Effective Beam Management (BM) in 5G advanced networks is essential for ensuring reliable connectivity, especially in environments characterized by high mobility and varying signal conditions. Traditional methods depend on frequent measurements of Synchronization Signal Block (SSB) quality which increases power consumption and negatively impact battery life. In this paper, we propose a novel spatiotemporal prediction model that leverages Deep Learning (DL) techniques to forecast future SSB Received Signal Received Power (RSRP) in both space and time domain. By predicting the best SSB index in advance, our model eliminates the need of continuous SSB measurements. This approach significantly reduces power consumption, thereby enhancing device battery life. The model combines Convolutional and Long Short-Term Memory (LSTM) networks, which capture temporal dependencies, with Fully Connected (FC) network for spatial feature extraction, resulting in a robust prediction mechanism. We also provided the mechanism to choose between the DL and legacy mechanism to have balance between global performance and power consumption.

In this paper, we propose a reinforcement learning based algorithm for rate-profile construction of Arikan’s Polarization Adjusted Convolutional (PAC) codes. This method can be used for any blocklength, rate, list size under successive cancellation list (SCL) decoding and convolutional precoding polynomial. To the best of our knowledge, we present, for the first time, a set of new reward and update strategies which help the reinforcement learning agent discover much better rate-profiles than those present in existing literature.Simulation results show that PAC codes constructed with the proposed algorithm perform better in terms of frame erasure rate (FER) compared to the PAC codes constructed with contemporary rate profiling designs for various list lengths. Further, by using a (64, 32) PAC code as an example, it is shown that the choice of convolutional precoding polynomial can have a significant impact on rate-profile construction of PAC codes.

The well known Maddah-Ali-Niesen (MAN) coded caching scheme for users with dedicated cache is extended for use in multi-access coded cache scheme where the number of users need not be same as the number of caches in the system. The well known MAN scheme is recoverable as a special case of the multi-access system considered. The performance of this scheme is compared with the existing works on multi-access coded caching. To be able to compare the performance of different multi-access schemes with different number of users for the same number of caches, the terminology of per user rate (rate divided by the number of users) introduced in [11] is used.

Recently multi-access coded caching schemes with number of users different from the number of caches obtained from a special class of resolvable designs called Cross Resolvable Designs (CRDs) have been reported and a new performance metric called rate-per-user has been introduced by Digvijay et al (“Multi-Access Coded Caching Schemes From Cross Resolvable Designs” in IEEE Transactions on Communications, May 2021). In this paper, we present a generalization of this work resulting in multi-access coded caching schemes with improved rate-per-user.

We present a novel caching and coded delivery scheme for a multi-access network where multiple users can have access to the same cache (shared cache) and multiple caches can be accessed by the same user. This scheme is obtained from resolvable designs satisfying certain conditions which we call cross resolvable designs. To be able to compare different multi-access coded schemes with different number of users we normalize the rate of the schemes by the number of users served. Based on this per-user-rate we show that our scheme performs better than the well known Maddah-Ali - Niesen (MaN) scheme and the recently proposed (“Multi-access coded caching: gains beyond cache-redundancy” by Serbetci, Parrinello and Elia) SPE scheme. It is shown that the resolvable designs from affine planes are cross resolvable designs and our scheme based on these performs better than the MaN scheme for large memory size cases. The exact size beyond which our performance is better is also presented. The SPE scheme considers only the cases where the product of the number of users and the normalized cache size is 2, whereas the proposed scheme allows different choices depending on the choice of the cross resolvable design.