Dynamic NOMA-Based Computation Offloading in Vehicular Platoons

Both the mobile edge computing (MEC) based and fog computing (FC) aided Internet of Vehicles (IoV) constitute promising paradigms of meeting the demands of low-latency pervasive computing. To this end, we construct a dynamic NOMA-based computation offloading scheme for vehicular platoons on highways, where the vehicles can offload their computing tasks to other platoon members. To cope with the rapidly fluctuating channel quality, we divide the timeline into successive time slots according to the channel's coherence time. Robust computing and offloading decisions are made for each time slot after taking the channel estimation errors into account. Considering a certain time slot, we first analytically characterize both the locally computed source data and the offloaded source data as well as the energy consumption of every vehicle in the platoons. We then formulate the problem of minimizing the long-term energy consumption by optimizing the allocation of both the communication and computing resources. To solve the problem formulated, we design an online algorithm based on the classic Lyapunov optimization method and block successive upper bound minimization (BSUM) method. Finally, the numerical simulation results characterize the performance of our algorithm and demonstrate its advantages both over the local computing scheme and the orthogonal multiple access (OMA)-based offloading scheme.Comment: 11 pages, 9 figure

A Prototype of Co-Frequency Co-Time Full Duplex Networking

Radio FD has emerged as an attractive technique capable of doubling the spectral efficiency over half duplex. However, for signal reception, an FD node needs to suppress its transmitter's signals quite significantly. In point to point communication systems, these transmitter signals are termed self-interference. When working with an FD mobile network, the self-interference problem becomes much more complicated because the receiver of an FD base station (BS) receives interference not only from its BS transmitter in its cell, but also from those in the surrounding cells. For the UL channel, self-interference extends to the problem of multiple interference. And, a similar interference problem can be found among the MSs over a DL channel. In both cases, the interference owing to the FD implementation spreads beyond the scope of the self-interference. This article describes the development of FD BSs that use antenna arrays to deal with the BSs' interference, and thus enable FD communication over the UL channel, where the theoretical focus is placed on how to use the antenna array to nullify the multiple interference and receive the signals of the desired MSs simultaneously. To complete the system construction, FD MSs have also been developed to enable DL transmission. A prototype system is described for the scenario of two cells and one FD MS for tests of FD communication over UL channels and DL channels in terms of video performance. Good video quality is demonstrated at both the BS and MS

Next-Generation Full Duplex Networking System Empowered by Reconfigurable Intelligent Surfaces

Full duplex (FD) radio has attracted extensive attention due to its co-time and co-frequency transceiving capability. {However, the potential gain brought by FD radios is closely related to the management of self-interference (SI), which imposes high or even stringent requirements on SI cancellation (SIC) techniques. When the FD deployment evolves into next-generation mobile networking, the SI problem becomes more complicated, significantly limiting its potential gains.} In this paper, we conceive a multi-cell FD networking scheme by deploying a reconfigurable intelligent surface (RIS) at the cell boundary to configure the radio environment proactively. To achieve the full potential of the system, we aim to maximize the sum rate (SR) of multiple cells by jointly optimizing the transmit precoding (TPC) matrices at FD base stations (BSs) and users and the phase shift matrix at RIS. Since the original problem is non-convex, we reformulate and decouple it into a pair of subproblems by utilizing the relationship between the SR and minimum mean square error (MMSE). The optimal solutions of TPC matrices are obtained in closed form, while both complex circle manifold (CCM) and successive convex approximation (SCA) based algorithms are developed to resolve the phase shift matrix suboptimally. Our simulation results show that introducing an RIS into an FD networking system not only improves the overall SR significantly but also enhances the cell edge performance prominently. More importantly, we validate that the RIS deployment with optimized phase shifts can reduce the requirement for SIC and the number of BS antennas, which further reduces the hardware cost and power consumption, especially with a sufficient number of reflecting elements. As a result, the utilization of an RIS enables the originally cumbersome FD networking system to become efficient and practical.Comment: 15 pages, 14 figure

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Dynamic NOMA-based computation offloading in vehicular platoons

Both the Mobile edge computing (MEC)-based and fog computing (FC)-aided Internet of Vehicles (IoV) constitute promising paradigms of meeting the demands of low-latency pervasive computing. To this end, we construct a dynamic NOMAbased computation offloading scheme for vehicular platoons on highways, where the vehicles can offload their computing tasks to other platoon members. To cope with the rapidly fluctuating channel quality, we divide the timeline into successive time slots according to the channel’s coherence time. Robust computing and offloading decisions are made for each time slot after taking the channel estimation errors into account. Considering a certain time slot, we first analytically characterize both the locally computed source data and the offloaded source data as well as the energy consumption of every vehicle in the platoons. We then formulate the problem of minimizing the long-term maximum task queue by optimizing the allocation of both the communication and computing resources. To solve the problem formulated, we design an online algorithm based on the classic Lyapunov optimization method and successive convex approximation (SCA) method. Finally, the numerical simulation results characterize the performance of our algorithm and demonstrate its advantages both over the local computing scheme and the orthogonal multiple access (OMA)-based offloading scheme

NOMA-SM for cooperatively enhancing vehicle-to-vehicle transmissions

Inspired by the robustness of spatial modulation (SM) against channel correlation and the benefits of non-orthogonal multiple access (NOMA), in this paper, we intrinsically amalgamate them into NOMA-SM in order to deal with the deleterious effects of wireless vehicle-to-vehicle (V2V) environments as well as to support improved bandwidth efficiency. Specifically, a spatio-temporally correlated Rician channel is considered for a V2V scenario. We derive the capacity of NOMA-SM and a pair of analytical capacity upper bounds in closed form. A power allocation optimization scheme is formulated accordingly and the optimal solution is demonstrated to be achievable with the aid of our proposed algorithm. By investigating the bit error ratio (BER) performance of NOMA with different multiple-antenna techniques and the bandwidth efficiency of SM combined with distinct multiple access methods, NOMA and SM are shown to cooperatively improve V2V transmissions.</p