Cross-layer Resource Allocation Problem Research Articles

Buffer-Aware User Selection and Resource Allocation for an Opportunistic Cognitive Radio Network: A Cross-Layer Approach

In this paper, we focus on a cross-layer resource allocation problem for an opportunistic cognitive radio network, where secondary users (SUs) share a primary network’s licensed spectrum only when the primary network is sensed to be idle. We consider cooperative spectrum sensing, where SUs participate in sensing only when a benefit from resource allocation is guaranteed. Number of SUs for cooperative spectrum sensing is an important parameter as it defines both sensing and resource allocation performance. The fusion centre captures interaction between physical layer spectrum sensing, channel condition, and higher layer data buffer while selecting SUs for cooperative sensing. We consider heterogeneous data types at SUs’ data buffers during SU selection to make our system model more general. We form a mixed integer non-linear optimization problem, from which we select SUs’ set for sensing and resource allocation, appropriate sensing thresholds, and corresponding resource allocation parameters. Due to it’s combinatorial nature, the optimization problem is non-trivial. We devise an optimal algorithm to solve the optimization problem. Moreover, we also propose a computationally efficient and near-optimal greedy algorithm with suitable performance bounds. We also show advantage of our proposed optimal algorithms compared to other traditional methods through extensive simulations.

Energy-Efficient Cross-Layer Resource Allocation for Heterogeneous Wireless Access

In this paper, an uplink cross-layer resource allocation problem based on imperfect channel state information (CSI) is modeled as min-max fractional stochastic programming for heterogeneous wireless access. The resource allocation is subject to constraints in delay, service outage probability, system radio bandwidth, and total power consumption. The joint bandwidth and power allocations are based on CSI at the physical layer and queue state information (QSI) at the link layer. In order to determine the transmission rate of each mobile terminal according to the queue buffer occupancy, a probability upper bound of exceeding the maximum packet delay in terms of a required transmission rate is presented based on M/D/1 model. Then, the bandwidth and power allocation problem is transformed into bi-convex programming, and an optimal distributed bandwidth and power allocation algorithm is proposed. To reduce computational complexity, a suboptimal distributed bandwidth and power allocation algorithm is presented. Simulation results demonstrate that the proposed algorithms improve the energy efficiency greatly.

Confidentiality-Preserving Control of Uplink Cellular Wireless Networks Using Hybrid ARQ

We consider the problem of cross-layer resource allocation with information-theoretic secrecy for uplink transmissions in time-varying cellular wireless networks. Particularly, each node in an uplink cellular network injects two types of traffic, confidential and open at rates chosen in order to maximize a global utility function while keeping the data queues stable and meeting a constraint on the secrecy outage probability. The transmitting node only knows the distribution of channel gains. Our scheme is based on Hybrid Automatic Repeat Request (HARQ) transmission with incremental redundancy. We prove that our scheme achieves a utility, arbitrarily close to the maximum achievable. Numerical experiments are performed to verify the analytical results and to show the efficacy of the dynamic control algorithm.

Cross-Layer Resource Allocation With Elastic Service Scaling in Cloud Radio Access Network

Cloud radio access network (C-RAN) aims to improve spectrum and energy efficiency of wireless networks by migrating conventional distributed base station functionalities into a centralized cloud baseband unit (BBU) pool. We propose and investigate a cross-layer resource allocation model for C-RAN to minimize the overall system power consumption in the BBU pool, fiber links and the remote radio heads (RRHs). We characterize the cross-layer resource allocation problem as a mixed-integer nonlinear programming (MINLP), which jointly considers elastic service scaling, RRH selection, and joint beamforming. The MINLP is however a combinatorial optimization problem and NP-hard. We relax the original MINLP problem into an extended sum-utility maximization (ESUM) problem, and propose two different solution approaches. We also propose a low-complexity Shaping-and-Pruning (SP) algorithm to obtain a sparse solution for the active RRH set. Simulation results suggest that the average sparsity of the solution given by our SP algorithm is close to that obtained by a recently proposed greedy selection algorithm, which has higher computational complexity. Furthermore, our proposed cross-layer resource allocation is more energy efficient than the greedy selection and successive selection algorithms.

Cross-layer resource allocation in wireless multi-hop networks with outdated channel state information

The cross-layer resource allocation problem in wireless multi-hop networks (WMHNs) has been extensively studied in the past few years. Most of these studies assume that every node has the perfect channel state information (CSI) of other nodes. In practical settings, however, the networks are generally dynamic and CSI usually becomes outdated when it is used, due to the time-variant channel and feedback delay. To deal with this issue, we study the cross-layer resource allocation problem in dynamic WMHNs with outdated CSI under channel conditions where there is correlation between the outdated CSI and current CSI. Two major contributions are made in this work: (1) a closed-form expression of conditional average capacity is derived under the signal-to-interference-plus-noise ratio (SINR) model; (2) a joint optimization problem of congestion control, power control, and channel allocation in the context of outdated CSI is formulated and solved in both centralized and distributed manners. Simulation results show that the network utility can be improved significantly using our proposed algorithm.

Control of Wireless Networks With Secrecy

We consider the problem of cross-layer resource allocation in time-varying cellular wireless networks and incorporate information theoretic secrecy as a quality-of-service constraint. Specifically, each node in the network injects two types of traffic, private and open, at rates chosen in order to maximize a global utility function, subject to network stability and secrecy constraints. The secrecy constraint enforces an arbitrarily low mutual information leakage from the source to every node in the network, except for the sink node. We first obtain the achievable rate region for the problem for single- and multiuser systems assuming that the nodes have full channel state information (CSI) of their neighbors. Then, we provide a joint flow control, scheduling, and private encoding scheme, which does not rely on the knowledge of the prior distribution of the gain of any channel. We prove that our scheme achieves a utility arbitrarily close to the maximum achievable utility. Numerical experiments are performed to verify the analytical results and to show the efficacy of the dynamic control algorithm.

Adaptive Cross-layer Resource Allocation by HNN in OFDM-MISO System

This paper presents an adaptive cross-layer resource allocation problem with the fairness in multi-user OFDMMISO communication systems, and provides two solutions with Hopfield Neural Network (HNN) and Genetic Algorithm (GA) for the problem. We utilized HNN’s characteristics such as parallel processing, fast convergence speed and easy convergence to the optimum, to solve this problem under the conditions of proportional fairness for satisfying system performances and users’ requirements. The method is simplified in the computation by dividing the bit-loading matrix into three matrixes. The simulation results show that HNN and GA can effectively solve optimization problems of resource allocation in such system, and results of selected HNN and GA methods are more effective than that of the traditional method.

Control of wireless networks with rechargeable batteries [transactions papers

We consider the problem of cross-layer resource allocation for wireless networks operating with rechargeable batteries under general arrival, channel state and recharge processes. The objective is to maximize total system utility, defined as a function of the long-term rate achieved per link, while satisfying energy and power constraints. A policy with decoupled admission control and power allocation decisions is proposed that achieves asymptotic optimality for sufficiently large battery capacity to maximum transmission power ratio (explicit bounds are provided). We present first a downlink resource allocation scenario; the analysis is then extended to multihop networks. The policy is evaluated via simulations and is seen to perform very well even in the non-asymptotic regime. This policy is particularly suitable for sensor networks, which typically satisfy the asymptotic conditions required by our methodology.