This paper proposes, and evaluates the benefits of, a hybrid central cloud (CC) and mobile edge computing (MEC) platform, especially introduced to balance the network resources for joint communication and computation. The transmission is further empowered by splitting the users’ messages into private and common parts, to mitigate the interference within the CC and MEC platforms. While several power-hungry, computationally-limited unmanned aerial vehicles (UAVs) are deployed at the cell-edge to boost the CC connectivity and relieve part of its computation burden, the CC connects to the base-stations via capacity-limited fronthauls. The paper then considers the problem of maximizing the weighted sum-rate subject to fronthaul and computation capacity, achievable rates, power, delay, and data-split constraints. Thereby determining the beamforming vectors associated with the private and common messages, the computation allocations, and the data-split factors. Such intricate non-convex optimization problem is tackled using an iterative algorithm that relies on well-chosen discrete relaxation, successive convex approximation, and fractional programming, and can be compellingly implemented in a distributed fashion. The simulations illustrate the proposed algorithm’s capabilities for empowering joint communication and computation, and highlight the pronounced role of rate-splitting and common message decoding in alleviating large-scale interference in hybrid CC/MEC networks.

The tremendous growth of data traffic in mobile communication networks (MCNs) and the associated exponential increase in mobile devices’ numbers necessitate the use of multi-cloud radio access networks (MC-RANs) as a viable solution to cope with the requirements of next-generation MCNs (6G). In MC-RANs, each central processor (CP) manages the signal processing of its own set of base stations (BSs), and so the system performance becomes a function of the joint intra-cloud and inter-cloud interference mitigation techniques. To this end, this paper considers the problem of maximizing the network-wide energy efficiency (EE) subject to user-to-cloud association, fronthaul capacity, maximum transmit power, and achievable rate constraints, so as to determine the joint beamforming vector of each user and the user-to-cloud association strategy. The paper tackles the non-convex and mixed discrete-continuous nature of the problem formulation using fractional programming (FP) and inner-convex approximation (ICA) techniques, as well as l0-norm relaxation heuristics, and shows how the proposed approach can be implemented in a distributed fashion via a reasonable amount of information exchange across the CPs. The paper simulations highlight the appreciable algorithmic efficiency of the proposed approach over state-of-the-art schemes.

The potential of intelligent reflecting surfaces (IRSs) is investigated as a promising technique for enhancing the energy efficiency of wireless networks. Specifically, the IRS enables passive beamsteering by employing many low-cost individually controllable reflect elements. The resulting change of the channel state, however, not only increases the signal quality but also the interference at the users. To counteract this negative side effect, we employ rate splitting (RS), which inherently is able to mitigate the impact of interference. We facilitate practical implementation by considering a Cloud Radio Access Network (C-RAN) at the cost of finite fronthaul-link capacities, which necessitate the allocation of sensible user-centric clusters to ensure energy-efficient transmissions. Dynamic methods for RS and the user clustering are proposed to account for the interdependencies of the individual techniques. Numerical results show that the dynamic RS method establishes synergistic benefits between RS and the IRS. Additionally, the dynamic user clustering and the IRS cooperate synergistically, reflected by increased individual gains for the dynamic clustering. Interestingly, with an increasing fronthaul capacity, the gain of the dynamic user clustering decreases, while the gain of the dynamic RS method increases. Around the resulting intersection, both methods affect the system concurrently, improving the energy efficiency drastically.

The concept of intelligent reflecting surfaces (IRSs) is considered as a promising technology for increasing the efficiency of mobile wireless networks. This is achieved by employing a vast amount of low-cost individually adjustable passive reflect elements, that are able to apply changes to the reflected signal. To this end, the IRS makes the environment real-time controllable and can be adjusted to significantly increase the received signal quality at the users by passive beamsteering. However, the changes to the reflected signals have an effect on all users near the IRS, which makes it impossible to optimize the changes to positively influence every transmission affected by the reflections. This results in some users not only experiencing better signal quality, but also an increase in received interference. To mitigate this negative side effect of the IRS, this paper utilizes the rate splitting (RS) technique, which enables the mitigation of interference within the network in such a way that it also mitigates the increased interference caused by the IRS. To investigate the effects on the overall power savings, that can be achieved by combining both techniques, we minimize the required transmit power, needed to satisfy per-user quality-of-service (QoS) constraints. Numerical results show the improved power savings, that can be gained by utilizing the IRS and the RS technique simultaneously. In fact, the concurrent use of both techniques yields power savings, which are beyond the cumulative power savings of using each technique separately.

Cloud radio access networks (C-RAN) enable a network platform for beyond the fifth generation of communication networks (B5G), which incorporates the advances in cloud computing technologies to modern radio access networks. Recently, rate splitting multiple access (RSMA), relying on multi-antenna rate splitting (RS) at the transmitter and successive interference cancellation (SIC) at the receivers, has been shown to manage the interference in multi-antenna communication networks efficiently. This paper considers applying RSMA in C-RAN. We address the practical challenge of a transmitter that only knows the statistical channel state information (CSI) of the users. To this end, the paper investigates the problem of stochastic coordinated beamforming (SCB) optimization to maximize the ergodic sum-rate (ESR) in the network. Furthermore, we propose a scalable and robust RS scheme where the number of the common streams to be decoded at each user scales linearly with the number of users, and the common stream selection only depends on the statistical CSI. The setup leads to a challenging stochastic and non-convex optimization problem. A sample average approximation (SAA) and weighted minimum mean square error (WMMSE) based algorithm is adopted to tackle the intractable stochastic non-convex optimization and guarantee convergence to a stationary point asymptotically. The numerical simulations demonstrate the efficiency of the proposed RS strategy and show a gain up to 27% in the achievable ESR compared with state-of-the-art schemes, namely treating interference as noise (TIN) and non-orthogonal multiple access (NOMA) schemes.

Rate-splitting multiple access (RSMA), recognized as a promising technique for future communication systems to generalize and outperform existing multiple access techniques, has been shown to enhance the spectral and energy efficiencies of multi-user multi-antenna broadcast channels (BCs). In this work, motivated by the benefits of RSMA discovered in multi-antenna BCs, we investigate the performance of RSMA in cloud radio access networks (C-RANs). Specifically, the beamforming vectors, message splits, and stream-to-base stations (BSs) allocation are jointly designed with the aim to maximize the sum rate subject to per-BS power constraints and fronthaul capacity constraints. Numerical results demonstrate that RSMA boosts the sum rate in C-RAN especially in strong interference regimes. Therefore, RSMA is a more promising transmission technique for C-RAN than other conventional transmission schemes such as treating interference as noise (TIN) or orthogonal multiple access schemes.

In this paper, we are concerned with indoor localization based on multiple-antenna channel measurements. Indoor localization is an active area of research due to its great importance in many applications. We propose a hybrid algorithm which combines the benefits of two techniques, namely signal processing and machine learning. We validate our algorithm based on real measurements acquired from two practical setups. Our approach shows a very promising performance in the IEEE CTW 2019 - Positioning Algorithm Competition where the algorithm achieves an accuracy within RMSE values below 10 cm. We further build a setup in another indoor environment, where the algorithm still proves a very good performance compared to state-of-the art techniques used in indoor localization tasks.

We jointly design an information-theoretic transmit and receive radar beamformers for spatially near multiple extended targets. We maximize the mutual information (MI) between the received signals and the targets signatures that allows the extraction of the unknown features, which may include shape, dimensions, and material. However, high interference caused by spatially near targets might obstruct the information extraction, and directing the beamformers toward the steering vector as done in conventional beamformers does not solve this problem, especially for extended targets. In this letter, an iterative algorithm is presented to solve this problem using alternative minimization, dividing it into two blocks. The first block is solving for the transmit beamformers successively using block coordinate descent, and the second one is solving for the receiver beamformers using the minimum variance distortionless response. We also show the effect of using our beamformers on the waveform design problem. Numerical results indicate that this algorithm can achieve substantially higher MI than the existing conventional methods. Thus, except for some degenerate cases, having fixed beamformers instead of optimized ones lead to significant performance degradation.

An emerging trend of next generation communication systems is to provide network edges with additional capabilities such as additional storage resources in the form of caches to reduce file delivery latency. To investigate this aspect, we study the fundamental limits of a cache-aided wireless network consisting of one central base station, M transceivers and K receivers from a latency-centric perspective. We use the normalized delivery time (NDT) to capture the per-bit latency for the worst-case file request pattern at high signal-to-noise ratios (SNR), normalized with respect to a reference interference-free system with unlimited transceiver cache capabilities. For various special cases that satisfy K+M≤4, we establish the optimal tradeoff between cache storage and latency. This is facilitated through establishing a novel converse (for arbitrary M and K) and an achievability scheme on the NDT. Our achievability scheme is a synergistic combination of multicasting, zero-forcing beamforming and interference alignment.

Cloud radio access networks (C-RAN) are expected to be the backbone of next generation communication networks. In order to reduce the backhaul cost, which is a bottleneck in C-RAN, popular files are cached in local memories at remote radio heads (RRH's) and thus in close proximity to the users demanding it. In this paper we investigate different caching strategies with the objective to reduce backhaul and transmit power cost. It turns out that the problem of jointly minimizing the transmit power and backhaul costs constitutes a mixed integer non linear program (MINLP). First, we introduce slack variables to formulate the problem as a standard mixed integer second order cone program (MI-SOCP). With this formulation we can get the global optimal solution with reduced computational costs. However, in large-scale networks with large number of users and RRH's, using MI-SOCP is either inefficient or even intractable in some cases. Therefore, we introduce an inflation based polynomial time algorithm. We show, with numerical simulations, that our approach provides close-to-optimal solutions with much smaller amount of time compared to that needed for getting the global optimal. We also show that our approach is more efficient than the other state of the art algorithms, besides it always yields an integer feasible solution as opposed to other relaxation techniques. We also point out the essential impact of caching scheme on the trade-off between transmit power and backhaul costs. It turns out that content redundancy at the local caches, enables the RRH's to cooperate for transmit power cost reduction, but at the expense of increased backhaul cost.