No abstract

To meet the bold requirements of future generation networks, emerging technologies such as opportunistic spectrum access, multi-tier networks, full-duplexing and cooperative networks have to be exploited. In this paper, we propose to blend all the above and globally optimize a relay-aided cognitive radio network composed of a licensed link and an opportunistic link, which is helped by a full-duplex relay node. The opportunistic transmission is allowed provided that a minimum Quality of Service (QoS) constraint is met at the licensed user. First, we derive the achievable rate region under two relaying schemes, namely Decode-and-Forward (DF) and Compress-and-Forward (CF). Then we investigate the optimal power allocation policies for the opportunistic user and the relay under an overall power constraint. The resulting optimization problems are non-convex programs because of the non-trivial operations at the relay (for both CF and DF) and, for DF relaying, the non-convex QoS constraint. Remarkably, the optimal solution is stated in closed-form for CF, whereas it is obtained numerically for DF. Finally, we evaluate numerically the network performance under the two relaying schemes. It turns out that DF outperforms CF only when the relay is close to the opportunistic transmitter and that CF relaying is always useful. INDEX TERMS Full-duplex relaying, opportunistic spectrum access, optimal power allocation

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For finite blocklength polar codes, the minimum distance and the number of low weight codewords are essential to obtain good performance under successive cancellation list decoding with moderate and high list sizes. In this paper, we propose a code design method to decrease the number of low weight codewords for some information lengths with a very low computational complexity. In the proposed method, some information bits are encoded by several rows of the polar encoding matrix, i.e., each of the dynamic frozen bits is chosen the same as one of the preceding information bits. The dynamic frozen bit index set is determined by using the connection between the binary representation of the row indices and the number of common 1-bit positions of any given rows. The resulting design is shown to perform as well as polarization-adjusted-convolutional codes [9] under successive cancellation list decoding but with significant computational complexity savings. These findings pave the way for the use of polar codes in applications with stringent complexity and with low energy consumption constraints.