We propose in this paper a novel hierarchical multi-grid orthogonal matching pursuit based angle estimation scheme. The proposed scheme accurately estimates the angles of departure (AoDs) and the angles of arrival (AoAs) of a point-to-point ultra massive multiple input multiple output (UM-MIMO) system, ensuring ultra-resolution estimation accuracy. Specifically, we define the on-grid error range in the conventional compressive sensing (CS) estimators and build high-resolution grids centered at the estimated discrete on-grid angles. The proposed scheme proceeds hierarchically through multiple levels to refine the angle estimations. Numerical simulations demonstrate the efficacy and superiority of the proposed scheme over recent state-of-the-art literature, where sub-millidegree angle estimation accuracy can be attained.

This paper focuses on exploring the wideband beam split effect in the near-field region under the adoption of practical true time delay (TTD) elements. We formulate the design of the wideband hybrid analog precoder as a joint optimization problem taking into account the hardware limitations of TTDs. To tackle this non-convex problem, we propose a greedy beamfocusing (GB) approach with two stages utilizing the delta-delay-phase precoding (DDPP) architecture in the near-field. The proposed approach designs the hybrid precoder while accounting for TTDs' limitations in its first stage. In the second stage, this approach adapts the resulting time delay values of TTDs to enable operation of the DDPP architecture in the near-field region. Numerical simulations depict that the state-of-art beamfocusing schemes in the near-field can realize only low-resolution beamsteering under the adoption of practical TTDs. In contrast, our proposed beamfocusing approach can achieve continuous beamsteering with an average array gain over users higher than 0.9 at all angles.

The exploitation of the substantial bandwidths available in the terahertz (THz) band has recently attracted considerable interest. However, beam squint effect is a significant obstacle in the design of wideband hybrid beamformers. The beam squint effect causes the radiation beam to deviate from the desired direction, resulting in substantial gain losses and hindering the effective utilization of available bandwidths. Delay-phase precoding (DPP), a combination of true time delay (TTD) elements and phase shifters (PSs) in the analog domain, has emerged as a potential solution to overcome the beam squint effect and maintain practical system design. However, existing hybrid precoding schemes that assume infinite resolution and unbounded range TTDs, as well as infinite resolution PSs, impose a significant burden in terms of hardware complexity and power consumption. In this paper, we introduce the Delta-Delay-Phase Precoding (DDPP) architecture, which significantly reduces the required delay range of TTDs, outperforming existing state-of-the-art solutions in the literature. Importantly, the proposed architecture maintains consistently superior performance, even as the number of antenna elements increases, making it scalable for UM-MIMO systems. Additionally, we propose hardware-aware designs for the hybrid analog precoder to combat beam squint effect while complying with the hardware limitations of TTDs and PSs. We formulate the design of TTDs and PSs as a joint optimization problem subject to their finite-resolution constraints. To tackle this non-convex optimization problem, we propose an iterative precoding algorithm based on alternating minimization. Simulation results demonstrate the superiority of the proposed hybrid analog precoding schemes over recent literature works. Particularly, the proposed precoding schemes achieve near-optimal performance despite the hardware limitations of TTDs and PSs.

Beam squint becomes a real challenge for the conventional wideband hybrid beamformers in ultra massive multiple input multiple output (UM-MIMO) systems at Terahertz (THz) band, as it causes serious beam gain loss and, hence, impedes the full utilization of the huge vacant bandwidths at this band. With the aim of combating this effect, delay-phase precoding has emerged as an energy-efficient solution, in which a mixture of true time delay (TTD) elements and phase shifters (PSs) are proposed to convert phase-controlled analog precoding into delay-phase controlled analog precoding. Nevertheless, existing infinite-resolution and large-range TTD elements are impractical due to the hardware cost and power consumption. In this paper, we propose a joint iterative optimization of the TTD and PS values based on alternating minimization considering low-resolution and limited-range TTD elements. Simulation results reveal the supremacy and robustness of the proposed algorithm in dealing with the hardware limitations of TTDs.

In this study, the authors secure the mixed radio frequency/free space optical (FSO) relay‐aided interference aligned system using a proposed physical layer security algorithm. This algorithm reduces the quality of the received signal at the eavesdropper through two procedures. First, it minimises the data transmission power from the legitimate users and the relays. Second, it jams the eavesdropper by broadcasting artificial noise from the users and the relays. Therefore, a joint optimisation problem is formulated to degrade the received signal at the eavesdropper from the users and the relays and to maximise the jamming artificial noise power, which is solved using an iterative optimisation algorithm beside a semi‐definitive programming algorithm. Furthermore, the pre‐coding and decoding matrices of the users and the relay are designed to enable the legitimate users to cancel the artificial noise, while the eavesdropper is disabled from distinguishing the artificial noise from the real streams. Moreover, the security performance of the proposed algorithm is analysed, and the impact of the FSO link's state on the security performance is studied. The extensive simulation results show the efficiency of the proposed algorithm and illustrate the role of the FSO link's state on the security performance.

This paper proposes a novel physical layer security algorithm in interference alignment (IA) based multiuser communication system with a single amplify-and-forward (AF) relay in the coexistence of an eavesdropper that tries to hide its existence. Thus, the relay and the users cannot estimate the eavesdroppers channel state information (CSI). In the proposed algorithm, the users broadcasts artificial noise in the null of the relay at the multiple access (MAC) phase, and the relay jams the eavesdropper by broadcasting the artificial noise in the null of users at the broadcast (BC) phase. Therefore, the proposed algorithm is formulated as a joint optimization problem consisting of two semidefinite programming (SDP) problems aiming at maximizing the power of the artificial noise and preserving the quality- of-services (QoS) of the users. An iterative optimization algorithm is proposed to solve the joint problem. Extensive simulation results are provided to show the effectiveness of the proposed algorithm for IA based relaying networks.

Channel state information (CSI) acquisition is a crucial issue in downlink FDD-based massive multi-input multioutput (MIMO) networks, where the channel reciprocity is not applicable. Thus, users are expected to feedback the bestmatch quantized channels to serving transmitters. Hence, an extensively large size of the feedback overhead is needed, which is linearly scaled at each user with the number of transmit antennas at the base-station (BS). In turn, the uplink (UL) channel capacity may be consumed and the overall performance becomes fundamentally limited by the downlink (DL) channel quantization precision. An alternative CSI acquisition scheme is critically needed. In this paper, we propose a novel FDD massive MIMO system based on a spatial DL channel estimation scheme; it relies on the statistical spatial correlation of the UL and DL channel clusters, given an arbitrary frequency band gap between the UL and DL channels. A transformation matrix is constructed to precode the observed UL channel on the estimated dominant DL angles of departure. The proposed scheme significantly outperforms the recent state-of-the-art techniques, without the cost of user feedback overhead bits and prior knowledge of the channel statistics.

In this paper, we investigate the resource management problem in orthogonal frequency division multiplexing (OFDM) based multiple-input multiple-output (MIMO) cognitive radio (CR) systems. We propose performing resource allocation based on interference alignment (IA) in order to improve the spectral efficiency of CR systems without affecting the quality of service of the primary system. IA plays a role in the proposed algorithm to enable the secondary users (SUs) to cooperate and share the available spectrum, which leads to a considerable increase in the spectral efficiency of CR systems. However, IA based spectrum sharing is restricted to a certain number of SUs per subcarrier in order to satisfy the IA feasibility conditions. Accordingly, the resource allocation problem is formulated as a mixed-integer optimization problem, which is considered an NP-hard problem. To reduce the computational complexity of the problem, a two-phases efficient sub-optimal algorithm is proposed. In the first phase, frequency-clustering is performed in order to satisfy the IA feasibility conditions, where each subcarrier is assigned to a feasible number of SUs. Whenever possible, frequency-clustering stage considers the fairness among the SUs. In the second stage, the available power is allocated among the subcarriers and SUs without violating the constraints that limit the maximum interference induced to the primary system. Simulation results show that IA with frequency-clustering achieves a significant sum rate increase compared to CR systems with orthogonal multiple access transmission techniques.

In this paper, the problem of resource allocation in overloaded orthogonal frequency division multiplexing (OFDM) based multiple-input multiple-output (MIMO) cognitive radio (CR) system is considered. The objective is to allocate the different subcarrier and distribute the available user power in order to maximize the CR system throughput. The interference induced to the primary system should not be harmful and hence, should not exceed the prescribed limit. Interference alignment (IA) technique is employed in order to achieve an efficient use of the available radio resources. Without affecting the quality of service of the primary system, IA enables the secondary users to share the available spectrum which increases the CR system degrees-of-freedom. Due to IA feasibility condition, the spectrum sharing with perfect IA is restricted to a certain number of user per subcarrier. Accordingly, the resource management problem is formulated as a mixed-integer optimization problem which is considered as an NP-hard problem. To reduce the computational complexity of the problem, a two-phase efficient sub-optimal algorithm is proposed. Frequency-clustering is performed in the first phase to the overcome IA feasibility conditions while the power is distributed among subcarriers in the second phase. Simulations show that IA technique achieves a significant sum-rate increase of CR systems compared with the traditional CR systems that use orthogonal multiple access transmission techniques.