In this paper, we consider the multiuser MIMO scheduling problem for dense wireless networks with access point cooperation. The problem is to maximize the aggregate throughput within a single cluster of access points, while maintaining a general fairness criterion. To alleviate the protocol overhead and sustain the performance of both stationary and mobile users, we propose a mobility-aware scheduling approach, which places users into stationary and mobile groups based on a novel CSI similarity metric. The algorithm then schedules the two groups into separate time slots. To balance fairness between stationary and mobile user groups, we adaptively determine their transmission time fractions. Different scheduling strategies are applied for the two groups. For stationary users, we collect CSI infrequently and perform a computationally expensive scheduling algorithm that is highly optimized to maximize throughput while maintaining fairness. For mobile users, we do per-time-slot CSI measurement and schedule users for each time slot using a very fast but less-optimized algorithm. Numerical results demonstrate that, when accounting for CSI feedback and scheduling overheads, our proposed scheduling algorithm with mobility awareness maintains very good fairness and provides substantial performance gains compared to conventional approaches that do not separate mobile and stationary users.

To mitigate the co-channel interference across adjacent access points (APs) and break the performance bottleneck in dense wireless networks, multiple APs are expected to share the channel state information and cooperatively process the user signals. While the inter-user interference can be eliminated using Block Diagonalization (BD) precoding technique, the number of simultaneous users is limited by the total number of transmit antennas. In this paper, we propose a novel pairing-and-binary-tree-based user selection algorithm (PBUS) to address the user selection issue for multi-user MIMO in dense environments. With the fitness metric evaluated for each pair of users, a binary tree is constructed to store multiple candidate user groups. The best user group is then selected from these candidates. PBUS can achieve both good sum-rate performance and low computational complexity, and also has the flexibility to trade off sum-rate performance and computational complexity.

This paper studies the fair scheduling problem for dense wireless networks with AP cooperation and MIMO links. The problem is to maximize the aggregate throughput while meeting a specified fairness objective. The proposed scheduling algorithm works with a novel single-slot throughput optimization procedure to first generate a set of candidate communication sets that are both high-performing and have good representation among all users. For a given set of candidate communication sets, our algorithm then produces a communication schedule that achieves near-optimal aggregate throughput among solutions that meet the specified fairness objective. Simulation results show that our proposed scheduling algorithm achieves high aggregate throughput while maintaining very good fairness. We also include a mechanism to relax the fairness constraint by a specified amount, which produces a range of solutions that permit a tradeoff between performance and fairness.

This paper addresses the problem of weighted sum rate (WSR) maximization in dense wireless networks with cooperative access points (APs) subject to a per-AP power constraint. We propose a combined optimization procedure that performs both user selection and MIMO weight calculation and scales well as the number of users increases. User selection eliminates some undesirable users, while MIMO weight calculation determines the precoders and combiners for all active nodes. A new performance metric, which takes into account available power, channel quality and orthogonality, and user weights, is used to perform an initial phase of user selection. A WSR maximization algorithm is then executed to optimize MIMO weights of selected users. The proposed algorithm includes additional user selection, i.e. certain users not eliminated in the first phase will be assigned zero-power stream during its execution. Numerical results show that our proposed algorithm achieves about 25% higher aggregate performance than the best existing algorithm while having a substantially lower running time. In fact, the running time is nearly constant as the number of users increases due to the very fast initial user selection phase.

In this paper, we investigate the resource allocation problem in an OFDM-based cognitive radio system with multiple relays. We address the formulated intractable problem by a two-stage procedure which corresponds to two subproblems: subchannel allocation and power distribution. For each subproblem, a fast algorithm is proposed by exploiting its special structure. Our proposed algorithms can achieve performance close to the upper bound. The remarkable characteristic of the proposed algorithm is that the approximate linear complexity is significantly lower than that of standard techniques, making it promising for applications. Simulation results also validate the superior performance of the proposed algorithm.

In this paper, we study the power allocation in Orthogonal Frequency Division Multiplexing (OFDM)-based Cognitive Radio (CR) networks with cooperative relay. Since the energy consumption is growing at a staggering rate, green radio, which puts emphasis on the energy-efficiency (EE) in wireless networks, is becoming increasingly important. Therefore, we try to maximize the EE of a relaying CR system, under the consideration of many practical limitations, such as transmission power budget, interference threshold of the primary users and the traffic demand. We first convert the formulated problem into a convex optimization one via its hypogragh form, which can be solved by the barrier method. By exploiting the special structure of the formulated convex optimization problem, we further speed up the computation of Newton step during the barrier method, significantly reducing the complexity of the algorithm. Numerical results validate that our proposal can exploit the overall EE of the CR system, while the algorithm converges efficiently and stably.

Cognitive Radio (CR) is an attractive technology to deal with current spectrum scarcity problem, while cooperative relay can make distributed receivers benefit from spatial diversity and combat severe fading in wireless environment. CR with cooperative relay is potentially a promising paradigm for developing spectrum-efficient wireless systems. In this paper, we study the resource allocation in Orthogonal Frequency Division Multiplexing (OFDM)-based CR networks with cooperative relays. Since the formulated optimization task defines a mixed integer programming problem that is generally hard to solve, we propose a two-stage method to produce near optimal solutions. Particularly, by jointly considering the Signal-to-Noise Ratios (SNRs) of OFDM subchannels and the interferences introduced to primary users, we propose an efficient subchannel assignment scheme for the CR system, as well as transmission mode selection strategy. Furthermore, we develop a fast algorithm to distribute power among subchannels, which can always work out the optimal power allocation with a reasonable complexity by exploiting the structure of the problem. Numerical results show that our proposal can significantly increase the throughput of the CR system compared with other schemes, and the proposed algorithm converges quickly and stably.

In this paper we study the Resource Allocation (RA) in Orthogonal Frequency Division Multiplexing (OFDM)-based Cognitive Radio (CR) networks, under the consideration of many practical limitations such as imperfect spectrum sensing, limited transmission power, different traffic demands of secondary users, etc. The general RA optimization framework leads to a complex mixed integer programming task which is computationally intractable. We propose to address this hard task in two steps. For the first step, we perform subchannel allocation to satisfy heterogeneous users' rate requirements roughly and remove the intractable integer constraints of the optimization problem. For the second step, we perform power distribution among the OFDM subchannels. By exploiting the problem structure to speedup the Newton step, we propose a barrier-based method which is able to achieve the optimal power distribution with an almost linear complexity, significantly better than the complexity of standard techniques. Moreover, we propose a method which is able to approximate the optimal solution with a constant complexity. Numerical results validate that our proposal exploits the overall capacity of CR systems well subjected to different traffic demands of users and interference constraints with given power budget.

In this paper, we investigate the energy-efficient resource allocation in orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) networks, where we try to maximize the system energy-efficiency under the consideration of many practical limitations, such as transmission power budget of the CR system, interference threshold of primary users and traffic demands of secondary users. Our general objective formulation leads to a challenging mixed integer programming problem that is hard to solve. To make it computationally tractable, we employ a time-sharing method to transform it into a non-linear fractional programming problem, which can be further converted into an equivalent convex optimization problem by using its hypogragh form. Based on these transformations, it is possible to obtain (near) optimal solution by standard optimization technique. However, the complexity of the standard technique is too high for this real-time optimization task. By exploiting the structure of the problem extensively, we develop an efficient barrier method to work out the (near) optimal solution with a reasonable complexity, significantly better than the standard technique. Numerical results show that our proposal can maximize the energy efficiency of the CR system, whilst the proposed algorithm performs quickly and stably.

Cognitive radio (CR) network with cooperative relay is potentially a promising technique to solve current spectrum inefficiency and spectrum scarcity problem. In this paper, we study the power allocation in cognitive radio (CR) networks with cooperative relay. The interference to the licensed primary system and the power budget of the CR network are jointly considered. The resulting model is general and the formulated optimization task generally hard to solve. To deal with the intractable coupling variables in the concerned problem, we propose an alternating optimization method, which can work out the optimal solution in an iteration manner and converges rapidly. Experiment results show that our proposed method can significantly increase system capacity, comparing with other representative ones. Furthermore, the proposed method gives a general power optimization framework for CR networks with cooperative relay and can be extended to other scenarios easily.

In this paper we study the resource allocation in OFDM-based cognitive radio (CR) networks, under the consideration of many practical limitations such as imperfect spectrum sensing, limited transmission power, different traffic demands of secondary users, etc. We formulated this general problem as a mixed integer programming task. Considering that this optimization task is computationally intractable, we propose to address it in two steps. For the first step, we perform subchannel allocation to satisfy heterogeneous users' rate requirement roughly and remove the integer constraints of the optimization problem. For the second step, we perform power allocation among the subchannels. By exploiting the problem structure to speedup the Newton step, we propose a Barrier-based method which is able to achieve the optimal power distribution with a complexity of O(N), where N is the number of active OFDM subchannels, significantly better than the complexity of O(N3) of standard techniques. Moreover, we proposed a method which is able to approximate the optimal solution with a constant complexity. Numerical results validate that our proposal exploits the overall capacity of CR systems well subjected to different traffic demands of users.

In this paper we study the resource allocation in multiuser orthogonal frequency division multiplexing (OFDM)-based cognitive radio (CR) networks, where secondary users (SUs) have flexible traffic demands, including heterogeneous real-time (RT) and non-real-time (NRT) services. We try to maximize the sum capacity of the NRT users and maintain the minimal rate requirements of the RT users simultaneously. Additionally, the interference introduced to primary users, which is generated by the access of the SUs, should be kept below a predefined threshold, which makes the optimization task more complex. The contribution of this work is two folds. First, we show that the formulated optimization problem has a special structure which can be exploited to implement a fast barrier method to obtain the optimal solution with a reasonable complexity. Second, we propose an effective measurement criterion to normalize OFDM subchannels' achievable rates, based on which we develop simple but efficient heuristic algorithm for subchannel assignment and power distribution. Simulation results show that our proposed resource allocation schemes work quite well for concerned wireless scenarios. The fast barrier method converges very fast and can always work out the optimal solution, while the heuristic algorithm produces solution close to the optimal with much lower complexity.