Abstract:
A combined CDM/TDM (code division multiplexing/time division multiplexing) transmitter and method are provided which employ fractional slot assignment by assigning fractional CDM channel gains to multiple slots for at least one packet with a goal that a sum of fractional CDM gains assigned each packet over a scheduling period meet a required threshold. In some embodiments, the transmitter is adapted to determine which users to transmit during a given slot, and to determine fractional CDM channel gains using an optimization of fractional CDM channel gain and user assignment which maximizes a number of users each of which is assigned a respective group of one or more slots with fractional CDM channel gains which sum to the required threshold for the user.

Description:
FIELD OF THE INVENTION 
   The invention relates to a scheduling method and apparatus for systems employing combined code division multiplexing and time division multiplexing. 
   BACKGROUND OF THE INVENTION 
   The forward link of 1×EV-DV (see for example “Physical Layer Standard for cdma2000 Spread Spectrum Systems”, IS2000 Release C. 3GPP2 C.S0002-C) uses code division multiplexing (CDM) within time division multiplexing (TDM) (1.25, 2.5 or 5 ms slots) on a shared channel (SHCH) that allows flexible slot scheduling and slots for real time services such as voice and video for multiple users. Each time slot has a set of Walsh codes (WC), and has its power allocation split between one or more voice users. 1×EV-DV allows one data user and multiple voice users per slot. Voice users may take several of the WC codes. For instance, full rate voice users can use either one or two SHCH W 16  codes. Half, quarter and eighth rate voice uses convolutional coding and uses only one W 16  code. 
   1×EV-DV allows users to be scheduled over a certain number of slots in every frame (e.g., 20 ms) by allocating Walsh code gains that are to be controlled based on channel estimations from each user. There is no solution for doing this allocation in a manner that maximizes voice capacity. 
   Voice capacity of 1×EV-DV strongly depends on a scheduling scheme. This implies that an efficient scheduling algorithm is needed in order to maximize the voice capacity. However, scheduling users efficiently over slots in each frame is a very difficult problem because it is a combinatorial optimization problem that is NP-hard (non-polynomial-hard). 
   Many scheduling algorithms have been proposed in the literature for various purposes. However, there are no scheduling algorithms that can be applied effectively directly to 1×EV-DV. Schemes which have been proposed were to obtain a feasible solution but these do not provide any quality guarantee. For instance, Zig-Zeg or round-robin schemes have been proposed for the forward link due to the difficulty of the NP-hard characteristics of the scheduling problem. 
   SUMMARY OF THE INVENTION 
   According to one broad aspect, the invention provides a combined CDM/TDM (code division multiplexing/time division multiplexing) transmitter employing fractional slot assignment by assigning fractional CDM channel gains to multiple slots for at least one packet with a goal that a sum of fractional CDM gains assigned each packet over a scheduling period meet a required threshold. 
   In some embodiments, the transmitter is adapted to determine which users to transmit during a given slot, and to determine fractional CDM channel gains using an optimization of fractional CDM channel gain and user assignment which maximizes a number of users each of which is assigned a respective group of one or more slots with fractional CDM channel gains which sum to the required threshold for the user. 
   In some embodiments, the transmitter is adapted to determine which users to transmit during a given slot, and to determine fractional CDM channel gains using an optimization of fractional CDM channel gain and user assignment which jointly maximizes a number of users each of which is assigned a respective group of one or more slots with fractional CDM channel gains which sum to the required threshold for the user, and maximizes the total amount of CDM channel gain assigned subject to a maximum. 
   In some embodiments, the transmitter is adapted to, at the start of each frame start the scheduling operation afresh, with the scheduling period having a number of slots to be scheduled N s  initially equalling a total number of slots N f  being scheduled for the frame, and a number of users being scheduled N u  initially equalling a total number of users to be scheduled; for current slot k, perform a scheduling operation for slot k through slot N f  by: obtain inputs to the scheduling operation comprising a CDM channel gain for each user and each slot; determine the fractional CDM channel gains by optimizing the CDM channel gains for each user and each slot to produce optimized CDM channel gains so as to maximize a number of users scheduled during the plurality of slots with a total CDM channel gain as great as the required threshold function of channel estimates for each user and each slot, with an optimized CDM channel gain of zero for a given slot and user meaning the user is not scheduled during the slot; allocate users having an optimized CDM channel gain greater than zero for slot k to the current slot k using the optimized CDM channel gain for that user and slot as the fractional CDM channel gain; for users having a voice packet transmitted after the current slot, decrement N u ; increment k and decrement N s =N f −k. 
   In some embodiments, the transmitter is further adapted to, after each slot, decrement the required threshold for each user scheduled by an amount equal to the optimized CDM channel gain assigned during the slot. 
   In some embodiments, the transmitter is further adapted to, after a given user&#39;s required threshold has been decremented to zero, and an acknowledgement of successful transmission has not been received within a specified time, reset the given user&#39;s required threshold. 
   In some embodments, the CDM channels are Walsh code channels. 
   In some embodiments, obtaining inputs to the scheduling operation comprises determining CHE ik , a channel estimate for user i in slot k; determining ω ik , a number of CDM channels allocated to user i in slot k; determining ξ, the required threshold; determining φ ik , a CDM channel gain for user i in slot k. 
   In some embodiments, the transmitter is further adapted to, determine G i , a processing gain for user i; determine β, a portion of power to be allocated to traffic channel; wherein determining φ ik  is done according to φ ik =ξ/α ik  where α ik =G i ·β·CHE ik . 
   In some embodiments, optimizing the CDM channel gains for each user and each slot, with an optimized channel gain of zero for a given slot meaning the user is not scheduled during the slot comprises performing an optimization of an objective function which maximizes a number of users that will be allocated enough CDM channel gain over one or more slots to meet the required threshold to determine u ik , an optimization factor for user i in slot k, where u ik &gt;0 if user i is allocated in slot k, and u ik =0 otherwise subject to constraints that a summation of CDM channel gain of users allocated in a slot should be less than or equal to a total available CDM channel gain, and a summation of the number of CDM channels of users allocated in a slot should be less than or equal to total available number of CDM channels per slot; determining optimized CDM channel gain according to φ ik *=φ ik u ik  for user i in slot k. 
   In some embodiments, the optimization is performed using a Lagrangean relaxation technique employing decomposition and subgradiant optimization. 
   According to one broad aspect, the invention provides a combined orthogonal channel/TDM system comprising a transmitter adapted to transmit using a plurality of orthogonal channels during each of sequence of time division mulitplexed slots; the transmitter employing fractional slot assignment to multiple slots for at least one packet with a goal that a sum of fractional orthogonal channel gains assigned each packet over a scheduling period meet a required threshold; a receiver adapted to perform diversity combining of fractionally assigned slots. 
   In some embodiments, the orthogonal channels are CDM channels. 
   In some embodiments, the orthogonal channels are OFDM sub-carriers. 
   According to one broad aspect, the invention provides a method of scheduling users in a combined orthogonal channel/TDM (time division multiplexing) frame structure, with each frame containing a plurality of time slots, and a plurality of orthogonal channels during each time slot, each user having a required threshold, the method comprising scheduling at least one user&#39;s packet to be transmitted during a first slot of said plurality of time slots and to be retransmitted during at least one second slot of said plurality of time slots with a fractional orthogonal channel gain during each of the first slot and the at least one second slot being less than the target transmit power for the user. 
   In some embodiments, the orthogonal channels are CDM channels. 
   In some embodiments, the orthogonal channels are OFDM sub-carriers. 
   In some embodiments, a method further comprises determining which users to transmit during a given time slot and determining fractional orthogonal channel gains using an optimization of fractional orthogonal channel gain user assignment which maximizes a number of users each of which is assigned a respective group of one or more slots with fractional orthogonal channel gains which sum to the required threshold for the user. 
   In some embodiments, a method further comprises determining which users to transmit during a given time slot and determining fractional orthogonal channel gains using an optimization of fractional orthogonal channel gain user assignment which jointly maximizes a number of users each of which is assigned a respective group of one or more slots with fractional orthogonal channel gains which sum to the required threshold for the user, and maximizes the total amount of channel gain assigned subject to a maximum in each slot. 
   According to one broad aspect, the invention provides a method comprising transmitting packets for multiple users using a combined orthogonal channel/TDM (time division multiplexing) frame structure, with each frame containing a plurality of time slots, and a plurality of orthogonal channels during each time slot; performing a scheduling operation over a plurality of said slots in a manner which maximizes a number of users scheduled during the plurality of slots as a function of a channel estimate for each user and each slot, the scheduling operation determining which users are transmitted during each slot, for each user to be transmitted how many of the plurality of orthogonal channels each user is to be allocated, and how much of a total available transmit power is to be allocated to each user during each slot. 
   In some embodiments, each user has a required threshold, the method further comprising at the start of each frame starting the scheduling operation afresh, with a number of slots to be scheduled N s  initially equalling a total number of slots N f  being scheduled for the frame, and a number of users being scheduled N u  initially equalling a total number of users to be scheduled; for current slot k, performing a scheduling operation for slot k through slot N f  by: obtaining inputs to the scheduling operation comprising a CDM channel gain for each user and each slot; determining a fractional orthogonal channel gain for each user and slot by optimizing the orthogonal channel gains for each user and each slot to produce optimized orthogonal channel gains so as to maximize a number of users scheduled during the plurality of slots with a total channel gain as great as the required threshold as a function of a channel estimate for each user and each slot, with an optimized orthogonal channel gain of zero for a given slot and user meaning the user is not scheduled during the slot; allocating users having an optimized channel gain greater than zero for slot k to the current slot k using the optimized channel gain for that user and slot as the fractional orthogonal channel gain; for users having a voice packet transmitted after the current slot, decrementing N u ; incrementing k and decrementing N s =N f −k. 
   In some embodiments, a method further comprises, after each slot, decrementing the required threshold for each user scheduled by an amount equal to the optimized orthogonal channel gain assigned during the slot. 
   In some embodiments, a method further comprises after a given user&#39;s required threshold has been decremented to zero, and an acknowledgement of successful transmission has not been received within a specified time, resetting the given user&#39;s required threshold. 
   In some embodiments, the orthogonal channels are Walsh code channels. 
   In some embodiments, the orthogonal channels are OFDM sub-carriers. 
   In some embodiments, obtaining inputs to the scheduling operation comprises determining CHE ik , a channel estimate for user i in slot k; determining ω ik , a number of orthogonal channels allocated to user i in slot k; determining ξ, the required threshold; determining φ ik , an orthogonal channel gain for user i in slot k. 
   In some embodiments, a method further comprises determining G i , a processing gain for user i; determining β, a portion of power to be allocated to traffic channel; wherein determining φ ik  is done according to φ ik =ξ/α ik  where α ik =G i ·β·CHE ik . 
   In some embodiments, optimizing the orthogonal channel gains for each user and each slot, with an optimized channel gain of zero for a given slot meaning the user is not scheduled during the slot comprises performing an optimization of an objective function which maximizes a number of users that will be allocated enough orthogonal channel gain over one or more slots to meet the required threshold to determine u ik , an optimization factor for user i in slot k, where u ik &gt;0 if user i is allocated in slot k, and u ik =0 otherwise subject to constraints that a summation of orthogonal channel gain of users allocated in a slot should be less than or equal to a total available channel gain, and a summation of the number of orthogonal channels of users allocated in a slot should be less than or equal to total available number of orthogonal channels per slot; determining the optimized orthogonal channel gains according to φ ik *=φ ik u ik  for user i in slot 
   
     
       
         
           k 
           , 
           
             
               ϕ 
               i 
             
             = 
             
               
                 ∑ 
                 k 
               
               ⁢ 
               
                 
                   ϕ 
                   ik 
                   * 
                 
                 . 
               
             
           
         
       
     
   
   In some embodiments, the objective function is: 
             Z   y     =       max   y     ⁢     {       ∑     i   =   1       N   u       ⁢     y   i       }             
or an equivalent thereof such that
 
                       ∑     i   =   1       N   u       ⁢       ϕ   ik     ⁢     u   ik         ≤   1     ⁢                     k   =   1     ,   2   ,   …   ⁢           ,     N   s                       ∑     i   =   1       N   u       ⁢       ω   ik     ⁢     Φ   ⁡     (     u   ik     )           ≤     WC   k       ⁢                     k   =   1     ,   2   ,   …   ⁢           ,     N   s                   y   i     =     Φ   ⁡     (         ∑     k   =   1       N   s       ⁢       α   ik     ⁢     ϕ   ik     ⁢     u   ik         -   ξ     )                 i   =   1     ,   2   ,   …   ⁢           ,     N   u                 0 ≦u   ik ≦1 i= 1,2 , . . . ,N   u   , k= 1,2,  . . . ,N   s     y   i ∈{0,1 }i= 1,2, . . . ,N   u . 
   In some embodiments, the objective function is: 
             Z   y     =       min   y     ⁢     {     -     {         ∑     i   =   1       N   u       ⁢     y   i       +       C   1     ⁢   ξ   ⁢       ∑     i   =   1       N   u       ⁢       ∑     k   =   1       N   s       ⁢     u   ik             }       }             
or an equivalent thereof.
 
   In some embodiments, the optimization is performed using a Lagrangean relaxation technique employing decomposition and subgradiant optimization. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
     Various embodiments of the invention will now be described in further detail with reference to the attached drawings in which: 
       FIG. 1  is an illustration of an example TDM/CDM frame structure; 
       FIG. 2  is an example of how users might be scheduled during a particular frame having the structure of  FIG. 1 ; 
       FIG. 3  is a block diagram of a packet transmitter provided by an embodiment of the invention; 
       FIG. 4  is a flowchart of the functionality of the scheduler of  FIG. 3 , provided by an embodiment of the invention; 
       FIG. 5  is a diagram illustrating how a user&#39;s WC gain may be allocated across multiple slots in a frame; and 
       FIG. 6  is a flowchart of priority based (Eb/Nt)req allocation in WC gain calculation. 
   

   DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS 
   Embodiments of the invention provide methods of scheduling voice users in a combined TDM/CDM environment. The invention will be described in the context of a combined TDM/CDM system in which time slots are arranged in frames. In each frame or slot, an orthogonal set of codes are used to provide multiple channels. In the examples described below, there are 16 time slots per frame, and the orthogonal set of codes are Walsh codes. It is to be understood that other sets of orthogonal channels could alternatively be employed in each time slot, and that other frame structures may be used which contain more or less than 16 slots per frame. For example, a set of orthogonal sub-carriers may be transmitted during each slot, this being the approach used by OFDM (orthogonal frequency division multiplexing) systems. 
   A TDM/CDM frame structure is shown in  FIG. 1 . The frame represents the resource available at the base station which is to be scheduled for multiple users. The frame is comprised of 16 TDM slots labelled TS 0  through TS 15 . Each slot has the capability of carrying up to some number of orthogonal WC channels. These are shown stacked vertically for each slot. In the illustrated example, it is assumed that up to 16 WC 16  codes can be transmitted on each slot. These are labelled WC 1 through WC 16 although not necessarily all of these are transmitted during a given slot. An example of an actual transmit frame is shown in  FIG. 2  where user identifiers U 0  through U 49  are stacked within each time slot. The presence of a user identifier in a given time slot means that user is allocated at least one WC during that slot. Thus, for example during TS 6  only U 40  is transmitting, while during TS 9 , U 27 , U 35  and U 36  are transmitting. 
   It is assumed that each voice user needs to be scheduled at least once per frame to maintain a required voice quality. Such scheduling is done on a per transmitter basis. A base station having multiple transmit sectors is assumed to have multiple transmitters, and the scheduling is performed for each sector. 
   It is also assumed that during a given slot, the transmitter has a finite maximum amount of total transmit power, which is to be allocated to different users by assigning them with different WC gain. For the purpose of simplification, the sum of WC gain is being normalized to one so that the WC gain value for a given user will represent the fraction of the total WC gain the given user is to be allocated. 
   The scheduling method determines how much WC gain should be allocated to each user in the current sector and determines which time slot should be assigned for each user, and how many users should be allocated in each time slot. The scheduling method determines how the WC gain of each user is to be divided over multiple slots in order to maximize power utilization, so as to be combined at the receiver using diversity combining. 
   With the methods provided, when applied in the 1×EV-DV context, users can be scheduled efficiently over slots based on the channel estimation and WC gain control, and the voice capacity of 1×EV-DV can be maximized. 
   Referring now to  FIG. 3 , shown is a block diagram of a transmitter provided by an embodiment of the invention. Shown is a traffic source  30  connected to a user queue  32 . The packets in the user queue  32  are scheduled for transmission by the scheduler  48  provided by another embodiment of the invention. A channel estimator  34  is connected to the scheduler  48 . The scheduler  48  is connected to a transmission queue  44 . The transmission queue  44  is connected to transmission block  42  which represents functionality responsible for the actual transmission of packets. The transmission block  42  is connected through a success testing logic  46  to a retransmission queue  38  which is in turn connected back to the user queue  32 .  FIG. 3  only shows the details of a single user. However, with the exception of the scheduler  48  and transmission queue  44 , the blocks are repeated for each user. The transmitter might form part of a basestation for example. 
   In operation, the traffic source  30  generates packets. Typically, for a voice user, these will be generated periodically but this is not essential. Packets generated by the traffic source  30  are added to the user queue  32 . The channel estimator  34  generates channel estimates CHE ik  for each user i and each slot k and passes these to the scheduler  48 . On the basis of the channel estimates CHE ik  and the user queues  32 , the scheduler  48  determines in which slots to place which users. The output of the scheduler is a set of values φ ik * representing for each slot and each user the optimized WC gain in slot k for user i. For each slot, users having non-zero values of φ ik * have a packet scheduled with the appropriate WC gain by adding the packet to the transmission queue  44 . The packet is then transmitted by the transmission block  42 . In the event the transmission of a given packet is successful as indicated by the success testing logic  46 , then the processing ends for that packet. On the other hand, in the event the transmission of a given packet is unsuccessful as indicated by the success testing logic  46 , then the packet is added to the retransmission queue  38 . 
   Channel Estimation: 
   As indicated above, channel estimates CHE ik  are required as an input to the scheduler  48 . A channel estimate is required for each user and for each slot in a frame and the channel estimator  34  is shown in  FIG. 3  to handle this functionality. There are many known methods of developing such channel estimates, and the invention does not rely on any specific method. For example, a prediction method may be used to provide channel estimates for each slot as a function of history data maintained for each channel estimate. Other suitable methods of obtaining channel estimates include for example using the latest channel quality feedbacks or using the Weiner-Hopf method. 
   The CHE can be defined as CHE ik =(C/I) ik =P ik /Ψ ik  where Ψ ik  is an interference power and P ik  is a received power for user i in slot k. 
   Scheduler 
   According to an embodiment of the invention, WC gain is allowed to be split over several slots in a given frame. The key idea of this scheme is to maximize voice capacity by maximum utilization of the transmit Tx power of the transmitter. In conventional TDM scheduling approaches a given TDM slot is allocated to a given user only and the entire required transmit power is used. In this embodiment, instead the required transmit power is allowed to be distributed over several slots. This means that even though a given slot may not have enough available power to satisfy a given user&#39;s power constraint, it may be that the left over available powers of several slots combined may be used to satisfy a given user&#39;s power constraint. 
     FIG. 4  is a high level flowchart of the operation of the scheduler  48 . The first step (step  4 - 1 ) is to calculate the WC gains φ ik , for each user i, in slot k, with the sum of the WC gains φ ik  being equal to the total WC gain φ i . 
   The next step (step  4 - 2 ) is to determine number of WC(s). 
   Finally, in step  4 - 3  the WC gains calculated in step  4 - 1  are optimized to produce optimized WC gains φ ik ★. This involves determining optimization multipliers u ik  using a combinatorial optimization technique and then computing the optimized WC gain values according to φ ik *=φ ik u ik . 
   Referring now to  FIG. 5 , shown is an illustration of how WC gain can be split over several slots in each frame, this splitting being optimized by a mathematical model. The detailed descriptions on the model and solution algorithm provided are in the following sections. In  FIG. 5 , the horizontal axis is time, and a frame j is shown to contain a number of different time slots TS 0  through TS 15 . As indicated above, each time slot has multiple orthogonal channels. In the event the algorithm determines that for a particular user i, three different slots k 1 , k 2 , k 3 , are to be used, the WC gain applied to these slots is φ ik     1   *=φ ik     1   u ik     1   , φ ik     2   *=φ ik     2   u ik     2   , and φ ik     3   *=φ ik     3   *u ik     3    respectively. 
   The first step executed by the scheduling algorithm is WC gain calculation. By the definition of E b /N t  (energy per bit/spectral density, of thermal noise plus interference):
 
( E   b   /N   t ) tk =( W/R   t )·φ ik   ·β·CHE   ik .
 
Where G i =(W/R i ) is a processing gain, φ ik  is the WC gain for user i (to be determined) in slot k, β is the fraction of total transmit power allocated to voice traffic, and CHE ik  is the channel estimate for user i in slot k. From the requirement that (E b /N t ) ik ≧ξ=(E b /N t ) req , where ξ=(E b /N t ) req  is a required value for (E b /N t ), one can write:
 
( W/R   i )·φ ik   ·β·CHE   ik =( E   b   /N   t ) req .
 
ξ=(E b /N t ) req  is a threshold representing a required (E b /N t ) at a receiver which is assumed constant for all users in this description, although the algorithm can easily be modified to handle different values for this threshold. Also, as described below, if priority based scheduling is employed, then two different values of required gain for each user may be employed. Then, the WC gain φ ik  for user i in slot k can be solved to yield:
 
φ ik =ξ/( G   i   ·β·CHE   ik )=ξ[ dB ]−( G   i   +β+CHE   ik )[ dB]. 
 
The WC gain φ ik  represents a minimum gain that must be allocated to the user in order that the user will receive a signal satisfying the required (E b /N t ) constraint if the packet of user i is transmitted only in slot k.
 
   Preferably, priority based (Eb/Nt)req allocation is employed in the WC gain calculation step. The key idea of this priority scheme is to increase the probability of transmitting the current voice frame successfully for users with higher priority. 
   One simple way of implementing a priority scheme will now be described. The method is summarized in the flowchart of  FIG. 6 . If there is frame error for a given user, the priority is increased for example priority=priority+1. 
   At the end of each frame, users having a positive (&gt;0) value of priority are identified, for example on a so-called “black list”. At the end of every frame if a user is not in the “Black List” the priority value of the user is decreased, i.e., priority=priority−1. The priority is then taken into consideration during the calculation of the WC gains φ ik . 
   For example, the priority calculated by the priority scheme may be used as follows: 
   If the priority of a user is positive (0&gt;) (yes path step  6 - 3 ) then a maximum value of (Eb/Nt)req is used (step  6 - 5 ) in calculating the WC gains φ ik  at step  6 - 6 ; 
   Else if the priority of a user is less than or equal to zero (no path step  6 - 3 ) then a minimum value of (Eb/Nt)req is used (step  6 - 4 ) in calculating the WC gains φ ik  at step  6 - 6  as described previously. 
   The main object of this priority scheme is to increase the probability of transmitting the current voice frame successfully for users with higher priority. 
   The remaining steps of the scheduler, namely step  4 - 2  of calculating the number of WC(s), and step  4 - 3  of optimize WC Gains are described below. 
   Mathematical Model 
   A complete mathematical model will now be presented. The input parameters, output parameters and decision variables are as follows (some already introduced previously): 
   Input Parameters 
   CHE ik : channel estimate for user i in slot k; 
   ω ik : Number of WC(s) allocated to user i in slot k—this is a constant for all k for a given user i representing how many WCs that user needs when transmitting; 
   ξ: Required (Eb/Nt); /★ this is typically a constant ★/ 
   G i : Processing gain for user i; 
   β: Portion of power to be allocated to traffic channel, e.g., 0.7—the remainder of the power is typically allocated to overhead functions; 
   φ i : total WC gain for user i; 
   φ ik : WC gain for user i in slot k, φ ik =ξ/α ik  where α ik =G i ·β·CHE ik . 
   Output Parameters: 
   u ik : optimization factor for user i in slot k. u ik &gt;0 if user i is allocated in slot k, and u ik =0 otherwise; 
   φ ik *=φ ik u ik : optimized WC gain for user i in slot k, 
             ϕ   i     =       ∑   k     ⁢     ϕ   ik   *             
Measure (Objective Function) and Constraints
 
   Objective: Maximize number of users allocated in slots and transmitted successfully 
   Constraints: 1. Summation of WC gain of users allocated in a slot should be less than or equal to 1;
         2. Summation of the number of WC of users allocated in a slot should be less than or equal to total available number of WC per slot.
 
Mathematical Formulation
       

                   Z   y     =       max   y     ⁢       {       ∑     i   =   1       N   u       ⁢     y   i       }     ⁢           ⁢     /     *           ⁢   objective   ⁢           ⁢   function   ⁢           *     /                 (   1   )               
subject to:
 
                             ∑     i   =   1       N   u       ⁢       ϕ   ik     ⁢     u   ik         ≤   1     ⁢                     k   =   1     ,   2   ,   …   ⁢           ,     N   s                     ⁢       /     *           ⁢   WC   ⁢           ⁢   gain   ⁢           ⁢   constraints   *     /                     (   2   )                           ∑     i   =   1       N   u       ⁢       ω   ik     ⁢     Φ   ⁡     (     u   ik     )           ≤     WC   k       ⁢                     k   =   1     ,   2   ,   …   ⁢           ,     N   s                     ⁢       /     *           ⁢   WC   ⁢           ⁢   numbers   ⁢           ⁢   constraints   ⁢           *     /                     (   3   )                       y   i     =     Φ   ⁡     (         ∑     k   =   1       N   s       ⁢       α   ik     ⁢     ϕ   ik     ⁢     u   ik         -   ξ     )                 i   =   1     ,   2   ,   …   ⁢           ,     N   u                     ⁢       /     *           ⁢     Eb   /   Nt     ⁢           ⁢   requirement   ⁢           *     /                     (   4   )               0 ≦u   ik ≦1 i= 1,2,  . . . ,N   u   , k= 1,2,  . . . ,N   s   (5)   y   t ∈{0,1 }i= 1,2,  . . . ,N   u   (6) 
where N u  is number of users to be scheduled, N s  is number of slots over which scheduling is being performed for this iteration, WC k  is total available number of WC in slot k, u ik : optimization factor for user i in slot k. u ik &gt;0 if user i is allocated in slot k, and u ik =0 otherwise; φ ik u ik  is the actual Walsh code gain allocated for user i in slot k, and where
 
   
     
       
         
           
             Φ 
             ⁡ 
             
               ( 
               x 
               ) 
             
           
           = 
           
             { 
             
               
                 
                   
                     1 
                   
                   
                     
                       
                         
                           if 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           x 
                         
                         &gt; 
                         0 
                       
                       ⁢ 
                       
                           
                       
                     
                   
                 
                 
                   
                     0 
                   
                   
                     otherwise 
                   
                 
               
               . 
             
           
         
       
     
   
   Objective function (1) represents that the number of users satisfying the condition of 
               ∑     k   =   1       N   s       ⁢       G   i     ⁢   β   ⁢           ⁢     CHE   ik     ⁢     ϕ   ik     ⁢     u   ik         &gt;   ξ         
is to be maximized.
 
   Constraint (2) represents that the total WC gains allocated to users who are to be assigned to the same slot should be less than equal to one, where a WC gain for a user will be split over some slots by u ik . 
   Constraint (3) represents that the total number of WCs for users being assigned in a slot should be less than or equal to the total available WC number for that slot. 
   Constraint (4) represents those users satisfying Eb/Nt requirement 
           (       i   .   e   .     ,         ∑     k   =   1       N   s       ⁢       G   i     ⁢   β   ⁢           ⁢     CHE   ik     ⁢     ϕ   ik     ⁢     u   ik         &gt;   ξ       )         
are to be scheduled (users not satisfying the condition may be scheduled too).
 
   Constraints (5) and (6) are decision variable constraints. We let u ik  be continuous variable in order to split a WC gain over some slots for soft combining and incremental redundancy, and to maximize power resource utilization. 
   The above problem is a MIP (Mixed Integer Programming) problem, which is NP-hard. The above math programming is difficult to solve since Φ(x) is not a linear function. It can however be converted to a linear form. 
   The above objective function (1) is equivalent to: 
   
     
       
         
           
             Z 
             y 
           
           = 
           
             
               min 
               y 
             
             ⁢ 
             
               { 
               
                 - 
                 
                   
                     ∑ 
                     
                       i 
                       = 
                       1 
                     
                     
                       N 
                       u 
                     
                   
                   ⁢ 
                   
                     y 
                     i 
                   
                 
               
               } 
             
           
         
       
     
   
   Preferably, an additional term 
           ξ   ⁢       ∑     i   =   1       N   u       ⁢       ∑     k   =   1       N   s       ⁢     u   ik               
is added to the objective function in order to obtain better solution and for the efficiency of our algorithm. This added term represents a constant multiplied by the fraction of the total available WC gain which is allocated over the slots being scheduled. Any term which is representative of this fraction may be employed, and this may not necessarily take the form of the above example. This yields a revised objective function:
 
   
     
       
         
           
             Z 
             
               y 
               , 
               u 
             
           
           = 
           
             
               min 
               
                 y 
                 , 
                 u 
               
             
             ⁢ 
             
               { 
               
                 - 
                 
                   { 
                   
                     
                       
                         ∑ 
                         
                           i 
                           = 
                           1 
                         
                         
                           N 
                           u 
                         
                       
                       ⁢ 
                       
                         y 
                         i 
                       
                     
                     + 
                     
                       ξ 
                       ⁢ 
                       
                         
                           ∑ 
                           
                             i 
                             = 
                             1 
                           
                           
                             N 
                             u 
                           
                         
                         ⁢ 
                         
                           
                             ∑ 
                             
                               k 
                               = 
                               1 
                             
                             
                               N 
                               s 
                             
                           
                           ⁢ 
                           
                             u 
                             ik 
                           
                         
                       
                     
                   
                   } 
                 
               
               } 
             
           
         
       
     
   
   The above problem can be converted (see appendix A) to the following problem: 
   Problem[Scheduler( )]: 
   Minimize: 
                   Z     y   ,   u   ,   x       =       min     y   ,   u   ,   x       ⁢     {       -     {         C   1     ⁢       ∑   i     ⁢     y   i         +       C   2     ⁢   ξ   ⁢       ∑   i     ⁢       ∑   k     ⁢     u   ik             }       +       K   1     ⁢       ∑   i     ⁢       ∑   k     ⁢     x   ik           +       K   2     ⁢       ∑   i     ⁢     x   i           }               (   7   )               
subject to:
 
                     ∑   i     ⁢       ϕ   ik     ⁢     u   ik         ≤     1   ⁢           ⁢     ∀   k               (   8   )                   ∑   i     ⁢       ω   ik     ⁢     x   ik         ≤       WC   k     ⁢           ⁢     ∀   k               (   9   )               u ik ≦M 1 x ik ∀i,k  (10) y i =x i ∀i  (11) 
                       ∑   k     ⁢     u   ik       -   1     ≤       M   2     ⁢     x     i   ⁢               ⁢     ∀   i               (   12   )               1≧u ik ≧0∀i,k y,∈{0,1}, x ik ∈{0,1}, x i ∈{0,1}∀i,k 
where C 1 (&gt;K 2 ), C 2 , K 1 (=K 2 ), K 2  are coefficients, M 1 =1, 0&lt;M 2 &lt;1.
 
   Note that we have replaced α ik φ ik  with ξ in Problem[Scheduler( )] according to the definition of φ ik =ξ/α ik . In (10), if u ik =0 then x ik  may be 0 or 1 but it will be 0 due to minimizing 
             K   1     ⁢       ∑   i     ⁢       ∑   k     ⁢     x   ik               
in the objective function. In (12), if
 
                 ∑   k     ⁢     u   ik       -   1     ≤     0   ⁢     (           ∑   k     ⁢       α   ik     ⁢     ϕ   ik     ⁢     u   ik         -   ξ     ≤   0     )             
then x i  may be 0 or 1 but it will be 0 due to minimizing
 
             K   2     ⁢       ∑   i     ⁢     x   i             
in the objective function.
 
Complexity
 
   Problem[Scheduler( )] is a sort of MIP (Mixed Integer Programming) and NP-hard problem. However, every term is linear, so the problem can be solved by linear or integer programming techniques. 
   Scheduling Algorithm 
   In this section, the detailed procedures of the algorithm are introduced. 
   Inputs: 
   
       
       
         
           1. Number of slots remaining, N s    
           2. Number of users remaining, N u    
           3. CHE ik  (Calculate φ ik ) 
           4. Vocoder rates (Calculate ω ik  and α ik )
 
Outputs:
 
           1. {u ik  ∀i,k} 
           2. φ ik *=φ ik u ik * 
           3. ω ik    
         
       
       Step 1) Get Inputs 
       Step 2) Solve problem for {u ik  ∀i,k} 
       Step 3) Allocate users having u ik &gt;0 to the current slot k with φ ik u ik  WC gain; 
       Step 4) Update N s  and N u    
       Step 5) Goto Step 1 
     
  
   At the start of each frame the whole scheduling process starts afresh. At that time, N s  will equal the total number of slots being scheduled for a frame. The scheduling algorithm is repeated every slot with N s  being decremented each iteration. Similarly, at the start of each frame N u  will equal the total number of users to be scheduled. Then, as users have their voice packets successfully transmitted N u  is decremented. More specifically, for users having a voice packet transmitted, meaning that one or more slots have been assigned, transmitted for the user and an acknowledgement received N u  is decremented. In some embodiments, for each user scheduled, the target gain for the user is decremented for use in subsequent slots. In some embodiments, if, after a user&#39;s target gain has been allocated over one or more slots, and an acknowledgement is not received within an acceptable round trip delay, preferably the user&#39;s target gain is reset to the initial target value. 
   An Example Implementation is Presented in Appendix C. 
   In the example implementation, Lagrangian optimization is employed. Any suitable optimization method may be employed, such as branch and bound, Primal dual, Ellipsoid, Polyhedral methods, etc. 
   Numerous modifications and variations of the present invention are possible in light of the above teachings. It is therefore to be understood that within the scope of the appended claims, the invention may be practised otherwise than as specifically described herein. 
   Appendix A: Problem Conversion 
   In this appendix, conversion of φ(x) to a linear form is described. 
   Let x ik =Φ(u ik )∀i,k . Then, 
             x   ik     =     {         1           if   ⁢           ⁢     u   ik       &gt;   0             0           if   ⁢           ⁢     u   ik       =   0                   
by the definition of Φ(•) and since u ik &gt;0.
 
As equivalent constraints, we can express it as below:
 u ik ≦M 1 x ik ∀i,k  (A1) 
where M 1  is a constant value called a “Big M”, which is larger than or equal to any value of u ik . In (A1), if u ik &gt;0 then x ik  should be equal to 1 and if u ik =0 then x ik  may be 0 or 1 but it will be 0 due to the associated objective function term. We set M 1 =1 since 1≧u ik ≧0.
 
   Hence, (3) can be converted to the following equivalent constraints: 
               ∑   i             ⁢       ω   ik     ⁢     x   ik         ≤       WC   k     ⁢           ⁢     ∀   k             u ik ≦M 1 x ik ∀i,k x ik ∈{0,1}∀i,k 
               Let   ⁢           ⁢     x   i       =       Φ   ⁡     (         ∑   k             ⁢     u   ik       -   1     )       ⁢     ∀     i   .           ⁢     
     ⁢   Then           ,       x   i     =     {           1               if   ⁢           ⁢       ∑   k             ⁢     u   ik         -   1     &gt;   0     ⁢                     0             if   ⁢           ⁢       ∑   k             ⁢     u   ik         -   1     ≤   0           ⁢   by   ⁢           ⁢   the   ⁢           ⁢   definition   ⁢           ⁢   of   ⁢           ⁢       Φ   ⁡     (   ·   )       .                 
As equivalent constraints, we can express it as below:
 
                       ∑   k             ⁢     u   ik       -   1     ≤       M   2     ⁢     x   i     ⁢           ⁢     ∀   i               (   A2   )               
where M 2  is a constant value called a “Big M”, which is larger than any value of
 
               ∑   k             ⁢     u   ik       -   1.         
In (A2), if
 
                 ∑   k             ⁢     u   ik       -   1     &gt;   0         
then x i  should be equal to 1 and if
 
                 ∑   k             ⁢     u   ik       -   1     ≤   0         
then x i  may be 0 or 1 but it will be 0 due to the associated objective function term. We set 0&lt;M 2 &lt;&lt;1.
 
Hence, (4) can be converted to the following equivalent constraints:
 y i =x i ∀i 
                 ∑   k             ⁢     u   ik       -   1     ≤       M   2     ⁢     x   i     ⁢           ⁢     ∀   i             x i ∈{0,1}∀i 
Appendix-B: Solution Approach
 
A.1 Algorithm for the Relaxed Problem
 
Lagrangean relaxation technique (decomposition and subgradient optimization) is applied.
 
Since (10) and (12) are constraints that make the problem difficult, we relax (10) and (12).
 
RP(Relaxed Problem):
 
               Z     y   ,   u   ,   x       ⁡     (   λ   )       =       min     y   ,   u   ,   x       ⁢     {             -     {         C   1     ⁢       ∑   i             ⁢     y   i         +       C   2     ⁢   ξ   ⁢       ∑   i             ⁢       ∑   k             ⁢     u   ik             }       +       K   1     ⁢       ∑   i             ⁢       ∑   k             ⁢     x   ik           +       K   2     ⁢       ∑   i             ⁢     x   i                       +       ∑   i             ⁢       ∑   k             ⁢       λ   ik     ⁡     (       u   ik     -       M   1     ⁢     x   ik         )             +       ∑   i             ⁢       λ   i     ⁡     (         ∑   k             ⁢     u   ik       -   1   -       M   2     ⁢     x   i         )                 }             
s.t.
 
               ∑   i             ⁢       ϕ   ik     ⁢     u   ik         ≤     1   ⁢           ⁢     ∀   k                       ∑   i             ⁢       ω   ik     ⁢     x   ik         ≤       WC   k     ⁢           ⁢     ∀   k             y i =x i ∀i 1≧u ik ≧0∀i,k y i ∈{0,1}, x ik ∈{0,1}, x i ∈{0,1}∀i,k 
where λ ik ,λ i ≧0∀i,k are Lagrangean multipliers.
 
The above problem can be decomposed as follows:
 
Subproblem (y,x), Subproblem (u), and Subproblem (x).
 
Subproblem(y,x): /★ subproblem having only y,x ★/
 
               Z   y     ⁡     (   λ   )       ⁢       min     y   ,   x       ⁢     {         -     C   1       ⁢       ∑   i             ⁢     y   i         -       M   2     ⁢       ∑   i             ⁢       λ   i     ⁢     x   i           +       K   2     ⁢       ∑   i             ⁢     x   i           }             
s.t.
 y i =x i ∀i y i ,x i ∈{0,1}∀i 
This subproblem can be decomposed further for i as below:
 
Subp(y,x:i):
 
               Z   y     ⁡     (     λ   :   i     )       =         min     y   ,     x   ∈     {     0   ,   1     }           ⁢       {         -     C   1       ⁢     y   i       +       (       K   2     -       M   2     ⁢     λ   i         )     ⁢     x   i         }     ⁢           ⁢     s   .           ⁢   t   .           ⁢     y   i           =     x   i             
Subproblem(u): /★ subproblem having only u ★/
 
               Z   u     ⁡     (   λ   )       =       min   u     ⁢     {         -     C   2       ⁢   ξ   ⁢       ∑   i     ⁢       ∑   k     ⁢     u     i   ⁢           ⁢   k             +       ∑   i     ⁢       ∑   k     ⁢       λ     i   ⁢           ⁢   k       ⁢     u     i   ⁢           ⁢   k             +       ∑   i     ⁢       ∑   k     ⁢       λ     i   ⁢               ⁢     u     i   ⁢           ⁢   k               }             
s.t.
 
               ∑   i     ⁢       ϕ     i   ⁢           ⁢   k       ⁢     u     i   ⁢           ⁢   k           ≤     1   ⁢           ⁢     ∀           ⁢   k             1≧u ik ≧0∀i,k 
   This subproblem can be decomposed further for k as below: 
   Subp(u:k): 
   
     
       
         
           
             
               
                 
                   Z 
                   u 
                 
                 ⁡ 
                 
                   ( 
                   
                     λ 
                     : 
                     k 
                   
                   ) 
                 
               
               ⁢ 
               
                 
                   min 
                   
                     0 
                     ≤ 
                     u 
                     ≤ 
                     1 
                   
                 
                 ⁢ 
                 
                   
                     { 
                     
                       
                         ∑ 
                         i 
                       
                       ⁢ 
                       
                         
                           ( 
                           
                             
                               λ 
                               
                                 i 
                                 ⁢ 
                                 
                                     
                                 
                                 ⁢ 
                                 k 
                               
                             
                             + 
                             
                               λ 
                               i 
                             
                             - 
                             
                               
                                 C 
                                 2 
                               
                               ⁢ 
                               ξ 
                             
                           
                           ) 
                         
                         ⁢ 
                         
                           u 
                           
                             i 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             k 
                           
                         
                       
                     
                     } 
                   
                   ⁢ 
                   
                       
                   
                   ⁢ 
                   
                     s 
                     . 
                     t 
                     . 
                     
                         
                     
                     ⁢ 
                     
                       
                         ∑ 
                         i 
                       
                       ⁢ 
                       
                         
                           ϕ 
                           
                             i 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             k 
                           
                         
                         ⁢ 
                         
                           u 
                           
                             i 
                             ⁢ 
                             
                                 
                             
                             ⁢ 
                             k 
                           
                         
                       
                     
                   
                 
               
             
             ≤ 
             1 
           
           ⁢ 
           
               
           
         
       
     
   
   This is a LP problem. We can use Simplex algorithm. However, we propose a simpler algorithm than Simplex algorithm. User i having minimum value of (λ ik +λ i −C 2 ξ) and minimum value of φ ik  should be chosen. If there are some users such that (λ ik +λ i −C 2 ξ)&lt;0, then the user i having minimum value of (λ ik +λ i −C 2 ξ)/φ ik  should be chosen firstly. If there are only users such that (λ ik +λ i −C 2 ξ)≧0, then the user i having minimum value of (λ ik +λ i −C 2 ξ)φ ik  should be chosen firstly. In the constraints, the contribution of the user i chosen is φ ik , and if the right hand side (RHS) is greater than the φ ik  then the RHS should be changed to 1−φ ik  after choosing the user i. The value of u ik  will be equal to 1 or be partial value that is less than 1. These steps are repeated. 
   Subproblem(x): /★ subproblem having only x ★/ 
               Z   x     ⁡     (   λ   )       =       min   x     ⁢     {         K   1     ⁢       ∑   i     ⁢       ∑   k     ⁢     x     i   ⁢           ⁢   k             -       ∑   i     ⁢       ∑   k     ⁢       λ     i   ⁢           ⁢   k       ⁢     M   1     ⁢     x     i   ⁢           ⁢   k               }             
s.t.
 
               ∑   i     ⁢       ω     i   ⁢           ⁢   k       ⁢     x     i   ⁢           ⁢   k           ≤       WC   k     ⁢           ⁢     ∀           ⁢   k             x ik ∈{0,1}∀i,k 
   This problem can be decomposed for k as below: 
   Subp(x:k): 
   
     
       
         
           
             
               Z 
               x 
             
             ⁡ 
             
               ( 
               
                 λ 
                 : 
                 k 
               
               ) 
             
           
           = 
           
             
               
                 min 
                 
                   x 
                   ∈ 
                   
                     { 
                     
                       0 
                       , 
                       1 
                     
                     } 
                   
                 
               
               ⁢ 
               
                 
                   { 
                   
                     
                       ∑ 
                       i 
                     
                     ⁢ 
                     
                       
                         ( 
                         
                           
                             K 
                             1 
                           
                           - 
                           
                             
                               λ 
                               
                                 i 
                                 ⁢ 
                                 
                                     
                                 
                                 ⁢ 
                                 k 
                               
                             
                             ⁢ 
                             
                               M 
                               1 
                             
                           
                         
                         ) 
                       
                       ⁢ 
                       
                         x 
                         
                           i 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           k 
                         
                       
                     
                   
                   } 
                 
                 ⁢ 
                 
                     
                 
                 ⁢ 
                 
                   s 
                   . 
                   t 
                   . 
                   
                       
                   
                   ⁢ 
                   
                     
                       ∑ 
                       i 
                     
                     ⁢ 
                     
                       
                         ω 
                         
                           i 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           k 
                         
                       
                       ⁢ 
                       
                         x 
                         
                           i 
                           ⁢ 
                           
                               
                           
                           ⁢ 
                           k 
                         
                       
                     
                   
                 
               
             
             ≤ 
             
               WC 
               k 
             
           
         
       
     
   
   This problem is a 0-1 knapsack problem since x ik  are 0-1 variables. We can solve this problem by a knapsack algorithm. However, Subp(x:k) is similar to Subp(u:k), so that we use our algorithm developed for Subproblem(u) with slight modification. 
   A.2 Algorithm for Obtaining Feasible Solutions of Problem[Scheduler( )] 
   A heuristic procedure is needed to obtain good solutions that are feasible to Problem[Scheduler( )] by using the solutions of RP. Only one case (i.e., u ik &gt;0 and x ik =0) is infeasible to (10) and only one case (i.e., 
               ∑   k     ⁢     u     i   ⁢           ⁢   k         &gt;   0         
and x i =0) is infeasible to (12) in ( Y , U , X ). It is feasible to (10) and (12) as defined in Appendix C.
 
Appendix C Pseudo-Code
 
procedure Near optimal algorithm for Problem[Scheduler( )]: NOPTALGO( )
 
begin
 while GAP(%)&lt;ε do         set multiplier λ={λ i ,λ ik  ∀i,k} be an initial value   solve Subproblem(y,x) and get the solution  Y ={y i  ∀i} by procedure Subproblem(y,x):   solve Subproblem(u) and get the solution  U ={u ik  ∀i,k} by procedure Subproblem(u):   solve Subproblem(x) and get the solution  X ={x ik  ∀i,k} by procedure Subproblem(x):   set the lower bound be       
             LB   ⁡     (   λ   )       =         Z     y   ∈           ⁢     Y   _         ⁡     (   λ   )       +       Z     u   ∈     U   _         ⁡     (   λ   )       +       Z     x   ∈     X   _         ⁡     (   λ   )       -       ∑     i   =   1       ⁢     λ   i               
get the solution (  Y ,Ū,  X ) of Problem[Scheduler( )] by MAKEFS( )
         set the upper bound be UB=Z y∈  Y ,x∈  X       solve       
             Z   ⁡     (     λ   *     )       =       max   λ     ⁢           ⁢       Z     y   ,   u   ,   x       ⁡     (   λ   )               
for optimizing λ by the subgradient optimization technique
         set GAP(%)=(UB−LB(λ))/UB×100   end {while}   get near optimal solutions U*={u ik *∀i,k}   end {Near optimal algorithm for Problem[Scheduler( )]: NOPTALGO( ) }   procedure Subproblem(x)
 
for k=1,2, . . . , N s  do
 
Define  I   &lt;0   ={i |( K   1 −λ ik   M   1 )&lt;0, i= 1,2, . . .  N   u }and
 
 I   &gt;0   ={i |( K   1 −λ ik   M   1 )≧0, i= 1,2, . . .  N   u }.
 
set RHS=WC and x ik =0 for i∈I
   while (RHS&gt;0) do   if (I &lt;0 ≠Ø) then
 
find  i   0   =argmin{i |( K   1 −λ ik   M   1 )/ω ik   ,i∈I   &lt;0 }
 
if (RHS≧ω i     0     k ) then
   set x i     0     k =1   update I 21 0 =I &lt;0 −{i 0 } and RHS=RHS−ω i     0     k  
 
else
 
 RHS=RHS−ω   i     0     k  
 
end {if}
   else if (I &gt;0 ≠Ø)
 
find  i   0   =argmin{i |( K   1 −λ ik   M   1 )ω ik   , i∈I   &gt;0 }
   if (RHS≧ω i     0     k ) then   set x i     0   k=1   update I &gt;0 =I &gt;0 −{i 0 } RHS=RHS−ω i     0     k      else
 
 RHS=RHS−ω   i     0     k  
 
end {if}
   end {if}   end {while}   end {for k}   get the solution  X ={x ik  ∀i,k}   end {Subproblem(x)}   procedure Subproblem(u)   for k=1,2, . . . , N s  do
 
Define  I   &lt;0   ={i| (λ ik +λ i   −C   2 ξ)&lt;0, i= 1,2,  . . . N   u } and
 
 I   &gt;0   ={i| (λ ik +λ i   −C   2 ξ)≧0 ,i= 1,2, . . .  N   u }.
 
set RHS=1 and u ik =0 for i∈I
   while ((RHS&gt;0) and (I &lt;0 ≠Ø or I &gt;0 ≠Ø)) do   if (I &lt;0 ≠Ø) then
 
find  i   0   =argmin{i |(λ ik +λ i   −C   2 ξ)/φ ik   , i∈I   &lt;0 }
 
if (RHS≧φ i     0     k ) then
   set u i     0     k =1/★ maximally allocated ★/   update 1 &lt;0 =I &lt;0 −{i 0 } and RHS=RHS−φ i     0     k      else if (0&lt;RHS&lt;φ i     0     k ) then   set u i     0     k =RHS/φ i     0     k /★ partially allocated ★/   update I &lt;0 =I &lt;0 −{i 0 } and RHS=RHS−φ i     0     k      end {if}   else if (I &gt;0 ≠Ø)
 
find  i   0   =argmin{i |(λ ik +λ i   −C   2 ξ)φ ik   , i∈I   &gt;0 }
 
if (RHS≧φ i     0     k ) then
   set u i     0     k =1/Ø maximally allocated Ø/   update I &gt;0 =I &gt;0 −{i 0 } and RHS=RHS−φ i     0     k      else if (0&lt;RHS&lt;φ i     0     k ) then   set u i     0     k =RHS/φ i     0     k /Ø partially allocated Ø/   update I &gt;0 =I &gt;0 −{i 0 } and RHS=RHS−φ i     0     k      end {if}   end {if}   end {while}   end {for k}   get the solution  U ={u ik  ∀i,k}   end {Subproblem(u)}   procedure Subproblem(y,x)   for i=1,2, . . . , N u  do   if (−C 1 +K 2 −M 2 λ i )&lt;0 then   set y i =x i =1   else   set y i =x i =0   end {for}   get the solution  Y ={y i  ∀i} and  X ={x i  ∀i}   end {Subproblem(y,x)}   procedure Infeasible solution to feasible solution: MAKEFS( )   begin   get  U ={u ik  ∀i,k},  X =}x ik  ∀i,k} and  X ={x i  ∀i} from RP   set (  IY ,  IU ,  IX )=( Y , U ,  X )   for i=1,2, . . . , N u  do   /★ these solutions are infeasible to (10) ★/   for k=1,2, . . . , N s  do   if (u ik &gt;0 and x ik =0) then   set u ik =0 in  IU  /★ make it be feasible to (10) ★/   end {if}   end {for k}   /★ these solutions are infeasible to (12) ★/   if       
           (         ∑   k     ⁢     u   ik       &lt;   1     )         
then
         set x t =y i =0 in  IX  and  IY  /★ make it be feasible to (12) ★/   else{       
               /     *           ⁢       ∑   k             ⁢     u   ik         ≥     1   ⁢           *     /             Define  K={k|u   ik  in    IU }           if(x i =y i =0) then   set RHS=M 2 −1   else /★ x i =y i =1★/   set RHS=M 2 +1   end {if}   while ((RHS&gt;0)&amp;&amp;(K≠Ø)) do /★ make it be feasible to (12) ★/
 
find  k   0   =argmax{k∈K|u   ik  in    IU } 
   if (RHS≧u ik     0   ) then   save u ik     0    in  IU     else if (0&lt;RHS&lt;u ik     0   ) then   set u ik     0   =RHS   save u ik     0    in  IU     end {if}   update RHS=RHS−u ik     0        set K=K−{k 0 }   end {while}   set u ik =0 for all k≠k 0 s in  IU     end {if}   end {for i}   /★ the following procedure is necessary because there may be unscheduled users, i.e., u ik =0   even though some slot have slack capacity in WC gain and WC number perspective ★/   for k=1,2, . . . , N s  do   calculate       
               s   1     ⁡     (   k   )       =     1   -       ∑   i     ⁢       ϕ   ik     ⁢     u   ik                 
with u ik  in  IU  /★ slack capacity in WC gain at slot k ★/
         calculate       
               s   2     ⁡     (   k   )       =       WC   k     -       ∑   i     ⁢       ω   ik     ⁢     x   ik                 
with x ik  in  IU  /★ slack capacity in WC number at slot k ★/
         end {for k}   find user i such that u ik =0/★ this user may be scheduled if there are slack capacity ★/   Let I m ={i|u ik =0} /★ unscheduled user set ★/
 
for  i∈I   m  do
   if (x i =1) then RHS=M 2 +1/★ in constraint (12) ★/   else RHS=1   end {if}   for k=1,2, . . . , N s  do   if(s 1 (k)&gt;0 and (ω ik &lt;s 2 (k)&gt;0)) then   set u ik =min(s 1 (k)/φ ik ,1) in  IU /★ schedule this user at slot k ★/   set x ik =1 in  IX  /★ due to constraint (10) ★/   update s 1 (k) and S 2 (k)   check       
               ∑   k     ⁢     u   ik       ≤   RHS         
/★ check for constraint (12) ★/
         end {if}   end {for k}   end {for i}   set (  Y , Ū,  X )=(  IY ,  IU ,  IX )/★ (  Y , Ū,  X ) is a feasible to Problem[Scheduler( )] ★/   end {Infeasible solution to feasible solution: MAKEFS( )}