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Received May 31, 2021, accepted June 12, 2021, date of publication June 23, 2021, date of current version July 1, 2021.
Digital Object Identifier 10.1109/ACCESS.2021.3091802
Interference-Based Consensus and Transaction
Validation Mechanisms for Blockchain-Based
Spectrum Management
YIFEI LIANG 1, CONG LU1, YOUPING ZHAO 1, (Senior Member, IEEE),
AND CHEN SUN 2, (Senior Member, IEEE)
1School of Electronics and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
2Sony (China) Ltd., Beijing 100028, China
Corresponding authors: Youping Zhao (yozhao@bjtu.edu.cn) and Chen Sun (Chen.Sun@sony.com)
This work was supported in part by the Research and Development Center China, Sony (China) Ltd.
ABSTRACT The convergence of dynamic spectrum access (DSA) and blockchain has been regarded as
the new paradigm of spectrum management. Because of the inherent properties of blockchain, such as
decentralization and tamper-resistance, the deployment of blockchain in future networks has advantages to
address problems exposed in traditional centralized spectrum management systems, such as high security risk
and low allocation efficiency. In this article, we first compare blockchain-based spectrum management with
the traditional centralized approach and then present a reference architecture for blockchain-based spectrum
management. In particular, we propose an interference-based consensus mechanism, which can be employed
to improve transaction efficiency and reduce system overhead while promoting spectrum sharing. The
proposed consensus mechanism is based on the comparison of aggregated interference experienced by each
node, such that the node that suffers the most aggregated interference will obtain the accounting right as a
compensation. Furthermore, to avoid harmful interference caused by spectrum traders, an interference-based
transaction validation mechanism is designed to validate the spectrum transactions stored in the blocks.
Different from existing transaction validation mechanisms in which every transaction needs to be validated
by all nodes, a ‘‘transaction validation area’’ is determined for each spectrum transaction, and only the nodes
located in the validation area need to validate the transaction. The simulation results show that the system
fairness and nodes’ signal-to-interference-and-noise power ratio (SINR) can be improved by adopting the
proposed mechanisms while reducing the system overhead.
INDEX TERMS Blockchain, consensus mechanism, spectrum management, transaction validation.
I. INTRODUCTION
With the commercialization of fifth-generation (5G) mobile
communications, increasing numbers of wireless services
are emerging. In the 5G era, there are three major
application scenarios: enhanced mobile broadband commu-
nications (eMBB), massive machine type communications
(mMTC) and ultra-reliable and low-latency communications
(URLLC). With the dramatically increasing number of new
applications such as virtual/augmented reality (VR/AR),
autonomous driving, and Internet of Things (IoT), as well as
numerous future applications, the demand on radio spectrum
The associate editor coordinating the review of this manuscript and
approving it for publication was Mauro Fadda .
continues to increase significantly [1], [2]. However, the radio
spectrum is still limited. The traditional centralized spectrum
management approach can no longer satisfy the require-
ments of higher data rates and lower latency for the next
generation of mobile communication, namely, 6G [1]. The
United States President’s Council of Advisors on Science
and Technology (PCAST) report [3] emphasized the need for
creative thinking to address the overarching spectrum crisis
in spectrum allocation, utilization and management. How to
satisfy the ever-increasing demand for spectrum and improve
the spectrum management efficiency for future dense het-
erogeneous networks has become a major research issue for
6G. Apart from the problems of low spectrum utilization and
low spectrum allocation efficiency, there are other problems
VOLUME 9, 2021 This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/ 90757
Y. Liang et al.: Interference-Based Consensus and Transaction Validation Mechanisms
in centralized spectrum management systems, such as high
security risk regarding users’ data, high maintenance costs
and a lack of incentive mechanisms [4].
Over the past twenty years, dynamic spectrum access
(DSA) has been investigated extensively to improve spec-
trum utilization [4], [5]. DSA is a spectrum sharing method;
i.e., when the primary users (PUs) do not use the channel,
the secondary users (SUs) can access the channel dynami-
cally. However, the SUs must evacuate the occupied channel
when the PU occupies the channel again [5], [6]. DSA can be
employed to improve the efficiency of spectrum utilization.
Distributed spectrum management is becoming the trend in
research to meet the highly dynamic spectrum demand in 6G
scenarios. The distributed method can improve spectrum
allocation efficiency, relieve the processing pressure of the
centralized spectrum management systems, and reduce the
security risk caused by malicious attack to the centralized
database [7]. At the Mobile World Congress Americas held
in September 2018, Ms. Jessica Rosenworcel, commissioner
of the Federal Communications Commission (FCC), said
that the FCC should use blockchain to manage the wireless
spectrum [8]. The convergence of DSA and blockchain has
become a hot research topic for 6G.
Blockchain is an integrated application of distributed data
storage, point-to-point transmission, consensus mechanism,
encryption and other technologies [9], [10]. It allows trans-
actions to be finished without any central entity. In recent
years, the architecture of blockchain has been thoroughly
studied, and the throughput of blockchain has been greatly
improved [11]. Furthermore, blockchain has been applied to
many fields, such as crowd sensing [12], industrial IoT [13],
smart contracts [14] and data verification [15]. Due to its
inherent characteristics, blockchain has been considered the
key enabler for 6G telecommunication systems. Spectrum
transactions can be executed without central authorization,
and the transaction information recorded in blockchain is
immutable, which makes it more efficient and safer for spec-
trum sharing. As a consequence, telecom operators, research
institutions and spectrum regulators around the world have
begun to explore the potential applications of blockchain.
Blockchain was first discussed at the IEEE DSA network
group meeting in March 2017; France’s spectrum regula-
tor (ANFR) had experimented with the use of blockchain
technology to manage 2.4 GHz, 5 GHz and other frequency
bands in 2018. Blockchain has significant advantages to solve
the problems exposed in centralized spectrum management
systems [16], as summarized in Table 1. First, users’ data
are difficult to recover when the centralized database suffers
from a malicious attack. However, this problem can be simply
avoided by using blockchain to encrypt the users’ data and
store these data in a distributed manner. Second, the lack of
incentive mechanisms in centralized spectrum management
systems to encourage PUs to share their spectrum [4] poses a
problem. In blockchain systems, there are various methods to
award users, such as virtual currency [9], [10], and spectrum
owners can lend their spectrum and be rewarded by automatic
TABLE 1. Comparison between centralized spectrum management and
blockchain-based spectrum management.
execution of the smart contract. Moreover, the spectrum
allocation efficiency will be decreased with the increasing
number of nodes in centralized systems, especially in IoT
scenarios. Decentralization schemes of blockchain can be
used to solve that problem [16]. Other problems, such as
vulnerability to malicious attack and high maintenance cost,
can also be solved with blockchain.
Recently, significant research progress has been made in
blockchain-based spectrum management. The potential use
cases of blockchain in the Citizens Broadband Radio Ser-
vice (CBRS) band are discussed in [17]. Four use cases are
summarized, namely, lightweight transactions, provenance
tracking, interorganizational recordkeeping and multiparty
integration. In recent blockchain-based spectrum manage-
ment studies, blockchain is commonly used as a trusted
database for transaction information, spectrum sensing data
and auction results. In [18], a blockchain verification protocol
is proposed for enabling and securing spectrum sharing. The
blockchain is used as a decentralized database to verify spec-
trum sharing between cognitive radio networks. This method
can be used to access available licensed spectrum without
the need for constant spectrum sensing. However, harmful
interference caused by buyer nodes to other nodes is not
considered.
Access authentication is one of the key applications of
blockchain-based management approaches. A blockchain-
based distributed scheme is proposed to improve the security
of the systems and users’ QoS in [19]. The blockchain-based
scheme in wireless virtualization can ensure the security
in the transactions between Primary Wireless Resource
Owners (PWROs) and Mobile Virtual Network Opera-
tors (MVNOs) and can also prevent PWROs from over-
committing their resources by stopping double-spending
attacks, which eventually helps MVNOs to meet the QoS
requirements of their users. However, mathematical anal-
ysis and extensive evaluation of the framework are still
needed. A multi-operator spectrum sharing smart contract
is designed in [20]. Spectrum trading is implemented based
on a permissioned blockchain without the need of a trustless
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Y. Liang et al.: Interference-Based Consensus and Transaction Validation Mechanisms
spectrum broker. Additionally, blockchain is applied in spec-
trum trading for unmanned aerial vehicle (UAV)-assisted
cellular networks in [21]. However, design of consensus
and transaction validation mechanisms suitable for spectrum
sharing scenarios are not considered in these works.
The consensus mechanism (also known as the account-
ing right determination mechanism in blockchain) is applied
in a distributed system to ensure an unambiguous order of
transactions, thereby ensuring the consistency of the sys-
tem [22]. The performance of the blockchain system, such
as latency and transaction throughput, is largely determined
by its adopted consensus mechanism. Currently, the different
consensus mechanisms adopted by blockchain systems can
be divided into proof-based consensus (such as proof of
work (PoW) [23], proof of stake (PoS) [24] and proof of
honesty [25]) and voting-based consensus (such as practical
byzantine fault tolerance (PBFT) and Raft [26]). However,
none of these mechanisms is specially designed for spec-
trum sharing scenarios and cannot be applied directly in
blockchain-based spectrum management systems. For exam-
ple, PoW has long been criticized for its waste of resources
(such as power resources). Moreover, the long transaction
validation time makes it relatively unsuitable for the com-
mercial application of lightweight spectrum transactions.
PoS may lead to centralization and unfairness. The char-
acteristics of spectrum sharing (such as potential harmful
interference avoidance) are not considered in the existing
blockchain mechanisms. Therefore, a fair and efficient con-
sensus mechanism specially tailored for spectrum sharing
scenarios remains to be developed. In this paper, we propose
an interference-based consensus mechanism and a spectrum
transaction validation mechanism. To the best of our knowl-
edge, we are the first to take harmful interference mitigation
into consideration when designing consensus mechanisms.
Our main contributions are as follows.
•We compare blockchain-based spectrum management
and the traditional centralized approach. We then present
a reference architecture for blockchain-based spectrum
management.
•We propose an interference-based consensus mech-
anism for spectrum trading. Consensus is reached
by evaluating the interference caused by spectrum
transactions. The spectrum transaction efficiency of
blockchain-based spectrum management systems can
be improved by adopting the proposed consensus
mechanism.
•We propose a transaction validation mechanism to avoid
harmful interference to other coexisting nodes. The
transaction validation area is proposed. Each transaction
has to be validated by the nodes located in the validation
area before it is accepted.
The rest of this paper is organized as follows. In Section II,
we compare blockchain-based spectrum management and
the traditional centralized approach and present a reference
architecture for blockchain-based spectrum management.
In Section III, we propose the interference-based consensus
and transaction validation mechanisms. In Section IV, simula-
tion results are presented and discussed. Section V concludes
this paper.
II. COMPARISON BETWEEN CENTRALIZED SPECTRUM
MANAGEMENT AND BLOCKCHAIN-BASED SPECTRUM
MANAGEMENT
In this section, we review the major problems associated
with traditional centralized spectrum management. Then,
we elaborate the advantages of blockchain-based spec-
trum management and present a reference architecture of
blockchain-based spectrum management.
A. MAJOR PROBLEMS OF TRADITIONAL CENTR ALIZED
SPECTRUM MANAGEMENT
Spectrum management systems can be classified into cen-
tralized systems and distributed systems. Centralized sys-
tems can perform overall planning for the entire network
and implement the optimal spectrum allocation scheme to
improve the system performance. Moreover, the central-
ized approach seems easier to develop and maintain [27].
However, with upcoming new wireless services, various
demanding QoS requirements need to be satisfied dynami-
cally. Additionally, the number of radio devices will increase
explosively with the widespread applications of the IoT,
resulting in an extremely high density of radio connections,
e.g., greater than 1 million/km2. Centralized spectrum man-
agement has the following major problems:
1) SECURITY RISK OF USERS’ DATA
Currently, users’ data are stored in centralized databases
maintained by service providers such as telecom operators.
Although the anti-attack capability of a centralized database
is good enough to prevent the most malicious attacks, inci-
dents such as credit card data leakage or theft cannot be
entirely avoided. Moreover, users’ data may pose a threat
to privacy or integrity if access and use of the information
is not appropriately secured [8]. For example, the use of
a centralized spectrum database in DSA systems offers a
pragmatic approach for enabling spectrum sharing between
PUs and SUs. PUs are usually government or military users,
while SUs are typically commercial users. Therefore, the sys-
tem incurs a number of security and privacy concerns by
unintentionally facilitating the collection and aggregation of
sensitive information by SUs.
2) LACK OF INCENTIVE MECHANISMS
As the demand for spectrum continues to increase, it will
become increasingly difficult to meet the demand through the
legacy spectrum policy based on assignment. Moreover, it has
become difficult for centralized systems to satisfy the demand
for spectrum and system requirements of all users. DSA has
been regarded as the key technology to address the spectrum
scarcity problem. However, the PUs might be unwilling to
share their spectrum with other SUs while considering secu-
rity risks and lack of financial rewards. Therefore, appropriate
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Y. Liang et al.: Interference-Based Consensus and Transaction Validation Mechanisms
incentive mechanisms need to be designed to encourage PUs
to share their spectrum [4].
3) LOW SPECTRUM ALLOCATION EFFICIENCY
The end-to-end latency requirement in 6G could be much
less than 1 ms in order to satisfy the requirements of vehicle-
to-everything (V2X) communication scenarios. This means
that the centralized systems need to have the capability
to realize efficient spectrum allocation with extremely low
latency, which tends to be a challenging task for centralized
systems. With the number of radio devices increasing expo-
nentially, it is challenging for centralized spectrum manage-
ment systems to allocate spectrum efficiently for all types of
users in a timely manner.
B. ADVANTAGES OF BLOCKCHAIN-BASED SPECTRUM
MANAGEMENT
Blockchains are distributed databases that can be securely and
iteratively updated. The working mechanism of blockchain
is introduced in [9]. The core characteristic of blockchain
is decentralization, in which there is no centralized entity.
Moreover, encryption technology and timestamps are used to
prevent users’ data from being maliciously tampered with.
Other technologies (such as consensus mechanisms, smart
contracts and P2P transmission) are also used in blockchain.
The advantages of blockchain-based spectrum management
are described in detail as follows:
1) SECURITY OF USERS’ DATA CAN BE ENSURED
Blockchain has advantages to ensure the security of users’
data. First, the distributed data storage method and the use
of encryption technology make it easier to recover the users’
data regardless of which node has been maliciously attacked.
At the same time, the risk of manipulation of users’ data by
a central management entity can be entirely avoided. Finally,
the use of a consensus mechanism makes it almost impossible
to tamper with the blockchain records because malicious
attackers need to manipulate the most users, which is almost
impossible.
2) VARIOUS INCENTIVE METHODS ENCOURAGE PUS TO
SHARE THEIR SPECTRUM
In the blockchain, digital currency, such as bitcoin (BTC) or
ether coin (ETH), can be issued to award the nodes that have
the accounting right. The BTC/ETH-based incentive mecha-
nism can be used to encourage more participants to join the
network and compete for the accounting right. Similar mech-
anisms can be applied in blockchain-based spectrum manage-
ment systems. For example, a digital currency reward (such
as spectrum coin) can be applied to encourage PUs to share
their spectrum with other users. Spectrum trading rules (such
as the price of the spectrum) can be determined in advance
in the smart contract. Spectrum transactions can be executed
automatically through smart contracts, and spectrum owners
can share their spectrum and be rewarded automatically.
FIGURE 1. Architecture of blockchain-based spectrum management.
Note: The yellow blocks are the modules that can be innovated when
integrating spectrum management functionality into blockchain.
3) DISTRIBUTED MANAGEMENT IMPROVES THE SPECTRUM
ALLOCATION EFFICIENCY
In centralized systems, all the spectrum management proce-
dures are executed by central entities, which leads to high
processing delay. Blockchain-based spectrum management
methods eliminate the central authority and replace it with
a distributed ledger to realize spectrum transactions. There-
fore, the processing pressure can be relieved significantly.
As a consequence, the allocation efficiency is improved [22].
Blockchain-based systems make it possible to satisfy the
extremely low latency (1 ms or less) requirement in 6G
networks.
C. BLOCKCHAIN-BASED SPECTRUM MANAGEMENT
ARCHITECTURE
As shown in Fig. 1, the blockchain-based spectrum man-
agement reference architecture is composed of a data layer,
network layer, consensus layer, incentive layer, contract layer
and application layer. In this figure, yellow blocks are the
modules that can be innovated when integrating spectrum
management functionality into blockchain. The functions of
these layers are discussed as follows.
•Data Layer: The data layer encapsulates the underlying
data blocks and related data encryption and timestamp
technologies. The block size, chain structure, encryption
method and composition of transaction information are
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Y. Liang et al.: Interference-Based Consensus and Transaction Validation Mechanisms
determined in this layer. In the data layer, transaction
records are organized according to a specific structure
in order to ensure that the transactions in blockchain
systems are immutable. For example, the bitcoin sys-
tem uses a Merkle tree to store the hash value of each
transaction in the blocks [9]. Blocks are chained together
by the hash pointer in chronological order. To improve
the transaction speed and system scalability, block-less
data structures have been adopted in recent blockchain
networks. In IOTA [28], transactions are structured as
a directed acyclic graph (DAG). Despite the organiza-
tion structure of transaction data, asymmetric encryption
mechanisms are adopted to prevent tampering of the
transaction data. When integrating spectrum manage-
ment functionality into blockchain, the block size, block
generation interval and other parameters can be opti-
mized to adapt to the actual spectrum sharing scenarios.
•Network Layer: Transaction information transmission
and transaction validation mechanisms are determined
in this layer. Transaction information is transmitted
via a P2P network or telecommunication network, and
the blocks are connected to the blockchain after being
validated by using validation mechanisms. In existing
blockchain-based systems, a transaction is validated
when the buyers have enough balance in their account.
In spectrum sharing scenarios, spectrum trading must
consider spectrum access rules, primary user protec-
tion and harmful interference mitigation. In this paper,
an interference-based transaction validation mechanism
is proposed, and spectrum transactions will be validated
only when the spectrum trading action does not cause
harmful interference to coexisting nodes.
•Consensus Layer: Consensus mechanisms are adopted
to maintain the consistency of blockchain-based sys-
tems. Various consensus mechanisms that can be used to
realize the decentralization of the systems are contained
in this layer, such as PoW, PoS and delegated proof
of stake (DPoS). These mechanisms can be divided
into proof-based consensus and voting-based consen-
sus. In proof-based consensus mechanisms, nodes are
required to solve a mathematical puzzle or to show
that they are more eligible than other nodes to win
the accounting right. In voting-based mechanisms, con-
sensus is reached by the communications of each
node. However, existing consensus mechanisms do not
fit well in spectrum sharing scenarios. In this paper,
an interference-based consensus mechanism is pro-
posed. Nodes that suffered most aggregated interference
in the last round are given the accounting right in the
next round.
•Incentive Layer: An incentive layer is applied for
promoting spectrum sharing among spectrum users.
More PUs will be encouraged to share their spectrum
by adopting appropriate pricing and incentive mecha-
nisms together with digital coin issuance mechanisms.
In recent studies, different spectrum sharing incentive
methods are proposed, and spectrum coin is proposed
to promote spectrum sharing [29]–[31].
•Contract Layer: The contract layer mainly includes var-
ious scripts, algorithms and smart contracts for spec-
trum trading, which is the basis of the programmable
feature of blockchain. Spectrum owners, infrastructure
providers and network slice brokers can be integrated
into the blockchain platform, and spectrum transac-
tions can be executed automatically based on the smart
contract.
•Application Layer: The characteristics of blockchain
enable it to serve spectrum management. The application
layer provides various application scenarios and use
cases of blockchain in spectrum management, includ-
ing spectrum trading, industrial management in IoT use
cases, etc. In each scenario, smart contracts can be cus-
tomized according to specific needs.
III. MECHANISM DESIGN FOR BLOCKCHAIN-BASED
SPECTRUM MANAGEMENT
In this section, an interference-based consensus mechanism
is designed to improve the spectrum transaction efficiency
of blockchain-based spectrum management systems. Further-
more, the transaction validation mechanism is designed to
avoid harmful interference with other coexisting nodes.
A. INTERFERENC E-BASED CONSENSUS MECHANISM
Fig. 2 shows the system scenario, in which the nodes rep-
resent spectrum traders in spectrum management system,
such as eNBs from different micro-operators or some other
secondary spectrum users. Additionaly, Nblocks have been
connected to the blockchain. The accounting right of Block-
(N+1) needs to be determined, and only one node will
be selected. All the transactions stored in Block-Nhave
been completed, and the buyer of each transaction may
cause harmful interference to the other nodes. As illustrated
in Fig. 2, there are Ntrs transactions stored in Block-N. Taking
the first 3 transactions as an example, the buyers are Node-1,
Node-3 and Node-5. Other coexistent nodes may suffer from
interference caused by those 3 buyers. Other coexistent nodes
can calculate the aggregated interference according to the
information of those 3 transactions in Block-N. Considering
that interference decreases with distance, it is reasonable to
neglect the interference from far-away nodes. Accordingly,
the concept of ‘‘protection area’’ is proposed to reduce the
system overhead. Each node will determine its protection
area, and the interference emitted from the buyer nodes out-
side of the protection area will be ignored. The radius of the
protection area for Node-ican be calculated by
Ri=λ
4π·α
sPmax ·GTx ·GRx
Ii
th
,(1)
where λis the wavelength of the carrier frequency; αis
the pathloss coefficient between the interfering node and
the victim receiving node; GTx and GRx are the transmitter
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Y. Liang et al.: Interference-Based Consensus and Transaction Validation Mechanisms
FIGURE 2. Illustration of the interference-based consensus mechanism.
antenna gain and the receiver antenna gain, respectively; Pmax
is the maximum transmit power of all nodes; and Ii
th is the
interference threshold of the i-th node.
To improve the system fairness, only the nodes with the
competition right can compete for the accounting right of
Block-(N+1). The competition right factor (CRF) of each
node can be determined according to the available spectrum
and spectrum coins held by each node. The more spectrum
and spectrum coins held by the node, the higher CRF that
node obtains. Only the nodes with a CRF value higher than
the CRF threshold (CRFth) have the right to compete for
accounting rights. The accounting right determination mech-
anism can be presented briefly as follows: for the nodes
that have the competition right, compare their experienced
aggregated interference suffered from the buyer nodes of the
transactions stored in Block-Nand then select the node that
suffers from the largest aggregated interference to receive the
accounting right of Block-(N+1).
For example, assuming that Node-4 in Fig. 2 suffers the
largest aggregated interference from the buyer nodes of the
transactions stored in Block-N, Node-4 will be selected to
have the accounting right of Block-(N+1). Node-4’s block
will be connected to the blockchain after being validated by
the other coexisting nodes, and Node-4 will receive spectrum
coins as a reward.
The procedures of the interference-based consensus mech-
anism are detailed in Algorithm 1.
B. TRANSACTION VALIDATION MECHANISM
Block-(N+1) cannot be immediately connected to the
blockchain even though the ownership of the accounting right
has been determined. Furthermore, the transactions stored in
that block will not be completed immediately. The system
scenario is shown in Fig. 3. Assuming that Node-phas the
accounting right of Block-(N+1), transactions stored in
Node-p’s block need to be validated because some spectrum
transactions may be invalid. For example, transaction infor-
mation may have been maliciously tampered or the balance
in the buyer’s wallet may be not enough to pay for the spec-
trum transaction. More importantly, the buyers (i.e., Node-2,
Node-4) of the spectrum transactions may cause harmful
interference to the neighboring nodes. Therefore, the problem
is how to identify harmful interference and design an appro-
priate transaction validation mechanism.
Since there is no central node to conduct interference
validation, the traders of each transaction need to provide nec-
essary information, such as the traders’ position and transmit
power, to the other nodes for interference calculation. As the
interference generally decreases with distance, interference
can be ignored when the interfering nodes are far away from
the receiving node. Different from the existing transaction
validation mechanisms in which each transaction needs to
be validated by all nodes in the blockchain, in the proposed
interference-based transaction validation mechanism, a trans-
action validation area is determined for each transaction, and
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Y. Liang et al.: Interference-Based Consensus and Transaction Validation Mechanisms
Algorithm 1 Procedures of the Interference-Based
Consensus Mechanism for Blockchain-Based Spectrum
Management
Input: Transaction information contained in Block-N
Output: Accounting right determination of Block-(N+1)
1: Assume block-Nhas been connected to the blockchain
and the accounting right of Block-(N+1) needs to be
determined;
2: Assume Node-khas the accounting right of Block-N;
3: for Node-i∈whole node set of the blockchain do
4: if CRFi>CRFth then
5: Create Node-i’s own block containing spectrum
transactions;
6: Calculate the radius of the protection area Ri;
7: Calculate the aggregated interference caused by
transactions in Block-Nfor Node-i;
8: Submit the aggregated interference information to
Node-k;
9: end if
10: end for
11: Compare the aggregated interference experienced by dif-
ferent nodes;
12: if Node-psuffers the largest aggregated interference
then
13: Node-pgains the accounting right of Block-(N+1);
14: Broadcast the accounting right determination result to
all coexistent nodes;
15: end if
FIGURE 3. Illustration of the interference-based transaction validation
mechanism.
only the nodes located in the validation area need to validate
the transaction. In this way, the system overhead is reduced
significantly.
The validation mechanism can be summarized as fol-
lows: for each transaction, every node (e.g., Node-i) located
in the transaction validation area calculates its signal-to-
interference-plus-noise ratio (SINR). Then, Node-icompares
its SINR with the SINR threshold (SINRth). If SINR >
SINRth, Node-ijudges that the transaction is valid after
verifying the relevant transaction information (e.g. account
balance); otherwise, SINR <SINRth, which indicates that
Algorithm 2 Procedures of the Interference-Based Transac-
tion Validation Mechanism for Blockchain-Based Spectrum
Management
1: for Transaction i∈Node-p’s block do
2: Calculate the radius of the validation area of transac-
tion i;
3: Store the transactions information and validation area
information into Node-p’s block;
4: end for
5: Send Node-p’s block to the other coexistent nodes;
6: for Transaction i∈Node-p’s block do
7: for Node-jin the validation area of transaction ido
8: if SINR >SINRth then
9: if Information of transaction iis valid then
10: Node-jconfirms transaction i;
11: else
12: Node-jrejects transaction i;
13: end if
14: else
15: Node-jrejects transaction i;
16: end if
17: end for
18: Calculate the passing rate (Pri) of transaction i;
19: if Pri>Prth then
20: Transaction iis validated;
21: else
22: Transaction iis rejected;
23: end if
24: end for
25: if ∀transaction i∈Node-p’s block is validated then
26: Node-p’s block is validated;
27: else
28: Node-p’s block is rejected;
29: end if
this transaction results in harmful interference to Node-i.
In this case, Node-ijudges that the transaction is invalid.
Finally, the transaction is determined to be valid only if the
passing rate is higher than the predefined passing rate thresh-
old (Prth). Note that the passing rate is defined as the ratio
between the number of nodes that judge that the transaction
is valid and the total number of nodes in the validation area
of that transaction.
The procedures of the interference-based transaction vali-
dation mechanism are detailed in Algorithm 2.
IV. SIMULATION ANALYSIS
In this section, we evaluate the performance of the
interference-based consensus and transaction mechanisms
via simulation.
A. PERFORMANCE COMPARISON: FAIRNESS AN D USER
SATISFACTION
In the simulation scenario, there are 100 nodes distributed
randomly. Each node owns different amount of spectrum.
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When the spectrum it owns cannot meet the demand, it needs
to purchase spectrum from other nodes. The total number of
transactions stored in Block-Nvaries from 50 to 80. The num-
ber of buyers is the same as the number of transactions. The
transmit power of each node is either 10 dBm or 20 dBm. The
pathloss coefficient is 2.5. The CRF of each node depends on
how much available spectrum and spectrum coins it holds.
The CRF threshold is set as 0.4, which indicates that the
nodes with a CRF lower than 0.4 cannot compete for the
accounting right. The node with the accounting right will
receive the spectrum coins reward issued by the spectrum
management system. The spectrum coins can be used to trade
with other nodes to obtain spectrum. Fig. 4 shows the number
of spectrum coins and the difference in the satisfaction factor
of each node after 100 blocks are connected to the blockchain.
The satisfaction factor of the i-th node is defined as
δi=Ni
sp/Di
sp,(2)
where Ni
sp is the total spectrum held by the i-th node, and Di
sp
is the total spectrum demand of the i-th node.
As seen from Fig. 4, the spectrum coins are concentrated
at a few nodes when the PoS mechanism is used. On the
other hand, the distribution of spectrum coins of each node is
relatively average when using the proposed mechanism, and
the standard deviation of the distribution of spectrum coins
also indicates that point. When the PoS mechanism is used,
the standard deviation of the distribution of spectrum coins
is 374, and when the proposed mechanism is used, the stan-
dard deviation is 67. Therefore, it can be concluded that
the system fairness can be improved by using the proposed
accounting right determination mechanism. The difference
in the satisfaction factor of each node is shown in Figure 4
(the brown curve) and is defined by the difference between
the satisfaction factor when using the proposed mechanism
and that of the same node while using the PoS mechanism.
As shown in Fig. 4, the satisfaction factor of most nodes
increases by using the proposed mechanism.
The PoS mechanism is based on the stake of each node.
As a result, the node with higher stake has higher probability
to obtain the accounting right. By contrast, all nodes have
the same probability to obtain the accounting right when
using the proposed mechanism. Therefore, the system fair-
ness and satisfaction factor can be improved by using the
interference-based consensus mechanism.
B. PERFORMANCE GAIN I N TERMS OF SINR
IMPROVEMENT AND SYSTEM OVERHEAD REDUCTION
In the simulation scenario, the total number of nodes varies
from 200 to 4000, and the active nodes ratio is 40%; i.e., 40%
of the nodes in the scenario are trading at the same time. The
transmit power of each node is either 10 dBm or 20 dBm. The
pathloss coefficient is set as 2.5. The interference threshold
(Ith) is set as −96 dBm when calculating the radius of the
‘‘validation area’’. The SINR threshold is 20 dB. The SINR
of each node and system overhead are compared.
FIGURE 4. Comparison between the interference-based consensus
mechanism and PoS mechanism in terms of the distribution of spectrum
coins and the difference in the satisfaction factor.
As seen from Fig. 5, the SINR of each node can be
improved significantly by using the proposed mechanism.
When the number of nodes increases, the SINR difference
further increases. When the total number of coexistent nodes
reaches 3000, the difference in user SINR reaches 10 dB. The
reason is that the transactions that will cause harmful inter-
ference to other nodes are invalid and will not be executed.
Therefore, we can conclude that the proposed transaction
validation mechanism can improve the SINR of each node
and can also work well in dense network scenarios.
The system overhead is compared in Fig. 6. In this sim-
ulation, the system overhead is represented by how many
times the validation procedure is executed. The total number
of nodes in the simulated scenario is Nnode, and the number
of transactions is Ntrs. Without using the interference-based
transaction validation mechanism, the total number of trans-
action validation is as large as Ntr =Nnode ·Ntrs since each
transaction needs to be validated by all the nodes. However,
the number of nodes that need to validate each transaction
(Nper−tr ) is significantly less than Nnode when using the pro-
posed mechanism. Therefore, the total number of validations
will be significantly reduced by using the interference-based
transaction validation mechanism. Note that in Fig. 6, the sys-
tem overhead reduction ratio is defined as
η=N∗
tr /Ntr ,(3)
where N∗
tr is the total number of validations when adopting
the proposed mechanism (in which each transaction needs to
be validated by those nodes inside the validation area only),
and Ntr is the total number of validations when adopting
the traditional mechanism (in which each transaction needs
to be validated by all nodes). There are several factors that
can affect the system’s computational complexity, such as
interference threshold and the ratio of active nodes. As shown
in Fig. 6, the total system overhead increases with the ratio of
active nodes while decreasing with the interference threshold.
90764 VOLUME 9, 2021
Y. Liang et al.: Interference-Based Consensus and Transaction Validation Mechanisms
FIGURE 5. Comparison of nodes’ SINR.
FIGURE 6. Comparison of system overhead.
And the system overhead is greatly reduced while adopt-
ing the proposed transaction validation mechanism, because
fewer nodes participate in the transaction verification process.
V. CONCLUDING REMARKS
In this paper, we compare blockchain-based spectrum man-
agement with the traditional centralized approach and present
a reference architecture of blockchain-based spectrum
management, which can be employed in the next gener-
ation of mobile communications, namely, 6G. In particu-
lar, interference-based consensus and transaction validation
mechanisms are proposed based on the reference architecture.
The simulation results show that the system performance
(such as system fairness, nodes’ satisfaction factor, and
nodes’ SINR) can be improved significantly while reducing
the system overhead.
It should also be noted that there are still many open
issues to be addressed. A series of mechanisms needs to
be developed to support the proposed blockchain-based
spectrum management architecture, which includes a block
generation mechanism, incentive mechanism, and pricing
mechanism, among many other mechanisms. Moreover,
a blockchain-based DSA test platform needs to be built to
test the proposed mechanisms and evaluate the system per-
formance in various 5G and/or 6G mobile communication
scenarios. Finally, a blockchain-based spectrum management
system that can be employed by various practical wireless
networks or vertical applications could be the direction of
future research and development.
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YIFEI LIANG received the B.S. degree in com-
munication engineering from Beijing Jiaotong
University, Beijing, China, in 2019, where he
is currently pursuing the Ph.D. degree with the
School of Electronic and Information Engineer-
ing. His research interests include intelligent
communications and blockchain-based spectrum
management.
CONG LU received the B.S. and M.S. degrees
in communication engineering from Beijing Jiao-
tong University, Beijing, China, in 2017 and
2020, respectively. His research interest includes
blockchain-based spectrum management.
YOUPING ZHAO (Senior Member, IEEE)
received the B.S. and M.S. degrees from Tsinghua
University, Beijing, China, in 1994 and 1996,
respectively, and the Ph.D. degree from Virginia
Tech, Blacksburg, VA, USA, in 2007. He is cur-
rently a Professor with Beijing Jiaotong Uni-
versity, Beijing. His current research interests
include cognitive radio and intelligent communi-
cations for next-generation wireless communica-
tions. He received the Best Editor Award of China
Communications, in 2018. Since 2017, he has been on the Editorial Board
of China Communications cosponsored by IEEE Communications Society.
CHEN SUN (Senior Member, IEEE) received
the Ph.D. degree in electrical engineering from
Nanyang Technological University, Singapore,
in 2005. From August 2004 to May 2008, he was
a Researcher with ATR Wave Engineering Lab-
oratories, Japan, working on adaptive beamform-
ing and direction-finding algorithms of parasitic
array antennas as well as a theoretical analysis
of cooperative wireless networks. In June 2008,
he joined the National Institute of Information
and Communications Technology (NICT), Japan, as an Expert Researcher
working on distributed sensing and dynamic spectrum access in TVWS.
He is currently the Director of the Sony Research and Development Center,
Wireless Network Research Department, Beijing, China. He received the
IEEE Standards Association Working Group Chair Award for Leadership,
in 2011, the IEEE 802.11af Outstanding Contributions Award, in 2014,
and the IEEE 802.19.1 Outstanding Contributions Award, in 2018. From
2013 to 2015, he served as the Technical Editor of the IEEE 1900.6 Standard,
in 2011 and the Rapporteur of the European Telecommunications Standards
Institute Reconfigurable Radio Systems EN 301 144. He currently serves as
the Technical Editor for the IEEE 802.19.1a Working Group.
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