In this paper, we consider a multiuser mobile edge computing (MEC) system, where a mixed-integer offloading strategy is used to assist the resource assignment for task offloading.
The impact of the quantization error on the convergence time is evaluated and the trade-off among model accuracy and timely execution is revealed.
To support these requirements, the third generation partnership project (3GPP) has introduced enhanced grant-free (GF) transmission in the uplink (UL), with multiple active configured-grants (CGs) for URLLC UEs.
Moreover, we formulate a model for the calculation of the absorption coefficient of any generic biological tissue.
Finally, a data-embedded MS-QP (DE-MS-QP) waveform is constructed through time-domain extension of the MS-QP sequence, generating null frequency points on each subband for data transmission.
As it has been discussed in the first part of this work, the utilization of advanced multiple access protocols and the joint optimization of the communication and computing resources can facilitate the reduction of delay for wireless federated learning (WFL), which is of paramount importance for the efficient integration of WFL in the sixth generation of wireless networks (6G).
Conventional machine learning techniques are conducted in a centralized manner.
To the best of the authors knowledge, this is the first time that LGSO algorithms are applied to the optimal power allocation problem in IoT networks.
To facilitate the performance analysis of a RRS-assisted system, first, we present novel closed-form expressions for the probability density function, the cumulative distribution function, the moments, and the characteristic function of the distribution of the sum of double-Nakagami-m random vectors, whose amplitudes follow the double-Nakagami-m distribution, i. e., the distribution of the product of two Nakagami-m random variables, and phases follow the circular uniform distribution.
Information Theory Signal Processing Information Theory Applications
Existing detection methods have mainly focused on specific noise models, which are not robust enough with unknown noise statistics.
This indicates that the proposed algorithm reaches almost the optimal efficiency in practical scenarios, and thereby it is applicable for large-scale systems.
More importantly, simulation results reveal that a 3-bit resolution for discrete phase shifts is sufficient to achieve near-optimal outage performance.
We adopt the partial offloading policy, in which each user can partition its computation task into offloading and locally computing parts.
Accurate channel state information (CSI) feedback plays a vital role in improving the performance gain of massive multiple-input multiple-output (m-MIMO) systems, where the dilemma is excessive CSI overhead versus limited feedback bandwith.
Next, we discuss the state of the art ML methodologies that are used to countermeasure the aforementioned challenges.
In the present work, we introduce a novel cochlear implant (CI) architecture, namely all-optical CI (AOCI), which directly converts acoustic to optical signals capable of stimulating the cochlear neurons.
Based on this framework, we define the latent access failure probability to characterize URLLC reliability and latency performances.