INTRODUCTION
Over last few years, the use of radio spectrum has increased dramatically in
wireless communications. As the fundamental and most important resource, the
radio spectrum is shared among a variety of services temporally for a vast array
of different purposes (Leaves et al., 2004; Yan
et al., 2012; Giacomazzi et al., 2012;
Xu et al., 2012). Due to the nature of beyond
3G or 4G mobile systems, such as distribution, reconfiguration, diversity etc.,
to utilize efficiently the precious radio spectrum, to accommodate as many services
as possible and provide operators maximal profits is an issue (Maharjan
et al., 2011).
Traditionally, spectrum is licensed with a particular radio standard and spatially
managed by different operators to deliver their services, called Fixed Spectrum
Management (FSM). Interference between different RANs (Radio Access Networks)
have been avoided successfully in the current FSM policy. However, FSM adopted
can not cope with the progress of future heterogeneous networks. Simultaneously,
traditional FSM results in low utilization efficiency and spectrum holes because
of dynamic demands for spectrum (McHenry, 2003). Throughout
the last decade, considering the scarcity and high economic value of spectrum,
worldwide research efforts have been made in Dynamic Spectrum Management (DSM)
(Leaves et al., 2001, 2002,
2004; Dimitrakopoulos et al.,
2004). Several mechanisms have been proposed to enhance the efficiency of
spectrum utilization, e.g. European research project DRiVE and OverDRiVE (Leaves
et al., 2001, 2002, 2004).
With the rapid development of SDR and the introduction of reconfiguration concepts
(Dimitrakopoulos et al., 2004), DSM no longer
remains a utopia but becomes a reality. Howevery, aforementioned works have
shown that win-win strategy for DSM and operators is still in the air.
In this study, efforts are dedicated to designing a reasonable DSM scheme to
give a comprehensive attention to enhance the spectrum efficiency and maximize
the operator’s profits. Three contributions are made in this study. First,
a bilateral bargaining based DSM architecture and some necessary concepts are
described to make the spectrum trading applicable. Second, the bilateral bargaining
DSM (BBDSM) algorithm and its implementation procedure are proposed. This BBDSM
takes advantage of Chatterjee and Samuelson (1983) (C
and S) bilateral bargaining model in game theory to explicitly address the profit
improvement of the RANs. Finally, the proposed BBDSM is compared with existing
FSM based on two imperfect load prediction methods, history value prediction
and current value prediction, using extensive simulations. Results show that
BBDSM is definitely effective in improving the spectrum utilization and increasing
the operator’s profits.
SYSTEM MODEL
In this section, the basic BBDSM architecture is introduced as shown in Fig. 1. Specifically, some new defined concepts supporting the proposed BBDSM scheme are emphasized.
BBDSM architecture: Spectrum market which originates from the definition
of spectrum pool (Dimitrakopoulos et al., 2004),
is a logical spot where distributed RANs could do spectrum transactions. In
the spectrum market, if some RAN can satisfies its own service requirements
and have spare spectrum as well it can lease his extra spectrum out to maximize
spectrum efficiency and his profits. On the other hand, if some RAN lacks of
spectrum owing to increasing services it becomes a consumer of the spectrum
market. In order to make more profits and satisfy as many service demands as
possible, the RAN will try to rent spectrum from others to proceed with his
service provisioning. Once there is a market, there should be policies to regulate
the operation of trading behaviors. Regulator is a repository of these principles,
e.g., trading regulations, greedy behavior bans and so on. To fulfill the spectrum
transactions, each RAN is attached to an intelligent Trading Agent (TA). Similar
to agents in real business, TAs take charge of the transactions and make a series
of important decisions, e.g., when to lease/rent the spectrum, how much spectrum
to lease/rent, at what price to lease/rent the spectrum et al.
Furthermore, to facilitate the spectrum trading, the entire spectrum block
is divided into Spectrum Trading Units (STU) in a size of the smallest channel
in a fixed service channel raster for a given band. The trouble in controlling
and managing the interferences between RANs (Leaves et
al., 2004) is ignored in the proposed BBDSM.
As mentioned in previous works Leaves et al. (2001,
2002), DSM is periodical and proactive operation and
typical period is 0.5 h, i.e., for the set T = {1, 2,…}, the DSM interval
between t and t+1 is half an hour. This feature is important for the spectrum
trading because spectrum leasing or renting will expire automatically after
the specified interval of time. Moreover, DSM at t is essential for providing
services and profit earning at t+1, whose payoff function depends on the results
of BBDSM at t and the practical load at t+1.
RAN’s profits: The total profits of each RAN in BBDSM can be divided into two parts: One is the profits of service provision utilizing his original spectrum (i.e., spectrum pre-assigned to that RAN); the other is profits from spectrum trading.
Service providing profits: If TA of the RAN utilizes a STU to provide services for one period it will obtain payoff:
where, Psprov is the earned income of services and Pcost is his own basic cost of the spectrum.
Spectrum trading profits: In view of the proactive features of BBDSM,
practical traffic demands may present some deviations from predicting traffic
demands.
|
| Fig. 1: |
Architecture for BBDSM in reconfigurable systems |
If actual load is less than the predicting value, investment made by renting
TA on trading spectrum at period t may bring no profits at period t+1; on the
contrary, investment made by renting TA may pose deficient which lower profits
and lead to the denial of service at period t+1.To guarantee the income of leasing
TAs, like in the real market, a simple risk capital mechanism is embodied in
BBDSM and the spectrum trading profits allocating can be fulfilled in two steps:
Advance payment at t and profit settlement in t+1. With the premise that renting
TA comes into terms with leasing TA and decides to choose this TA to trade with
it should deliver advance payment to leasing TA beforehand. Regarding the risks
of imperfection of load prediction, this payment is nonrefundable. Renting TA
bargains with leasing TA at t over the amount of advance payment per STU with
the assumption that this STU of spectrum is used to satisfy the service requirements
and make profits. Advance payment per STU is defined as P advance which is in
accord with the bilateral bargaining results between renting TA and leasing
TA. Hence, the profit of leasing TA from spectrum trading can be denoted as
follows:
After the practical service demands are satisfied at t+1, profit settlement stage takes on. Renting TA’s profits per STU earned by the trading spectrum is:
BBDSM SCHEME
Based on the architecture and definitions above, BBDSM scheme is elaborated
in this section. Due to the decentralized properties of RANs, TAs can’t
collect all the necessary trading information, thus the negotiations between
renting TA and leasing TA can be considered as a Bayesian game with incomplete
information. As one of the most classic models in that game, C and S bargaining
model by Chatterjee and Samuelson (1983) is adopted
for the profit allocation in BBDSM. The Bayesian equilibrium and related implementation
procedures are investigated in this part as well.
BDSM algorithm under incomplete information general description: Chatterjee
and Samuelson (1983) studied a bilateral bargaining under incomplete information
in 1983. Two players, a buyer and a seller, are trying to transact a good. The
buyer and seller name a price; if the bid price is above the ask price, the
good is sold for the average of the two prices and if the ask price is above
the bid price, the seller retains the good. Each trader knows his own valuation
for the good. However, there is incomplete information on each side concerning
the side’s valuation.
In the case of BBDSM, since renting TAs’ and leasing TAs’ profits depend on the earning of spectrum trading, they can be also regarded as two players bargaining to transact spectrum. The bilateral bargaining between them can be abstracted into a Bayesian game model as follows:
| • |
Player (I): Renting TA and leasing TA are two players
i∈ N = {1,2} |
| • |
Valuation of the spectrum (c, v): c is defined as the cost valuation
of the trading spectrum per STU from the aspect of the leasing TA and v
stands for the value of spectrum to the renting TA |
| • |
Bid and ask prices (Sbid, Sask): Both renting
TA and leasing TA hide their expected prices. They progressively change
their bid or ask prices (Sbid, Sask) so as to acquire
better profits and in this way their expected prices are revealed step by
step. The bargaining strategy is a means to reveal and can also be used
to determine, the expected prices of the renting TA and leasing TA |
| • |
Profits (Pleasing, Prenting): The trade takes
place at the average of the two price (Sbid+Sask)/2
which is equal to Padvance, if and only if renting TA’s
price exceeds leasing TA’s price i.e., Sask≤Sbid.
The utility functions of the renting TA and leasing TA, respectively, are: |
Bargaining strategies: Now, the key problem for both renting TA and
leasing TA is how to design the optimal bids or asks to achieve maximum benefit,
relying on incomplete information about the opponent. Starting with the basic
concepts presented in Chatterjee and Samuelson (1983),
a novel approach for spectrum bargaining analysis is developed here.
Suppose the leasing TA estimates the renting TA will use a linear bargaining strategy:
where, Sbid(lease)(v) is the estimation made by the leasing TA of what the renting TA’s offer will be.
Correspondingly, the renting TA estimates that the leasing TA will use a linear bargaining strategy:
where, Sask(rent)(c) is the renting TA’s estimate of the leasing TA’s offer.
It should be noted that the leasing TA have no information about the value
of v and the renting TA does not know c. Coefficient αbid and
βbid are the estimation of leasing TA (αbid≥0,
βbid≥0) and coefficient αask and βask
are estimated by the renting TA (αask≥0, βask≥0).
As mentioned above, leasing TA and renting TA develop their bargaining strategies
to expand their own benefits:
where, Sask is the asking price of the seller to be determined, E [Sbid(lease)(v)|Sbid(lease)(v)≥Sask] is the expectation (mean) of the estimated bid price of the renting TA when this estimated bid price is larger than or equal to the ask price of the leasing TA and Prob [Sbid(lease)(v)≥Sask] is the probability that the estimated bid of the renting TA is larger than or equal to the ask of the leasing TA.
In consistent with renting TA, the leasing TA’s profit maximizing objective can be illustrated as:
Bayesian equilibrium of the game is supposed as (Sbid*, Sask*) which represent the optimal strategies maximizing the profits of renting RAN and leasing RAN, respectively. It can be obtained from:
In the effort to receive more profits and deduce the optimal strategies Sbid*
and Sask* from Eq. 10, 11,
E [Sbid(lease)(v)| Sbid(lease)(v)≥Sask],
Prob [Sbid(lease)(v)≥Sask], E[Sask(rent)
(c)| Sbid≥Sask(rent)(c)] and Prob
[Sbid≥Sask(rent)(c)] must be satisfied.
With the valuation of c and v based on load prediction, leasing TA can have
an estimate of Sbid(lease) (v) and renting TA can have
an estimate of Sask(rent) (c). When this estimations are
embodied into the functions, E [Sbid(lease) (v)| Sbid(lease)
(v)≥Sask], Prob [Sbid(lease) (v)≥Sask],
E [Sask(rent) (c)| Sbid≥Sask(rent)
(c)] and Prob [Sbid≥Sask(rent) (c)] can
be calculated and consequently the Bayesian equilibrium can be developed from
Eq. 10, 11.
TA’s valuation of c and v should be dynamically adapted in consistent
with the supply demand relation of the market in near future. To be specific,
spectrum will appreciate provided that demand exceeds supply in the market and
prospective spectrum demand is optimistic. On the other hand, spectrum will
depreciate provided that supply exceeds demand in the market and prospective
spectrum demand is pessimistic. However, it is almost impossible for the individual
TA to predict and assemble all the necessary information of the whole market
accurately. Instead, either renting or leasing TA adapts its own estimate of
v or c according to the spectrum load prediction, i.e., with current time t,
if renting TA predicts that its loads increase at t+1 which means this TA is
in more urgent need of the spectrum, v is supposed to be in higher price. On
the contrary, if renting TA predicts its loads decrease at t+1 it will lower
v. Correspondingly, if leasing TA predicts that its loads increase at t+1 which
means the spare spectrum is shrinking, c will be in higher price. And if this
TA predicts its loads decrease at t+1 which means the TA have more available
spectrum to lease and it is eager to trade, estimation of spectrum descend.
Meanwhile, massive v can be statistically taken by renting TA as variable with
a certain probability distribution (e.g., uniform distribution, triangular distribution)
and so it does with c by the leasing TA. To be simple, in the following part,
the bargaining strategies of both parties are investigated in the scenario of
c and v with a uniform distribution.
Optimal bilateral bargaining strategies under a uniform distribution: If TA of the RAN utilizes a STU to provide services for one period it will obtain payoff.
The renting TA assumes c to be a variable uniformly distributed in [c1,
c2] and the leasing TA assumes v to be a variable uniformly distributed
in [v1, v2]. As defined in Eq. 6-7,
Sbid(lease) (v) and Sask(rent) (c)
are variables uniformly distributed in [αbid+βbid*v1,
αbid+βbid*v2] and [αask+βask*c1,
αask+βask*c2], respectively. It is
obvious that the probability density for c is 1/(c2-c1)
and for v is 1/(v2 – v1). Similar probability density
can also be found for Sbid(lease) (v) and Sask(rent)
(c).
With the assumption of a uniform distribution for v, Prob [Sbid(lease)
(v)≥Sask] can be obtained through simple mathematical manipulation:
For Sbid(lease)(v) is uniformly distributed in [αbid+βbid*v1, αbid+βbid*v2], E[Sbid(lease)(v)| Sbid(lease)(v)≥Sask] can be easily deduced as:
Correspondingly, formulations can be established for Prob[Sbid≥Sask(rent)(c)] and E[Sask(rent)(c)| Sbid≥Sask(rent)(c)] under the assumption of a uniform distribution for c, i.e.:
In terms of Bayesian equilibrium, Eq. 12 and 13
are substituted into Eq. 8 and then from Eq.
10, the optimal strategy of the leasing TA can be given as:
Therefore, when there is equilibrium, the optimal strategy for the leasing TA is to offer an asking price which is the sum of 1/3 the upper limit of his estimation of the buyer’s bid and 2/3 his own lowest valuation of the trading spectrum.
Similarly, taking Eq. 14 and 15 into
Eq. 9 and then from Eq. 11, when the game
reaches the equilibrium, the optimal bidding strategy of the renting TA would
be:
Hence, the optimal strategy for the renting TA is to ask in a price which is the sum of 1/3 the lower limit of his estimation of the leasing TA’s ask and 2/3 his own highest valuation.
BBDSM IMPLEMENTATION
BBDSM runs periodically. In each period, TAs takes five main steps: profit settlement, self-checking, spectrum value estimation, bilateral bargaining and decision making as depicted in Fig. 2.
Profit settlement: If current TA was renting TA in period (t-1) and spectrum trading has made profits in terms of practical load during period t, profits earned by service will be received the providing conforming to the agreement reached in the last period. Considering the imperfect load prediction and risk of the advance payment, there may be surplus or deficit for current TA.
Self-checking: TA checks his predicting status of spectrum usage and compares it with Th which is defined as the trading threshold to classify all the RANs into two categories with respect to the predicting spectrum using ratio rp:
where, Sp is the forecasting load and Sa is the number of total STUs originally assigned to that RAN.
| • |
If rp>Th, according to load prediction,
current RAN needs to rent some spectrum from the others in the next period
and his TA can be regarded as renting TA |
| • |
If 0 <rp<Th, according to load prediction,
current RAN has some spare spectrum to share with the other in the next
period and his TA can be regarded as leasing TA |
Spectrum value estimation: The spectrum value estimation of the renting
TA (c) and the leasing TA (v) will change independently in accordance with the
variations in load prediction as mentioned above.
Bilateral bargaining: Each renting TA will negotiate with each leasing TA. With the premise that all the TAs are endowed with high intelligence, each pair of renting TA and leasing TA can find their optimal strategies quickly to equilibrate the bilateral bargaining game of spectrum trading and come to terms with each other in a short time (i.e. for only a few seconds) which is negligible compared with BBDSM period.
Decision making: As shown in Fig. 2, renting TA will weigh all potential bilateral bargaining contracts and apply for the spectrum trading with most profitable leasing TA. With respect to the leasing TA, if there is more than one trading candidates (e.g., m candidate renting TAs) it will choose the most profitable renting TA to lease the spectrum out.
The overall algorithm of BBDSM can be illustrated in Fig. 2.
SIMULATION AND ANALYSIS
Simulation model: A simulation was created to model three overlaid RANs, UMTS, GSM and DVB-T and assess their profits as well as other performances with and without BBDSM, over a span of 24 h.
For illustrative purpose, various service demands for spectrum (e.g., voice load, video load, data load and et al.) across different RANs are abstracted in STUs. Providing in the market, the whole block of spectrum is in the size of 190 STUs, where UMTS, GSM and DVB-T own 50, 40 and 100 STUs, respectively. As for BBDSM, the income of services per STU Psprov is defined as 1, the basic cost of the spectrum belonging to current TA Pcost is 0.4 and Th is set to 1 To be simple, given c1 = v1 = 0 and c2 = v2 = 1 i.e., c and v are uniformly distributed in [0, 1]. When the bilateral bargaining game arrives at equilibrium, there would be:
Given c1 = v1 = 0 and c2 = v2
= 1 and substitute them into Eq. 16-17
and from Eq. 19-20, there would be αbid
= 1/12, βbid = 2/3, αask = 1/4 and βask
= 2/3.
|
| Fig. 2: |
Proposed BBDSM algorithm |
| Table 1: |
Simulation parameters |
|
|
Meanwhile, similar to Leaves et al. (2002),
BBDSM is executed every 0.5 h in the simulations. The main parameters for simulation
are summarized in Table 1.
Moreover, two simple imperfect prediction schemes are employed to estimate the future load in the proposed BBDSM:
| • |
Current value scheme |
| • |
History value scheme |
|
| Fig. 3(a-c): |
Time varying normalised traffic demands of a day (predicting
load vs practical load), (a) DVB-T, (b) UMTS and (c) GSM |
Current value scheme takes the value of the current load at t measured as an
estimate of the load of t+1; history value scheme refers to load distribution
models by Leaves et al. (2001) and Almeida
et al. (1999). Double-gaussian and trapezoidal functions are adopted
to simulate UMTS and GSM historical statistical load pattern correspondingly.
Since, UMTS, GSM and DVB-T have different time-varying loads as shown in Fig. 3, BBDSM is embodied to dynamically trade the spectrum in STUs among the three RANs on a temporal basis.
As shown in Fig. 3, practical load always deviates from the predicted value when model DVB-T, UMTS and \GSM are applied. This is because that the prediction value is estimated based on load distribution. Therefore, unexpected load peaks or slopes may be caused by certain events, e.g., traffic jam, important sports events or a public holiday. On the other hand, BBDSM scheme takes practical load into account and can avoid this kind of deviation.
RESULTS
The performance metrics to be compared include the profits versus time (24
h, 48 BBDSM periods) in the three RANs. Moreover, the effect of users’
satisfactory degree is measured using the ratio of service demands satisfied
in different busy hour of each RAN. The spectrum efficiency of the overlapped
RANs is also taken into account.
Firstly, the profits of UMTS, GSM and DVB-T with FSM and BBDSM are investigated in Fig. 4 across all the 24 h of a day in practical environment. It is shown that compared with conventional FSM, all the three RANs definitely make more profits with proposed BBDSM scheme. The differences lie in that BBDSM allows for the spectrum trading and negotiation which is not available in FSM. Therefore, in BBDSM whenever any RANs have difficulty in supporting their prospective service demands, they will bilateral bargain to trade spectrum with the other RANs which have available spectrum and improve the profits. Especially for DVB-T, during UMTS and GSM busy hour, the increase of his profits is approximately up to 20% on average.
Secondly, BBDSM shows better effect on users’ satisfactory degree than
FSM in busy hour. The users’ satisfactory degree drops rapidly when the
load is heavy in FSM.
|
| Fig. 4(a-c): |
Individual RAN’s profits with different schemes, (a)
FSM, (b) BBDSM (history value prediction) and (c) BBDSM (current value prediction) |
|
| Fig. 5(a-c): |
Users’ satisfactory ratio in each RAN’s busy hour,
(a) FSM, (b) BBDSM (history value prediction) and (c) BBDSM (current value
prediction) |
However, no matter based on current value predicting scheme or history value
predicting scheme, BBDSM scheme keeps the users’ satisfactory degree fairly
high (approximately above 85%) all the time for it makes best use of the whole
block of the spectrum in the market. Related results are illustrated in Fig.
5.
Finally, the spectrum efficiency of the three RANs is analyzed.
|
| Fig. 6: |
Spectrum efficiency improvement in busy hour (from 13-22 p.m.) |
As demonstrated in Fig. 6 it is obvious that during the
time of transactions happen frequently (e.g., from 13-22 p.m.), the more STUs
of spectrum traded, the better spectrum efficiency achieved.
CONCLUSION
In this study, a bilateral bargaining scheme is investigated under incomplete
information for DSM to better operators’ profits in spectrum trading. With
the help of proposed BBDSM algorithm based on C and S bargaining model in game
theory and its implementation procedure, renting T/As and leasing T/As can negotiate
with each other and cope with spectrum trading with the most appropriate and
profitable choice for themselves. Simulation results demonstrate that proposed
approach remarkably outperforms existing FSM in expanding individual operator’s
profits, bettering users’ satisfactory degree and improving the utilization
of spectrum.
ACKNOWLEDGMENTS
This work was supported in part by NSFC (Grant No. 61202393, 61272461, 61070176 and 61170218), CPSF (Grant No. 2012M521797, 2013M542370), Educational Commission of Shaanxi Province, China (Grant No. 12JK0936, 12JK0937, 2010JK854, 2010JC25, 2010JC24 and 2013JK1139), SRFDP (Grant No. 20106101110018), International Cooperation Foundation of Shaanxi Province, China (Grant No. 2013KW01-02), Natural Science Foundation of Shaanxi Province, China (Grant No. 2012JQ8038) and the Key Project of Chinese Ministry of Education (Grant No. 211181). This study is also supported by the Specialized Research Fund for the Doctoral Program of Higher Education of China (Grant No. 20136118120010) and China Postdoctoral Science Foundation (No.2013M542370).
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