In this paper, the sum cell rate based on altruistic and egoistic multicell distributed beamforming (MDBF) is studied with local channel state Information (CSI). To start with, we provide two sufficient conditions for implementing altruistic and egoistic strategy based on the traditional method, and give the proof of those condition. Second, a MDBF method based on the altruistic and egoistic strategy is proposed, where the altruistic strategy is implemented with the internal penalty function. Finally, simulation results demonstrate that the effectiveness of the sufficient conditions and the proposed method has the different performance and advantages.

In this paper, we propose a relaying strategy for single-carrier relay networks with frequency selective channels, where each relay node delays its received signal before amplify-and-forward processing it. We propose a computationally efficient delay design method which reduces the number of delay candidates. To further reduce computational complexity, we develop a simplified delay design method which reduces the number of weight computations. Also, we extend the design method to the case where only partial channel state information of relay-to-destination channels is available. Simulation results show that the proposed relaying strategy outperforms a conventional amplify-and-forward relaying strategy and achieves the performance close to that of a more complex filter-and-forward relaying strategy. It is also shown that the proposed delay design method achieves near-optimum performance.

In this paper, the multicell distributed beamforming (MDBF) design problem of suppressing intra-cell interference (InCI) and inter-cell interference (ICI) is studied. To start with, in order to decrease the InCI and ICI caused by a user, we propose a gradient-iteration altruistic algorithm to derive the beamforming vectors. The convergence of the proposed iterative algorithm is proved. Second, a metric function is established to restrict the ICI and maximize cell rate. This function depends on only local channel state information (CSI) and does not need additional CSIs. Moreover, an MDBF algorithm with the metric function is proposed. This proposed algorithm utilizes gradient iteration to maximize the metric function to improve sum rate of the cell. Finally, simulation results demonstrate that the proposed algorithm can achieve higher cell rates while offering more advantages to suppress InCI and ICI than the traditional ones.

This paper studies an underlay-based cognitive two-way relay network which consists of a primary network (PN) and a secondary network (SN). Two secondary users (SUs) exchange information with the aid of multiple single-antenna amplify-and-forward relays while a primary transmitter communicates with a primary receiver in the same spectrum. Unlike the existing contributions, the transmit powers of the SUs and the distributed beamforming weights of the relays are jointly optimized to minimize the sum interference power from the SN to the PN under the quality-of-service (QoS) constraints of the SUs determined by their output signal-to-interference-plus-noise ratio (SINR) and the transmit power constraints of the SUs and relays. This approach leads to a non-convex optimization problem which is computationally intractable in general. We first investigate two necessary conditions that optimal solutions should satisfy. Then, the non-convex minimization problem is solved analytically based on the obtained conditions for single-relay scenarios. For multi-relay scenarios, an iterative numerical algorithm is proposed to find suboptimal solutions with low computational complexity. It is shown that starting with an arbitrarily initial feasible point, the limit point of the solution sequence derived from the iterative algorithm satisfies the two necessary conditions. To apply this algorithm, two approaches are developed to find an initial feasible point. Finally, simulation results show that on average, the proposed low-complexity solution considerably outperforms the scheme without source power control and performs close to the optimal solution obtained by a grid search technique which has prohibitively high computational complexity.

In this study, a new method for Decode-Distributed Beamforming (D-DB) relaying is proposed. Each relay node decodes the source symbol by maximum likelihood detection. The detected symbol is entered into the stored Quantized Equal-gain (QE) codebook, where the label of the phase region is provided by a feedback link from the destination node. Therefore, the proposed relay network forms a Decode-Distributed QE (D-DQE) relay network. The performances of the D-DQE codebooks are examined by Monte-Carlo simulations, in which the feedback links and channel estimations are assumed to be error-free. The simulation results reveal that the symbol error rates of the D-DQE relay system improve the error performance of the QE codebooks when relay nodes are close to the source node. When error-free feedback bits are provided, the performance of the proposed D-DQE is better than that of Alamouti's Decode-Distributed Space-Time Coding (D-DSTC) relay network. The weakest relays are rejected to improve the performance of the D-DQE codebooks and reduce the number of feedback bits. This relay network is called Decode-Relay Rejection for Distributed Beamforming (D-RRDB) relay networks.

Three synchronization issues, i.e., phase, frequency, and symbol time, have to be properly controlled to achieve distributed beamforming gain. In orthogonal frequency division multiplexing (OFDM) systems, frequency offset in cooperating signals is more important than other synchronization issues since it results in SNR degradation as well as inter-carrier interference (ICI). In this paper, the impact of frequency offset in distributed beamforming is analyzed for OFDM systems. ICI resulting from frequency offset between cooperating signals is also investigated and approximated. Performance degradation due to frequency offset is shown with various numbers of cooperating signals and offset values. We show that frequency offset between cooperating signals is critical in OFDM systems since it leads to interference from the other subcarriers as well as power loss in the desired signal.