Resource Allocation In Small Cell Networks Research Articles

Energy-efficient resource allocation for small-cell networks: A stable queue perspective

The small-cell technology is promising for spectral-efficiency enhancement. However, it usually requires a huge amount of energy consumption. In this paper, queue state information and channel state information are jointly utilized to minimize the time average of overall energy consumption for a multi-carrier small-cell network, where the inter-cell interference is an intractable problem. Based on the Lyapunov optimization theory, the problem could be solved by dynamically optimizing the problem of user assignment, carrier allocation and power allocation in each time slot. As the optimization problem is NP-hard, we propose a heuristic iteration algorithm to solve it. Numerical results verify that the heuristic algorithm offers an approximate performance as the brute-force algorithm. Moreover, it could bring down the overall energy consumption to different degrees according to the variation of traffic load. Meanwhile, it could achieve the same sum rate as the algorithm which focuses on maximizing system sum rate.

Distributed Virtual Resource Allocation in Small-Cell Networks With Full-Duplex Self-Backhauls and Virtualization

Wireless network virtualization has attracted great attention from both academia and industry. Another emerging technology for next-generation wireless networks is in-band full-duplex (FD) communications. Due to its promising performance, FD communication has been considered to be an effective way of achieving self-backhauls for small cells. In this paper, we introduce wireless virtualization into small-cell networks and propose a virtualized small-cell network architecture with FD self-backhauls. We formulate the virtual-resource-allocation problem in virtualized small-cell networks with FD self-backhauls as an optimization problem. Since the formulated problem is a mixed combinatorial and nonconvex optimization problem, its computational complexity is high. Moreover, the centralized scheme may suffer from signaling overhead, outdated dynamics information, and scalability issues. To solve it efficiently, we divide the original problem into two subproblems. For the first subproblem, we transfer it to a convex optimization problem and then solve it by an efficient alternating direction method of multipliers (ADMM)-based distributed algorithm. The second subproblem is a convex problem, which can be solved by each infrastructure provider. Extensive simulations are conducted with different system configurations to show the effectiveness of the proposed scheme.

Partially-Distributed Resource Allocation in Small-Cell Networks

We propose a four-stage hierarchical resource allocation scheme for the downlink of a large-scale small-cell network in the context of orthogonal frequency-division multiple access (OFDMA). Since interference limits the capabilities of such networks, resource allocation and interference management are crucial. However, obtaining the globally optimum resource allocation is exponentially complex and mathematically intractable. Here, we develop a partially decentralized algorithm to obtain an effective solution. The three major advantages of our work are as follows: 1) as opposed to a fixed resource allocation, we consider load demand at each access point (AP) when allocating spectrum; 2) to prevent overloaded APs, our scheme is dynamic in the sense that as the users move from one AP to the other, so do the allocated resources, if necessary, and such considerations generally result in huge computational complexity, which brings us to the third advantage: 3) we tackle complexity by introducing a hierarchical scheme comprising four phases: user association, load estimation, interference management via graph coloring, and scheduling. We provide mathematical analysis for the first three steps modeling the user and AP locations as Poisson point processes. Finally, we provide the results of numerical simulations to illustrate the efficacy of our scheme.

Joint Optimization of Collaborative Sensing and Radio Resource Allocation in Small-Cell Networks

The current trend towards heterogeneous networks requires some sort of self-organization capability, in terms of self-optimization and self-configuration. The basic steps enabling self-optimization typically require listening to the environment, learning and adapting resource allocation consequently. In this work, we propose a joint optimization of sensing parameters and radio resource allocation in order to maximize the opportunistic throughput, under the constraint of limiting undue interference towards primary users. The method applies to wireless networks where the sensing nodes are allowed to cooperate to improve their local decision capabilities using a fully decentralized approach based on distributed consensus. The proposed optimization maximizes the opportunistic throughput, taking into account decision errors, sensing time and time necessary to achieve a consensus over the sensed variables. Because of the lack of knowledge of the channel between the cognitive users and the macro-users, the interference constraint is formulated in probabilistic form, depending on the spatial distribution of the nodes and on the channel fading model. Our formulation allows us to find the optimal false alarm rate and, consequently, the optimal decision thresholds jointly with the optimal power/bit allocation. Finally, we show that, given a total power budget constraint, there is an optimal way of distributing the available power between the power spent to achieve consensus and the power spent for transmitting the data payload.