Resource Allocation in Cell-Free MU-MIMO Multicarrier System with Finite and Infinite Blocklength

The explosive growth of data results in more scarce spectrum resources. It is important to optimize the system performance under limited resources. In this paper, we investigate how to achieve weighted throughput (WTP) maximization for cell-free (CF) multiuser MIMO (MU-MIMO) multicarrier (MC) systems through resource allocation (RA), in the cases of finite blocklength (FBL) and infinite blocklength (INFBL) regimes. To ensure the quality of service (QoS) of each user, particularly for the block error rate (BLER) and latency in the FBL regime, the WTP gets maximized under the constraints of total power consumption and required QoS metrics. Since the channels vary in different subcarriers (SCs) and inter-user interference strengths, the WTP can be maximized by scheduling the best users in each time-frequency (TF) resource and advanced beamforming design, while the resources can be fully utilized. With this motivation, we propose a joint user scheduling (US) and beamforming design algorithm based on the successive convex approximation (SCA) and gene-aided (GA) algorithms, to address a mixed integer nonlinear programming (MINLP) problem. Numerical results demonstrate that the proposed RA outperforms the comparison schemes. And the CF system in our scenario is capable of achieving higher spectral efficiency than the centralized antenna systems (CAS).

Optimization of the energy efficiency in Smart Internet of Vehicles assisted by MEC

Smart Internet of Vehicles (IoV) as a promising application in Internet of Things (IoT) emerges with the development of the fifth generation mobile communication (5G). Nevertheless, the heterogeneous requirements of sufficient battery capacity, powerful computing ability and energy efficiency for electric vehicles face great challenges due to the explosive data growth in 5G and the sixth generation of mobile communication (6G) networks. In order to alleviate the deficiencies mentioned above, this paper proposes a mobile edge computing (MEC) enabled IoV system, in which electric vehicle nodes (eVNs) upload and download data through an anchor node (AN) which is integrated with a MEC server. Meanwhile, the anchor node transmitters radio signal to electric vehicles with simultaneous wireless information and power transfer (SWIPT) technology so as to compensate the battery limitation of eletric vehicles. Moreover, the spectrum efficiency is further improved by multi-input and multi-output (MIMO) and full-duplex (FD) technologies which is equipped at the anchor node. In consideration of the issues above, we maximize the average energy efficiency of electric vehicles by jointly optimize the CPU frequency, vehicle transmitting power, computing tasks and uplink rate. Since the problem is nonconvex, we propose a novel alternate interior-point iterative scheme (AIIS) under the constraints of computing tasks, energy consumption and time latency. Results and discussion section verifies the effectiveness of the proposed AIIS scheme comparing with the benchmark schemes.