Multi-target detection is one of the primary tasks in radar-based localization and sensing, typically built on phased array antennas. However, the bulky hardware in the phased array restricts its potential for enhancing detection accuracy, since the cost and power of the phased array can become unaffordable as its physical aperture scales up to pursue higher beam shaping capabilities. To resolve this issue, we propose a radar system enabled by reconfigurable holographic surfaces (RHSs), a novel meta-surface antenna composed of meta-material elements with cost-effective and power-efficient hardware, which performs multi-target detection in an adaptive manner. Different from the phase-control structure in the phased array, the RHS is able to apply beamforming by controlling the radiation amplitudes of its elements. Consequently, traditional beamforming schemes designed for phased arrays cannot be directly applied to RHSs due to this structural difference. To tackle this challenge, a wave-form and amplitude optimization algorithm (WAOA) is designed to jointly optimize the radar waveform and RHS amplitudes in order to improve the detection accuracy. Simulation results reveal that the proposed RHS-enabled radar increases the probability of detection by 0.13 compared to phased array radars when six iterations of adaptive detection are performed given the same hardware cost.

Ultra-massive multiple-input multiple-output (MIMO) is expected to be one of the key enablers in the forthcoming 6G networks to handle various user demands by exploiting spatial diversity. In this paper, a new paradigm termed holographic radio is considered for ultra-massive MIMO via integrating numerous antenna elements into a compact space, thereby achieving a spatially quasi-continuous aperture and realizing high beampattern gain. We propose a practical path to implement holographic radio by a novel metasurface-based antenna called a reconfigurable holographic surface (RHS). Specifically, the RHS is capable of holographic beamforming over the spatially quasi-continuous apertures by incorporating densely packed tunable metamaterial elements with low power consumption. To enhance the performance of the RHS as an antenna array for achieving ultra-massive MIMO, a holographic beamforming optimization algorithm is developed for beampattern gain maximization based on the hardware design and full-wave analyses of RHSs. We then implement a prototype of an RHS and build an RHS-aided communication platform to further substantiate the feasibility of RHS-enabled holographic radio. Both simulation and experimental results verify the effectiveness of the proposed holographic beamforming optimization algorithm. It is also proved that the RHS-aided communication platform is capable of supporting real-time transmission of high-definition video.

Ultra-dense low-Earth-orbit (LEO) satellite communication networks have significant potential for providing high-speed data services. To compensate the severe path loss in satellite communications, a key conceptual enabler is the holographic multiple input multiple output (HMIMO) with a spatially continuous aperture which can achieve a high directive gain with a small antenna size. In this paper, we consider a novel metamaterial antenna called a reconfigurable holographic surface (RHS) integrated with a user terminal (UT) to support LEO satellite communications. Composing of densely packing sub-wavelength metamaterial elements, the RHS can realize continuous or quasi-continuous apertures and provide a practical way towards the implementation of HMIMO. To obtain the desired beam directions towards the satellites, we propose a LEO satellite tracking scheme based on the temporal variation law such that frequent satellite positioning can be avoided. A holographic beamforming algorithm for sum rate maximization is then developed where a closed-form for the optimal holographic beamformer is derived. The robustness of the algorithm against the tracking errors of the satellites’ positions is also proved. Simulation results verify the theoretical analysis and show that the RHS outperforms the traditional phased array of the same physical dimension in terms of the sum rate when the compact element spacing of the RHS leads to much more RHS elements. Moreover, the RHS also provides a more cost-effective solution for pursuing high data rate compared with the phased array.

The next generation wireless communications aiming at enhancing capacity and massive connectivity significantly over high-frequency bands urge the development of novel multiple access technologies. In this paper, we propose a new type of space-division multiple access (SDMA), called holographic-pattern division multiple access (HDMA). We develop the principle for HDMA with the main idea of mapping the intended signals for receivers to a superposed holographic pattern. The multi-user holographic beamforming scheme for HDMA is then presented. Based on the theoretical analysis, we find that there exists an optimal holographic pattern such that the sum rate with simple zero-forcing precoding can achieve the asymptotic capacity of the HDMA system. Simulation results verify the theoretical analysis and show that the HDMA scheme outperforms the traditional SDMA scheme in terms of both the cost-efficiency and the sum rate.

The future sixth generation (6G) networks look forward to providing revolutionary mobile connectivity and high-throughput data services through low-cost communication devices. Benefiting from the programmability and tunability of metamaterials, the reconfigurable holographic surface (RHS) inlaid with numerous metamaterial radiation elements shows its great potential to achieve such a bold vision. By leveraging the holographic principle, the RHS serves as an ultra-thin and lightweight antenna integrated with the transceiver to generate desirable directional beams with low hardware cost and power consumption. In this paper, we consider a downlink RHS-aided multi-user communication system where a base station (BS) equipped with an RHS transmits signals to users. We propose an RHS-based hybrid beamforming scheme where the digital beamforming and the holographic beamforming are performed at the BS and RHS, respectively, together with the receive combining at each user. We formulate a sum-rate maximization problem for RHS-based hybrid beamforming and decompose it into three sub-problems. A joint sum-rate maximization algorithm is then developed to solve the sub-problems in an alternating manner. Simulation results show that based on the proposed RHS-aided hybrid beamforming scheme, a moderate-sized RHS is enough to achieve a satisfactory sum-rate, which is also higher than a same-sized phased array can achieve.

In this article, we consider a downlink ultra-dense LEO-based multi-terminal satellite system where multiple satellites fly in formation to serve a number of ground terminal stations for data transmission. Benefited from the dense satellite constellation, high channel capacity can be achieved via the massive virtual antenna array formed by multiple satellites. We aim at exploring how the satellite distribution and formation size influence the channel capacity in this case. We first derive the upper bound of the channel capacity in the general multi-antenna satellite networks. The single-antenna satellite case is considered where we present the specific forms of the derived capacity bounds to prove that the capacity first increases linearly then increases more and more slowly with the satellite formation size, and there exists an optimal LEO satellite distribution to achieve the maximum capacity of the system. Simulation results verify our theoretical analysis and show that such statements also hold for the general multi-antenna satellite case.

Recently, the ultra-dense low Earth orbit (LEO) satellite constellation over high-frequency band has served as a potential solution for high-capacity backhaul data services. In this paper, we consider an ultra-dense LEO-based terrestrial-satellite network where terrestrial users can access the network through the LEO-assisted backhaul. We aim to minimize the number of satellites in the constellation while satisfying the backhaul requirement of each user terminal (UT). We first derive the average total backhaul capacity of each UT, based on which a three-dimensional constellation optimization algorithm is proposed to minimize the number of satellites in the constellation. Simulation results verify our theoretical capacity analysis and show that for any given coverage ratio requirement, the corresponding optimized LEO satellite constellation can be obtained by the proposed three-dimensional constellation optimization algorithm. Given the same number of deployed LEO satellites, the average coverage ratio of the proposed LEO satellite constellation is at least 10 percentage points higher than that of Telesat constellation.

Recently, the ultra-dense low earth orbit (LEO)satellite communication networks over high-frequency band have served as a potential solution for high-capacity backhaul data services. In this paper, we consider an ultra-dense LEO-based terrestrial-satellite network where terrestrial users can access the network through the LEO-assisted backhaul. We aim to minimize the number of satellites in the LEO satellite constellation while satisfying the backhaul requirement of each terrestrial-satellite terminal (TST). We first derive the average total backhaul capacity of each TST, based on which a three-dimensional constellation optimization algorithm is proposed to minimize the number of satellites in the LEO satellite constellation. Simulation results verify our theoretical capacity analysis and show that for any given coverage percentage requirement, the corresponding optimized LEO satellite constellation can be obtained by the proposed three-dimensional constellation optimization algorithm.

In this paper, we consider an uplink ultra-dense LEO-based multi-terminal satellite system where each ground terminal station connects to multiple satellites for data transmission. Benefited from the dense satellite constellation, high channel capacity can be achieved via the spatial diversity of multiple satellites. To evaluate the multi-satellite channel capacity performance, we first derive the lower bound and upper bound of the channel capacity in uplink LEO-based multi-terminal systems with multiple single-antenna satellites. Based on the capacity bounds, we theoretically prove that the capacity grows almost linearly with the number of satellites, and there exists an optimal LEO satellite distribution to achieve the maximum capacity of the system. We then illustrate that such statements also hold for the multi-antenna satellite case where the upper bound of the channel capacity and the lower bound of the achievable rate after receive beamforming are derived separately. Simulation results verify our theoretical analysis.

Recently, the ultra-dense low earth orbit (LEO) satellite constellation over high-frequency band has served as a potential solution for terrestrial data offloading owing to its seamless coverage and high-capacity backhaul. In this paper, we consider an integrated ultra-dense LEO-based satellite-terrestrial network where the terrestrial operator (TO) can offload its subscribed users to the LEO satellite network owned by the satellite operator (SO) for satellite-backhauled network access. However, data offloading consumes extra resources of the SO and degrades the quality-of-service of the SO's original users. Therefore, we aim to design a pricing mechanism based on the Stackelberg game to motivate both operators for data offloading, and the Stackelberg equilibrium is achieved by jointly optimizing the C-band user association, Ka-band spectrum allocation, and data service pricing. Simulation results show that our proposed pricing mechanism can motivate two operators for offloading efficiently. The influence of available frequency resources, data service prices, and the number of LEO satellites on the system performance are also discussed.

In this paper, we consider an ultra-dense LEO-based satellite-terrestrial network where the traditional operator (TO) cannot satisfy the increasing data demand of its subscribed users due to the limited backhaul capacity of traditional small cells. Therefore, the TO offloads its users to LEO-based small cells owned by the satellite operator (SO) for satellite-backhauled network access. To motivate both operators for data offloading and maximize their revenues, we propose a pricing mechanism for data offloading based on the Stackelberg game. An iterative optimization algorithm framework is developed by jointly considering user association, Ka-band spectrum allocation and pricing to achieve the Stackelberg equilibrium. The user association scheme and pricing scheme to maximize the TO and the SO's revenues are designed separately. The closed-form optimal solution for user association problem is derived, and the iterative pricing mechanism is also designed. Simulation results show that our proposed pricing scheme can motivate two operators for offloading efficiently. The influence of frequency resources and the number of LEO satellites is also discussed.

Due to the limited backhaul capacity of traditional small cells (TSC), it is non-trivial for the traditional operator (TO) to satisfy the increasing data demand of users. To relieve this issue, we propose a data offloading scheme, where the TO offloads TSC users to LEO-based small cells owned by the satellite operator for satellite-backhauled network access. An iterative optimal algorithm based on the Stackelberg game is developed to maximize both operators' revenues. Simulation results show the effectiveness of our scheme.

In this paper, we consider a cooperative autonomous driving system where a vehicle overtakes the one in front based on collective perception. To avoid collisions with vehicles on the other lane, we propose a V2X-based cooperative collision avoidance scheme. The overtaking vehicle estimates its distance with the neighbors via V2V communications and decides whether to overtake or not. Two cases where the distance information is obtained independently and cooperatively are taken into account. We derive the probability of collision avoidance, and analyze the influence of different factors such as speed and density of vehicles on the system performance. Simulation results verify our analysis and show the improvement brought by the cooperative case compared to the independent case.