For processors with vectorial computing units like DSP, it is very important to ensure vector load/store operations alignment of memory blocks, and minimize space wastage when making memory allocations. In this paper, we design and implement a memory management method, vector memory pool, suitable for embedded vector processors. By partitioning an entire block of memory space into many aligned vector objects and making efficiently use of vector processing units, the processing of memory manipulation library functions such as memset/memcpy is accelerated. The implementation and comparative verification of vector memory pool on RT-Thread Nano based on SWIFT DSP was completed, and the running efficiency reached a tens of times improvement compared to the original method.
The sixth generation (6G) mobile networks need to meet various performance requirements such as the number of connections, latency, reliability, and energy efficiency. In particular, for the Internet of Things (IoT) scenarios with short packet transmission, it is necessary to analyze and optimize various performances while achieving massive connections. In addition, practical constraints (such as imperfect channel state information, limitations of classical Shannon’s capacity, inter-cell interference, massive user interference, etc.) further aggravate the difficulties of theoretical analysis and performance improvement. We propose a performance analysis and optimization strategy for short packet transmission systems based on cell-free massive multiple-input multiple-output (CF mMIMO), which points out the idea of improving system performance with large-scale connections under practical constraints. Furthermore, with the combination of simultaneous wireless information and power transfer (SWIPT) technology and finite blocklength (FBL) information theory, we derive the closed-form expressions of downlink signal-to-interference-plus-noise ratio (SINR), achievable data rate, and energy collected based on CF mMIMO. Simulation results verify the effectiveness of the proposed strategy, which is also expected to support massive ultra-reliable and low latency communications (mURLLC) with ultra-high energy efficiency or spectral efficiency in the future.
With the rapid development of Internet of Things (IoT) technology, the number of large numbers of Machine Type Communication (MTC) devices involved in M2M has increased dramatically. When large scale MTC devices access the base station at the same time in a short period of time, this can cause traffic overload and lead to a sharp drop in the success rate of access of MTC devices. 3GPP has proposed the access class barring (ACB) scheme to defer access requests from certain activated MTC devices to avoid congestion at the base station (BS). In this paper, we propose a dynamic ACB scheme for grouping MTC devices and a resource allocation scheme for preamble. First, MTC devices are classified into two categories according to their characteristics: delay-sensitive and energy-constrained. The two categories use separate preamble resources, and a temporary ACB factor is calculated for each time slot based on the current preamble resources and the number of devices. The preamble resources are reallocated based on this temporary ACB factor using reinforcement learning methods, and then the ACB factor is dynamically adjusted according to the new preamble resources. Simulation results show that the solution improves the access success rate of M2M devices, reducing the total service time of delay-sensitive devices by 40 $$\%$$ compared to the traditional solution, while reducing the access collision rate of energy-constrained devices by 30 $$\%$$ .
Orthogonal time frequency space (OTFS) modulation can provide significant error performance than orthogonal frequency division multiplexing (OFDM) modulation in the high-speed scenario. However, the fractional Doppler effects cause Doppler diffusion. In this paper, we analyze the Doppler diffusion from both the formula and geometric levels. In order to alleviate Doppler diffusion, we propose an adaptive orthogonal basis scheme by using the Doppler shifts feedback of the receiver. Our scheme alter the matrix of the inverse symplectic finite Fourier transform (ISFFT) by the feedback. This scheme makes it possible to estimate the channel more accurately. In the simulation results, we show that the bit error rate (BER) and block error rate (BLER) performance of our proposed scheme is not affected compared with OTFS. In addition, our scheme can work even if the Doppler domain dimension is limited and the Doppler shifts feedback of the receiver is inaccurate.
With the development of fifth generation (5G) technology, mobile edge computing (MEC) is becoming an essential architecture which is envisioned as a cloud extension version. MEC system can push the resources from cloud side to edge side, aiming to solve many computation intensive problems. The task offloading policy is vital and has an important influence on MEC system. Meanwhile, privacy leakage may occur during the task offloading period which may degrade MEC system performance. The attention on these issues is lack according to existing works. Inspired by this, we present a privacy-preserving aware Multi-Armed Bandits based task allocation algorithm, Privacy Upper Confidence Bound (pUCB), to find a balance between the privacy preserving and the efficiency of task processing. In addition, we take regret analysis of the proposed algorithm. The extensive simulation results show that pUCB scheme can achieve a higher optimal rate, a lower lock rate and less total time cost comparing with traditional Multi-arm bandits (MAB) based algorithm.
