Licensed Shared Access (LSA) is a new concept proposed by the radio spectrum policy group in order to optimize spectrum usage: a Mobile Network Operator (MNO) can access temporarily to other incumbent's spectrum after obtaining a license.The licensing process is made via an auction mechanism. The mechanisms proposed in the literature for the LSA context are oneshot auction mechanisms which allocate all the available spectrum as a one block. In this paper, first we show how to increase the performances of those auctions (in terms of revenue, efficiency and fairness of the allocation)while preserving truthful bidding by splitting spectrum and converting single block auctions to multiblock auctions. Simulations results illustrate how the appropriate number of blocks allow to increase the aforementioned metrics. Second, we show how to convert one-shot mechanisms to equivalent ascending mechanisms (in terms of allocations and payments) so that we add transparency and privacy to the auction.

For decades, Simultaneous Ascending Auction (SAA) has been the most popular mechanism used for spectrum auctions. It has recently been employed by many countries for the allocation of 5G licences. Although SAA presents relatively simple rules, it induces a complex strategic game for which the optimal bidding strategy is unknown. Considering the fact that sometimes billions of euros are at stake in an SAA, establishing an efficient bidding strategy is crucial. In this work, we model the auction as a $n$ -player simultaneous move game with complete information and propose the first efficient bidding algorithm that tackles simultaneously its four major strategic issues: the exposure problem , the own price effect , budget constraints and the eligibility management problem . Our solution, called $SMS^\alpha$ , is based on Simultaneous Move Monte Carlo Tree Search (SM-MCTS) and relies on a new method for the prediction of closing prices. By introducing a new reward function in $SMS^\alpha$ , we give the possibility to bidders to define their own level of risk-aversion. Through extensive numerical experiments on instances of realistic size, we show that $SMS^\alpha$ largely outperforms state-of-the-art algorithms, notably by achieving higher expected utility while taking less risks.

Licensed shared access is a new sharing concept that allows Mobile Network Operators (MNOs) to share the 2.3–2.4 GHz bandwidth with its owner. This sharing can be done after obtaining a license from the regulator. The allocation is made among groups such that two base stations in the same group can use the same spectrum simultaneously. In this context, different auction schemes were proposed, however they are all one-shot auctions. In this paper, we propose an ascending implementation of the well-known Vickrey-Clarke-Groves mechanism (VCG) when the regulator has K identical blocks of spectrum to allocate. The implementation is based on the clinching auction. Ascending auctions are more transparent than one-shot auctions because bidders see the evolution of the auction. In addition, ascending auctions preserve privacy because bidders do not reveal necessarily their valuations.

For decades, Simultaneous Ascending Auction (SAA) has been the most popular mechanism used for spectrum auctions. It has recently been employed by many countries for the allocation of 5G licences. Although SAA presents relatively simple rules, it induces a complex strategic game for which the optimal bidding strategy is unknown. Considering the fact that sometimes billions of euros are at stake in an SAA, establishing an efficient bidding strategy is crucial. In this work, we model the auction as a $n$-player simultaneous move game with complete information and propose the first efficient bidding algorithm that tackles simultaneously its four main strategic issues: the $\textit{exposure problem}$, the $\textit{own price effect}$, $\textit{budget constraints}$ and the $\textit{eligibility management problem}$. Our solution, called $SMS^α$, is based on Simultaneous Move Monte Carlo Tree Search (SM-MCTS) and relies on a new method for the prediction of closing prices. By introducing a new reward function in $SMS^α$, we give the possibility to bidders to define their own level of risk-aversion. Through extensive numerical experiments on instances of realistic size, we show that $SMS^α$ largely outperforms state-of-the-art algorithms, notably by achieving higher expected utility while taking less risks.