In finding the optimal solution of virtual-path bandwidth allocation for large-scale networks, existing searching algorithms frequently call the process which calculate the bandwidth for given call blocking probability (CBP) and traffic loads. This is an inverse process of calculating CBP for given traffic loads and bandwidth. Because there is no analytic expression of calculating CBP, the process of calculating bandwidth with given CBP and traffic adopts an iteration algorithm. It leads to a tedious computation process. In this letter, a fast bandwidth evaluation algorithm is proposed and applied to the field of virtual path bandwidth allocation that aims at minimizing the worst call blocking probabilities in the network. The algorithm is proved to be accurate and fast. Finally, we provide comparison curves for the exact optimal CBPs obtained in the case of using OPBM against that of DCLPBM aided by the fast bandwidth evaluation algorithm.

A K-user parallel concatenated code (PCC) is proposed for a Gaussian multiple-access channel with symbol synchronization, equal-power, and equal-rate users. In this code, each user employs a PCC with M+1 component codes, where the first component code is a rate-1/q repetition code and the other M component codes are the same rate-1 recursive convolutional (RC) codes. By designing the repetition coding rate and the RC component code, the K-user PCC achieve reliable transmission for a given number of users and noise level. Two decoding schemes are considered: low-density parity-check (LDPC)-like decoding and Turbo-like decoding. For each decoding scheme, a fixed point analysis is given to optimize the parameters: the rate of repetition component code 1/q, the number of RC component codes M, or the RC component codes themselves. The analysis shows that an accumulate code is the optimal RC component code for a K-user PCC, in the sense of achieving the maximum sum rate. The K-user PCC with an accumulate component code achieves a larger sum rate in the high rate region than the conventional scheme of an error correction code serially concatenated with spreading under similar encoding and decoding complexity.

Information-theoretic limits of a multi-way relay channel with direct links (MWRC-DL), where multiple users exchange their messages through a relay terminal and direct links, are discussed in this paper. Under the assumption that a restricted encoder is employed at each user, an outer bound on the capacity region is derived first. Then, a decode-and-forward (DF) strategy is proposed and the corresponding rate region is characterized. The explicit outer bound and the achievable rate region for the Gaussian MWRC-DL are also derived. Numerical examples are provided to demonstrate the performance of the proposed DF strategy.

A K-user interleave-division multiple-access (IDMA) system with symbol-synchronous and equal-power users is considered. In this IDMA system, the spreading, interleaving, and multiple-access channel (MAC) are jointly considered as an equivalent channel, referred to as an IDMA channel. Given channel signal-to-noise ratio (SNR), the sum capacity of the IDMA channel is only determined by a K-user spread-interleave (SI) matrix. First, it is shown that to maximize the sum capacity of the IDMA channel, rows or columns of its K-user SI matrix should be pairwise orthogonal. The optimal K-user SI matrix is constructed. Second, for the IDMA system with each user employing the same spreading sequence followed by random interleaving, it is shown that, as the number of users approaches infinity, the sum capacity of the IDMA channel converges to a determinate value, which is achieved by a balanced spreading sequence. Moreover, when both the number of users and the data length approach infinity, this determinate value of sum capacity is achieved by an arbitrary spreading sequence. Furthermore, for a finite number of users, an optimal spreading sequence is derived by minimizing an expected column correlation of the K-user SI matrix. It shows that this optimal spreading sequence provides the maximum ergodic sum capacity.

The selection of mutation strategy greatly affects the performance of differential evolution algorithm (DE). For different types of optimization problems, different mutation strategies should be selected. How to choose a suitable mutation strategy for different problems is a challenging task. To deal with this challenge, this paper proposes a novel DE algorithm based on local fitness landscape, called FLIDE. In the proposed method, fitness landscape information is obtained to guide the selection of mutation operators. In this way, different problems can be solved with proper evolutionary mechanisms. Moreover, a population adjustment method is used to balance the search ability and population diversity. On one hand, the diversity of the population in the early stage is enhanced with a relative large population. One the other hand, the computational cost is reduced in the later stage with a relative small population. The evolutionary information is utilized as much as possible to guide the search direction. The proposed method is compared with five popular algorithms on 30 test functions with different characteristics. Experimental results show that the proposed FLIDE is more effective on problems with high dimensions.