Biling Zhang

A coalitional game scheme for cooperative interference management in cloud radio access networks

Cloud radio access networks C-RANs are promising technological paradigms to improve capacity and extend coverage. However, the severe interference resulting from high density deployment of remote radio heads RRHs prevents to achieve high spectrum efficiency. How to mitigate the interference is a key challenging work. This paper investigates the cooperative interference management among RRHs in C-RANs. By forming coalitions and performing non-coherent joint transmission within a coalition, the intra-coalition interference among RRHs can be successfully suppressed. Considering that RRHs are selfish and rational, in this paper, the cooperation of RRHs is formulated as a coalition formation game scheme, where the necessary conditions for an RRH to cooperate are derived. To find the solution for the proposed game, an enhanced concept of defection order is proposed, based on which a self-optimal coalition formation algorithm is proposed. The performances of the proposed algorithm, in terms of convergence, complexity and fairness, are analysed as well. Simulation results show that our proposed algorithm yields significant improvements of system performance compared with both the non-cooperative scheme and the conventional broadcasting-based coalitional scheme.Copyright

Fair Resource Allocation in OFDMA Two-Hop Cooperative Relaying Cellular Networks

This paper discusses on the fair resource allocation in OFDMA two-hop cooperative relaying cellular networks which consist of a single base station (BS), dedicated fixed relay stations (RSs) and user stations (USs). By assuming the relay strategy employed on each subcarrier is adaptively selected among direct transmission, amplify-and-forward (AF) and decode-and-forward (DF) according to different channel conditions, we formulate a fair subcarrier assignment, relay station and relay strategy selection problem with QoS constraints in terms of user’s minimum rate requirement. The formulated problem is a Binary Integer Programming (BIP) problem which is NP-complete, so a simple suboptimal algorithm is proposed to manage the network resources. Simulations demonstrate the proposed algorithm obtains near optimal solution with low complexity and achieves a good tradeoff between the overall system performance and the fairness among users.

A Contract Game for Direct Energy Trading in Smart Grid

Direct trading is a promising approach to simultaneously achieve trading benefits and reduce transmission line losses in smart grid. However, due to the selfish nature, small-scale electricity suppliers (SESs) and electricity consumers (ECs) will not participate direct trading if the trading does not bring them benefits. Therefore, how to provide proper economic incentives for these two parties to take part in direct trading is an essential issue. Nevertheless, the asymmetry trading information between them makes the problem challenging. In this paper, we propose a contract-based direct trading framework to tackle this challenge, in which the decision making process of ECs and SESs in the presence of asymmetric information is modeled as a contract game. In the proposed game, the EC designs a contract which contains its trading strategies toward all types of SESs. Through the contract, the EC not only attracts SESs to sell electricity but also maximizes its own revenue. The SESs, on the other hand, get maximal benefits if they truthfully select the contract items of their own types. We derive theoretically the optimal contract for the short-term market where the supply of SESs is deterministic. Then we extend our study to the long-term market where the supply of SESs is encountering significant uncertainty. Finally, simulation results are shown to verify the effectiveness of the proposed scheme.

A dynamic coalition formation framework for interference management in dense small-cell networks

In response to the drastic increase in the traffic loads being served by existing cellular wireless networks, small cells are envisioned as a promising solution to extend the coverage and offload the traffic from the overburdened macrocells. However, the interference management amongst densely deployed small cells remains a technical challenge. In this paper, a neighbourhood cooperation-based interference mitigation scheme is proposed. By forming coalitions, the transmissions of small-cell base stations SBSs within the coalition are coordinated, and the co-tier interference amongst them is thus suppressed. Considering the facts that SBSs are selfish and rational, the cooperative behaviour of the neighbourhood SBSs is formulated as a coalition game in partition form. To achieve a final stable coalition structure, the concept of recursive core is first introduced. Then a simple distributed merge-only algorithm is proposed, and its performance, in terms of stability, convergence and complexity, is theoretically analysed. The dynamic process of the coalition formation is further investigated. Simulation results show that the proposed algorithm can improve the individual throughput of SBS by 5.6% and 27.3%, respectively, when compared with the classical and non-cooperative cases. Copyright

Proactive relay selection in distributed multi-source cooperative networks

This paper investigates proactive relay selection (PRS) in distributed cooperative networks with multiple potential relays and multiple source-destination pairs. A simple PRS scheme is proposed and its outage probability is analyzed. Compared with the reactive relay selection (RRS) scheme proposed in [6], our PRS scheme has a loss on the outage performance. However, its significance lies in three aspects: 1) It is fully decentralized; 2) It has less signalling overhead; 3) It saves the network energy consumption. This shows that our PRS scheme is specifically valuable to networks which do not allow centralized control and are energy constrained, such as ad hoc or sensor networks.

A coalitional game based interference management in cloud small cell networks

Small cells have recently emerged as an effective technology in meeting the increased demand for cellular network capacity. Cloud radio access networks (C-RANs), composed of baseband units (BBU), remote radio heads (RRH) and the optical transport network, have been introduced to harness the capacity benefits of small cells at reduced capital and operational expenses. However, dense deployment of RRHs causes severer interference among them and thus lower the spectrum efficiency. In this paper, we investigate the problem of cooperative interference management among RRHs in C-RAN based small cell networks, which are also referred to as cloud small cell networks (C-SCNs). A coalitional game in simplified-partition form, where RRHs cooperate by forming coalitions to eliminate the intra-coalition interference, is formulated. To analyze the formation of coalitions, a novel concept defection order is introduced, based on which the necessary conditions for RRHs to cooperate is derived, and a coalition formation algorithm called SOCFA is proposed as well. Simulation results show that our proposed algorithm yields a significant advantage reaching up to 28% relative to the non-cooperative scheme, in terms of aggregate system utility.

An indirect reciprocity based incentive framework for cooperative spectrum sensing

To overcome the hidden terminal problem a secondary user (SU) may encounter, cooperative spectrum sensing (CSS) is proposed and gained much attention in the last decades. However, due to the selfish nature, SUs may not cooperate unconditionally as most previous works have assumed. Therefore, how to stimulate SUs to play cooperatively is an important issue. In this paper, we propose a reputation-based CSS incentive framework, where the cooperation stimulation problem is modeled as an indirect reciprocity game. In the proposed game, SUs choose how to report their sensing results to the fusion center (FC) and gain reputations, based on which they can access a certain amount of vacant licensed channels in the future. For the proposed game, we derive theoretically the optimal action rule, according to which the SU will truthfully report its result when the estimated average energy is equal to or higher than the given threshold and vice versa. The decision accuracy of the FC thereby can be greatly improved. Moreover, we derive the condition under which the optimal action rule is evolutionarily stable. Finally, simulation results are shown to verify the effectiveness of the proposed scheme.

Indirect-Reciprocity Data Fusion Game and Application to Cooperative Spectrum Sensing

Data sharing is one critical step to implement data fusion, and how to encourage sensors to share their data is an important issue. In this paper, we propose a reputation-based incentive framework, where the data sharing stimulation problem is modeled as an indirect reciprocity game. In the proposed game, sensors choose how to report their results to the fusion center and gain reputations, based on which they can obtain certain benefits in the future. Taking the sensing and fusion accuracy into account, reputation distribution is introduced in the proposed game, where we prove theoretically the Nash equilibrium of the game and its uniqueness. Furthermore, we apply the proposed scheme to the cooperative spectrum sensing. We show that within an appropriate cost-to-gain ration, the optimal strategy for the secondary users is to report when the average received energy is above a given threshold and keep silence otherwise. Such an optimal strategy is also proved to be a desirable evolutionarily stable strategy. Finally, simulation results are shown to verify the theoretical results and demonstrate that compared with the existing schemes, our proposed scheme achieves better operating characteristic curve and higher system throughput with convincing performance on fairness.