Full-Duplex Relaying for Opportunistic Spectrum Access Under an Overall Power Constraint

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

Unsupervised deep learning to solve power allocation problems in cognitive relay networks

“…The system under study, depicted in Fig. 1, is composed of a primary user or transmitter U P and its destination D P ; a secondary full-duplex relay; and a secondary user U S and its destination D S , similarly to [5], [8], [9]. The received signals at the relay, primary and secondary destinations write as where i ∈ {P, S}, j ∈ {P, S}\i; X P , X S and X R , of average power P P , P S and P R respectively, denote the message send by the primary user, the secondary user and the relay respectively; Z R and Z i denote the AWGN at the relay and at destination D i of variance N R and N i respectively.…”

“…We let g (g ij , ∀i, ∀j) denote the vector collecting all channel gains in the network. We assume that the relay performs full-duplex decode-and-forward (DF) and that the relay can cancel out any self-interference as in [5], [8], [9]. Also, the messages sent by the secondary user and the relay are treated as additional noise at the primary destination; and the primary message is treated as additional noise throughout the secondary network.…”

“…The simultaneous blend of all the above techniques holds the potential to improve both the spectral efficiency and network throughput while providing optimal resource allocation policies. In this paper, we consider a relay-aided cognitive radio network similarly to [8], where the opportunistic network is assisted by a full-duplex relay performing Decode-and-Forward (DF). Our objective is to find the optimal power allocation policy maximizing the opportunistic achievable Shannon rate under a primary quality of service (QoS) constraint by exploiting machine (deep) learning.…”

“…Our objective is to find the optimal power allocation policy maximizing the opportunistic achievable Shannon rate under a primary quality of service (QoS) constraint by exploiting machine (deep) learning. The secondary transmitter and the relay have individual power constraints unlike in [8], where an overall power constraint was assumed.…”

“…Related works: Because of the non-linear and complex relay operations, the resulting power allocation problems in such relay-aided cognitive networks are non-convex and can be solved in closed-form only in special cases, such as: negligible interference links [5], negligible opportunistic direct links [9], Compress-and-Forward relaying [8]. Outside these very specific cases, such power allocation problems become difficult to tackle and even intractable using traditional techniques based on convex optimization and game theory.…”