The use of small cells is a promising way of providing the required capacity in future cellular networks through densification. Self-organizing network (SON) techniques are required in small cells, particularly in femtocells where plug-and-play user deployments are used. However, these techniques have tended to be implemented by vendors with proprietary algorithms that use low-level UE measurements which are not easily accessible to network operators. This makes the management of femtocells in a multi-vendor environment difficult. In this paper, we present a centralized SON technique to perform load balancing, coverage optimization and manage sleep modes that utilizes high-level measurements made at the femtocells which are easily available through the widely used TR-069 device management protocol standard. This allows network operators the flexibility of implementing and coordinating the SON techniques in multi-vendor femtocell deployments. We show that, despite the limited amount of information used, the proposed solution is able to effectively optimize the coverage in order to balance load and manage sleep modes whilst ensuring that no coverage gaps occur.

In this paper, a distributed algorithm for dynamic frequency reuse scheme in OFDMA downlink cellular networks is proposed. Each cell is divided into two regions, namely, inner and outer regions. The proposed distributed algorithm is based on minimizing the total interference at all users in each region. Unlike other fractional frequency reuse (FFR) schemes, the main advantage of the proposed algorithm is that it adapts to the network channel conditions. Simulation results show that the proposed algorithm provides better performance than that of FFR, in terms of both total system throughput and average user bit-rate.

Self-configuration of network parameters is a desired network management feature, especially in dense small cell deployments. One of the key parameters that requires such autonomous set up is the cell ID. Its configuration to a large extent influences the network performance affecting such mechanisms as network access, decoding, handover, and is therefore of a very high importance. In this paper a new centralised method for Physical Layer Cell Identity (PCI) assignment is presented. The proposal is based on the network information available via the TR-069 management protocol, which makes it applicable also to multi-vendor deployments. In contrast with the existing schemes, the proposed algorithm mitigates not only the colliding and confusing assignments but also prevents neighbours from using different PCIs but introducing overlap in their reference signal pattern, which has not been addressed before. The results show a significant reduction of assignment conflicts when compared with the baseline 3rd Generation Partnership Project (3GPP) approach.

Energy-efficient resource allocation is considered for cognitive cooperative radio networks (CCRNs) with imperfect spectrum sensing. The optimization problem of maximizing energy efficiency (EE) is formulated over the transmission power and sensing time subject to some practical limitations, such as the individual power constraint for secondary source and relay, the quality of service (QoS) for the secondary system, and effective protection for the primary user (PU). Given the complexity of this problem, two simplified versions (i.e., perfect and imperfect sensing cases) are studied in this paper. We transform the considered problem in fractional form into an equivalent optimization problem in subtractive form. Then, for perfect sensing, the Lagrange dual decomposition and iterative algorithm are applied to acquire the optimal power allocation policy; for imperfect sensing, an exhaustive search and iterative algorithm are proposed to obtain the optimal sensing time and corresponding power allocation. Finally, numerical results show that the EE design greatly improves EE compared with the conventional spectrum-efficient design.

Recently, an adaptive peak cancellation was proposed to reduce the high peak-to-average power ratio (PAPR), while keeping the out-of-band (OoB) power leakage as well as an in-band distortion power (EVM) below the pre-determined (permissible) level. However, the peak cancellation in MIMO-OFDM systems was not considered. In this paper, we propose a peak cancellation method for linearly pre-coded MIMO-OFDM systems using eigen-beam space division multiplexing (E-SDM). We evaluate and discuss the performance of the system using the proposed peak cancellation in terms of bit error rate (BER), complementary cum-mulative distribution function (CCDF) of PAPR and the system's computational complexity. Our results show the improvements with respect to both the achievable BER and PAPR with the proposed peak cancellation in E-SDM systems under the restriction of OoB power radiation.

In this paper, an optimization model for solving the channel assignment problem in multi-cell WLANs is proposed. This model is based on maximizing the minimum distance between access points (APs) that work on the same channel. The proposed model is formulated in the form of a mixed integer linear program (MILP). The main advantage of the proposed algorithm is that it ensures non-overlapping channel assignment with no overhead power measurements. The proposed channel assignment algorithm can be implemented within practical time frames for different topology sizes. Simulation results indicate that the proposed algorithm exhibits better performance than that of the pick-first greedy algorithm and the single channel assignment method.