shrank unexpectedly by 0.3% in august. manchester crown court hears how a nurse, accused of murdering babies on a neonatal ward, tried to kill a premature baby girl on two consecutive night shifts. after the latest russian attacks on ukraine, nato says sending air defence systems there is a top priority. blood supplies fall to a critically low level in england, prompting nhs blood and transplant to declare its first ever amber alert hospitals may have to postpone some elective surgery. and scientists successfully transplant tissue from a human brain to that of a newborn rat and say the hybrid organoids can not only play the 1970s video game, pong, but will also help with research into brain disorders. but will also help with research we but will also help with research will be talking live the we will be talking live to one of the experts involved. good afternoon and welcome to bbc news. the prime minister has said she ll absolutely stick to her promise not to cut public
Grant-free random access is promising in achieving massive connectivity with sporadic transmissions in massive machine type communications (mMTC) for internet of things (IoT) applications, where the hand-shaking between the access point (AP) and users is skipped, leading to high multiple access efficiency. In grant-free random access, the AP needs to identify the active users and perform channel estimation and signal detection. Conventionally, pilot signals are required for the AP to achieve user activity detection and channel estimation before active user signal detection, which may still result in substantial overhead and latency. In this paper, to further reduce the overhead and latency, we investigate the problem of grant-free random access without the use of pilot signals in a millimeter wave (mmWave) multiple input and multiple output (MIMO) system, where the AP performs blind joint user activity detection, channel estimation and signal detection (UACESD). We show that the blind
We investigate signal detection in multiple-input-multiple-output (MIMO) communication systems with hardware impairments, such as power amplifier nonlinearity and in-phase/quadrature imbalance. To deal with the complex combined effects of hardware imperfections, neural network (NN) techniques, in particular deep neural networks (DNNs), have been studied to directly compensate for the impact of hardware impairments. However, it is difficult to train a DNN with limited pilot signals, hindering its practical application. In this work, we investigate how to achieve efficient Bayesian signal detection in MIMO systems with hardware imperfections. Characterizing combined hardware imperfections often leads to complicated signal models, making Bayesian signal detection challenging. To address this issue, we first train an NN to ‘model’ the MIMO system with hardware imperfections and then perform Bayesian inference based on the trained NN. Modelling the MIMO system with NN enables the design
Dual pulse shaping (DPS) transmissions enable the use of A/D and D/A converters with half of the symbol rate, alleviating the requirement of high speed conversion devices in wideband millimeter wave communications. In this letter, we focus on DPS equalization and propose a unitary approximate message passing (UAMP) based equalization technique. Two DPS transmission schemes are considered, and by exploiting the special structure of the system transfer matrix, two low-complexity equalizers are developed with the fast Fourier transform (FFT). Simulation results show that significant performance gains can be achieved by the UAMP-based equalizers, compared to the conventional DPS equalizer.