Multi-RAT Heterogeneous Networks Research Articles

Dynamic RAT Selection and Transceiver Optimization for Mobile-Edge Computing Over Multi-RAT Heterogeneous Networks

Mobile-edge computing (MEC) integrated with multiple radio access technologies (multi-RATs) is a promising technique for satisfying the growing low-latency computation demand of intelligent Internet of Things (IoTs) applications. Under the wireless MapReduce framework for executing nomographic functions, this article investigates the joint RAT selection and transceiver design for over-the-air (OTA) aggregation of intermediate values (IVAs) in multi-RAT MEC systems, while taking into account the energy budget constraint for the local computing and IVA transmission per wireless device (WD), so as to adapt to the instantaneous communication opportunities in multiple RATs and the dynamic computational task loads. To provide a complete Pareto optimal solution, we minimize the weighted sum of the computational mean squared error (MSE) of the aggregated IVA at the RAT receivers, the IVA transmission cost of the WDs, and the associated transmission time delay. The joint RAT selection and transceiver design problem for OTA aggregation of the IVAs is a nonconvex mixed-integer problem, which is NP hard. We develop a low-complexity algorithm to solve the challenge by continuous relaxation and alternating optimization. Specifically, the optimal receive beamforming vectors at the gNB/access points (APs) are shown to be the minimum MSE (MMSE) filters. Exploiting the hidden convexity of the remaining subproblem, we obtain an efficient iterative algorithm by alternating between the RAT selection and the transmit coefficient variables for OTA aggregation of IVAs. Extensive numerical results verify the effectiveness of our proposed design as compared to other existing schemes.

A novel approach to fairness-aware energy efficiency in multi-RAT heterogeneous networks

Heterogeneous cellular networks are a viable solution in response to the growing demand for broadband services in the new-generation wireless networks. It is more challenging to manage resources (e.g., user association and power control) in multi-radio access technology (multi-RAT) HetNets than in conventional single-tier cellular networks due to the higher computational complexity and energy efficiency constraints. In addition, given the novel services and resource limitation of the user layer, energy efficiency and fairness assurance are critical issues in the uplink. Considering the uplink fairness criterion, this paper proposes a dynamic optimization model which maximizes the total uplink/downlink energy efficiency in addition to providing the essential coverage and capacity of heterogeneous cellular networks. Based on the non-convex characteristics of the energy efficiency maximization model, the mathematical model can be formulated into two subproblems, i.e., resource optimization and user association. In this approach, a subgradient method is applied for fair resource management and also successive convex approximation and dual decomposition methods are adopted to solve the proportional fairness problem. The simulation results exhibit a considerable throughput increase of 25% and 18% on average for random and hotspot user distributions, respectively. Based on the numerical results, the adaptive radio resource management method reduces the rate of vertical handoffs between RATs, which leads to more stable communications for the users. It also proved that the proposed approach managed to significantly improve the total network energy efficiency by up to 40%.

Edge-Centric Bandit Learning for Task-Offloading Allocations in Multi-RAT Heterogeneous Networks

The exponential growth of data traffic from mobile devices leads to a need of heterogeneous networks (HetNets) which integrate multiple radio access technologies (multi-RATs) to allocate task-offloading with quick coordination. In this paper, we present a novel mobile edge computing (MEC) architecture for multi-RAT HetNets, and propose an MEC-centric offloading decision mechanism. By formulating the intended task as a multi-armed bandit (MAB) problem, we develop a fronthaul-aware upper confidence bound (FA-UCB) algorithm that is able to deal with uncertainty and asymmetry of network state information. It is rigorously established that the proposed FA-UCB algorithm has a sublinear regret bound against the optimal benchmark with full a-priori knowledge, given that the backhaul delays are independently and identically distributed over time. Furthermore, under a restless martingale (RM) bandit condition, we put forth a generalized RM-FA-UCB algorithm that can achieve a sublinear regret bound even under non-stationary network dynamics. Numerical results demonstrate the merits of the proposed schemes and algorithms.

Cell ID Management in Multi-Vendor and Multi-RAT Heterogeneous Networks

With the ever increasing cell densities in wireless networks, it is desired to enable more self-X features, such as self-configuration. Setting the network parameters in an autonomous manner is not only more time efficient but also increases network performance and reduces the probability of a human error. Among numerous network settings, one of the key parameters that requires such autonomous configuration is the cell ID (CID). It is used during fundamental procedures, such as network access, decoding, or handover, and therefore, its configuration is crucial for network operation. A complete CID management framework together with a centralized method for CID assignment is presented in this paper. It is not only applicable to multiple mobile standards but is also compatible with multi-vendor equipment, since it is based on the TR-069 management protocol. The proposed approach mitigates CID conflicts when there is a high level of reuse. Moreover, in the 4G case it also prevents neighbors from using different CID but with the same reference signal pattern, which avoids significant interference. The proposed method is evaluated in an enterprise small cell scenario, and in comparison with the baseline third generation partnership project approach, significant reductions of assignment conflicts are demonstrated.

On the Limits of Coexisting Coverage and Capacity in Multi-RAT Heterogeneous Networks

This paper devises a general modeling and analyzing framework for a heterogeneous wireless network (HetNet) in which several wireless subnetworks coexist and use multiple radio access technologies (multi-RATs). The coexisting coverage and network capacity in such a multi-RAT HetNet are hardly investigated in prior works. To characterize the coexisting interactions in a multi-RAT HetNet, in this paper, we consider a HetNet consisting of $K$ tiers of access points (APs) and two different RATs, RAT- $ \mathsf {L}$ and RAT- $ \mathsf {U}$ , are adopted in the HetNet. RAT- $ \mathsf {L}$ is adopted by the APs in the first $K-1$ tiers and APs in the $K$ th tier only use RAT- $ \mathsf {U}$ . Both noncrossing-RAT and crossing-RAT user association scenarios are considered. In each scenario, the void probability and the channel access probability of the APs in each tier are first found and then the tight lower bounds and their lowest limits on the proposed coexisting coverage and network capacity are derived. We show that multi-RAT networks in general can achieve higher link coverage and capacity by using opportunistic carrier sense multiple access with collision avoidance that avoids/alleviates severe interfering between all coexisting APs. Also, crossing-RAT user association is shown to achieve much higher coexisting coverage and network capacity than noncrossing-RAT user association. Finally, numerical simulations for the LTE-U and WiFi networks coexisting in the HetNet validate our findings.