A new design of an all-pass IIR filters based two-channel quadrature mirror filter bank (All-pass-IIR-2c-QM-FB) is proposed for the first time. The proposed design is based on two optimization problems. The first one minimizes the weighted linear phase error of the whole filter bank system and weighted stop-band design error of the low-pass analysis filter. The second one minimizes the linear phase error of the whole filter bank system for the given stop-band design error requirement of the low-pass analysis filter. An iterative technique is proposed to solve these two problems. For the first optimization problem, the proposed iterative technique has no requirement for the initial solution whereas solving the second optimization problem using the proposed technique needs a suitable initial solution. The obtained solution of the first optimization problem serves as the initial solution of the second optimization problem. Simulation experiment validates the effectiveness and obvious superiority of the proposed design of an All-pass-IIR-2c-QM-FB.

The matrix decomposition (MD) based FIR filter design technique can synthesize a traditional FIR filter with much less implementation complexity. A scheme for obtaining more sparse coefficients of a MD-FIR filter is proposed, which consists of two parts. The first part is the previous procedure of designing a MD-FIR filter (i.e., design an initial MD-FIR filter using a certain MD method and optimize its coefficients). The second part is the proposed procedure of obtaining more sparse coefficients, where the MD-FIR filters' coefficients also need to be optimized. The performances of the various initial MD-FIR filters, which are obtained based on the various MD methods, in the implementation of this scheme are experimentally compared. The MD-FIR filter's coefficients can be effectively optimized by the trust-region iterative-gradient-searching (TR-IGS) algorithm. We present further results on the TR-IGS. The error bound for each iteration of TR-IGS is analyzed. A convergent online implementation scheme of the TR-IGS is presented and analyzed theoretically and experimentally. The proof of the convergence is provided. A sufficient condition for determining whether a theoretical termination point of the scheme is a strict local optimum point is provided. The step-size optimization problem for the TR-IGS is analyzed theoretically and experimentally.

The intensive downlink (DL) inter-cell interference created by cell range expansion (CRE) of picocells is an urgent problem needing to be solved under macro-pico heterogeneous network (HetNet) scenario. A promising approach is taking advantage of additional degree of freedom brought by carrier aggregation (CA). However, poorly arranged carrier configurations or interference coordination schemes can lead to a degradation of system overall performance. In this paper, we propose a novel dynamic interference coordination scheme based on carrier aggregation to alleviate the DL interference from macrocells to users located in the cell range expansion area. Novel carrier configuration pattern and dynamic power control scheme based on price algorithm are applied to mitigate detrimental interference to picocell-edge users and improve the availability of macrocells. Simulation results show that the proposed scheme can boost the picocell-edge throughput significantly while ameliorating the overall system throughput.

In this paper, we investigate the energy efficiency problem in macro-relay network. Considering users with different locations and rate requirements, our objective is to serve all users with strict rate guarantee and meanwhile minimize the total energy consumption of the network. First, we formulate an integer optimization problem with the variables as stationuser connections. Then we analyze the complexity of the optimal brute-force search (BFS) method and propose a practical algorithm, i.e., serving station selection for energy minimization (SSSEM). The proposed SSSEM algorithm dynamically alters user's serving station according to both the radio frequency energy and the circuit energy, thus minimizing the total energy consumption of the network. Extensive simulations are conducted and the results show that, compared with existing schemes, the proposed SSSEM algorithm can significantly reduce the total energy consumption of the network, and meanwhile achieve the optimum of BFS.

In this paper, we propose a novel opportunistic scheduling algorithm in multi-cell cooperation scenario, i.e., joint multi-user diversity (MUD) exploitation and inter-cell interference (ICI) mitigation based scheduling (MEIMS). Our algorithm jointly considers the intended channel condition of the scheduled user from its serving cell and the orthogonality between that and the corresponding interference channels to concurrently scheduled users in neighboring cells so as to exploit MUD and mitigate ICI simultaneously. The performance of our algorithm is evaluated through simulation. Results show that, our scheme can significantly enhance the received signal to interference plus noise ratio (SINR) with relatively better fairness guarantee, thus achieves the largest throughput and utility comparing to several well-known scheduling algorithms.

3GPP LTE networks can provide a higher capacity by adopting advanced physical layer techniques and serve users with different Quality of Service (QoS) requirements. However, unbalanced user distributions and strong inter-cell interference (ICI) still deteriorate network performances severely. Since fractional frequency reuse (FFR) technique is recommended to mitigate ICI, we investigate the load balancing problem in a 3GPP LTE multi-cell FFR network with heterogenous services in this paper. Firstly we formulate a multi-objective optimization problem, whose objectives are intra- and inter-cell load balancing index for users with QoS requirements and total utility function for users without QoS requirements. Then we analyze the complexity of the problem and propose a practical algorithm which includes QoS aware intra- and inter-cell handover and call admission control. Extensive simulations are conducted, the results show that our algorithm can lead to significantly better performances, i.e., a lower new call blocking rate for users with QoS requirements, a larger utility for users without QoS requirements at the cost of a bit degradation of total throughput.

In this paper, we propose a novel opportunistic scheduling algorithm, joint useful and interference channel based scheduling (JUICS), in coordinated multi-point (CoMP) transmission scenario. Our scheme jointly considers the useful channel condition of the scheduled user from its serving cell and the orthogonality between that and the corresponding interference channels to concurrently scheduled users in neighboring CoMP cells, thus to exploit multi-user diversity (MUD) and mitigate inter-cell interference (ICI) simultaneously. The performance of the proposed algorithm is evaluated through simulation in terms of the cumulative distribution performance of the received signal to interference plus noise ratio (SINR) and that of the scheduled times of all CoMP users. Results show that, with limited complexity and overhead, our scheme can significantly enhance the received SINR with relatively better fairness guarantee, thus to achieve the largest throughput and utility comparing to several well-known scheduling algorithms.

In this paper, we investigate call admission control (CAC) with opportunistic scheduling and propose a novel CAC algorithm for users with quality of service (QoS) requirements. Our main contribution is threefold. First, we verify that, compared with several other scheduling schemes, cumulative distributed function based scheduling (CS) makes the best tradeoff between efficiency and fairness in full-load scenario and exploits the best opportunism with absolutely fair resource allocation. Then we deduce and validate the multi-user diversity gain (MDG) of CS, which determines its long-term average performance and is used for estimation of resource occupation in CAC algorithm design. After that, we use opportunistic round robin (ORR) method to calculate the statistical low performance bound of CS, and propose CS/ORR based CAC (COCAC) algorithm, which guarantees the heterogeneous minimum rate requirement (MRRs) of both new access users and existing ones. Finally, we evaluate the performance of the proposed COCAC algorithm via simulation. Results show that COCAC can significantly reduce new call block probability, effectively make use of system resources, as well as strictly guarantee all users' MRRs.

In this paper, we investigate load balancing problem in 3GPP Long Term Evolution (LTE) networks and propose our solution which considers users with different Quality-of-Service (QoS) requirements. Load unbalance among neighboring cells often yields negative impacts on user experience and network performance, and it has mainly been considered for only data services without QoS guarantee. However, 3GPP LTE network aims to support multi-class services with different QoS requirements, on which the influence of load unbalance is quite different. For those with minimum rate requirements, it may result in high block probability, while for others without rate requirements, the throughput of boundary users may be degraded. In this paper, we incorporate all the differences into a network utility maximization framework and formulate it as a multi-objective optimization problem. The objectives in the problem are load balancing index of services with QoS requirements and the total utility of other services, and the constraints are physical resource limits and QoS demands. Then we analyze the complexity of the problem, and propose our solution, which includes a QoS guaranteed hybrid scheduling scheme, handover of users with and without QoS requirements, and a call admission control algorithm. Extensive simulation is conducted and the results show that the proposed framework leads to significantly better load balancing, and thus the decrease in call block probability of users with QoS requirements, and the increase in throughput of boundary best effort users.

The total energy minimization in wireless multihop networks using two-way network coding is investigated in this paper. The problem is first formulated as a linear programming problem, then decomposed into two sub-problems using the La grangian decomposition, and finally solved with the subgradient method. After that, the backpressure based algorithm is proposed to solve the problem in a distributed manner. The performance of the algorithm is evaluated first in a simple topology for analysis and then in a random topology with different number of flows for practical consideration. Simulation results show that the convergence time increases as the number of nodes in the network, and the energy cost per packet can be saved up to 30% by using two-way network coding.

This paper deals with lifetime maximization for energy constrained wireless networks with inter-session network coding. The problem of lifetime maximization is first formulated, and then transformed into a linear programming problem. Using Lagrangian dual decomposition, it is further converted into a dual problem that consists of two subproblems: lifetime optimization and network optimization, and then it is solved by utilizing dual subgradient method. Convex combination is used for primary recovery from the dual solutions. Performance of the algorithm is evaluated through simulation. Results illustrate that theoretical analysis matches practical calculation well, and that inter-session network coding can prolong the network lifetime up to 50% compared with the one without network coding.

Load imbalance that deteriorates the system performance is a severe problem existing in 3GPP LTE networks. To deal with this problem, we propose in this paper a load balancing framework, which aims at balancing the load in the entire network, while keeping the network throughput as high as possible. In this framework, the objective is formulated as a network-wide utility function balancing network throughput and load distribution, and then it is transformed to an integer optimization problem under resource allocation constraints. After that, the complexity of the problem is analyzed, network structure constraints are presented, and a practical suboptimal algorithm, called Heaviest-First Load Balancing (HFLB), is proposed. Extensive simulation is made and the results show that using the HFLB algorithm the network can get significantly better load balancing while maintaining the same network throughput at the price of a bit more handovers compared with the traditional signal strength-based handover algorithm.