Artificial intelligence has recently permeated every field, and current research trends in wireless communications naturally involve leveraging machine learning (ML) for communications processing. Numerous previous research studies have theoretically demonstrated that intelligentizing physical layer holds the promise of enhancing performance for the 6G era. In this article, we demonstrate the practical implementation of intelligentizing random access (RA) through real-time channel learning (CL). Real-time CL adapts an ML model to the variations of wireless channel in real time and requires extremely short and low-complexity training. Our research encompasses algorithmic design through the implementation of an off-the-shelf testbed which incorporates real-time CL in softwarized modem. We initially review the fundamental algorithms of communications processing for RA and then proceed to design a proper ML model for real-time CL. The overall intelligentized RA detector is then implemented using the concept of softwarized modem. The implementation achieves real-time CL via efficient interoperation of communications processing and the ML model. Experiments with the implementation confirm that intelligentization through real-time CL is feasible and results in an SNR gain up to 2.7 dB for RA scenarios.

Mobile edge computing(MEC) has emerged as an attractive solution by executing computation-intensive services at a powerful edge server instead of mobiles. Two types of data are necessary to this end. One is user-specific data acquired from mobiles, calledcomputation offloading(CO). The other is service-specific data downloaded from a central cloud, calledservice caching(SC). It is noteworthy that CO and SC decisions are coupled when each user’sservice preference(SP) is personalized. Specifically, noting that the optimal SC is to cache services likely to be requested more frequently, the resultant SC tends to be biased to the SP of the user whose offloading rate is high. On the other hand, such an SC decision causes longer computing latency of users with a relatively low offloading rate, which ultimately limits a CO decision for agile MEC services. This work tackles this issue from a sum-utility maximization perspective under radio-resource and computation-latency constraints. The average computation latency is first derived in closed-form by modeling a computation as a stochastic process following a hyper-exponential distribution. Based on it, we first consider the case for homogeneous SP where CO and SC decisions are decoupled. Thus, SC can be deterministically controlled using the homogeneous SP, while CO decision is independently determined, lying between water-filling and channel-inversion allocations. Next, we design a joint CO-and-SC policy for heterogeneous SP. CO and SC decisions are iteratively optimized with the other fixed by leveraging the homogeneous SP’s result. The optimal stopping rules are derived, guaranteeing the sum-utility enhancement. The proposed algorithm’s effectiveness is verified by simulations that the proposed CO-and-SC design for heterogenous SP always outperforms that for homogeneous SP.

Based on a convex approach with side-information, we propose transmit beamforming designs for two-user multiple-input single-output fading interference channels. Main contribution of this paper is to provide a novel methodology for solving a non-convex optimization problem efficiently based on the effective side-information. Consequently, the proposed scheme exhibits near-optimal average sum-rate performance under single user detection with Gaussian inputs, which is validated through numerical results. As a by-product, we show that the proposed scheme requires almost no parameter optimization on the average over multiple coding blocks.

The multiplexing gain (MUXG) of the K-user interference channel (IC) with partially connected interfering links is analyzed. The motivation for the partially connected IC comes from the fact that not all interferences are equally strong in practice. We introduce the idea of the graphical representation of interference alignment (IA) condition. We show that for a K-user partially connected MIMO IC with M antennas at each transmitter and receiver, the transmit beamforming matrices achieving the MUXG of KM/2 can be generated sequentially almost surely if the interference channel has a graphical representation which follows certain criteria. We also present a novel scheme, where we use the idea of partially connected IC to obtain improvements in conventional distributed interference alignment algorithm for fully connected IC. The improvements are obtained in terms of greater average sum-rate and faster convergence.

We analyze achievable multiplexing gains (MUXGs) of fully connected K-user line-of-sight (LOS) interference channels (ICs). An array of polarimetric antennas, each composed of three orthogonal electric dipoles and three orthogonal magnetic dipoles in which all six elements are co-located, is used throughout this paper. For a K-user LOS IC with single polarization, the maximum achievable MUXG is only K, regardless of the number of transmit and receive antennas at each node due to the key-hole effect. If polarimetric antennas are used at each node, a trivial upper bound on the MUXG is now 2K. In this paper, we consider zero-forcing (ZF) and interference alignment (IA) schemes to achieve this upper bound. We show the optimal MUXG of 2K is achievable if M ≥ ((K+1)/6) polarimetric antennas are used at each node for any K. In addition, using the proposed ZF scheme, we obtain minimal dipole configurations at each node that achieve this upper bound for K <; 5. Furthermore, we analyze achievable MUXGs using the distributed interference alignment (DIA) algorithm. Although IA can generally provide a higher MUXG than ZF for LOS ICs, we observe that the number of required dipole elements to achieve the optimal MUXG of 2K under a ZF scheme is the same as that required under IA for all cases we consider.

The separability in parallel Gaussian interference channels (PGICs) is studied in this paper. We generalize the separability results in one-sided PGICs (OPGICs) by Sung et al. to two-sided PGICs (TPGICs). Specifically, for strong and mixed TPGICs, we show necessary and sufficient conditions for the separability. For this, we show diagonal covariance matrices are sum-rate optimal for strong and mixed TPGICs.