TW201448625A - Robust uplink transmission and joint base station scheduling distributed compression - Google Patents

Abstract

Systems, methods, and/or techniques for providing an uplink with compression are contemplated. Such systems, methods, and/or techniques may include performing joint base station scheduling and distributed compression. The joint base station scheduling and distribution compression may include a two-phase joint selection and compression algorithm. Additionally, the joint base station scheduling and distribution compression may be configured to be performed jointly using sparsity-inducing optimization. A covariance matrix, channel information, a correlation matrix, a linear transformation matrix, a sum-rate metric, a compression strategy, a determined codebook, a sparsity inducing term, a block-coordinate ascent algorithm, one or more test channels, convergence criterion, and the like may also be used to perform the joint base station scheduling and distribution compression.

Description

Robust uplink transmission and joint base station scheduling compression

CROSS-REFERENCE TO RELATED APPLICATIONS [0001] This application claims the benefit of the benefit of the benefit of the benefit of the benefit of the benefit of the benefit of the benefit of the benefit of the present disclosure. U.S. Provisional Patent Application Serial No. 61/659,856, filed on Jun. 14, entitled "SYSTEMS AND METHODS FOR PROVIDING JOINT BASE STATION SCHEDULING AND DISTRIBUTED COMPRESSION," .

Current wireless communication system deployments and networks are facing "bandwidth crises" due to the ever-increasing demands for devices and high data rate applications, such as those performed thereon. The bandwidth can be defined by the network bit rate of a logical or physical communication path in a digital communication system. The bandwidth in bits per second may also refer to the consumption bandwidth corresponding to the throughput achieved, such as the average rate of successful data transmission over the communication path.

Systems, methods, and/or techniques for providing compression to an up chain are contemplated. The system, method, and/or technique can include performing joint base station scheduling and decentralized compression. Joint base station scheduling and distributed compression may include a two-stage joint selection and compression algorithm. Additionally, joint base station scheduling and distributed compression can be configured to be jointly performed using sparsity inducing optimization. Covariance matrix, channel information, correlation matrix, linear transformation matrix, total rate metric, compression strategy, determined codebook, sparse guidance terminology, block coordination ascending algorithm, one or more test channels, convergence criteria, etc. can also be Used to perform joint base station scheduling and distributed compression. For example, according to one embodiment, performing joint base station scheduling and distributed compression may include one or more of the following: receiving at least one of: channel information, calculated for use in two stages of joint selection and compression The test channel and the signal of the selected base station of the algorithm; determining a compression strategy; compressing the received signal according to the compression strategy; and transmitting an index associated with the compressed signal, and the like. Systems, methods, and/or techniques for providing compression to an up chain are contemplated. The system, method, and/or technique includes performing decentralized source coding and compression. Decentralized source coding and compression can be configured to be performed via sequential source coding with boundary information. Additionally, a covariance matrix (eg, which depends on the channel implementation of one or more base stations), channel information, correlation matrices, linear transformation matrices, total rate metrics, compression strategies, determined codebooks, etc. can be used Perform decentralized source encoding and compression. For example, according to one embodiment, performing decentralized source coding and compression may include one or more of: receiving channel information, determined order for compression, correlation matrix and/or signal; providing received signal Derived information; applying a linear transformation matrix to the received signal; adjusting the precision of the predefined quantized codebook based on the linear transformation matrix; performing a componet-wise quantization of the linear transformation output to obtain a predefined a point in the quantized codebook; an index associated with a point in the predefined quantized codebook; a total rate metric calculated from the one or more system parameters; a compression strategy determined; a received signal based on the compression strategy Compress; and so on. Embodiments encompass techniques for performing decentralized codebooks and compression, the techniques can include receiving at least one of: channel information, determined order for compression, correlation matrices, and signals. The technique also includes providing information derived from the received signal. In addition, the technique also includes applying a linear transformation matrix to the received signal. Techniques also include adjusting the accuracy of a predefined quantization codebook based on a linear transformation matrix. Further techniques include performing component form quantization of the linear transform output to obtain points in a predefined quantized codebook. The technique also includes transmitting an index associated with a point in the predefined quantized codebook. And the technique also includes calculating a total rate metric based on one or more system parameters. Moreover, the techniques also include determining a compression strategy and/or compressing the received signal in accordance with the compression strategy. Embodiments encompass techniques for performing joint base station scheduling and distributed compression. One or more techniques may include receiving channel information by the device. The device can communicate with one or more base stations. The techniques can include selecting the one or more base stations by the device. The techniques also include determining, by the device, one or more compression strategies. The techniques also include reporting, by the device, one or more test channels to the one or more base stations. The techniques include performing rate control by the apparatus and/or performing decoding on compressed information received by the apparatus. Embodiments encompass techniques for performing a winding with compression. One or more techniques include receiving channel information by the device. The device can communicate with one or more base stations. The techniques also include determining, by the device, an order of the one or more base stations for compression. The techniques also include reporting, by the apparatus, one or more correlation matrices to the one or more base stations. The techniques also include determining, by the device, one or more compression strategies. The techniques also include reporting, by the device, one or more test channels to the one or more base stations. Moreover, the techniques further include performing rate control by the apparatus and/or performing decoding on compressed information received by the apparatus. Embodiments encompass a device in which the device can communicate with a cloud communication network. The device includes a processor. The processor can be configured to receive channel information from at least the device. The device can communicate with one or more base stations. The processor can be configured to select one or more base stations by the device. The processor can also be configured to determine one or more compression strategies by the device. The processor can be configured to report one or more test channels to the one or more base stations by the device. Moreover, the processor can be configured to perform rate control by the apparatus and/or perform decoding on compressed information received by the apparatus. The present disclosure is provided to introduce a selection of concepts in a simplified form, which is also described in the following detailed description. The Summary is not intended to identify key features or essential features of the claimed subject matter, and is not intended to limit the scope of the claimed subject matter. Further, the claimed subject matter is not limited to any limitation to any or all disadvantages noted in any part of the invention.

Example embodiments are described in detail below with reference to the various drawings. While the invention has been described with respect to the specific embodiments of the invention, it is understood that Systems and methods are provided for performing and/or utilizing a joint base station scheduling and decentralized source compression scheme at a remote radio head connected to a central processor in an uplink transmission scenario. Systems and methods are provided for providing and/or utilizing a decentralized source compression scheme at a remote radio head connected to a central processor in an uplink transmission scenario. The proposed decentralized compression scheme can be used when the remote radio head has imperfect channel state information in the network. FIG. 1A depicts an illustration of an example communication system 100 in which one or more disclosed embodiments may be implemented. Communication system 100 may be a multiple access system that provides content such as voice, material, video, messaging, broadcast, etc. to multiple wireless users. Communication system 100 can enable multiple wireless users to access such content through the sharing of system resources, including wireless bandwidth. For example, communication system 100 can use one or more channel access methods, such as code division multiple access (CDMA), time division multiple access (TDMA), frequency division multiple access (FDMA), orthogonal FDMA (OFDMA). Single carrier FDMA (SC-FDMA) and the like. As shown in FIG. 1A, communication system 100 can include wireless transmit/receive units (WTRUs) 102a, 102b, 102c, and/or 102d (generally or collectively referred to as WTRU 102), Radio Access Network (RAN) 103/104/ 105, core network 106/107/109, public switched telephone network (PSTN) 108, internet 110 and other networks 112, but it will be understood that the disclosed embodiments may encompass any number of WTRUs, base stations , network, and/or network components. Each of the WTRUs 102a, 102b, 102c, and/or 102d may be any type of device configured to operate and/or communicate in wireless communication. By way of example, the WTRUs 102a, 102b, 102c, and/or 102d may be configured to transmit and/or receive wireless signals, and may include user equipment (UE), mobile stations, fixed or mobile subscriber units, pagers, mobile phones, Personal digital assistants (PDAs), smart phones, portable computers, netbooks, personal computers, wireless sensors, consumer electronics, and more. Communication system 100 can also include a base station 114a and a base station 114b. Each of the base stations 114a, 114b can be configured to have a wireless interface with at least one of the WTRUs 102a, 102b, 102c, and/or 102d to facilitate access to one or more communication networks (eg, a core network) Any type of device of 106/107/109, Internet 110 and/or network 112). For example, base stations 114a and/or 114b may be base station transceiver stations (BTSs), Node Bs, eNodeBs, home Node Bs, home eNodeBs, site controllers, access points (APs), wireless routers, and Similar device. Although base stations 114a, 114b are each depicted as a single element, it will be understood that base stations 114a, 114b may include any number of interconnected base stations and/or network elements. The base station 114a may be part of the RAN 103/104/105, which may also include other bases such as a site controller (BSC), a radio network controller (RNC), a relay node, and the like. Station and/or network elements (not shown). Base station 114a and/or base station 114b may be configured to transmit and/or receive wireless signals within a particular geographic area, which may be referred to as cells (not shown). Cells can also be divided into cell sectors. For example, a cell associated with base station 114a can be divided into three sectors. Thus, in one embodiment, base station 114a may include three transceivers, i.e., one transceiver for each sector of the cell. In another embodiment, base station 114a may use multiple input multiple output (MIMO) technology, and thus multiple transceivers for each sector of the cell may be used. The base stations 114a and/or 114b may communicate with one or more of the WTRUs 102a, 102b, 102c, and/or 102d via the null plane 115/116/117, which may be any suitable Wireless communication links (such as radio frequency (RF), microwave, infrared (IR), ultraviolet (UV), visible light, etc.). The null intermediaries 115/116/117 can be established using any suitable radio access technology (RAT). More specifically, as previously discussed, communication system 100 can be a multiple access system and can utilize one or more channel access schemes such as CDMA, TDMA, FDMA, OFDMA, SC-FDMA, and the like. For example, base station 114a and WTRUs 102a, 102b and/or 102c in RAN 103/104/105 may implement a radio technology such as Universal Mobile Telecommunications System (UMTS) Terrestrial Radio Access (UTRA), which may use broadband CDMA (WCDMA) is used to establish the null intermediaries 115/116/117. WCDMA may include, for example, High Speed Packet Access (HSPA) and/or Evolved HSPA (HSPA+). HSPA may include High Speed Downlink Packet Access (HSDPA) and/or High Speed Uplink Packet Access (HSUPA). In another embodiment, base station 114a and WTRUs 102a, 102b and/or 102c may implement a radio technology such as Evolved UMTS Terrestrial Radio Access (E-UTRA), which may use Long Term Evolution (LTE) and/or Or LTE-Advanced (LTE-A) to establish an empty intermediate plane 115/116/117. In other embodiments, base station 114a and WTRUs 102a, 102b and/or 102c may implement such as IEEE 802.16 (ie, Worldwide Interoperability for Microwave Access (WiMAX)), CDMA2000, CDMA2000 1x, CDMA2000 EV-DO, Temporary Standard 2000 (IS) -2000), Temporary Standard 95 (IS-95), Provisional Standard 856 (IS-856), Global System for Mobile Communications (GSM), Enhanced Data Rate GSM Evolution (EDGE), GSM EDGE (GERAN) . For example, the base station 114b in FIG. 1A can be a wireless router, a home Node B, a home eNodeB, or an access point, and any suitable RAT can be used for facilitating, for example, a company, a home, a vehicle, A local area communication connection such as a campus. In one embodiment, base station 114b and WTRUs 102c, 102d may implement a radio technology such as IEEE 802.11 to establish a wireless local area network (WLAN). In another embodiment, base station 114b and WTRUs 102c, 102d may implement a radio technology such as IEEE 802.15 to establish a wireless personal area network (WPAN). In yet another embodiment, base station 114b and WTRUs 102c, 102d may use cell-based RATs (e.g., WCDMA, CDMA2000, GSM, LTE, LTE-A, etc.) to establish picocell cells and femtocells. Yuan (femtocell). As shown in FIG. 1A, the base station 114b can have a direct connection to the Internet 110. Thus, base station 114b does not have to access Internet 110 via core network 106/107/109. The RAN 103/104/105 can communicate with a core network 106/107/109, which can be configured to provide voice, data, application, and/or voice over internet protocol (VoIP) services to Any type of network of one or more of the WTRUs 102a, 102b, 102c, and/or 102d. For example, the core network 106/107/109 can provide call control, billing services, mobile location based services, prepaid calling, internetworking, video distribution, etc., and/or perform advanced security functions such as user authentication. Although not shown in FIG. 1A, it is to be understood that the RAN 103/104/105 and/or the core network 106/107/109 may communicate directly or indirectly with other RANs, which may be used with the RAN 103/ 104/105 the same RAT or a different RAT. For example, in addition to being connected to the RAN 103/104/105, which may employ E-UTRA radio technology, the core network 106/107/109 may also be in communication with other RANs (not shown) that use GSM radio technology. The core network 106/107/109 may also serve as a gateway for the WTRUs 102a, 102b, 102c, and/or 102d to access the PSTN 108, the Internet 110, and/or other networks 112. The PSTN 108 may include a circuit switched telephone network that provides Plain Old Telephone Service (POTS). The Internet 110 may include a global system interconnecting computer networks and devices using public communication protocols such as TCP in the Transmission Control Protocol (TCP)/Internet Protocol (IP) Internet Protocol Suite. , User Datagram Protocol (UDP) and IP. Network 112 may include a wireless or wired communication network that is owned and/or operated by other service providers. For example, network 112 may include another core network connected to one or more RANs that may use the same RAT as RAN 103/104/105 or a different RAT. Some or all of the WTRUs 102a, 102b, 102c, and/or 102d in the communication system 100 may include multi-mode capabilities, ie, the WTRUs 102a, 102b, 102c, and/or 102d may be included for communication over multiple communication links Multiple transceivers that communicate with different wireless networks. For example, the WTRU 102c shown in FIG. 1A can be configured to communicate with a base station 114a that uses a cell-based radio technology and with a base station 114b that uses an IEEE 802 radio technology. FIG. 1B depicts a system block diagram of an example WTRU 102. As shown in FIG. 1B, the WTRU 102 may include a processor 118, a transceiver 120, a transmit/receive element 122, a speaker/microphone 124, a numeric keypad 126, a display/touch pad 128, a non-removable memory 130, and a removable Memory 132, power supply 134, global positioning system chipset 136, and other peripheral devices 138. It is to be understood that the WTRU 102 may include any subset of the above-described elements while remaining consistent with the above embodiments. Moreover, embodiments encompass nodes represented by base stations 114a and 114b and/or base stations 114a and 114b (such as, but not limited to, transceiver stations (BTS), Node B, site controllers, access points (APs), homes Node B, evolved Home Node B (eNode B), Home Evolved Node B (HeNB), Home Evolved Node B Gateway and Proxy Node, etc. may include those described in FIG. 1B and described herein. Multiple or all of the components. The processor 118 can be a general purpose processor, a special purpose processor, a conventional processor, a digital signal processor (DSP), a plurality of microprocessors, one or more microprocessors associated with the DSP core, a controller, Microcontrollers, Dedicated Integrated Circuits (ASICs), Field Programmable Gate Array (FPGA) circuits, any other type of integrated circuit (IC), state machine, etc. Processor 118 may perform signal coding, data processing, power control, input/output processing, and/or any other functionality that enables WTRU 102 to operate in a wireless environment. The processor 118 can be coupled to a transceiver 120 that can be coupled to the transmit/receive element 122. Although processor 118 and transceiver 120 are depicted as separate components in FIG. 1B, it will be appreciated that processor 118 and transceiver 120 can be integrated together into an electronic package or wafer. Transmit/receive element 122 may be configured to transmit signals to or from a base station (e.g., base station 114a) via null intermediaries 115/116/117. For example, in one embodiment, the transmit/receive element 122 can be an antenna configured to transmit and/or receive RF signals. In another embodiment, the transmit/receive element 122 may be a transmitter/detector configured to transmit and/or receive, for example, IR, UV, or visible light signals. In yet another embodiment, the transmit/receive element 122 can be configured to transmit and receive both RF signals and optical signals. It is to be understood that the transmit/receive element 122 can be configured to transmit and/or receive any combination of wireless signals. Moreover, although the transmit/receive element 122 is depicted as a single element in FIG. 1B, the WTRU 102 may include any number of transmit/receive elements 122. More specifically, the WTRU 102 may use MIMO technology. Thus, in one embodiment, the WTRU 102 may include two or more transmit/receive elements 122 (e.g., multiple antennas) for transmitting and receiving wireless signals through the null intermediaries 115/116/117. The transceiver 120 can be configured to modulate a signal to be transmitted by the transmit/receive element 122 and configured to demodulate a signal received by the transmit/receive element 122. As noted above, the WTRU 102 may have multi-mode capabilities. Thus, the transceiver 120 can include multiple transceivers for enabling the WTRU 102 to communicate via multiple RATs, such as UTRA and IEEE 802.11. The processor 118 of the WTRU 102 may be coupled to a speaker/microphone 124, a numeric keypad 126, and/or a display/touch pad 128 (eg, a liquid crystal display (LCD) unit or an organic light emitting diode (OLED) display unit), and may User input data is received from the above device. Processor 118 may also output data to speaker/microphone 124, numeric keypad 126, and/or display/touchpad 128. In addition, processor 118 can access information from any type of suitable memory and store data to any type of suitable memory, such as non-removable memory 130 and/or removable. Memory 132. Non-removable memory 130 may include random access memory (RAM), readable memory (ROM), hard disk, or any other type of memory storage device. The removable memory 132 can include a Subscriber Identity Module (SIM) card, a memory stick, a secure digital (SD) memory card, and the like. In other embodiments, the processor 118 can access data from memory that is not physically located on the WTRU 102 and located on a server or home computer (not shown), and store the data in the memory. The processor 118 can receive power from the power source 134 and can be configured to distribute power to other components in the WTRU 102 and/or to control power to other elements in the WTRU 102. Power source 134 can be any device suitable for powering WTRU 102. For example, the power source 134 may include one or more dry cells (nickel cadmium (NiCd), nickel zinc (NiZn), nickel hydrogen (NiMH), lithium ion (Li-ion), etc.), solar cells, fuel cells, and the like. The processor 118 may also be coupled to a GPS chipset 136 that may be configured to provide location information (eg, longitude and latitude) regarding the current location of the WTRU 102. The WTRU may receive location information from the base station (e.g., base station 114a, 114b) plus or in place of GPS chipset 136 information through null intermediaries 115/116/117, and/or based on two or more neighboring base stations The timing of the received signal determines its position. It is to be understood that the WTRU 102 can obtain location information by any suitable location determination method while consistent with the embodiments. The processor 118 can also be coupled to other peripheral devices 138, which can include one or more software and/or hardware modules that provide additional features, functionality, and/or wireless or wired connections. For example, peripheral device 138 may include an accelerometer, an electronic compass (e-compass), a satellite transceiver, a digital camera (for photo or video), a universal serial bus (USB) port, a vibrating device, a television transceiver, and With headphones, Bluetooth R module, FM radio unit, digital music player, media player, video game player module, Internet browser and so on. FIG. 1C depicts a system block diagram of RAN 103 and core network 106 in accordance with an embodiment. As described above, the RAN 103 can communicate with the WTRUs 102a, 102b, and/or 102c over the null plane 115 using UTRA radio technology. The RAN 103 can also communicate with the core network 106. As shown in FIG. 1C, the RAN 103 may include Node Bs 140a, 140b, and/or 140c, wherein each of the Node Bs 140a, 140b, and/or 140c may include one or more transceivers that pass through the null plane 115 To communicate with the WTRUs 102a, 102b, and/or 102c. Each of Node Bs 140a, 140b, and/or 140c may be associated with a special unit (not shown) within the scope of RAN 103. The RAN 103 may also include RNCs 142a and/or 142b. It should be understood that the RAN 103 may include any number of Node Bs and RNCs while still being consistent with the implementation. As shown in FIG. 1C, Node Bs 140a and/or 140b can communicate with RNC 142a. Additionally, Node B 140c can communicate with RNC 142b. Node Bs 140a, 140b, and/or 140c can communicate with corresponding RNCs 142a, 142b via the Iub interface. The RNCs 142a, 142b can communicate with each other through the Iur interface. The RNCs 142a, 142b can be configured to control respective Node Bs 140a, 140b, and/or 140c to which they are connected, respectively. In addition, the RNCs 142a, 142b can be configured to implement or support other functions, such as outer loop power control, load control, admission control, packet scheduling, handover control, macro diversity, security functions, data encryption, and the like, respectively. The core network 106 shown in FIG. 1C may include a media gateway (MGW) 144, a mobile switching center (MSC) 146, a Serving GPRS Support Node (SGSN) 148, and/or a Gateway GPRS Support Node (GGSN) 150. . While each of the above elements is described as being part of the core network 106, it should be understood that any of these elements may be owned and/or operated by entities other than the core network operator. The RNC 142a in the RAN 103 can be connected to the MSC 146 in the core network 106 via the IuCS interface. The MSC 146 can be connected to the MGW 144. MSC 146 and MGW 144 may provide WTRUs 102a, 102b, and/or 102c with access to a circuit-switched network (e.g., PSTN 108) to facilitate communication between WTRUs 102a, 102b, and/or 102c and conventional landline communication devices. Communication. The RNC 142a in the RAN 103 can also be connected to the SGSN 148 in the core network 106 via the IuPS interface. The SGSN 148 can be connected to the GGSN 150. SGSN 148 and GGSN 150 may provide WTRUs 102a, 102b, and/or 102c with access to a packet switched network (e.g., Internet 110) to facilitate communication between WTRUs 102a, 102b, and/or 102c and IP-enabled devices. Communication. As noted above, the core network 106 can also be connected to other networks 112, which can include other wired or wireless networks that are owned and/or operated by other service providers. FIG. 1D depicts a system diagram of RAN 104 and core network 107 in accordance with an embodiment. As described above, the RAN 104 can communicate with the WTRUs 102a, 102b, and/or 102c over the null plane 116 using E-UTRA radio technology. The RAN 104 can also communicate with the core network 107. The RAN 104 may include eNodeBs 160a, 160b, and/or 160c, although it should be understood that the RAN 104 may include any number of eNodeBs while still being consistent with the embodiments. The eNodeBs 160a, 160b, and/or 160c may each include one or more transceivers that communicate with the WTRUs 102a, 102b, and/or 102c over the null plane 116. In an embodiment, the eNodeBs 160a, 160b, and/or 160c may use MIMO technology. Thus, for example, the eNodeB 160a can use multiple antennas to transmit wireless signals to and receive wireless information from the WTRU 102a. Each of the eNodeBs 160a, 160b, and/or 160c may be associated with a particular unit (not shown) and may be configured to process radio resource management decisions, handover decisions, users in the uplink and/or downlink schedule. As shown in FIG. 1D, the eNodeBs 160a, 160b, and/or 160c can communicate with each other through the X2 interface. The core network 107 shown in FIG. 1D may include a mobility management gateway (MME) 162, a service gateway 164, and a packet data network (PDN) gateway 166. While each of the above elements is described as being part of core network 107, it should be understood that any of these elements may be owned and/or operated by entities other than the core network operator. The MME 162 may be connected to each of the eNodeBs 160a, 160b, and/or 160c in the RAN 104 via the S1 interface and may act as a control node. For example, MME 162 may be responsible for authenticating users of WTRUs 102a, 102b, and/or 102c, bearer activation/deactivation, selecting a particular service gateway during initial connection of WTRUs 102a, 102b, and/or 102c, and the like. The MME 162 may also provide control plane functionality for the exchange between the RAN 104 and a RAN (not shown) that uses other radio technologies, such as GSM or WCDMA. Service gateway 164 may be connected to each of eNodeBs 160a, 160b, and/or 160c in RAN 104 via an S1 interface. The service gateway 164 can typically route and forward user data packets to the WTRUs 102a, 102b, and/or 102c, or route and forward user data packets from the WTRUs 102a, 102b, and/or 102c. The service gateway 164 may also perform other functions, such as anchoring the user plane during inter-eNode B handover, triggering paging when the downlink data is available to the WTRUs 102a, 102b, and/or 102c, for the WTRUs 102a, 102b, and/or 102c Manage and store contexts and more. Service gateway 164 may also be coupled to PDN gateway 166, which may provide WTRUs 102a, 102b, and/or 102c with access to a packet switched network (e.g., Internet 110) to facilitate WTRU 102a. Communication between 102b, and/or 102c and the IP-enabled device. The core network 107 can facilitate communication with other networks. For example, core network 107 may provide WTRUs 102a, 102b, and/or 102c with access to a circuit-switched network (e.g., PSTN 108) to facilitate WTRUs 102a, 102b, and/or 102c and conventional landline communication devices. Communication between. For example, core network 107 may include, or may communicate with, an IP gateway (eg, an IP Multimedia Subsystem (IMS) service) that interfaces between core network 107 and PSTN 108. In addition, core network 107 may provide access to WTRUs 102a, 102b, and/or 102c to network 112, which may include other wired or wireless networks that are owned and/or operated by other service providers. FIG. 1E depicts a system diagram of RAN 105 and core network 109 in accordance with an embodiment. The RAN 105 can communicate with the WTRUs 102a, 102b, and/or 102c over the null plane 117 using IEEE 802.16 radio technology. As will be discussed further below, the communication links between the different functional entities of the WTRUs 102a, 102b, and/or 102c, the RAN 105, and the core network 109 may be defined as reference points. As shown in FIG. 1E, the RAN 105 may include base stations 180a, 180b, and/or 180c and ASN gateway 182, although it should be understood that the RAN 105 may include any number of base stations and ASN gateways while still with implementations. be consistent. Base stations 180a, 180b, and/or 180c are associated with special units (not shown) in RAN 105, respectively, and may include one or more transceivers, respectively, that communicate with WTRUs 102a, 102b via null intermediaries 117. And/or 102c communicate. In one embodiment, base stations 180a, 180b, and/or 180c may use MIMO technology. Thus, for example, base station 180a can use multiple antennas to transmit wireless signals to, and receive wireless information from, WTRU 102a. Base stations 180a, 180b, and/or 180c may also provide mobility management functions such as handover triggering, tunnel establishment, radio resource management, traffic classification, quality of service (QoS) policy enforcement, and the like. The ASN gateway 182 can act as a traffic aggregation point and can be responsible for paging, caching, routing to the core network 109 of the user profile, and the like. The null interfacing plane 117 between the WTRUs 102a, 102b, and/or 102c and the RAN 105 may be defined as an Rl reference point that implements the IEEE 802.16 specification. In addition, each of the WTRUs 102a, 102b, and/or 102c can establish a logical interface (not shown) with the core network 109. The logical interface between the WTRUs 102a, 102b, and/or 102c and the core network 109 can be defined as an R2 reference point that can be used for authentication, authorization, IP host configuration management, and/or mobility management. The communication link between each of the base stations 180a, 180b, and/or 180c may be defined to include an agreed R8 reference point for facilitating data transfer between the WTRU and the base station. The communication link between the base stations 180a, 180b, 180c and the ASN gateway 182 can be defined as an R6 reference point. The R6 reference point may include an agreement for facilitating mobility management based on mobile events associated with each of the WTRUs 102a, 102b, and/or 102c. As shown in FIG. 1E, the RAN 105 can be connected to the core network 109. The communication link between the RAN 105 and the core network 109 can be defined, for example, as an R3 reference point that includes protocols for facilitating data transfer and mobility management capabilities. Core network 109 may include a Mobile IP Home Agent (MIP-HA) 184, Authentication, Authorization, Accounting (AAA) service 186, and gateway 188. While each of the above elements is described as being part of the core network 109, it should be understood that any of these elements may be owned and/or operated by entities other than the core network operator. The MIP-HA may be responsible for IP address management and may cause the WTRUs 102a, 102b, and/or 102c to roam between different ASNs and/or different core networks. The MIP-HA 184 may provide the WTRUs 102a, 102b, and/or 102c with access to a packet switched network (e.g., the Internet 110) to facilitate communication between the WTRUs 102a, 102b, and/or 102c and the IP enabling device. Communication. The AAA server 186 can be responsible for user authentication and support for user services. Gateway 188 can facilitate interaction with other networks. For example, gateway 188 can provide WTRUs 102a, 102b, and/or 102c with access to a circuit-switched network (e.g., PSTN 108) to facilitate communication between WTRUs 102a, 102b, and/or 102c and conventional landline communication devices. Communication. In addition, gateway 188 can provide access to network 112 to WTRUs 102a, 102b, and/or 102c, which can include other wired or wireless networks that are owned and/or operated by other service providers. Although not shown in FIG. 1E, it should be, can and/or will be understood that the RAN 105 can be connected to other ASNs and the core network 109 can be connected to other core networks. The communication link between the RAN 105 and other ASNs may be defined as an R4 reference point, which may include protocols for coordinating the mobility of the WTRUs 102a, 102b, and/or 102c between the RAN 105 and other ASNs. The communication link between core network 109 and other core networks may be defined as an R5 reference point, which may include protocols for facilitating interworking between the local core network and the visited core network. Embodiments recognize that current deployments of cell systems face "bandwidth crises" due to increased demand for high data rate applications. In order to alleviate the "bandwidth crisis", the cloud radio access network can be ported at the base station (BS) baseband processing or integrated into the "cloud" or the center of the Internet to which the BS is connected via the backhaul link. An efficient implementation of network MIMO, which is used when the unit is used, thereby simplifying the deployment and management of the BS and reducing BS energy consumption, can be enabled. On the uplink of the cloud radio access network, the BS can operate to relay "soft" information to the terminals of the cloud decoder associated with the received baseband signal. It may be that the signals received at different BSs may be correlated for other reasons, and a decentralized source coding strategy may be used. Embodiments recognize that decentralized source coding performance is sensitive to defects in received signals that can be compressed. Embodiments also recognize that efficient operation of a cloud including a portion of a radio access network may include parsimonious use of the base station, where base station energy consumption tends to be in a related contribution to the overall energy expenditure for the network. In order to alleviate the "bandwidth crisis", the cloud radio access network can be ported at the base station (BS) baseband processing or integrated into the "cloud" or the central unit in the network to which the BS is connected via the backhaul link. An efficient implementation of network MIMO, which is used to simplify deployment and management of the BS and reduce BS energy consumption, can be enabled. On the uplink of the cloud radio access network, the BS can operate to relay "soft" information to the terminals of the cloud decoder associated with the received baseband signal. Since signals received at different BSs can be correlated, a decentralized source coding strategy can be used. Unfortunately, decentralized source codeability tends to be sensitive to defects in the compressed received signal. Additionally, efficient operation of the cloud including the portion of the radio access network may include thrift use of the base station, which tends to be in a related contribution to the overall energy expenditure for the network. As described herein, systems and/or methods for distributed source coding and compression schemes for uplinks of cloud radio access networks (eg, which use associations between signals received at different base stations (BSs) Can be extended to include scheduling (eg, BS scheduling) in which multiple multi-antenna base stations (BSs) can be connected to the central unit via a capacity-limited back-load link (eg, as shown in FIG. 2) Cloud decoder). In this scenario, for example, the number of active base stations (BSs) can be reduced and thus the energy efficiency of the mobile network can be improved or improved. The system and/or method may include jointly performing the optimized optimization problem of compression and scheduling by introducing sparse boot terminology into the target function. Additionally or alternatively, the system and/or method may provide an iterative method or algorithm that is used to converge to a local optimization point. As described herein, distributed compression for uplinks of cloud radio access networks is provided (eg, using decentralized source encoded compression in the presence of multi-antenna BSs in a manner that focuses on robustness and efficiency), where many Multiple multi-antenna base stations (BSs) may be connected to the central unit via a capacity limited backhaul link (eg, a cloud decoder as shown in FIG. 2). Since signals received at different BSs can be correlated, a decentralized source coding strategy can potentially be beneficial and can be implemented via sequential source coding with boundary information. For compression using boundary information, the available compression strategy can be used based on criteria that maximize the achievable rate or minimize the mean square error. In each case, one or more or each BS may use the information about the particular covariance matrix to achieve the advantages of decentralized source coding. Since the covariance matrix may be implemented, among other reasons, depending on the channel corresponding to other BSs, a robust compression method may be provided and/or used, wherein the information about the covariance available at each BS may be, for example, not perfect. In some embodiments, the method can be formulated using a deterministic worst case approach and a scheme to achieve a stable point can be provided and/or used. According to example embodiments, the systems and/or methods described herein may be applicable to and use UEs, eNBs, Het-Nets, remote radio heads, etc., across L1-L2. Additionally, the same symbol of the probability mass function (pmf) and the probability density function (pdf), iep(x), indicating the distribution pmf or pdf of the random variable X that can be used. Similar symbols can also be used for union and conditional distribution. In example embodiments, the schemes, equations, and/or algorithms disclosed herein may be based on two (eg, unless otherwise specified). Additionally, given vector x = [x ₁ , x ₂ .., xn]TFor subsetsSЄ{1,2, ..., n}xSCan be defined to include items with i in any order_iThe vector S of x. symbol_xΣ can also be used for the correlation matrix of the random vector x, ie_xΣ = E[xx†];_XyΣ can represent the cross-correlation matrix_XyΣ = E[xy†];and_xΣ| Can represent the "conditional" correlation matrix for a given y x, ie_xΣ| = Σ_x −Σ_XyΣ− _y ¹Σ† _Xy. In one embodiment, the symbolⁿH can representn × nHermitian matrix set. System models can be provided and/or used as described herein. For example, including the total _B NA cell cluster of BSs may be provided and/or used (e.g., as shown in FIG. 2), wherein one or more or each BS may be an MBS or an HBS. _M N ActivitiesMSIt can also be provided and/or used. Figure 2 depicts an example implementation of a cloud with a cloud radio access network of one or more base stations (BS) such as Home BS (HBS) and Macro BS (MBS). In one or more embodiments, the set of BSs can be represented as_BN = {1}_B, ..., N. Additionally, one or more, or each ith BS can pass capacity_iC's limited capacity link is connected to the cloud decoder and can have_B,in antennas, and one or more, or each MS may have_{M, i} n antennas. The embodiments described herein may be described or represented as examples with respect to the winding. Will HIjDefined as between the jth MS and the ith BS_B,in×nM,jThe channel matrix, the entire channel matrix to BS i can be described as_M×n_B,in matrix: Hi= [Hi ₁ ···HiNM] (1) If clustered_MThe N MSs are synchronized, and at the discrete time channel (c.u.) of the given slot, the signal received by the i-th BS can be given as:yi= Hix + zi.(2) In (2), the vector x = [x₁ † ···x_NM †]†Can be ×_MN1 and may be a symbol vector (e.g., a special cluster) transmitted by the MS in the cluster. Noise vector ziCan depend on i and be distributed as zi ~ CN(0,I), fori Є {1, ..., NB}. In one or more embodiments, the noise covariance matrix can be selected as an identity without loss of generality because the received signal can be whitened by the BS. Additionally or alternatively, the channel matrix HiConstant in each time slot. Using standard random coding arguments, the coding strategy used by the MS in each time slot can be used and/or used on the transmitted signal.p(x), the transmitted signal can be factorized into(3) This is because signals transmitted by different MSs can be independent.

According to an example embodiment, distribution The parameters of the modulation and channel coding scheme employed, such as constellation, transmit power, beamforming, and (vs.) space-time coding, distance between codebooks, etc., may be represented (eg, in a concise manner) Wait. Signal x can be discrete (eg, taken from a discrete constellation). For example, if Signal is transmitted, signal Can be Expressed Discrete values are obtained in the constellation diagram. When special (eg, good) channel coding is used, such as turbo code or low density parity check (LDPC) code, if communication occurs on a Gaussian channel, the distribution Can be close to the Gaussian distribution. In an embodiment, the signal distribution transmitted by the ith MS Can be for a given covariance matrix Given .

The BS can communicate with the cloud by, for example, providing the cloud with soft information derived from the received signal. In one or more embodiments, the MS can provide, use, and/or utilize a compression policy that does not involve knowing one or more codebooks such as constellation atlas, MCS level, and the like. Using conventional rate-distortion theory, the compression strategy for the ith BS can be tested by the channel Providing and/or defining, the test channel Describes the signal to be compressed, y _i , and the description it will be passed to the cloud The relationship between.

As described herein (eg, in lemma 2), each BSi can be linearly transformed Applied to the received signal (for example, first) and then can perform a linear transformation output Component form quantization to obtain a predefined quantized codebook The point in the middle. After BSi can pass capacity Return link transmission and codebook Generate an index associated with the point to the cloud decoder. Quantization effects can be increased by adding Gaussian noise according to the implementation The way to be equivalently patterned. Furthermore, because component form quantization can be provided and/or used, the correlation matrix of the noise can be an identity matrix. The accuracy of the quantized codebook can be adjusted by adjusting the linear transformation matrix To control. According to an example embodiment, a matrix Can include , where a single matrix U can act as a conditional uncorrelated output element (eg, also known as a conditional KLT) and the precision of each compressed output element can pass through a diagonal matrix To control, which means if , then Elements can be compressed with better precision than the mth element. According to an embodiment, the compression can be limited to each received symbol Bit. Thereby, the size of the quantized codebook can be satisfied . The cloud can be targeted description of The signal x of the MS is jointly decoded. From standard signal-theoretical considerations, the total achievable rate can be given as:

Once targeted Linear transformation matrix Can be determined, total rate metrics (eg, mutual information) The system parameters can then be calculated as functions, such as SNR, input distribution And/or channel matrix And (possibly in some embodiments) a linear transformation matrix, . From the total rate generated, the codebook size employed by the MS can be determined (e.g., it can be assumed that continuous decoding at the cloud decoder in one or more embodiments).

Due to signals measured by different BSs Can be correlated, decentralized source coding schemes or techniques have the potential to improve description Quality. Specifically, if a compression test channel is given , if or when the capacity C _i satisfies the following conditions, the description Can be restored in the cloud.

among them According to an example embodiment, y _s may include And similarly targeted as well as Each y _i .

The optimization may be limited to (6) and the optimization space may include a BS permutation π. In some embodiments, question (7) is still complicated. For this scenario, the greedy method in Algorithm 1 can be used or applied to the selection of the arrangement π while making the test channel at one or more or each stage of the greedy method or algorithm Most optimized. The greedy algorithm may be based on a chained rule for mutual information that may cause or allow the total rate (4) to be expressed for the permutation π of the set {1, . . . , NB} as:

. Because of this algorithm, the arrangement Can be obtained and is a viable test channel (for example, from the sense that the limit (5) is satisfied). In one or more embodiments, the compression based on (9) in Algorithm 1 can be referred to as, for example, maximum rate compression.

The implementation of the greedy algorithm in Algorithm 1 can solve the problem (9) for one or more, or each ith, BS for a given order π. Furthermore, the problem (9) can be solved at the cloud decoder, where the cloud decoder then transmits the result to the ith BS. As described herein, in some embodiments, the cloud center can know the channel matrix H _i , i=1, . . . , N _B . Alternatively or additionally, once the order π can be determined by the cloud, the problem (9) can be resolved at one or more, or each ith BS. In some embodiments, the cloud can transmit some information to, for example, one or more, or each BS, as described herein.

In one or more embodiments, the maximum rate or maximized total rate as described herein can be compressed. Assume that the cloud decoder has boundary information And Next, it is possible to provide an optimized compression test channel for the i-th BS. Problem (9) of the program. The random vector involved in question (9) can satisfy the Markov chain. .

For ordering π and test channels The chosen algorithm 1 or greedy method can be provided or defined below. First, the set S can be initialized to an empty set, ie . Thereafter, for j =1, . . . , N _B , the following can be performed.

One or more, or each ith BS, can evaluate the channel by solving the following problems

In one or more embodiments, the optimal value of the problem can be expressed as And the optimized test channel is represented as . After that, with the maximum optimization value Can be selected as And added to the collection S such as And the set π *( j )= i .

In one or more embodiments, compression techniques and/or schemes based on MMSE compression may be provided and/or used, for example, based on a Gaussian input vector x , an optimized test channel for problem (9) It can be given by the Gaussian distribution mentioned in the following lemma (for example, Lemma 2). Optimized test channel for Gaussian distribution x Can be described as:

Where A _i is a matrix to be calculated and q _i ~CN ( O , I ) may be compressed noise, wherein the compressed noise is independent of x and z _i . And, define Problem (9) can be re-expressed as

Where the function Can be defined as:

as well as

In one or more embodiments, And .

Using the compression model (10), the boundary information available to the cloud decoder in the ith phase of the greedy algorithm Can be given as:

among them , j =1 , ... , i -1.

In one or more embodiments, minimum mean square error (MMSE) compression may be provided and/or used. For example, an MSE-based approach to question (9) can be provided and/or used. In particular, techniques or minimum MSE (MMSE) techniques aimed at minimizing MSE are provided. In some embodiments, the MMSE technique can be distinguished from maximum rate compression in terms of criteria that optimize the test channel (and possibly only in some embodiments). Thus, the greedy algorithm in Algorithm 1 can operate using MMSE compression and the difference from the maximum rate exists in the fact that the problem (9) can be replaced with the problem of the MMSE criteria described herein.

In one or more embodiments, direct MMSE can be provided and/or used with Borderless Information (NSI). For example, in some scenarios, compression can be done by making the received signal The MSE on it is minimized and ignores that the cloud decoder can have boundary information The purpose of the facts is to achieve or execute. This can produce the following compression criteria:

Where g (.) can be a function, such as an MMSE estimate that is affected at the decoder boundary. In one or more embodiments, the limitation in question (17) may involve The mutual information of the condition, because the boundary information at the cloud may not be leveraged.

In one or more embodiments, a Borderless Information (NSI) MMSE can be provided and/or used. For example, by considering the fact that the recovery of the clouds may not x y _i compression of interest can be obtained (e.g., potentially better compression), and thus using the MMSE criterion:

Where the function g (.) can have the same interpretation as previously described. The policy may be "indirect" the MMSE, but because it is compressed to provide x y _i is described as a target, wherein the x-described may not be measured directly at the BS.

By utilizing the boundary information An improved version of the direct and/or indirect MMSE strategy discussed above may be obtained, wherein the boundary information Such as can be available at the cloud decoder. Boundary information Can be used for various purposes such as, in order to reduce to Compression rate and / or description at the cloud decoder And border information Both are functions And get the final estimate.

In one or more embodiments, a direct MMSE with boundary information (SI) can be provided and/or used. For example, a direct MMSE method using boundary information can generate criteria:

Alternatively or additionally, an indirect MMSE with boundary information (SI) may be provided and/or used. For example, the indirect MMSE method can produce guidelines:

For example, in the proposition (e.g., Proposition 2), the MMSE-optimized compression standards listed above may be used as given by A _i (10), whereby And

among them For direct methods and For indirect methods (where ) and . The number of rows of the matrix U can be a covariance matrix for the case of NSI and SI, respectively. with Feature vector corresponding to the sorted feature value And

Where μ can be the condition thus satisfied .

In an embodiment, the argument of the proposition (eg, Proposition 2) and/or support for the proposition may follow a conditional KLT result.

In one or more embodiments described herein, robustness optimization compression can be provided and/or used. For example, as described above, the optimal compression caused by the scheme of problem (9) at the ith BS may depend on the covariance matrix in (13) of the vector of the transmitted signal. Where the vector of transmitted signals may be in a compressed version of the signal received by the BS in set S As a condition. Can be used at the cloud decoder. Usually, assuming a matrix Known (e.g., very well understood) at the ith BS is practical because it depends on the channel matrix of all BSs in set S (e.g., as shown in (13)). Thus, by assuming that the ith BS has an association to the actual matrix Matrix Estimated way to provide and/or use a robust version of the optimization problem (9) because

among them A deterministic Hermitian matrix that models the estimation error can be built. Error matrix according to an embodiment Belong to (for example, referred to as belonging to) collection , the set pair and matrix The non-deterministic establishment model at the relevant i-th BS.

In some embodiments, it may be for the purpose of defining a non-deterministic set, among other reasons One or more bounds can be applied to the matrix The eigenvalue and/or the eigenvector is given on a given measurement. According to the mutual information in (11) Can be expressed as Observation, of which And The uncertainty is The feature values are constrained.

This can be equated with constrained matrices in some constant vanes Norm. Specifically, in an embodiment, the non-deterministic set Hermitian matrix Collection of conditions of with Can be kept against the matrix Given lower and higher constraints on the eigenvalue .

In this embodiment (eg, under this model), the problem of deriving an optimized robust compression strategy can be formulated as:

thus And

In some embodiments, the problem (26) may not be convex and the closed-loop form of the scheme may be prohibitive. The next theorem derives a scheme for the KKT condition for problem (26), which can be referred to as a stationary point.

For example, in the theorem (eg, Theorem 1), the stable point for the problem (26) can be found as (10) by the matrix A _i , thereby Can be given as (15), where matrix U can be decomposed from eigenvalues Obtained and diagonal elements It can be calculated by solving the following mixed integer continuous problem:

St0<μ<1, , , where the function with Can be defined as:

as well as

and with ; and discrete sets Can be defined as:

Where Q ₁ and S ₁ are given as:

And

One or more embodiments encompass joint base station selection and compression (eg, decentralized compression) that may be provided and/or used as described via sparse guidance optimization. For example, in order to operate the network effectively, a subset of BSs available for N _B It can be passed to the cloud decoder in a given time slot. For example, these embodiments may be used when different BSs can share the same backhaul resources or energy consumption and green networking is important. Thus (for example, under this assumption), the system design can provide and/or require the selection of subset S as well as Compressed test channel s Choice. In one or more embodiments, the BS selects an exhaustive search that can use exponential complexity in the number N _B of BSs. Thus, in some embodiments, an efficient method of adding sparse boot terminology to a target function can be provided and/or used.

For example, the cost q _{i of} each spectral unit resource (eg, used per discrete time channel) can be associated to the i-th BS, which can measure the relative of each spectrum resource that initiates the i-th BS on each bit of revenue. cost. To address this idea, a single cell with one MBS and N _B -1 HBS as shown in Figure 3 can be provided and/or used. The HBS (eg, shown) may share the total backhaul capacity C _H for the cloud decoder as if the HBS were delivered to the cloud decoder via a shared wireless link. If or when MBS is active, a subset of HBS Can be scheduled into additional information such as Provides a cloud decoder at a given total loadback limit C _H . In one or more embodiments, the approaches presented herein can also be generalized to multiple complex systems having multiple cells.

In one or more embodiments, with It may represent a set including MBS and HBS, respectively. If or when the Gaussian test channel (10) has a given covariance matrix Used at each ith BS, the joint problem of HBS selection and compression optimized via sparse guidance can be formulated as:

Where 1 (.) is an indicator function that takes a value of 1 otherwise 0 if the argument state is true, and for simplicity q ₂ =...= q _NB = q _H . In (34), It may be a condition such as considering the fact that the MBS is an activity. According to an embodiment, the second item in the object of question (34) may be a vector of Norm. If the cost q _H is large enough, this item can make the scheme some matrix Set to 0 to keep the corresponding i-th HBS as non-boot. In order to avoid The non-smothness caused by the norm can be achieved by using the same vector Norm replacement The norm _- how modifies the problem (34). Question (34) can thus be re-formulated as follows:

among them And .

According to this formula, a two-stage approach to joint HBS selection and compression problems can be provided and/or used (eg, as shown in Algorithm 2). For example, in the first phase, a block coordinated ascending algorithm can be implemented to solve the problem (35). Therefore, you can get non-zero Subset of HBS . In the second phase, the block coordination ascending algorithm can be set by In the subset Run on it, all of them And q _H =0. In some embodiments, the second phase can be used to improve the test channel obtained in the first phase. For example, a two-stage algorithm for joint HBS selection and compression may be used by the entire communication stream such as shown in FIG. 4 (eg, at 6004 for HBS and 6006 for compression strategy).

For example, a two-stage joint HBS selection and compression algorithm (eg, Algorithm 2) may include or perform the following. In phase 1, problem (35) can be solved or calculated via a block coordinated ascending algorithm. For example, you can initialize n=0 and . for Can be updated to the following problem scenario.

In one or more embodiments, if the convergence criteria are not met, such as the update ) can be repeated and otherwise can be stopped (eg, the algorithm can be terminated). Once the algorithm is terminated, the obtained Can be expressed as , where i=2,...,N _B and Can be set.

In stage 2, use q _H =0 and in the set In the HBS, the block coordinated ascending algorithm can be applied to the problem (35).

Figure 4 depicts an example technique for base station (BS) selection and compression consistent with an embodiment (e.g., it may provide and/or use algorithm 2). As shown in FIG. 4, at 6002, channel information can be received and/or acquired. One or more HBSs can be selected at 6004. At 6006, a compression strategy or scheme can be determined (eg, a test channel can be calculated), and at 6008, a compressed test channel can be reported or sent. Rate control can then be performed at 6010 (eg, the total rate can be calculated, the codebook can be determined, and/or the codebook index can be provided or reported). At 6012, uplink communication can be provided (eg, signals can be generated from a codebook and associated signals can be provided and/or received). At 6014, compression can be performed (eg, a signal such as a received signal can be compressed according to a test channel). Thereafter, at 6016, decoding can be performed (eg, the codebook-based generated signal can be (eg, jointly) decoded based on a signal such as a description or compression).

In one or more embodiments, the problem (36) of the proposed algorithm (eg, algorithm 2) can be addressed as described herein. When other variables When determined as a value obtained from a previous iteration, the scheme or implementation may correspond to an update . The global maximum of the problem (36) can be obtained as shown below (for example, in Theorem 2).

For example (like according to Theorem 2), the solution to problem (36) can be given by matrix A _i from (10) ,among them Matrix Can be given as:

among them . The diagonal elements α ₁ , . . . , α _nB,i can be obtained as ,among them

Where l = 1, ..., n _{B, i} and q _H ' = log _e 2. q _H . The Lagrangian multiplier μ* can be obtained as follows. in case ,among them Can be given as:

Then μ*=0; otherwise μ* can be a unique value μ>0, thus ,among them .

Although the features and elements of the present invention have been described above in terms of specific combinations, those skilled in the art can understand that each feature or element can be used alone or in the absence of other features and elements. Any other combination of features and elements of the invention is used in various situations. Furthermore, the embodiments provided by the present invention can be implemented in a computer program, software or firmware executed by a computer or processor, wherein the computer program, software or firmware is embodied in a computer readable storage medium. Examples of computer readable media include electronic signals (transmitted over a wired or wireless connection) and computer readable storage media. Examples of computer readable storage media include, but are not limited to, read only memory (ROM), random access memory (RAM), scratchpad, cache memory, semiconductor memory devices, magnetic media (eg, internal hard Disc or removable disk), magneto-optical media, and optical media such as CD-ROMs and digital versatile discs (DVDs). The software related processor can be used to implement a radio frequency transceiver for use in a WTRU, UE, terminal, base station, RNC, or any host computer.

100. . . Communication Systems

102, 102a, 102b, 102c, 102d. . . Wireless transmit/receive unit (WTRU)

103, 104, 105. . . Radio access network (RAN)

106, 107, 109. . . Core network

108. . . Public switched telephone network (PSTN)

110. . . Internet

112. . . Other network

114a, 114b, 180a, 180b, 180c. . . Base station (BS)

115, 116, 117. . . Empty intermediary

118. . . processor

120. . . transceiver

122. . . Transmitting/receiving element

124. . . Speaker/microphone

126. . . Numeric keypad

128. . . Display/touchpad

130. . . Non-removable memory

132. . . Removable memory

134. . . power supply

136. . . Global Positioning System (GPS) chipset

138. . . Peripherals

140a, 140b, 140c. . . Node B

142a, 142b. . . Radio Network Controller (RNC)

144. . . Media Gateway (MGW)

146. . . Mobile switching center (MSC)

148. . . Serving GPRS Support Node (SGSN)

150. . . Gateway GPRS Support Node (GGSN)

160a, 160b, 160c. . . eNodeB

162. . . Mobility Management Gateway (MME)

164. . . Service gateway

166. . . Packet Data Network (PDN) gateway

184. . . Mobile IP Local Agent (MIP-HA)

186. . . Authentication, Authorization, and Accounting (AAA) services

188. . . Gateway

HBS. . . Home BS

IP. . . Internet protocol

Iub, IuCS, IuPS, iur, S1, X2. . . interface

MBS. . . Macro BS

R3: R6, R8. . . Reference point

The invention may be understood in more detail from the following description, which is given by way of example, and which can be understood in conjunction with the accompanying drawings, wherein: FIG. 1A depicts one or more of the disclosed embodiments may be practiced therein. A system diagram of an example communication system. FIG. 1B depicts a system diagram of an example wireless transmit/receive unit (WTRU) that may be used in a communication system as shown in FIG. 1A. Figure 1C depicts a system diagram of an example radio access network and an example core network that may be used in a communication system as shown in Figure 1A. Figure 1D depicts a system diagram of another example radio access network and another example core network, which may be used in a communication system as shown in Figure 1A. Figure 1E depicts a system diagram of another example radio access network and another example core network, which may be used in a communication system as shown in Figure 1A. Figure 2 depicts an example implementation of a cloud with a cloud radio access network of one or more base stations (BSs), such as Home BS (HBS) and Macro BS (MBS), consistent with an embodiment. Figure 3 depicts an example implementation of a cell of a cloud radio access network consistent with an embodiment. Figure 4 depicts an example technique for base station (BS) selection and compression consistent with an embodiment. Figure 5 depicts an example technique for providing compression to a base station (BS) for an uplink system consistent with an embodiment.

BS. . . Base station

HBS. . . Home BS

MBS. . . Macro BS

Claims (20)

A method for performing joint base station scheduling and distributed compression, the method comprising: receiving channel information by a device, the device communicating with one or more base stations; selecting the one or more bases by the device Determining, by the device, one or more compression policies; reporting, by the device, one or more test channels to the one or more base stations; performing rate control by the device; and receiving by the device The compressed information performs decoding. The method of claim 1, wherein selecting the base station comprises selecting one or more home base stations (HBS). The method of claim 1, wherein the device is a node in a cloud communication network. The method of claim 1, wherein the determining the one or more compression strategies comprises calculating one or more test channels for the one or more base stations using a coordinated ascending algorithm. The method of claim 1, wherein the selecting the one or more base stations comprises using a coordinated ascending algorithm to maximize a function of the covariance matrix elements. The method of claim 1, wherein the selecting the one or more base stations comprises using a sparse guidance optimization algorithm performed by the apparatus. The method of claim 1, wherein the performing rate control comprises at least one of: calculating a total rate, determining one or more used by one or more wireless transmit/receive units (WTRUs) And a code, and reporting to the one or more WTRUs an index corresponding to at least one of the one or more codebooks. The method of claim 1, wherein the performing the decoding comprises decoding at least one signal, the at least one signal being generated based on one or more codebooks determined by the apparatus. The method of claim 1, the method further comprising: generating one or more first signals by one or more wireless transmit/receive units (WTRUs), the one or more first signals being based on One or more codebooks determined by the device, the one or more WTRUs communicating with the one or more base stations; receiving one or more second signals by the one or more base stations; Or a plurality of base stations compressing the one or more second signals based on the one or more test channels received from the device; and transmitting an index associated with the compressed one or more second signals to The device. A method for performing uplink using compression, the method comprising: receiving channel information by a device, the device communicating with one or more base stations; determining, by the device, a sequence of the one or more base stations For compressing; reporting, by the device, one or more correlation matrices to the one or more base stations; determining one or more compression strategies by the device; from the device to the one or more bases The station reports one or more test channels; performing rate control by the device; and performing decoding on the compressed information received by the device. The method of claim 10, the method further comprising: generating one or more first signals by one or more wireless transmit/receive units (WTRUs), the one or more first signals being based on One or more codebooks determined by the device, the one or more WTRUs communicating with the one or more base stations; receiving one or more second signals by the one or more base stations; Or a plurality of base stations compressing the one or more second signals based on one or more test channels received from the device; and transmitting an index associated with the compressed one or more second signals to the Said device. The method of claim 10, wherein performing rate control comprises at least one of: calculating a total rate, determining one or more codes used by one or more wireless transmit/receive units (WTRUs) Or reporting to the one or more WTRUs an index corresponding to at least one of the one or more codebooks. The method of claim 12, wherein a total rate metric is calculated as a function of at least one of: a signal to noise ratio (SNR), an input distribution, one or more channel matrices, Or a linear converter. The method of claim 11, wherein the one or more base stations receive the reception of the one or more second signals comprising receiving at a different base station in the one or more base stations The correlation between the signals. The method of claim 14, wherein the transmitting the index associated with the compressed one or more second signals to the device is accomplished via one or more load-back links. A device for communicating with a cloud communication network, the device comprising: a processor configured to: at least: receive channel information by a device, the device communicating with one or more base stations; Determining, by the device, the one or more base stations; determining, by the device, one or more compression policies; reporting, by the device, one or more test channels to the one or more base stations; Rate control; and performing decoding on the compressed information received by the device. The apparatus of claim 16, wherein the processor is further configured to cause the one or more compression strategies to calculate one or for the one or more base stations by using a coordinated ascending algorithm Multiple test channels to determine. The apparatus of claim 16, wherein the processor is further configured to cause the one or more base stations to be selected using a coordinated ascending algorithm to maximize a function of the covariance matrix elements . The apparatus of claim 16, wherein the processor is further configured to: determine an order of the one or more base stations for compression; and report to the one or more base stations One or more correlation matrices. The device of claim 16, wherein the processor is further configured to cause the one or more base stations to be selected using a sparse boot optimization performed by the device.