US20160021666A2  Method and apparatus for coordinated powerzoneassignment in wireless backhaul networks  Google Patents
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 US20160021666A2 US20160021666A2 US14/093,011 US201314093011A US2016021666A2 US 20160021666 A2 US20160021666 A2 US 20160021666A2 US 201314093011 A US201314093011 A US 201314093011A US 2016021666 A2 US2016021666 A2 US 2016021666A2
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 H—ELECTRICITY
 H04—ELECTRIC COMMUNICATION TECHNIQUE
 H04W—WIRELESS COMMUNICATION NETWORKS
 H04W52/00—Power management, e.g. TPC [Transmission Power Control], power saving or power classes
 H04W52/04—TPC
 H04W52/30—TPC using constraints in the total amount of available transmission power
 H04W52/34—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading
 H04W52/346—TPC management, i.e. sharing limited amount of power among users or channels or data types, e.g. cell loading distributing total power among users or channels

 H—ELECTRICITY
 H04—ELECTRIC COMMUNICATION TECHNIQUE
 H04W—WIRELESS COMMUNICATION NETWORKS
 H04W72/00—Local resource management, e.g. wireless traffic scheduling or selection or allocation of wireless resources
 H04W72/04—Wireless resource allocation
 H04W72/044—Wireless resource allocation where an allocation plan is defined based on the type of the allocated resource
 H04W72/0473—Wireless resource allocation where an allocation plan is defined based on the type of the allocated resource the resource being transmission power
Abstract
Description
 This application claims priority from U.S. provisional patent application No. 61/731,429, filed Nov. 29, 2012, and U.S. provisional patent application No. 61/793,551, filed Mar. 15, 2013, both entitled “Method and Apparatus for Coordinated PowerZoneAssignment in Wireless Backhaul Networks”, which are incorporated herein by reference in their entirety.
 This invention relates to wireless backhaul for high capacity data networks, to systems and methods for powerzoneassignment in wireless backhaul networks, and more particularly to practical techniques for maximizing a network utility for wireless backhaul networks, including MicroCell and PicoCell networks.
 Coordinated resource allocation is expected to play a major role in improving the performance of densely deployed interferencelimited networks, where coordination can offer significant advantages in mitigating wireless interference. Soft Frequency Reuse (SFR), in particular, is one enhanced frequencyreuse technique proposed for LTEbased systems. SFR provides both the flexibility of utilizing the available bandwidth, and the capability to reduce high intersite interference levels associated with dense networks with aggressive frequency reuse.
 Powerzone assignment is innately similar to the conventional scheduling, which has been wellstudied in the literature of wireless networks. One of the most prominent solutions is the classical proportional fair scheduling solution, whose goal is to maximize the log of the longterm average rate. See for example, U.S. patent application Ser. No. 13/463,47, filed on 3 May 2012, by H. Dahrouj, W. Yu, T. Tang, and S. Beaudin, entitled “Interference Mitigation With Scheduling And Dynamic Power Spectrum Allocation For Wireless Networks”. Such as solution, however, is based on a preassigned association of hubs and Remote Backhaul Modules (RBMs), and is performed on a per hub basis with no hub coordination. Such an approach does not guarantee an optimal solution to the problem, may depend on the everchanging traffic of the wireless channel, and does not guarantee a onetoone mapping constraint.
 Thus, there is need for a method for scheduling resources in a backhaul network, e.g. for interference management, comprising power zone assignment, and in particular there is a need for practical methods using techniques with low complexity, fast convergence, and performance improvement as compared to conventional approaches.
 The present invention seeks to mitigate one or more disadvantages of these known systems and methods, or at least provide an alternative.
 Aspects of the present invention provide methods, systems, apparatuses and software products for scheduling resources in a wireless backhaul network comprising coordinated power zone assignment, achieved by maximizing a network utility across the network, and preferably optimally assigning RBMs to powerzones on a onetoone basis.
 A first aspect of the invention provides a method for scheduling resources in a wireless backhaul network comprising a plurality of N Hubs, each Hub serving a plurality of K Remote Backhaul Modules (RBMs), the method comprising: providing a power allocation of K power zones per Hub; selecting a network utility to be optimized across the backhaul network; based on measurements of channel gains for each HubRBM link, performing a coordinated power zone assignment across the backhaul network, comprising computing a onetoone power zone assignment of each of the NK RBMs to one of the NK power zones by maximizing the selected network utility across the backhaul network.
 Other aspects of the invention provide an apparatus, software, and a system for performing the methods described herein, as defined in claims. Thus, aspects of the present invention provide more efficient powerzoneassignment techniques which can be applied to wireless backhaul networks.
 Embodiments of the method are applicable to a backhaul network comprising several hubs, each serving several Remote Backhaul Modules (RBMs). Influenced by the idea of SoftFrequencyReuse (SFR) recently developed for LTEsystems, the design strategy is based on a 3sector model with frequency reuse 1 between the sectors. The backhaul network may preferably be a SFRbased backhaul network. SFR is introduced to increase the network capacity for areas with dense data traffic, and comprised of N hubs, where each hub can serve up to K Remote Backhaul Modules (RBMs) multiplexed across the K time/frequency zones, for a total number of NK RBMs and NK time/frequency zones, known as powerzones. Each of the powerzone operates at a preassigned power allocation. The performance of the system, therefore, widely depends on the optimal RBMtopowerzone assignment, i.e. finding the optimal assignment that associates each of the KN RBMs with one of the KN available powerzones, on a onetoone mapping basis.
 The powerzoneassignment problem is considered from a generic network utility perspective. It considers the problem of maximizing a network utility function across the whole network, where the maximization is over the possible RBMtopowerzone assignments on a onetoone basis. Practical methods are provided to solve the problem.
 In a method of one embodiment, called AuctionBased PowerZone Assignment solution, (ABPZA), the method is based on the auction approach, first proposed by D. P. Bertsekas in “The auction algorithm: A distributed relaxation method for the assignment problem,” Annals of Operations Research, vol. 14, pp. 105123, December 1988. The ABPZA method inherits the advantages of the auction approach. It, especially, offers closetoglobal optimal solution to the problem. Furthermore, it can be implemented in a distributed fashion across all hubs, and asynchronously at each hub.
 Methods according to other embodiments are based on simple heuristic methods which offer a decent performance improvement as compared to noncoordinated conventional systems. These methods are low in complexity, fast in convergence, and compatible with the physical constraints of SFRbased wireless backhaul system.
 In particular, inspired by the advantages of SoftFrequencyReuse techniques of LTEsystems, methods according to preferred embodiments are based on a 3sector SFRbased wireless backhaul network, with frequency reuse 1 between the sectors. For example, a backhaul network providing increased network capacity for areas with dense data traffic, comprises N transmitters known as hubs, serving KN Remote Backhaul Modules (RBMs) in total, where K is the number of frequency/time zones of every hub frame. Each of the zones, called powerzones, operates at a preassigned power allocation. The performance of the system, therefore, widely depends on the optimal RBMtopowerzone assignment strategy, i.e. the resource allocation problem of optimally scheduling each of the NK RBMs to one of the NK powerzones, on a onetoone basis, and in a coordinated manner, as opposed to conventional systems which schedule the RBMs entering the network in an uncoordinated way. Thus the present invention provides a method comprising coordinated powerzoneassignment for wireless backhaul networks. It considers the problem of maximizing a network utility function across the whole network, where the maximization is over all possible RBMtopowerzone assignments.
 A method according to one embodiment, called AuctionBased PowerZone Assignment solution, (ABPZA) is based on an auction approach, and innately inherits its advantages. ABPZA, especially, offers a closetoglobal optimal solution. It can be implemented in a distributed fashion across all hubs, and asynchronously at each hub. Another proposed embodiment of the method, although suboptimal, is referred to as a ClusteringandExhaustiveSearch PowerZoneAssignment (CESPZA) method. This method first assigns each RBM to one hub, utilizing a maximum ratebased clustering. The RBMtopowerzone assignment problem then reduces to a low complexity exhaustive search, which can be performed on a perhub basis. Embodiments of the methods disclosed in this application offer a performance improvement as compared to uncoordinated conventional systems. Both ABPZA and CESPZA are practical methods, and compatible with the physical constraints of SFRbased wireless backhaul systems, which make them amenable to practical implementation.
 The foregoing and other objects, features, aspects and advantages of the present invention will become more apparent from the following detailed description, taken in conjunction with the accompanying drawings, of preferred embodiments of the invention, which description is by way of example only.

FIG. 1 shows a schematic diagram of an exemplary wireless backhaul network comprising seven cells, 3 sectors per cell, 1 hub per sector, and 4 Remote Backhaul Modules (RBMs) per sector; 
FIG. 2 illustrates schematically the transmitted frames of 3 interfering hubs, showing the structure and power configuration of each frame; 
FIG. 3 shows a table that summarizes the system parameters of a wireless backhaul network used for simulations to evaluate the performance of methods according to embodiments of the invention; 
FIG. 4 is a bar chart showing the percentage gain of methods according to embodiments of the invention as compared to a conventional roundrobin approach; and 
FIG. 5 is a bar chart showing the percentage gain using methods with and without a perhub exhaustive search; 
FIG. 1 shows a schematic diagram of an exemplary wireless backhaul network 100 comprising seven cells, with three sectors per cell (i.e. sectors 1201, 1202 and 1203), one hub 102 per sector, and four Remote Backhaul Modules (RBMs) 104 per sector. Thus, in general, the system model is that of a wireless backhaul network comprising N hubs, each serving K RBMs, for KN RBMs in total. 
FIG. 2 shows an example of an SFRbased framing structure of three interfering hubs. The bandwidth f_{1 }of each hub transmission is divided into three orthogonal segments f_{2}, f_{3}, and f_{4}. The transmit frame structure of every hub comprises two time zones. The first time zone contains the data associated with one RBM, and utilizes the entire bandwidth. The second time zone contains the data for the other three RBMs, which are separated from each other in frequency, using different RBMtosubband assignments. Each of the subband zones, called powerzones, operates at a preassigned power allocation. The performance of the system, therefore, widely depends on the optimal RBMtopowerzone assignment.  The objective of methods according to embodiments of the invention is to maximize a network utility function across the whole wireless backhaul network, where the maximization is over the possible RBMtopowerzone assignments on a onetoone basis. More specifically, let (i, j) be the jth powerzone of the ith hub, and a_{ik} ^{j }the utility of assigning the RBM_{i }to powerzone (i, j), given all the KN available powerzones. Also let H be the set of hubs, Z the set of powerzones for every hub. The maximization problem then becomes:

$\mathrm{max}\ue89e\sum _{i,j,k}\ue89e\phantom{\rule{0.3em}{0.3ex}}\ue89e{a}_{\mathrm{ik}}^{j}\ue89e{x}_{\mathrm{ik}}^{j}$ $s.t.\phantom{\rule{0.8em}{0.8ex}}\ue89e\sum _{k}\ue89e\phantom{\rule{0.3em}{0.3ex}}\ue89e{x}_{i,k}^{j}=1,\left(i,j\right)\in H\times Z$ $\sum _{\left(i,j\right)\in H\times Z}\ue89e\phantom{\rule{0.3em}{0.3ex}}\ue89e{x}_{\mathrm{ik}}^{j}=1,{x}_{\mathrm{ik}}^{j}\in \left\{0,1\right\}$  where the above maximization is over the discrete binary variable x_{ik} ^{j}, which is 1 if RBM k is mapped to powerzone (i, j), and zero otherwise.
 The problem as formulated above corresponds to a onetoone assignment problem.
 In a method according to a first embodiment of the invention an auctionbased method is proposed to solve the problem. The method, called auctionbased powerzoneassignment (ABPZA) method, offers a closetoglobal optimal solution, i.e. within e of the global optimal solution. The auction approach was first proposed for the classical objecttoperson assignment problem by D. P. Bertsekas, in “The auction algorithm: A distributed relaxation method for the assignment problem,” Annals of Operations Research, vol. 14, pp. 105123, December 1988.
 To establish a social optimum among all RBMs and powerzones, ABPZA proceeds in rounds (or iterations) to update the assignment and prices, i.e. the bidder RBM i raises the price of its preferred power zone j by some bidding increment. Bidding increments and price increases help competition by making the RBM bidder's own preferred powerzone less attractive to other potential RBMs.
 ABPZA method can be summarized as follows:

 Initialize a positive scalar ε>0, introduced to guarantee the algorithm convergence.
 Start with an empty set of powerzoneassignment mappings, and a set of prices λ_{i} ^{j }satisfying the εcomplementary slackness condition:

max_{(l,m)} {a _{lk} ^{m}−λ_{l} ^{m} }−ε≦a _{ik} ^{j}−λ_{i} ^{j}.  1) Bidding Phase:

 a) For each unassigned RBM k, find the powerzone (i_{k},j_{k}) that maximizes the profit of RBM k, i.e.: (i_{k},j_{k})=argmax_{(i,j)}{a_{ik} ^{j}−λ_{i} ^{j}}.
 b) Compute b_{k}, defined as the best value offered by powerzone other than (i_{k},j_{k}), i.e., b_{k}=max_{(i,j)≠(i} _{ k } _{,j} _{ k } _{)}{a_{ik} ^{j}−λ_{i} ^{j}}.
 c) Compute β_{i} _{ k } _{,k} ^{j} ^{ k }, defined as the bid of RBM k for powerzone (i_{k},j_{k}):

β_{i} _{ k } _{,k} ^{j} ^{ k } =a _{i} _{ k } _{,k} ^{j} ^{ k } −b _{k}+ε. 
 d) Go to step 1(a); repeat for all unassigned RBMs.
 2) Assignment Phase:

 a) For each powerzone (i,j), find the RBM k_{i} ^{j }that offers the highest bid to (i,j), as found in step 1 above, i.e., k_{i} ^{j}=argmax_{k}β_{ik} ^{j}.
 b) Assign (i,j) to RBM k_{i} ^{j}, and set (i,j)'s price to this highest bid, i.e.,

${\lambda}_{i}^{j}={\beta}_{{\mathrm{ik}}_{i}^{j}}^{j}.$ 
 c) Go to step 2) a.; and repeat for all (i,j).
 3) Set ε=αε for some 0<α<1; go to step 1(a); and stop when

$\varepsilon <\frac{1}{\mathrm{NK}}.$  The ABPZA method inherits the advantages of the auction approach. It offers an E closetoglobal optimal solution to the problem. It can be implemented in distributed fashion across all hubs, and asynchronously at each hub.
 The ABPZA method enables jointly and globally determining both the RBMtopowerzone assignment and the RBMtohub association. The latter is also known as clustering.
 For systems with a prior knowledge of the clustering strategy, the problem reduces to a RBMtopowerzone assignment problem, on a perhub basis. The latter is especially applicable for the downlink of the system under consideration. Thus, based on this observation, a method according to another embodiment is proposed, called a ClusteringandExhaustiveSearch PowerZoneAssignment method (CESPZA). This method hinges upon the fact that, for clustering in the downlink direction, the assignment decision of every RBM belonging to a certain hub to some powerzone does not affect the assignment of RBMs belonging to other hubs. Given that only K RBMs would roam to every hub, it is sufficient for every hub to look over the 4K! possibilities of its own RBMtopowerzone assignment. CESPZA, therefore, first decides the clustering strategy based on one of the powerzones and one hub. Subsequently, CESPZA evokes an exhaustive search of RBMtopowerzone assignments, on a per hub basis. CESPZA is a simple method based on the gains a_{ik} ^{j}. It has low computational complexity, and already brings in a significant performance improvement as compared to conventional systems, as the simulation results presented herein suggest.
 More specifically, let A be the NKxNK matrix whose entries are defined as follows:

A _{k,(i1)K+j} =a _{ik} ^{j}.  At each step, find the largest entry of the matrix A, call it

${A}_{{k}_{x}^{\mathrm{max}},{k}_{y}^{\mathrm{max}}}.$  RBM k_{x} ^{max }then maps to powerzone

$\left({i}_{{k}_{x}^{\mathrm{max}}},{j}_{{k}_{x}^{\mathrm{max}}}\right),$  where:

${i}_{{k}_{x}^{\mathrm{max}}}=\lfloor \frac{{k}_{y}^{\mathrm{max}}1}{K}\rfloor +1,{j}_{{k}_{x}^{\mathrm{max}}}=\mathrm{mod}\ue8a0\left({k}_{y}^{\mathrm{max}}1,K\right)+1,$  where └ ┘ and mod(,) represent the floor and modulo operators, respectively. Next, delete the

${A}_{{k}_{x}^{\mathrm{max}}}\ue89e\mathrm{th}$  row and the

${A}_{{k}_{y}^{\mathrm{max}}}\ue89e\mathrm{th}$  column of the matrix A, so that

${A}_{{k}_{x}^{\mathrm{max}}}$  and power zone

${A}_{{k}_{y}^{\mathrm{max}}}$  are not involved in subsequent steps. Repeat this procedure until all the KN RBMs are divided into disjoint clusters of equal cardinality K.
 The above step is followed by an exhaustive search on perhub basis to rearrange the RBMs within each hub frame.
 The clustering part of the method of this embodiment requires a central processor which has access to all entries of the matrix A, and therefore has the power of assigning RBMs in a coordinated way. In conventional systems, however, networks are formed incrementally, i.e., RBMs enter the network, each one at a time. Whenever a RBM k enters the network, it only has information about its own individual utilities a_{ik} ^{j }for all (i, j). An uncoordinated strategy to assign RBMs to powerzone (and equivalently to hubs) can then be summarized as follows:

 1) Whenever a user k enters the network, consider row A(k,:) solely, and choose the index k_{y} ^{max }that corresponds to the maximum entry

${A}_{k,{k}_{y}^{\mathrm{max}}}.$ 
 2) Powerzone k_{y} ^{max }is reserved to RBM k and announced unavailable to all new comers (i.e., new RBMs).
 3) Repeat the process for all new corners k′(k′≠k).
 Because of the less coordinated nature of the incremental deployment, the additional perhub exhaustive search step here brings in significant additional gain.
 To compare the methods of the different embodiments proposed herein, we simulated the performance of the methods based on a wireless backhaul network, similar to the one shown in
FIG. 1 . System parameters for the simulations are shown inFIG. 3 .  The utility function a_{ik} ^{j}, used as an example in the simulations for illustration purposes, corresponds to the rate a_{ik} ^{j}=R_{ik} ^{j}=W_{i,j }log_{2}(1+SINR_{ik} ^{j}) of RBM k if it is assigned to powerzone (i,j). The power is allocated based on SFRsystems strategy. It allows each sector to utilize the entire bandwidth, while reducing the interference to the neighboring sectors. It also allows the inner RBMs to use the outer subbands, as a way to improve the overall system throughput.
 To show the gain of the coordinated scheduling methods,
FIG. 4 shows the percentage gain in sumrate for each the methods as compared to the simple roundrobin assignment. As shown inFIG. 4 , the auction algorithm, which offers a closetoglobaloptimal solution provides the best performance among the proposed methods. The other simple heuristic rateclustering method already shows a performance gain of within 6% of auction method. Most importantly,FIG. 4 especially shows the significant performance gain offered by coordinated powerzoneassignment methods, i.e. auction, rateclustering and geographicalclusteringplusexhaustivesearch methods, when compared to the uncoordinatedrateclusteringplusexhaustivesearch method. The auction method, in particular, offers up to 30% sumrate improvement over uncoordinated systems.  Furthermore,
FIG. 4 shows how all the proposed methods have a superior performance to a simple SFRbaseline method. Such a method does not account for the possible SINR values of different assignments. Instead, it simply assigns RBMs to powerzones based on local channel information.  To show the individual gains provided by both the clustering and the perhub exhaustive search steps, we show the percentage gain of the methods under different clustering strategies in
FIG. 5 . The first shows that the gain of the additional exhaustive search is negligible after rateclustering. This is especially due to the fact that rateclustering already accounts for the rates themselves. SINRclustering, however, does not account for the bandwidths allocations of the different powerzones. Therefore, as expected,FIG. 5 shows that the additional perhub exhaustive search succeeding the SINRclustering brings in a significant additional gain. The same goes for the uncoordinatedrateclustering, because of the uncoordinated nature of its incremental deployment.  In summary, Soft Frequency Reuse (SFR) is an enhanced frequency reuse technique, that provides both the flexibility of utilizing the entire available bandwidth, and the capability to reduce high intersite interference levels, associated with networks with full frequency reuse. This application discloses and examines the benefits of one type of coordinated scheduling in Soft Frequency Reuse (SFR)based systems. Considering, for example, the downlink of a 3sectorpercell SFRbased wireless backhaul network consisting of N access nodes (hubs), each serving K remote terminals (RBMs) multiplexed across the K time/frequency zones, with frequency reuse one between the sectors, and assuming a fixed transmit power, methods are disclosed to address the resource allocation problem of optimally scheduling each of the NK RBMs to one of the NK powerzones, on a onetoone basis, and in a coordinated manner, as opposed to conventional systems which schedule the RBMs entering the network in an uncoordinated way. One of the embodiments is based on the auction approach, and offers a closetoglobaloptimal solution. Other embodiments are based on first assigning RBMs to hubs heuristically, and then optimally scheduling RBMs within each hub. Simulation results show that coordinated scheduling offers significant performance improvement as compared to noncoordinated systems. The methods according to preferred embodiments offer a significant performance improvement as compared to conventional systems. These methods are low in complexity, and compatible with the physical constraints of SFRbased wireless systems. Thus, these methods are amenable to practical implementation. Methods according to embodiments of the invention described herein provide for interference management in wireless backhaul networks using coordinated power zone assignment. The methods disclosed herein are applicable to wireless backhaul networks comprising MicroCell and PicoCell networks, which involve a onetoone powerzoneassignment problem. The auctionbased powerzoneassignment (ABPZA) method offers a closetoglobal optimal solution. It can be implemented in a distributed fashion across all transmitters, and asynchronously at each transmitter. The ClusteringandExhaustiveSearch PowerZoneAssignment (CESPZA) method is another simple method, which can be implemented on a perhubbasis, given a preassigned clustering.
 Both the ABPZA and CESPZA methods provide coordinated powerzoneassignmentmethods across the entire backhaul network. These methods provide a significant performance improvement as compared to conventional noncoordinated systems, and can be implemented in a distributed fashion, and asynchronously across all hubs.
 A related article authored by the inventors entitled “Coordinated Scheduling for Wireless Backhaul Networks with Soft Frequency Reuse”, H. Dahrouj, W. Yu, T. Tang, J. Chow and R. Selea, 21st European Signal Processing Conference (EUSIPCO), Marrakech, Morocco, September 2013, is incorporated herein by reference in its entirety.
 Although embodiments of the invention have been described and illustrated in detail, it is to be clearly understood that the same is by way of illustration and example only and not to be taken by way of limitation, the scope of the present invention being limited only by the appended claims.
Claims (30)
max_{(l,m)} {a _{lk} ^{m}−λ_{l} ^{m} }−ε≦a _{ik} ^{j}−λ_{i} ^{j}.
β_{i} _{ k } _{,k} ^{j} ^{ k } =a _{i} _{ k } _{,k} ^{j} ^{ k } −b _{k}+ε.
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