Cognitive radio network (CRN) provides an effective way of improving efficiency and flexibility in spectrum usage. Due to the coexistence of secondary user (SU) and primary user (PU), managing interference is a critical issue to be addressed if we are to reap the full benefits. In this paper, we consider the problem of joint interference management and resource allocation in a multi-channel ad hoc CRN. We formulate the problem as an overlapping coalition formation game to maximize the sum rate of SU links while guaranteeing the quality of service (QoS) of PU links. In the game, each SU link can make an autonomous decision and is allowed to participate in one or more cooperative coalitions simultaneously to maximize its payoff. To obtain the solution of the formulated game, a distributed, self-organizing algorithm is proposed for performing coalition formation. We analyze the properties of the algorithm and show that SU links can cooperate to reach a final stable coalition structure. Compared with existing approaches, the proposed scheme achieves appreciable performance improvement in terms of the sum rate of SU links, which is demonstrated by simulation results.

Femtocell has been considered as a key promising technology to improve the capacity of a cellular system. However, the femtocells deployed inside a macrocell coverage are potentially suffered from excessive interference. This paper proposes a novel radio resource optimization in closed access femtocell networks based on bat algorithm. Bat algorithm is inspired by the behavior of bats in their echolocation process. While the original bat algorithm is designed to solve the complex optimization problem in continuous search space, the proposed modified bat algorithm extends the search optimization in a discrete search space which is suitable for radio resource allocation problem. The simulation results verify the convergence of the proposed optimization scheme to the global optimal solution and reveal that the proposed scheme based on modified bat algorithm facilitates the improvement of the femtocell network capacity.

An interference-aware dynamic channel assignment scheme is proposed with consideration of co-tier interference for the downlink of an OFDMA/FDD based dense small-cell network. The proposed scheme adaptively assigns subchannels to the small-cell user equipment (SUE) according to the given traffic load and interference effect from neighbor small-cell access points. The simulation results show that the proposed scheme outperforms the other schemes based on the graph coloring algorithm in terms of the mean SUE capacity.

In a network with dense deployment of multiple-input multiple-output (MIMO) small cells, coverage overlap between the small cells produces intercell-interference, which degrades system capacity. This paper proposes an intercell-interference management (IIM) scheme that aims to maximize system capacity by using both power control for intercell-interference coordination (ICIC) on the transmitter side and interference cancellation (IC) on the receiver side. The power control determines transmit power levels at the base stations (BSs) by employing a neural network (NN) algorithm over the backhaul. To further improve the signal to interference plus noise ratio (SINR), every user terminal (UT) employs a multiuser detector (MUD) as IC. The MUD detects not only the desired signals, but also some interfering signals to be cancelled from received signals. The receiver structure consists of branch metric generators (BMGs) and MUD. BMGs suppress residual interference and noise in the received signals by whitening matched filters (WMFs), and then generate metrices by using the WMFs' outputs and symbol candidates that the MUD provides. On the basis of the metrices, the MUD detects both the selected interfering signals and the desired signals. In addition, the MUD determines which interfering signals are detected by an SINR based replica selection algorithm. Computer simulations demonstrate that the SINR based replica selection algorithm, which is combined with channel encoders and packet interleavers, can significantly improve the system bit error rate (BER) and that combining IC at the receiver with NN power control at the transmitter can considerably increase the system capacity. Furthermore, it is shown that choosing the detected interfering signals by the replica selection algorithm can obtain system capacity with comparable loss and less computational complexity compared to the conventional greedy algorithm.

The heterogeneous network (HetNet), which deploys small cells such as picocells, femotcells, and relay nodes within macrocell, is regarded as a cost-efficient and energy-efficient approach to resolve increasing demand for data bandwidth and thus has received a lot of attention from research and industry. Since small cells share the same licensed spectrum with macrocells, concurrent transmission induces severe interference, which causes performance degradation, particularly when coordination among small cell base stations (BSs) is infeasible. Given the dense, massive, and unplanned deployment of small cells, mitigating interference in a distributed manner is a challenge and has been explored in recent papers. An efficient and innovative approach is to apply cognitive radio (CR) into HetNet, which enables small cells to sense and to adapt to their surrounding environments. Consequently, stations in each small cell are able to acquire additional information from surrounding environments and opportunistically operate in the spectrum hole, constrained by minimal inducing interference. This paper summarizes and highlights the CR-based interference mitigation approaches in orthogonal frequency division multiple access (OFDMA)-based HetNet networks. With special discussing the role of sensed information at small cells for the interference mitigation, this paper presents the potential cross-layer facilitation of the CR-enable HetNet.

Cognitive beamforming exploiting spatial opportunity is an attractive technique for secondary users to coexist with primary users in cognitive radio environments. If perfect channel state information of the interfering link is available, interference from a secondary transmitter to a primary receiver can be perfectly pre-nulled by choosing the ideal transmit beam. In practice, however, there is channel estimation error due to noise and the time-varying channels. To minimize the residual interference due to those channel estimation errors, channel prediction based on auto regressive (AR) model is introduced in this paper. Further, to cope with extremely rapidly-varying channels, a cognitive transmit power control technique is proposed as well. By combining channel prediction and transmit power control in cognitive beamforming, the cognitive users can share the spectrum with the primary users with a limited interference level in time-varying channels.

Since femtocells are deployed in a two tier cellular network, along with macrocells operating on the same channel, interference between them limits the overall performance of the network. Without any control of the femtocell operation, pre-deployed macrocells will experience severe interference, which is not consistent with the current femtocell deployment principle. In this paper, to resolve this problem, a mathematical framework that optimizes the downlink transmission power of femtocells is formulated. Based on the formulated framework, we derive the optimal value of the transmission power so that the transmission affects the pre-deployed macrocell's downlink performance at a minimum scale, while providing sufficient Quality of Service (QoS) to its served users. Furthermore, to reduce the complexity of the power control process, we propose an Interference Estimation scheme which approximates the interference levels between different pairs of macrocell and femtocell base stations. The feasibility of this estimation process is shown by deriving the lower and upper bound of the estimation error. Through simulations, compared to no power control, we show that our proposed method provides a 17.64% reduction in macro user's outage probability, 5.9 dB decrease of interference on cell-edge macrocell users, and a 1.41 times increase in average user throughput.

In multi-cell OFDMA-based networks, co-channel interference (CCI) is inevitable when the frequency reuse scheme is used. The CCI affects the performance of users, especially that of cell edge users. Several frequency reuse schemes and subcarrier allocation algorithms have been proposed to solve the CCI problem. Nevertheless, it is difficult to improve both the cell capacity and the performance of cell edge users since they have a trade-off. In this paper, we propose a new balanced frequency reuse (BFR) as a new frequency partitioning scheme that gives more power to the users in the outer region and allocates more subcarriers to the users in the inner region. In addition, we propose ordering and directional subcarrier allocation (ODSA) for our frequency partitioning proposal to mitigate the CCI effectively when cells have heterogeneous traffic loads. The performance of the proposed BFR with the ODSA algorithm is investigated via analyses and simulations. Performance evaluation shows that the proposed BFR with the ODSA algorithm can increase both the spectral efficiency and the performance of cell edge users if the transmission power is appropriately handled.

Todays cellular systems reach their limits for data rate due to the continuously increasing amount of subscribers using wireless service for business purposes or in leisure time (smartphone effect). Thus, recent research focuses on concepts for interference management for cellular OFDMA systems. This paper addresses various techniques related to this topic, while considering the concepts with lowest complexity and backhaul costs as promising candidates to be applied first. Starting from interference canceling receivers over multi-user MIMO using fixed precoding to multi-cell interference estimation, which improves the precision of link adaptation, we discuss closed-loop cooperative transmit beamforming using multiple base stations grouped into a wireless distributed network (WDN), which is denoted as coordinated multi-point joint transmission in the 3GPP LTE-Advanced standardization. It is obvious, the more sophisticated these techniques are, the higher the demands for feedback and backhaul become. Performance results are provided by employing multi-cell simulations according to recommendations from 3GPP. In addition, feasibility of coordinated multi-point joint transmission is demonstrated in a real-time prototype setup, i.e. in the Berlin LTE-Advance Testbed.

In a spectrum sharing system, lower-priority users are allowed to spatially reuse the spectrum allocated to higher-priority users as long as they do not disrupt communications of the latter. Therefore, to improve spectrum utilization, an important requirement for the former users is to manage the interference and ensure that the latter users can maintain reliable communications. In the present paper, a game theoretic framework of joint channel selection and power allocation for spectrum sharing in distributed cognitive radio networks is proposed. First, a utility function that captures the cooperative behavior to manage the interference and the satisfaction level to improve the throughput of the lower-priority users is defined. Next, based on the defined utility function, the proposed framework can be formulated as a potential game; thus, it is guaranteed to converge to a Nash equilibrium when the best response dynamic is performed. Simulation results show the convergence of the proposed potential game and reveal that performance improvements in terms of network throughput of the lower-priority users and outage probability of the higher-priority users can be achieved by the introduction of an adaptive coefficient adjustment scheme in the proposed utility function at the expense of the convergence to the Nash equilibrium.