Dynamic resource management for orthogonal frequency division multiple access wireless networks

The disclosed subject matter relates to dynamic resource management for wireless network components with a wireless communications environment. Personal base stations can be deployed in a substantially uncoordinated manner resulting in conflicts among wireless radio resources. Dynamically assigning subchannel and dynamically allocating power for subchannels can reduce these conflicts. Dynamic resource management can employ combinatorial auction schema such that assignment of subchannels can be considerate of selecting a power level for performance and to minimize overlapping subchannel interference. In an aspect, several methods can be employed to reduce the computational complexity of the general integer programming problem presented. These several methods can include a Combinatorial Auction with Greedy Algorithm scheme, a Random Equal Subchannel Partition scheme, a Local Combinatorial Auction scheme, and a Neighbors' Poor Channels scheme.

TECHNICAL FIELD

The various embodiments of the subject disclosure relate generally to wireless communications, and more particularly to dynamic management of subchannels and power levels of components associated with wireless communications.

BACKGROUND

Orthogonal Frequency-Division Multiple Access (OFDMA) is a multi-user version of Orthogonal Frequency-Division Multiplexing (OFDM). Multiple access is achieved in OFDMA by assigning subsets of subcarriers to individual users. This can allow simultaneous transmission from several users at a low data rate. OFDMA can be employed in wireless network components, including carrier base stations (BS or NodeB) and in personal base stations, such as femtocells, picocells, etc. These personal base stations can also sometimes be referred to as evolved NodeB or eNodeB. These personal base stations, e.g., femtocells, are small base stations that are usually installed in indoor environments to improve the data rate areas of poorer coverage by NodeBs. Since personal base stations can be deployed in an ad hoc manner and share the same frequency bands, interference mitigation becomes a concern from a resource management position.

As growing numbers of users are wirelessly accessing systems such as the interne and cellular telephone systems, successful and efficient deployment of personal base stations can provide for improved wireless network performance by filling coverage gaps or augmenting deficient coverage areas. In this regard, dynamic resource management for OFDMA-based wireless network components can play a role in performance of these valuable network resources.

SUMMARY

The following presents a simplified summary of the various embodiments of the subject disclosure in order to provide a basic understanding of some aspects described herein. This summary is not an extensive overview of the disclosed subject matter. It is intended to neither identify key or critical elements of the disclosed subject matter nor delineate the scope of the subject various embodiments of the subject disclosure. Its sole purpose is to present some concepts of the disclosed subject matter in a simplified form as a prelude to the more detailed description that is presented later.

An embodiment of the presently disclosed subject matter can include a system that includes at least one wireless radio component. A wireless radio component can be, for example, a femtocell or picocell access point. The wireless radio component can include a subchannel assignment component. The subchannel assignment component can dynamically assign subchannels of a set of subchannels. The wireless radio component can further include a power allocation component. The power allocation component can dynamically allocate power to the subchannels of the set of subchannels.

In a further embodiment, the disclosed subject matter can be in the form of computer-executable instructions stored on a computer-readable storage medium. The computer-executable instructions can include defining a variable as a function of power allocated to a subchannel of a wireless radio component. The computer-executable instructions can further include defining an access variable relating to an accessibility of the subchannel. A function, in terms of the valuation variable and the access variable, can then be solved to determine allocated power and a subchannel assignment.

In another embodiment, the disclosed subject matter can be in the form of a method. The method can include determining a first marginal utility value related to assigning a first subchannel and assigning the first subchannel. The method can continue to determining a second marginal utility value related to assigning a second subchannel and assigning the second subchannel.

In a further embodiment, the disclosed subject matter can be embodied as a system including a means for defining a variable as a function of power allocated to a subchannel of a wireless radio component. The system can further include a means for defining an access variable relating to an accessibility of the subchannel. The system can further include a means for determining allocated power for the subchannel and a subchannel assignment of the subchannel as a function of the valuation variable and the access variable.

The following description and the annexed drawings set forth in detail certain illustrative aspects of the disclosed subject matter. These aspects are indicative, however, of but a few of the various ways in which the principles of the various embodiments of the subject disclosure can be employed and the disclosed subject matter is intended to include all such aspects and their equivalents. Other advantages and distinctive features of the disclosed subject matter will become apparent from the following detailed description of the various embodiments of the subject disclosure when considered in conjunction with the drawings.

DETAILED DESCRIPTION

In OFDMA-based networks, radio spectrum is divided into parallel subchannels that can be assigned to different users, e.g., requestors. Where a plurality of requestors accesses a subchannel simultaneously in overlapping coverage regions, interference can degrade the subchannel and affect the data carrying capacity thereof. As such, various techniques have previously been employed to reduce interference with OFDMA subchannels. An example of these conventional techniques can include spatially manipulating wireless network components such that, at predetermined power levels, the coverage areas of each subchannel for a particular wireless network component don't overlap the coverage area of the same subchannel of another wireless network component. This has a distinct disadvantage in that the coverage areas need to be determined to reduce the likelihood of overlap and resulting interference.

Dynamic resource management for wireless network components, as disclosed herein, can employ the assignment of subchannels to a requestor and the allocation of transmission power for assigned subchannels in a manner that serves to optimize OFDMA resources. In an aspect, the general goal of subchannel assignment is to assign each piece of wireless radio resource to the most suitable requestor. The resource allocation methods in conventional literature, generally only consider the interference from other cells and, as such, treat the base station being a victim, but neglect the inference caused by the users to other base stations, e.g., where the users would be considered as aggressors. While this is quite reasonable for traditional cellular networks with proper cell-planning in which adjacent cells use different sub-bands, in networks employing personal base stations, the consideration of aggressive behavior can be useful in determining transmission power level allocation where adjacent personal base stations share the same spectrum. Further, user diversity can be exploited to mitigate interference by considering the interference condition of local and neighboring personal base stations.

For simplicity and clarity, it can be assumed that separated channels are assigned to carrier level network components, e.g., base stations, and to personal level network components, e.g., femtocells, picocells, etc. (hereinafter simply “fAP”), such that interference comes only from neighboring fAP but not from macro level BSs. As used herein, the term “optimized’ is used inclusively to indicate some level of optimization up to and including, but not limited to, an ideal optimization (e.g., an optimized result can be less optimal than an ideally optimized result). Exemplary numerical and simulation results demonstrate the validity of the disclosed subject matter. Thus, dynamic resource management for wireless network components can provide improved deployment of OFDMA subchannels.

Turning to the figures,FIG. 1illustrates a system100that can facilitate dynamic resource management for wireless network components in accordance with an aspect of the subject matter disclosed herein. System100can include mobile device102. Mobile device102can request wireless resources from wireless radio component110. In an aspect these wireless resources can be OFDMA subchannels. Wireless radio component110can be a personal base station, e.g., femtocell, picocell, etc. Wireless radio component110can, in another aspect, be a carrier base station, e.g., a macro-level base station, NodeB, etc. In an aspect, wireless radio component110can be communicatively coupled to a network though not specifically illustrated atFIG. 1. This network can be a local network, a regional network, a wide area network, or any other type of network. The network can carry data, voice, or combinations thereof, among other types of information. The network can be wired, wireless, or a combination thereof. Also of note, wireless radio component110can be one of a plurality of the same, or similar, wireless radio components included in the network, though not illustrated for clarity and brevity. Of further note, the network can comprise any number or other network components to facilitate communicative coupling over the network.

Wireless radio component110can further comprise subchannel assignment component (SAC)130and power allocation component (PAC)140. SAC130and PAC140can be communicatively coupled. In a further aspect, SAC130and PAC140can be a single component (not specifically illustrated) without departing from the present disclosure. SAC130can facilitate dynamic management of OFDMA subchannels associated with one or more wireless radio component110. In an aspect, SAC130can dynamically select a subchannel in response to a request for a subchannel. The request for a subchannel can be related to assigning a subchannel to facilitate a communicative coupling between mobile device102and wireless radio component110. Similarly, PAC140can facilitate dynamic management of OFDMA subchannels associated with one or more wireless radio component110. PAC140can dynamically allocate power to subchannels to facilitate transmission of information on the subchannel. In an aspect, PAC140can increase power on a subchannel to facilitate improved communication on a subchannel. In a further aspect, PAC140can decrease power on a subchannel to reduce the likelihood of interference with subchannels.

FIG. 2is an exemplary illustration of user diversity that can be related to dynamic resource management for wireless network components in accordance with an aspect of the disclosed subject matter disclosed herein. Part (a) ofFIG. 2illustrates two access points, AP1and AP2, and their corresponding overlapping coverage areas, designated by the dotted lines. A first mobile subscriber, MS1is located in the coverage area of AP1and a second mobile subscriber, MS2, is located in the coverage are of AP2. Given the spatial relationships of part (a), even when both AP1and AP2employ the same subchannels for communication with MS1and MS2respectively, there will be no interference on the subchannels because MS1is out of range of AP2and MS2is out of range of AP1.

In contrast to part (a), at part (b) ofFIG. 2, MS1is now located in the overlapping coverage region of both AP1and AP2. MS2remains only in the coverage are of MS2. The spatial relationships illustrated at part (b) would be problematic where both AP1and AP2are employing the same subchannels for communications with MS1and MS2. Where MS2is out of the coverage area of AP1, information transmitted by AP1on the same subchannel as employed by AP2would not reach MS2. However, information transmitted by AP1and AP2would be received by MS1where it is within the coverage area of both access points. This can be a cause of interference in the communication between MS1and AP1. This interference can be cured by ensuring that AP1and AP2use different subchannels when a mobile subscriber is located in the overlapping coverage area. Restricting AP1and AP2to different subchannels reduces the maximum possible communication bandwidth for the access points. As a non-limiting example, if AP1uses half of the available subchannels and AP2uses the other half of the available subchannels, the maximum bandwidth is also about half for each of the access points as compared to having access to all of the available subchannels (as would be possible at part (a)). Selection of subchannels for use at each access point can be facilitated by communicative coupling between the access points.

The addition of an additional mobile subscriber does not always require further dilution of the available bandwidth. At part (c) ofFIG. 2, a third mobile subscriber is introduced, MS1b, while the mobile subscriber located in the overlapping coverage area is relabeled MS1a. Communication with MS1band MS2, similar to that of part (a), can occur on any subchannel because they are not located in the overlapping portion of the coverage areas of AP1and AP2. However, communication between AP1and MS1ashould occur on subchannels not employed by AP2to reduce the likelihood of interference from AP2signals. As such, AP1and AP2can selectively employ separated subchannels, as in part (b), for communications with MS1aand MS2, respectively, while AP1can further employ the any subchannel that does not interfere with communications to MS1a, for communication with MS1b. This improved spectral efficiency can be termed user diversity or multi-user diversity.

Turning now toFIG. 3, presented is a diagram of a system300that can facilitate dynamic resource management for a plurality of wireless network components in accordance with an aspect of the subject matter disclosed herein. System300can include a plurality of wireless radio components,310and314. It will be noted that system300can include any number of wireless radio components, though only two are illustrated for clarity and brevity. Wireless radio component310can include wireless access point adjustment component (fAP)320. Wireless radio component314can include wireless access point adjustment component (fAP)324. In an aspect, a fAP, e.g.,320or324, can include a subchannel assignment component that can be the same as, or similar to, SAC130. In a further aspect, a fAP, e.g.,320or324, can include a power allocation component that can be the same as, or similar to, PAC140.

System300can include a plurality of mobile devices,302,304,306, communicatively coupled to the wireless radio components, e.g.,310and314. As illustrated, system300can include mobile devices302and306communicatively coupled to wireless radio component310and mobile device304communicatively coupled to wireless radio component314. Further, one or more of the wireless radio components of the plurality of wireless radio components can be communicatively coupled with one or more other wireless radio components. System300illustrates wireless radio component310communicatively coupled to wireless radio component314.

Wireless radio component310can dynamically select a first set of subchannels, by employing fAP320, for communication with mobile device302. Further, wireless radio component310can dynamically select a second set of subchannels, by employing fAP320, for communication with mobile device306. The first and second sets of subchannels can be different to reduce the likelihood of interference. Wireless radio component310can communicate information about the first and second set of selected subchannels to wireless radio component314. Wireless radio component314can dynamically select a third set of subchannels, by employing fAP324, for communication with mobile device304. The third set of subchannels can be different from the first and second sets of subchannels to reduce the likelihood of interference. In a further aspect, the third set of subchannels can dynamically select subchannels in use in the first or second sets of subchannels where they are not likely to cause interference because they conform to spatial conditions where the coverage regions of wireless radio component310and wireless radio component314don't overlap and contain one of the mobile devices, e.g.,302,304, or306, in a manner similar to that disclosed with regard toFIG. 2.

Subchannel Selection and Power Allocation

Each fAP can dynamically select subchannel assignment and transmission power control for each communicative coupling with a requestor. In an aspect, the decisions, including the required computations, can be formed by the fAP. In a further aspect, information exchange among a plurality of fAP comprising at least part of a network is allowed, so that each fAP can receive information about the other fAP in the network, particularly neighboring fAP that would be spatially predisposed to interference resulting from using the same subchannels. This information can include power allocation, interference condition, and/or subchannel assignment information. In one particular aspect, the resource allocation associated with dynamic resource management for wireless network components, for example, can be treated as an auction model, that is, a subchannel can be treated as item for bidding, and a requestor for a subchannel as a bidder. The design of this exemplary auction problem can fall into game theory, wherein the goal is to generate optimized dynamic management results. More specifically, the exemplary auction model can be a combinatorial auction problem in which each bidder, e.g., requestor, can be assigned a combination of subchannels such that the value, e.g. total throughput, can be maximized.

Let xi,kbe a binary variable which equals one if bidder i is accessible to subchannels k, and zero otherwise. Let vi(pi,k, p−i,k) be the valuation of the requestor i, with regard to subchannel k, where p−1,k=[p1,k, . . . , pi−1,k, pi−1,k, . . . , pN,k]. As a function of allocated power, the valuation can be defined as the Shannon capacity:

Ii,k=Σj≠ipj,khi,j,kis the total interference power measured by requestor i on subchannel k;

pi,kis the power allocated by requestor i on subchannel k;

hi,j,kis the channel gain between transmitter of a link j and receiver of a link i;

σn2is the thermal noise power level.

Then the global optimization problem can be formulated as:

Each requestor, e.g., user, seeking to access one or more subchannels can feasibly access a given set of subchannels, said set is determined in a subchannel assignment portion. Power allocation can be done in a game theory manner, as previously disclosed, and can be executed by each individual fAP, as follows: Let the set of assigned subchannels to requestor/user i be Si. The optimization problem to maximize the total capacity of the system then can be:

maximize⁢⁢∑i⁢∑k∈Si⁢vi⁡(pi,k;p-i,k)subject⁢⁢to⁢⁢∑k∈Si⁢pi,k≤Pmax⁢∀user⁢⁢i
with the Lagrangian,

J=∑i⁢∑k⁢vi⁡(pi,k;p-i,k)-∑i⁢λi⁡(∑k⁢pi,k-Pmax)(2)
An optimized power allocation pi,kcan then be found by differentiating J with respect to pi,k, and letting the result be zero:

Where personal base stations, e.g., femtocells, are employed in networks, these networks are often constructed dynamically and in a haphazard manner. That is to say, in contrast to macro level base stations, e.g., carrier base stations, that are often deployed in a highly planned manner with well understood parameters for coverage are overlap, attenuation, and expected usage, personal level base stations can be deployed with nearly no planning whatsoever. As an example, a new tenant in an office building could easily add several new femtocells within meters of a well established set of femtocells, and such newly deployed femtocells could easily cause significant interference with the previous channel and power allocations of the existing femtocell network. As such, channel selection and/or power allocation can be done in a decentralized manner. Utility functions for individual requestors, e.g., individual mobile devices/users, can maximize utility dynamically. The Nash equilibrium can be reached where a set of solutions are found for the utility functions. The utility function can be designed as quasi-linear, e.g., in the form of ui,k=vi,k−ti,k, where ti,kis the price of allocation power pi,k.

Then set

ti,k=∑j≠i⁢αi,j,k⁢hi,j,k⁢pi,k(4)
where αi,j,kis called the pricing factor, and observe that hi,j,kpi,kis the interference power caused by user i to the BS of user j, if user i and j share subchannel k. As such, a transmitter is ‘charged some price’ for each link with which it interferes, and that price is proportional to the level of interference, e.g., interference power.

The individual problem for user i can now be formulated so as to maximize the utility of user i:

Generally speaking, the Nash equilibrium doesn't always mean a true optimum, as disclosed hereinabove. However, by comparing the above equation with Eq. 2, we have:

αi,j,k=hj,j,k⁢pj,kIj,k⁡(Ij,k+hj,j,k⁢pj,k)=αj,k(5)pi,k*=(1ai,k+λj,k-hi,i,kIi,k)+(6)
Where, of note, the pricing factor is independent from i. As such, at each iteration, the pricing factor information can be shared among communicatively coupled fAPs, representing the current interference conditions.

FIGS. 4-7illustrate methods and/or flow diagrams in accordance with the disclosed subject matter. For simplicity of explanation, the methods are depicted and described as a series of acts. It is to be understood and appreciated that the various embodiments of the subject disclosure is not limited by the acts illustrated and/or by the order of acts, for example acts can occur in various orders and/or concurrently, and with other acts not presented and described herein. Furthermore, not all illustrated acts may be required to implement the methods in accordance with the disclosed subject matter. In addition, those skilled in the art will understand and appreciate that the methods could alternatively be represented as a series of interrelated states by way of state diagram or events. Additionally, it should be further appreciated that the methods disclosed hereinafter and throughout this specification are capable of being stored on an article of manufacture to facilitate transporting and transferring such methods to computers. The term article of manufacture, as used herein, can encompass a computer program accessible from any computer-readable device, carrier, or media.

FIG. 4illustrates a method400that facilitates dynamic resource management for wireless network components in accordance with an aspect of the disclosed subject matter. At410a valuation variable is defined as a function of power allocated to a subchannel of a set of subchannels. As such, a greater value can be associated to allocation of a power level that sufficiently exceeds background thermal noise and interference power. In one aspect, the valuation variable can be the same as, or similar to, vi(pi,k) as found in Eq. 1.

At420, an access variable can be defined relating to the accessibility of a subchannel to a requestor. The access variable can be binary, such that the value is 1 when the subchannel is accessible and 0 when the subchannel is not accessible. In an aspect, the valuation variable can be the same as, or similar to, xi,kas disclosed hereinabove.

At430, an optimization problem can be solved in terms of the valuation variable and the access variable. At this point method400can end. The optimization problem can be exhaustively solved by first allowing fractional values in place of the binary access variable, then finding optimal fractional values by solving the resulting linear equations, and then transforming the solutions back into integer values by applying tie-breaking methods. As previously disclosed, this process can be computationally intensive and alternative computational solution that are less rigorous are explored further herein for the sake of clarity and brevity. In an aspect, the optimization problem can be to maximize ΣiΣkεSivi(pi,k; p−i,k), as disclosed hereinabove.

FIG. 5illustrates a method500that facilitates dynamic resource management for wireless network components in accordance with an aspect of the disclosed subject matter. At510, a marginal utility value can be determined relating to assigning a first subchannel to each requestor of the subchannel. This marginal utility value can represent the value to each requestor of being assigned the first subchannel. As an example, where there are two requestors, the marginal value may be determined for each of the two channels. A comparison of the two determined marginal values can, for example, illustrate a greater marginal value for the second requestor. As such, the subchannel can be assigned to the second requestor wherein maximizing value is a goal.

At520, the first subchannel can be assigned to a requestor of the set of requestors. This assignment can be based on the determined marginal utility from510. As illustrated in the previous example, where the marginal value for the second requestor was greater than the marginal value determined for the first requestor for the same subchannel, it is logical to assign the subchannel to the second requestor where maximizing value is a predetermined objective. In an aspect this can be viewed as a greedy method in that after the marginal value is determined for each requestor seeking to be assigned the first subchannel, one of the requestors is assigned the subchannel based on the determined marginal values.

At530, a diminished set of requestors can be defined. The diminished set of requestors can be the set of requestors without the requestor that was assigned the first subchannel at520. That is, as a requestor is assigned a subchannel, the requestor is removed from the diminished set.

At540, a marginal utility value can be determined relating to assigning a second subchannel to each requestor from the diminished set of requestors. Determining the marginal utility value at540can be the same as, or similar to, determining the marginal utility value at510except for a diminished set of requestors.

At550, the second subchannel can be assigned to a requestor of the diminished set of requestors. At this point, method500can end. The assignment can be based on the determined marginal utility value from540. The assignment can be the same as, or similar to, the assignment done at520except for the second subchannel and for the diminished set of requestors.

In an aspect, method500can be expanded by iteratively diminishing the set of requestors, determining marginal utility values for further subchannels, and assigning those subchannels based on the corresponding iteration of the marginal utility values. It is to be noted that this iterative process can assign subchannels to each requestor in a set of requestors in order of diminishing marginal utility value (where there are more subchannels than requestors). Method500can facilitate assigning subchannels to requestors having the most to gain by being assigned a particular subchannel.

FIG. 6illustrates a method600that facilitates dynamic resource management for wireless network components in accordance with an aspect of the disclosed subject matter. At610, a first subchannel can be assigned to a first requestor of a set of requestors.

At620, a power level assignment can be determined for the first subchannel. This power level can be based on a level of performance for the power level and a correlated level of interference for assigning the power level. In an aspect, the determined power can consider the benefit of a high power level to range and signal to noise ratios (SNR) and also consider the disadvantages of a high power level that can interfere with neighboring users of the subchannel. As stated herein above, method600, at620, can consider allocating a power level as both an aggressor and a victim. As a non-limiting example, allocating a high power level to the subchannel can provide excellent range and a high SNR while also causing a high level of interference with other users of the subchannel. As a second non-limiting example, allocating a low power level can cause minimal interference with other users of the subchannel but can also result in limited range and a low SNR. As such, a power level can be dynamically allocated that satisfies both performance and interference concerns for the given conditions of the subchannel, requestors, and network. As an example, where there are no neighboring fAPs that employ the first assigned subchannel, a high level of power can be allocated to provide great SNR and range without concern about interference because the first subchannel is not also being used in close proximity.

At630, a second subchannel can be assigned. At640, the power level for the second subchannel can be determined. At this point method600can end. Method600illustrates less rigorous selection and assigning of subchannels as compared, for example, to method500, while compensating for the less nuances selection process by allocation of power levels that are considerate of both the utility and interference associated with the possibility of assigning the same subchannel to neighboring fAPs.

It is to be noted that assigning subchannels is done on a per fAP basis, e.g., each fAP can have the same set of subchannels. However, it is to be further noted that method600can consider interference levels for other fAPs in a network, particularly where the fAPs in a network are communicatively coupled, such as illustrated in system300. In an aspect, method600can rapidly assign subchannels with a level of impunity in that the allocated power levels of the assigned subchannels can specifically consider the impact of transmissions on the same subchannel on neighboring fAPs.

FIG. 7illustrates a method700that facilitates dynamic resource management for wireless network components in accordance with an aspect of the disclosed subject matter. At710, a power level can be determined for each subchannel of a set of subchannels. At720, a subchannel of the set of subchannels can be assigned to a requestor. At this point method700can end. The assignment of the subchannel can be based on the power level of the subchannel in comparison to other subchannel power levels. In an aspect, where a subchannel at a first fAP is lightly used, the subchannel can have a low power level allocated to it. This same subchannel can be employed at a second fAP specifically because the low power level of the subchannel on the first fAP is less likely to cause interference in the use of the subchannel on the second fAP. Method700can be described as assigning subchannels to requestors that are least heavily used by neighboring fAPs.

FIG. 8illustrates exemplary pseudo-code800for dynamic resource management for wireless network components in accordance with an aspect of the disclosed subject matter. Pseudo-code800illustrates determining a power level for a subchannel for each fAP in the network. Whereas each fAP can designate a different power level for the subchannel, as disclosed herein, these values can be employed in subchannel assignment. The determined power levels for the subchannel are then communicated to other fAPs in the network. This facilitates each fAP of the network having information regarding the power levels allocated to the subchannel at each other fAP in the network. The next subchannel can then be considered for each fAP in the network. As pseudo-code800iterates, each fAP in the network will become informed about the allocated power level of each subchannel at each fAP in the network. This information can then be leveraged in selecting subchannel assignments for requestors. Optimization, as stated hereinbefore, can be highly computationally intensive and an exhaustive solution can be time consuming. As such, alternate methods can be demonstrated in pseudo-code that can be computed more quickly and provide optimized, if not optimal, results for subchannel assignment and power level allocation.

FIG. 9illustrates exemplary pseudo-code900for dynamic resource management for wireless network components in accordance with an aspect of the disclosed subject matter. Pseudo-code900can be a form of a combinatorial auction with greedy algorithm (CAGA). Pseudo-code900can be deemed a greedy algorithm in that, at each iteration, a subchannel is assigned to a requestor. One subchannel can be allocated to one requestor in each round of the per-fAP adjustment procedure. This subchannel and requestor can be selected based on which requestor can expect the greatest improvement, wherein improvement is defined as marginal utility:
vi(ai|Si)=v(Si∪{ai})−vi(Si)  (7)
Where Sidenotes the allocation to requestor i and aiis the subchannel index that requestor i seeks to have assigned in a round. The resulting algorithm can be executed by each fAP in a network, where the normalized SINR for requestor i on subchannel k is defined as the signal-to-noise-interference ratio (SINR) with unit receiving power, or SINRi,knorm=hi,k2/(Ik+σn2)

In an aspect, pseudo-code900can sort a set of subchannels in decreasing order of normalized SINR. Pseudo-code900updates a marginal utility value of a subchannel for each requestor. The subchannel is then assigned to the requestor having the greatest marginal utility value for the channel at that iteration. Ties are broken arbitrarily. Pseudo-code900iterates through the set of subchannels, such that each subchannel is assigned to a requestor that valued the resource more than other requestors at a particular iteration. In an aspect, pseudo-code900can be similar to method500. Of note, in a flat fading channel, the capacity, as a function of allocated bandwidth, increases with decreasing marginal utility. This property holds for frequency selective channels, if the subchannels are sorted in decreasing order of SINR. It can be shown that the resulting capacity of each fAP is at least half of the optimal value. This algorithm runs in polynomial time.

While the CAGA provides the resulting capacity of each fAP is at least half of the optimal value, it is still computationally complexity, though less so than for pseudo-code800. Separating the subchannel assignment and power allocation phases by first fixing the subchannel assignment and then executing game-theoretic power adjustment can provide for less computationally intense processes. Of note, by fixing the subchannel assignment, for each new requestor, the serving fAP and neighboring fAP can adjust power iteratively, but an ‘auction’ procedure is not involved and no iteration is needed for subchannel assignment because it is fixed at the initialization stage. One fixed subchannel assignment is Random Equal Subchannel Partition (RESP). For each fAP running RESP, in the SA phase, the subchannels can be randomly permuted and equally divided to the requesters served by the fAP. In the PA phase, the per fAP adjustment procedures becomes:
pi(l)=BRi(pi(l-1), . . . ,pi−1(l-1),pi+1(l-1). . . ,pN(l-1),
where BRirepresents the best response of user i which corresponds to the power allocation shown in Eq. 6.

As a further simplifying alternative optimization technique, intercommunication between fAPs in network can be ignored in favor of strictly local adjustments to the SA and PA. A first example can be a Local Combinatorial Auction (LCA) Method. The LCA method can employ almost the same algorithm as in the CAGA method disclosed hereinabove, e.g., pseudo-code900. In the LCA method, for each requestor a local marginal utility value of a subchannel is determined. The subchannel is then assigned to the requestor having the greatest marginal utility value for the channel at that iteration. Power allocation is done using traditional water-filling algorithm. It can be imagined that the LCA method results in poorer performance than the CAGA method in that the fAP with a new requestor generates new interference to neighboring fAP, while there is no corresponding correction for this new interference at the victim fAP because the information is never communicated to the victim fAP as would be done in the CAGA method.

A second example of local adjustment can be the Neighbors' Poor Channels (NPC) Method. The NPC method can include assigning to each user the subchannels in which its neighbor allocates the least power resources. It can be assumed that if a neighboring fAP allocates less power in a subchannel, then there is a lower preference for this subchannel, such that the transmission power on that subchannel in a serving fAP generates less interference to its neighbors. As an example, for each subchannel k, the maximum power allocated in neighboring fAPs for each requestor, i, can be interrogated, and subchannel k can be assigned to the requestor with the minimum neighbor power. Of note, the neighboring and interference conditions are different for users in the serving fAP and user diversity can still be employed in the NPC method.

Turning now toFIG. 10, illustrated is exemplary pseudo-code1000for dynamic resource management for wireless network components in accordance with an aspect of the disclosed subject matter. Pseudo-code1000illustrates finding the minimum and maximum allocated power level for each subchannel for a set of neighboring fAPs. This allows a determination of which fAPs is least likely to be involved in interference for the given subchannel. The subchannel can then be assigned to requestors at a fAP that are least likely to cause interference by employing the subchannel. As an example, where a subchannel is assigned to a first fAP but has no power allocated to it because it is unused despite being allocated, it would be unlikely to suffer interference from the same subchannel being assigned to a neighboring fAP. In an aspect, it can be viewed as being analogous to utilizing unused frequency space in cognitive radios, e.g., when a broadcaster isn't actively using the frequency, it does little harm to let other use it until the broadcaster needs the frequency again.

Each of CAGA, RESP, NPC, and LCA can be modeled and numerical results can be obtained demonstrating behaviors of the various methods for given parameters. Numerical results are presented herein for a simulation of a 5×5 room array in a grid pattern deployment of fAPs, in which one fAP is installed in each of the 25 rooms. The rooms are modeled as being 10-by-10 meters in size. Further, system bandwidth is designated as 10 MHz, consisting of 1024 OFDM tones, grouped into 40 subchannels with 18 tones per subchannel, and the remaining tones are used as guard tones.

Further, path loss (PL) in dB for non line-of-sight (NLOS) propagation is given by an indoor small office model. It can be assumed there are light walls between any two rooms and nwcan be the number of walls between any two nodes. Let d be the distance in meters, and f be the carrier frequency in GHz. Thus, path loss can be represented by:
PL(d,f)=46.4+20 log10d+5nw+20 log10(f/5.0)  (8)
Shadowing can be modeled by a log-normal random variable 10′x/10, where x is a zero mean Gaussian random variable with standard deviation of 3.1 dB for LOS and 6 dB for NLOS cases. Frequency-selective fading can be simulated by the cluster-delay-line (CDL) model for the selected indoor small office model. In this model, the small-scaled wave propagation behavior is described by 16 clusters with different delay and ray power. Further, a single-input-single-output (SISO) antenna setting is modeled for simplicity. Requester/User arrival is modeled as a Poisson process, and each requestor/user is disconnected after uploading an exemplary file. Behavior is simulated for 30 minutes.

FIG. 11illustrates exemplary simulation results and exemplary numerical results1100in accordance with an aspect of the disclosed subject matter. In an aspect,FIG. 11shows the average user capacity in a dense deployment scheme, in which there is one fAP in each of the 25 small rooms. The two algorithms with iterative power adjustments, i.e., CAGA and RESP, outperform the baseline algorithms. Of note, the RESP algorithm performs slightly below the CAGA algorithm, but does so with much lower complexity and computational load.

FIG. 12illustrates exemplary simulation results and exemplary numerical results1200in accordance with an aspect of the disclosed subject matter. In addition to the average user capacity, the performance of individual users is of concern. As femtocell networks aim at providing high data rate services, a user success probability can be defined as the percentage of requestors/users with capacity higher than 1 Mbps.FIG. 12shows the requestor/user success probability for different requestor/user arrival rates. It can be shown that the success ratios of the two combinatorial auction style algorithms slightly drop at high user arrival rate, since they both aim at maximizing the total system throughput.

FIG. 13illustrates exemplary simulation results and exemplary numerical results1300in accordance with an aspect of the disclosed subject matter. The Shannon capacity formula illustrates that capacity grows linearly with channel bandwidth, and in logarithm with SNR. Although higher subchannel usage ratios do not always bring higher user capacity, they can be highly correlated.FIG. 13illustrates the average fAP subchannel usage ratio against the requestor/user arrival rates in a sparse deployment example. We observe that the LCA method suffers from low subchannel usage ratio for high user arrival rate, resulting in low throughput. The other three algorithms, i.e., CAGA, RESP, and NPC, keep the ratio above 50% in this particular example.

Referring toFIG. 14, illustrated is a block diagram of an exemplary, non-limiting electronic device1400that can utilize dynamic resource management for wireless network components in accordance with an aspect of the disclosed subject matter. The electronic device1400can include, but is not limited to, a computer, a laptop computer, or network equipment (e.g. routers, access points, femtocells, picocells), and the like.

Components of the electronic device1400can include, but are not limited to, a processor component1402, a system memory1404(with nonvolatile memory1406), and a system bus1408that can couple various system components including the system memory1404to the processor component1402. The system bus1408can be any of various types of bus structures including a memory bus or memory controller, a peripheral bus, or a local bus using any of a variety of bus architectures.

The system memory1404can include computer-readable storage media in the form of volatile and/or nonvolatile memory1406. A basic input/output system (BIOS), containing the basic routines that help to transfer information between elements within electronic device1400, such as during start-up, can be stored in memory1404. Memory1404can typically contain data and/or program modules that can be immediately accessible to and/or presently be operated on by processor component1402. By way of example, and not limitation, system memory1404can also include an operating system, application programs, other program modules, and program data. As a further example, system memory can include program modules for subchannel assignment and allocation of power as disclosed hereinabove.

The nonvolatile memory1406can be removable or non-removable. For example, the nonvolatile memory1406can be in the form of a removable memory card or a USB flash drive. In accordance with one aspect, the nonvolatile memory1406can include flash memory (e.g., single-bit flash memory, multi-bit flash memory), ROM, PROM, EPROM, EEPROM, and/or NVRAM (e.g., FeRAM), or a combination thereof, for example. Further, the flash memory can be comprised of NOR flash memory and/or NAND flash memory.

A user can enter commands and information into the electronic device1400through input devices (not illustrated) such as a keypad, microphone, tablet or touch screen although other input devices can also be utilized. These and other input devices can be connected to the processor component1402through input interface component1410that can be connected to the system bus1408. Other interface and bus structures, such as a parallel port, game port or a universal serial bus (USB) can also be utilized. A graphics subsystem (not illustrated) can also be connected to the system bus1408. A display device (not illustrated) can be also connected to the system bus1408via an interface, such as output interface component1412, which can in turn communicate with video memory. In addition to a display, the electronic device1400can also include other peripheral output devices such as speakers (not illustrated), which can be connected through output interface component1412. In an aspect, other electronic devices, e.g., other fAPs in a network can be communicatively coupled to electronic device1500by way of input interface component1410and output interface component1412, which can serve to facilitate transfer of subchannel and power allocation information among a plurality of fAPs.

It is to be understood and appreciated that the computer-implemented programs and software can be implemented within a standard computer architecture. While some aspects of the disclosure have been described above in the general context of computer-executable instructions that may run on one or more computers, those skilled in the art will recognize that the technology also can be implemented in combination with other program modules and/or as a combination of hardware and software.

Some portions of the detailed description may have been presented in terms of algorithms and/or symbolic representations of operations on data bits within a computer memory. These algorithmic descriptions and/or representations are the means employed by those cognizant in the art to most effectively convey the substance of their work to others equally skilled. An algorithm is here, generally, conceived to be a self-consistent sequence of acts leading to a desired result. The acts are those requiring physical manipulations of physical quantities. Typically, though not necessarily, these quantities take the form of electrical and/or magnetic signals capable of being stored, transferred, combined, compared, and/or otherwise manipulated.

It has proven convenient at times, principally for reasons of common usage, to refer to these signals as bits, values, elements, symbols, characters, terms, numbers, or the like. It should be borne in mind, however, that all of these and similar terms are to be associated with the appropriate physical quantities and are merely convenient labels applied to these quantities. Unless specifically stated otherwise as apparent from the foregoing discussion, it is appreciated that throughout the disclosed subject matter, discussions utilizing terms such as processing, computing, calculating, determining, and/or displaying, and the like, refer to the action and processes of computer systems, and/or similar consumer and/or industrial electronic devices and/or machines, that manipulate and/or transform data represented as physical (electrical and/or electronic) quantities within the computer's and/or machine's registers and memories into other data similarly represented as physical quantities within the machine and/or computer system memories or registers or other such information storage, transmission and/or display devices.