beam predictor in an end-to-end fashion. The learned codebook consists of site-specific probing beams that can capture particular characteristics of the propagation environment. The proposed method relies on beam sweeping of the learned probing codebook, does not require additional context information and is compatible with the beam sweeping-based beam alignment framework in 5G. We demonstrate using realistic ray-tracing data that the proposed method can achieve high beam alignment accuracy and signal-to-noise ratio (SNR) while significantly reducing the beam sweeping complexity and latency.

This paper studies the effect of the user hand grip on the design of beamforming codebooks for 5G millimeter-wave (mmWave) mobile handsets. The high-frequency structure simulator (HFSS) is used to characterize the radiation fields for fourteen possible handgrip profiles based on experiments we conducted. The loss from hand blockage on the antenna gains can be up to 20-25 dB, which implies that the possible hand grip profiles need to be taken into account while designing beam codebooks. Specifically, we consider three different codebook adaption schemes: a grip-aware scheme, where perfect knowledge of the hand grip is available; a semi-aware scheme, where just the application (voice call, messaging, etc.) and the orientation of the mobile handset is known; and a grip-agnostic scheme, where the codebook ignores hand blockage. Our results show that the ideal grip-aware scheme can provide more than 50% gain in terms of the spherical coverage over the agnostic scheme, depending on the grip and orientation. Encouragingly, the more practical semi-aware scheme we propose provides performance approaching the fully grip-aware scheme. Overall, we demonstrate that 5G mmWave handsets are different from pre-5G handsets: the user grip needs to be explicitly factored into the codebook design.

Communication systems with low-resolution analog-to-digital-converters (ADCs) can exploit channel state information at the transmitter and receiver. This paper presents codebook designs and performance analyses for limited feedback MIMO systems with finite-bit ADCs. A point-to-point single-user channel is firstly considered. When the received signal is sliced by 1-bit ADCs, the absolute phase at the receiver is important to align the phase of the received signals. A new codebook design for beamforming, which separately quantizes the channel direction and the residual phase, is therefore proposed. For the multi-bit case where the optimal transmission method is unknown, suboptimal Gaussian signaling and eigenvector beamforming is assumed to obtain a lower bound of the achievable rate. It is found that to limit the rate loss, more feedback bits are needed in the medium SNR regime than the low and high SNR regimes, which is quite different from the conventional infinite-bit ADC case. Second, a multi-user system where a multiple-antenna transmitter sends signals to multiple single-antenna receivers with finite-bit ADCs is considered. Based on the derived performance loss due to finite-bit ADCs and finite-bit CSI feedback, the number of bits per feedback should increase linearly with the ADC resolution in order to restrict the rate loss.

Hybrid analog/digital architectures and receivers with low-resolution analog-to-digital converters (ADCs) are two low power solutions for wireless systems with large antenna arrays, such as millimeter wave and massive multiple-input multiple-output systems. Most prior work represents two extreme cases in which either a small number of radio frequency (RF) chains with full-resolution ADCs, or low-resolution ADC with a number of RF chains equal to the number of antennas is assumed. In this paper, a generalized hybrid architecture with a small number of RF chains and a finite number of ADC bits is proposed. For this architecture, achievable rates with channel inversion and singular value decomposition-based transmission methods are derived. Results show that the achievable rate is comparable to that obtained by full-precision ADC receivers at low and medium SNRs. A trade-off between the achievable rate and power consumption for the different numbers of bits and RF chains is devised. This enables us to draw some conclusions on the number of ADC bits needed to maximize the system energy efficiency. Numerical simulations show that coarse ADC quantization is optimal under various system configurations. This means that hybrid combining with coarse quantization achieves better energy-rate trade-off compared with both hybrid combining with full-resolutions ADCs and 1-bit ADC combining.

In massive multiple-input multiple-output (MIMO) systems, it may not be power efficient to have a pair of high-resolution analog-to-digital converters (ADCs) for each antenna element. In this paper, a near maximum likelihood (nML) detector for uplink multiuser massive MIMO systems is proposed where each antenna is connected to a pair of one-bit ADCs, i.e., one for each real and imaginary component of the baseband signal. The exhaustive search over all the possible transmitted vectors required in the original maximum likelihood (ML) detection problem is relaxed to formulate an ML estimation problem. Then, the ML estimation problem is converted into a convex optimization problem which can be efficiently solved. Using the solution, the base station can perform simple symbol-by-symbol detection for the transmitted signals from multiple users. To further improve detection performance, we also develop a two-stage nML detector that exploits the structures of both the original ML and the proposed (one-stage) nML detectors. Numerical results show that the proposed nML detectors are efficient enough to simultaneously support multiple uplink users adopting higher-order constellations, e.g., 16 quadrature amplitude modulation. Since our detectors exploit the channel state information as part of the detection, an ML channel estimation technique with one-bit ADCs that shares the same structure with our proposed nML detector is also developed. The proposed detectors and channel estimator provide a complete low power solution for the uplink of a massive MIMO system.

From security perspective, a friendly relay may help to keep the confidential messages from being eavesdropped, while an untrusted relay may intentionally eavesdrop the messages when relaying. This paper studies the secure beamforming for multiple-input multiple-output (MIMO) two-way communications, where two source nodes exchange information with the help of an untrusted relay node. The relay adopts amplify-and-forward (AF) strategy and acts as both an essential helper and a potential eavesdropper. Our goal is to maximize the secrecy sum rate of the bidirectional links by jointly optimizing the source and relay beamformers. For the two-phase two-way relay scheme, we first derive the optimal structure of the relay beamformer and then propose an iterative algorithm to jointly optimize the source and relay beamformers. Then, a comprehensive study on the asymptotical performance is conducted by letting the source and relay powers approach zero or infinity. In particular, we show that when all powers approach infinity, the two-way relay scheme achieves the maximum secrecy rate if the transceiver beamformers are designed such that the received signals at the relay can be aligned to be parallel.

Two-way relaying can considerably improve spectral efficiency in relay-assisted bidirectional communications. However, the benefits and flexible structure of orthogonal frequency division multiplexing (OFDM)-based two-way relay systems is much less exploited. Moreover, most of existing works have not considered quality-of-service (QoS) provisioning for two-way relaying. In this paper, we consider the OFDM-based bidirectional transmission where a pair of users exchange information via the assistance of a decode-and-forward (DF) relay. Each user can communicate with the other via three transmission modes: direct transmission, one-way relaying, and two-way relaying. We jointly optimize the transmission policies, including power allocation, transmission mode selection, and subcarrier assignment in order to maximize the weighted sum rates of the two users with diverse QoS guarantees. This is formulated as a mixed integer programming problem. By using the dual method, we efficiently solve the problem in an asymptotically optimal manner. Simulation results show that the proposed resource allocation scheme can substantially improve system performance compared with conventional schemes. A number of interesting insights are also obtained via comprehensive simulations.

In this paper, we consider the orthogonal frequency division multiplexing (OFDM)-based bidirectional transmission where a pair of users exchange information with the assistance of a decode-and-forward (DF) relay. Each user can communicate with the other via three transmission modes: direct transmission, one-way relaying, and two-way relaying. We jointly optimize the transmission policies, including power allocation, transmission mode selection, and subcarrier-node assignment for maximizing the weighted sum rates of the two users with quality-of-service (QoS) guarantees. We formulate the joint optimization problem as a mixed integer programming problem. By using the dual method, we solve the problem efficiently in an asymptotically optimal manner. Particularly, we derive the capacity region of two-way DF relaying in parallel relay channels. Simulation results show that the proposed resource-allocation scheme can substantially improve system performance compared with the conventional schemes.

Hash functions are often used to protect the integrity of information. In general, the design of hash functions should satisfy three standards: pre-image resistance, second pre-image resistance and collision resistance. The design of hash functions in the literature assumes that the messages to be transmitted are equally probable. In this paper, we focus on the pre-image resistance and investigate the problem of mapping design for hash function utilizing the unequal occurrence probabilities of the messages. We first present a necessary condition for the optimal mapping and then introduce a heuristic algorithm. Simulation experiments are carried out to evaluate the performance of the proposed new design. It is shown that the probability of successful attack can be significantly reduced compared with the conventional design. Our algorithm can be useful in scenarios where the attacker has limited ability or time to estimate the probability distribution of the messages. To our best knowledge, this work is the first attempt of making use of the message distribution in designing hash functions for information security.

In this paper, we study the degrees of freedom of a multiple-input multiple-output (MIMO) two-way X relay channel, i.e., a system with two groups of source nodes and one relay node, where each of the two source nodes in one group wants to exchange independent messages with both the two source nodes in the other group via the relay node. We only consider the symmetric case where each source node is equipped with M antennas while the relay is equipped with N antennas. We first show that the upper bound of the degrees of freedom is 2N when N ≤ 2M. Then by applying physical layer network coding and joint interference cancellation, we propose a novel transmission scheme for the considered network. We show that this scheme can always achieve this upper bound when N ≤ ⌊4M/3⌋.

Providing physical-layer security for mobile users in future broadband wireless networks is of both theoretical and practical importance. In this paper, we formulate an analytical framework for resource allocation in a downlink OFDMA-based broadband network with coexistence of secure users (SU) and normal users (NU). The problem is formulated as joint power and subcarrier allocation with the objective of maximizing average aggregate information rate of all NU's while maintaining an average secrecy rate for each individual SU under a total transmit power constraint for the base station. We solve this problem in an asymptotically optimal manner using dual decomposition. Our analysis shows that an SU becomes a candidate competing for a subcarrier only if its channel gain on this subcarrier is the largest among all and exceeds the second largest by a certain threshold. Furthermore, while the power allocation for NU's follows the conventional water-filling principle, the power allocation for SU's depends on both its own channel gain and the largest channel gain among others. We also design a suboptimal algorithm to reduce the computational cost. Numerical studies are conducted to evaluate the performance of the proposed algorithms in terms of the achievable pair of information rate for NU's and secrecy rate for SU at different power consumptions.

Providing physical-layer security for mobile users in future broadband wireless networks is of both theoretical and practical importance. In this paper, we formulate an analytical framework for resource allocation in a downlink orthogonal frequency-division multiple access (OFDMA)-based broadband network with coexistence of secure users (SUs) and normal users (NUs). The SUs require secure data transmission at the physical layer while the NUs are served with conventional best-effort data traffic. The problem is formulated as joint power and subcarrier allocation with the objective of maximizing average aggregate information rate of all NUs while maintaining an average secrecy rate for each individual SU under a total transmit power constraint for the base station. We solve this problem in an asymptotically optimal manner using dual decomposition. Our analysis shows that an SU becomes a candidate competing for a subcarrier only if its channel gain on this subcarrier is the largest among all and exceeds the second largest by a certain threshold. Furthermore, while the power allocation for NUs follows the conventional water-filling principle, the power allocation for SUs depends on both its own channel gain and the largest channel gain among others. We also develop a suboptimal algorithm to reduce the computational cost. Numerical studies are conducted to evaluate the performance of the proposed algorithms in terms of the achievable pair of information rate for NU and secrecy rate for SU at different power consumptions.