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The coexistence of a macrocell and a number of femtocells often leads to a two-tier heterogeneous network, where the co-tier interference (CotIN) and cross-tier interference (CrotIN) both degrade users' quality of service. In order to suppress these interferences, we propose a precoding scheme in a heterogeneous network with cooperative femtocells, called CotIN elimination and CrotIN suppression with precoding criterion selection (ESPS) scheme. In this scheme, we first eliminate the CotINs of each user by applying the QR decomposition to channel matrix. Then the CrotINs of macrocell users and femtocell users are suppressed via the macrocell base station (MBS) and femtocell access points (FAPs) with precoding criterion selection, respectively. By using the ESPS scheme, spatial resources can be efficiently exploited. In addition, our ESPS scheme requires little information exchange between MBS and FAPs without iteration and thus significantly reduces the implementation complexity. Furthermore, the robustness is increased through introducing the information of channel uncertainty into the ESPS when channel estimation or quantization error exists. The performance analysis for the ESPS demonstrates that the ESPS is practical in the heterogeneous networks. Finally, simulation results show that the ESPS can decrease users' bit error rates and increase their transmission rates. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY In amplify-and-forward relay networks, the equivalent channel to the destination node is not independent of equivalent noise and the equivalent noise does not follow a Gaussian distribution. Therefore, it is difficult to directly estimate the equivalent channel based on traditional optimal rules. In this paper, we propose a two-pilot estimation (TPE) scheme that decomposes a non-Gaussian noise channel estimation problem into two channel estimation problems in Gaussian noise. In TPE scheme, the relay-destination channel is first estimated by one pilot and the other pilot is used to estimate the equivalent channel with the aid of the estimated relay-destination channel. Simulation results show that the TPE scheme can achieve less estimation error and larger system throughput than other existing channel estimators in slow fading case. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY Dynamic spectrum auction (DSA) has been considered as one of potential spectrum allocation approaches in cognitive femtocell networks. As a modified version of traditional spectrum auction, DSA should not only increase auction revenue but also improve spectrum utilization on finer time granularity. We propose a DSA algorithm based on a double optimization framework (DOF), which focuses on the optimization of auction revenue and spectrum utilization. The optimization processing consists of two stages. Firstly, a proper auction period is selected to balance the expected spectrum utilization and auction revenue. Then, the cognitive femtocell base station adjusts its reserve price with the repetition of auction to leverage over instant revenue and spectrum utilization. At the same time, the bidders can adjust their bidding price to improve utilities. Performance analysis shows that the DOF-based DSA algorithm has low complexity and can resist collusion, so it can be carried out frequently with small overhead. On the other hand, it is better than the greedy algorithm and Vickrey-Clarke-Groves auction on revenue. Simulation results show that the DOF-based DSA algorithm can keep a fine spectrum utilization and bring the cognitive femtocell base station more revenue in both single-unit award spectrum auction and multi-unit awards spectrum auction. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

SUMMARY In this paper, we propose a cross-layer based power allocation scheme with statistical delay QoS guarantees for the cognitive (secondary) amplify-and-forward relay link, which coexists with one primary link by sharing particular portion of the spectrum. Specifically, our derived power allocation scheme aims at maximizing the effective capacity of the cognitive relay link, which can be seen as the maximum arrival rate supported by the system under given QoS constraints. In our work, not only the average total transmit power and average interference power constraints are considered, but also the impact of the interference from the primary link to the cognitive relay link is taken into consideration. Simulation results show that the effective capacity of the cognitive relay link varies with the statistical QoS constraints. In particular, the stringent QoS constraint will cause low effective capacity. Moreover, we observe that the average total transmit power and average interference power are two important parameters, which will obviously impact the performance of the cognitive relay link. In addition, we find that the transmission of the primary link will significantly affect the performance of the cognitive relay link, such that a larger transmit power of the primary link will cause the performance degradation of the cognitive relay link. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

A high‐rate beamforming design is reported for amplify‐and‐forward wireless relay networks with simultaneous information and power transfer. To achieve high sum rate, a new relay beamformer is designed to support multi‐pair source–destination communication links as well as an energy receiver simultaneously, instead of serving one information transmitter at a time. An energy‐harvesting constrained sum‐rate maximisation problem for this design is formulated, then relaxed and reformulated into a series of convex problems, which can be iteratively solved in polynomial time, eventually leading to a high‐rate beamforming solution. Simulation results confirm the performance gain and fast convergence of the developed beamforming design. [ABSTRACT FROM AUTHOR]

Due to a huge number of M2M devices expected to access the network simultaneously, congestion and overload of networks are the core challenges to the 5G ultra-dense M2M networks design. In this paper, an optimal traffic-load control scheme is proposed to overcome the challenges. Since the base station lacks accurate information about the traffic-load of network resulting in an unachievable solution to the optimal control scheme in practice, we propose two different traffic-load estimation schemes corresponding to the two different access protocols. For the first protocol that the collision devices are not barred by an access probability, we propose a markov-chain based estimation scheme according to the collision state of network. For the second protocol that the collision devices are barred by an access probability, we estimate the traffic-load using the number of success devices and the average number of new active devices in each slot. Both estimation schemes ensure that the practical control schemes are very close to the theoretical optimal control scheme. In addition, we prove that the optimal performances of the two protocols are exactly the same in theory. Finally, the simulation results exhibit our conclusion and show the superior performances of our proposed scheme. [ABSTRACT FROM AUTHOR]