Adaptive and Economically-Robust Group Selling of Spectrum Slots for Cognitive Radio-Based Networks

Abstract

Auction theory has found vital application in cognitive radio to relieve spectrum scarcity by redistributing idle channels to those who value them most. However, countries have been slow to introduce spectrum auctions in the secondary market. This could be in part because a number of substantial conflicts could emerge for leasing the spectrum at the micro level. These representative conflicts include the lack of legislation, interference management, setting a reasonable price, etc. In addition, the heterogeneous nature of the spectrum precludes the true evaluation of non-identical channels. The information abstracted from the initial activity in terms of price paid for specific channels may not be a useful indicator for the valuation of another channel. Therefore, auction mechanisms to efficiently redistribute idle channels in the secondary market are of vital interest. In this paper, we first investigate such leading conflicts and then propose a novel Adaptive and Economically-Robust spectrum slot Group-selling scheme (AERG), for cognitive radio-based networks such as IoT, 5G and LTE-Advanced. This scheme enables group-selling behavior among the primary users to collectively sell their uplink slots that are individually not attractive to the buyers due to the auction overhead. AERG is based on two single-round sealed-bid reverse-auction mechanisms accomplished in three phases. In the first phase, participants adapt asks and bids to fairly evaluate uplink slots considering the dynamics of spectrum trading such as space and time. In the second phase, an inner-auction in each primary network is conducted to collect asks on group slots, and then, an outer-auction is held between primary and secondary networks. In the third phase, the winning primary network declares the winners of the inner-auction that can evenly share the revenue of the slots. Simulation results and logical proofs verify that AERG satisfies economic properties such as budget balance, truthfulness and individual rationality and improves the utilities of the participants.

Cognitive Radio with Machine Learning to Increase Spectral Efficiency in Indoor Applications on the 2.5 GHz Band

“…The licensed spectrum is limited, making new licenses scarce and expensive. At the same time, the proliferation of internet-connected devices can make the industrial-scientific-and-medical (ISM) bands highly congested [ 1 , 2 ]. CR, dynamic spectrum access (DSA), and ML are considered a possible solution, as they increase the spectral efficiency of currently available frequencies using “spectral windows” [ 3 , 4 , 5 ].…”

“…This allows secondary users (SUs) to use the spectrum vacancy to transmit. In other words, SUs dynamically accesses the licensed spectrum when (or where) it is temporally available [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ].…”

“…At the same time, the IoT continues to gain importance and popularity due to the ever-growing number of low-price and low-power smart devices, like sensors, actuators, and radio frequency ID (RFID) tags, that can connect to the internet anytime, anywhere and for anything. It is expected that, by 2025, the number of internet-connected devices will be 10 times larger than the human population [ 2 , 6 ]. IoT devices are, therefore, anticipated to demand a huge amount of wireless spectrum in the near future, which contrasts with the current issue of spectral scarcity [ 2 , 6 , 8 ].…”

“…It is expected that, by 2025, the number of internet-connected devices will be 10 times larger than the human population [ 2 , 6 ]. IoT devices are, therefore, anticipated to demand a huge amount of wireless spectrum in the near future, which contrasts with the current issue of spectral scarcity [ 2 , 6 , 8 ].…”

Handshake Sense Multiple Access Control for Cognitive Radio-Based IoT Networks

“…This situation will get worse if IoT-enabled SUs are densely deployed to act as competitors. Hence, the dynamic spectrum leasing, e.g., in [13,14], can increase the transmission power density and subsequently the interference level [15]. We need to consider the leaked powers from the adjacent SUs to the PU receivers especially in the decentralized networks due to hidden primary terminal problem [16,17,18].…”