Recently, reconfigurable intelligent surfaces (RIS) have attracted a lot of attention due to their capability of extending cell coverage by reflecting signals toward the receiver. In this letter, we analyze the coverage of a downlink RIS-assisted network with one base station (BS) and one user equipment (UE). Since the RIS orientation and the horizontal distance between the RIS and the BS have a significant influence on the cell coverage, we formulate an RIS placement optimization problem to maximize the cell coverage by optimizing the RIS orientation and horizontal distance. To solve the formulated problem, a coverage maximization algorithm (CMA) is proposed, where a closed-form optimal RIS orientation is obtained. Numerical results verify our analysis.
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Driven by great demands on low-latency services of the edge devices (EDs), mobile edge computing (MEC) has been proposed to enable the computing capacities at the edge of the radio access network. However, conventional MEC servers suffer some disadvantages such as limited computing capacity, preventing and computation-intensive tasks to be processed on time. To relief this issue, we propose the heterogeneous multi-layer MEC (HetMEC) where data that cannot be timely processed at the edge are allowed to be offloaded to the upper-layer MEC servers, and finally to the cloud center (CC) with more powerful computing capacity. We aim to minimize the system latency, i.e., the total computing and transmission time on all layers for the data generated by the EDs. We design the latency minimization algorithm by jointly coordinating the task assignment, computing, and transmission resources among the EDs, multi-layer MEC servers, and the CC. The simulation results indicate that our proposed algorithm can achieve a lower latency and higher processing rate than the conventional MEC scheme.
Intelligent reflecting surface (IRS), which is capable to adjust propagation conditions by controlling phase shifts of the reflected waves that impinge on the surface, has been widely analyzed for enhancing the performance of wireless systems. However, the reflective properties of widely studied IRSs restrict the service coverage to only one side of the surface. In this paper, to extend the wireless coverage of communication systems, we introduce the concept of intelligent omni-surface (IOS)-assisted communication. More precisely, IOS is an important instance of reconfigurable intelligent surface (RIS) that is capable to provide service coverage to the mobile users (MUs) in a reflective and a transmissive manner. We consider a downlink IOS-assisted communication system, where a multi-antenna small base station (SBS) and an IOS perform beamforming jointly, to improve the received power of multiple MUs on both sides of the IOS, through different reflective/transmissive channels. To maximize the sum-rate, we formulate a joint IOS phase shift design and SBS beamforming optimization problem, and propose an iterative algorithm to solve the resulting non-convex program efficiently. Both theoretical analysis and simulation results show that an IOS significantly extends the service coverage of the SBS when compared to an IRS.
The received signal strength (RSS) fingerprinting based localization is a widely used technique to locate mobile devices in cellular networks. However, traditional RSS finger-printing based techniques require a set of RSS values from multiple base stations (BSs) at each location, and the RSS from remote BSs is weak and easily affected by the noise and other variations of the propagation channel, which limits the localization accuracy. In this paper, we propose to utilize a reconfigurable intelligent surface (RIS) to improve the accuracy for the RSS fingerprinting based multi-user outdoor localization, which requires the RSS from only one BS. By modifying phase shifts of signals reflected by the RIS, we can create different RSS values at the same location using the signal from the nearest BS for localization. To optimize the RIS phase shifts, we formulate the localization error minimization (LEM) problem and propose an LEM algorithm. Simulation results validate the effectiveness of the proposed scheme.
In this paper, we study the platoon cooperation in the multi-lane cooperative platoon scenario, where platoons move cooperatively and communicate with each other by cellular vehicle-to-everything (V2X) communication. The platoon cooperation is important for the interference management and communication reliability enhancement, yet it is difficult, since the platoon formation, subchannel allocation, and power control of each platoon interact with each other and influence those of other platoons. To increase the number of vehicles in the platoon and reduce the power consumption, we propose a two-step resource allocation strategy in consideration of platoon formation, i.e., the resource allocation at the base station (BS) and within each platoon. A branch and bound algorithm is utilized for the resource allocation at the BS. We then design a distributed dynamic programming-based subchannel allocation and power control algorithm for the joint optimization of platoon formation, subchannel allocation, and power control. The simulation results evaluate the impact of the penalty factor and the latency requirement on the system performance.
To overcome spectrum congestion, a promising approach is to integrate sensing and communication (ISAC) functions in one hardware platform. Recently, metamaterial antennas, whose tunable radiation elements are arranged more densely than those of traditional multiple-input-multiple-output (MIMO) arrays, have been developed to enhance the sensing and communication performance by offering a finer controllability of the antenna beampattern. In this paper, we propose a holographic beamforming scheme, which is enabled by metamaterial antennas with tunable radiated amplitudes, that jointly performs sensing and communication. However, it is challenging to design the beamformer for ISAC functions by taking into account the unique amplitude-controlled structure of holographic beamforming. To address this challenge, we formulate an integrated sensing and communication problem to optimize the beamformer, and design a holographic beamforming optimization algorithm to efficiently solve the formulated problem. A lower bound for the maximum beampattern gain is provided through theoretical analysis, which reveals the potential performance enhancement gain that is obtained by densely deploying several elements in a metamaterial antenna. Simulation results substantiate the theoretical analysis and show that the maximum beamforming gain of a metamaterial antenna that utilizes the proposed holographic beamforming scheme can be increased by at least 50% compared with that of a traditional MIMO array of the same size. In addition, the cost of the proposed scheme is lower than that of a traditional MIMO scheme while providing the same ISAC performance.
We consider the cooperative detection for distributed sensing via low Earth orbit constellations. The source data gathered by the ground sensors are non-orthogonally sent to satellite access points, and then aggregated at a central satellite via inter-satellite links (ISLs). To overcome the intrinsic ISL bandwidth limitation, we consider the deep auto-encoding paradigm to jointly design the ISL transceivers among satellites, and propose a novel deep variational information bottleneck (DVIB) method which maximizes the end-to-end sensing accuracy under bandwidth constraints. Specifically, the mathematically untractable ISL bandwidth constraint is first transformed into an entropy-based format. Then a customized batch-norm layer is introduced, where the messages on ISLs are considered as latent variables and are regularized with entropy-constrained posterior for efficient compression. Compared to the benchmark, the proposed DVIB method is shown to simultaneously reduce the bandwidth overhead by 30% and enhance the sensing accuracy by 2–5 dB, validating the significance of relevant information extraction on ISLs.
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