1 / 13

Unlocking Wireless Performance with Co-operation in Base-Station Pools

Parul Gupta, IBM Research – India COMSNETS - Jan 8, 2010 . Unlocking Wireless Performance with Co-operation in Base-Station Pools. Overview. Why Co-operate? Base Station co-operation in present network architecture Pooled Base Station architecture

dewey
Download Presentation

Unlocking Wireless Performance with Co-operation in Base-Station Pools

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Parul Gupta, IBM Research – India COMSNETS - Jan 8, 2010 Unlocking Wireless Performance with Co-operation in Base-Station Pools

  2. Overview • Why Co-operate? • Base Station co-operation in present network architecture • Pooled Base Station architecture • Potential cost savings through pooled BS model for a few scenarios • Interference Avoidance • Interference Alignment • Uplink Macro-Diversity • Efficient handovers • Summary and Future work

  3. Why Co-operate? • There is demand for supporting many users withhigh data rates at high mobility. Challenges: • Spectrum is limited: Reuse desirable • For systems with spectrum reuse, capacity is fundamentally limited by interference • With the trend towards smaller cells for reducing transmit power and better reuse, handovers become more frequent • Base Stations (BS) can co-operate to • Spatially multiplex many independent data streams on the same channel. Prior work shows increased channel rank for such virtual arrays [1] • Distributed Transmit Beamforming • Interference Avoidance and Interference Cancellation • Load Balancing via joint-scheduling • Reduces latency during handoff, necessary for real-time applications like VoIP and streaming video [1] V. Jungnickel, S. Jaeckel, L. Thiele, L. Jiang, U. Krger, A. Brylka and C.V. Helmolt, “Capacity measurements in a cooperative MIMO network”,IEEE Transactions on Vehicular Technology, vol. 58, no. 5, pp. 2392-2405, Jun 2009.

  4. Co-operation in Distributed Network Architecture • Assumption of infinite backhaul not always true • US has 75% copper, 15% fiber and 10% microwave. • Companies like Clearwire are leasing T1 bundles for their new network deployment: • 6 T1s per Wimax BS in Manhattan! • Cost increases with each extra T1-line leased: $400 p.m. for 1.54 Mbps • Some co-operation schemes might still be possible in the distributed network architecture with limited backhaul • Schemes need to be designed appropriately for constraints, e.g. limited co-operation • There is a cost associated with communication over the backhaul: whether over a peer-peer BS interface (where exists) or a higher hierarchical element like RNC or ASN Gateway

  5. SMS/MMS SMS/MMS BS ManagementServer WAP GW IMS Edge gateway Content Service BS Web Service Edgegateway Billing BS BS PSTN Present 2G-3G Wireless Network architecture 4G Wireless Network with Co-located Base-Station Pools Access Network Core Network Service Network Mobile switch center BS cluster Radio network controller Service support node Radio network controller Gateway Internet BS cluster

  6. Base Station Pools eliminate communication costs in co-operation • Information resides in a common place, transparently accessible to all BSs • Make fine-grained communication possible • Co-operation schemes require exchanging high volumes of data in short times become realizable • In this work, we estimate the potential cost savings for a few such schemes

  7. Cell 1 Cell 2 Cell 3 Interference Avoidance Full Frequency Reuse System • Capacity of full frequency reuse systems gets limited due to interference, esp. for cell-edge users • Interference can be avoided with joint resource allocation and power control, e.g. Fractional Frequency Reuse • Less complex, but takes a capacity hit • Each BS needs to share its power information with neighbors Fractional Frequency Reuse System

  8. Interference Avoidance – Example Communication Cost • Relative Narrowband Transmit Power (RNTP) messages specified in LTE specifications can indicate interference in the Downlink • Contain a bitmap for each Resource Block (100 per slot in 20 MHz bandwidth) • Similarly for Uplink, Interference indicator messages restricted to once every 20 ms to avoid excess overhead

  9. Interference Cancelation • Rather than avoiding interference, co-operating BSs can pre-code the transmitted signals to minimize interference at the receiver • Interference alignment [1] • Asynchronous Interference mitigation [2] • More complex because of signal processing • Assumes all co-operating BSs have full Channel State Information (CSI) at the transmitter • Dimensionality of channel matrix with K transmitters and receivers: K2 • For sharing this information with all co-operating BSs, communication cost grows as K3 • Example backhaul calculations are done assuming the complex CSI for the 720 data subcarriers, 10 MHz Wimax channel, fed back every 10 ms • Note: Spectrum to feedback CSI to the transmitter potentially an issue. TDD systems can utilize channel reciprocity to estimate downlink-CSI [1] V. Cadambe and S. A. Jaffer, “Interference alignment and degrees of freedom for the K-users interference channel,” IEEE Transactions on Information Theory, vol. 54, no. 8, pp. 3425-3441, Aug 2008 [2] H. Zhang et. Al., “Asynchronous Interference Mitigation in Co-operative Base-Station Systems”, IEEE Transactions on Wireless Communications, Vol. 7, No. 1, Jan 2008

  10. y1 y2 h1 h2 x Uplink Macro-Diversity • Macro-Diversity schemes today (e.g. in Macro-Diversity Handover in Wimax) in the uplink rely on selection diversity • The extra gains due to Maximal Ratio Combining are untapped due to large amounts of data exchange and computation complexity • Example calculation shown for communication cost for 10 MHz Wimax channel, 2:1 DL:UL ratio, 5 ms frame, assuming 3 samples need to be transmitted per subcarrier • The amount of data to be transferred over the network is large, even for few quantization bits • Base-Station Pools eliminate this communication cost over the network, making MRC realizable

  11. Faster Handovers with Co-operation MS Serving BS (#1) Target BS (#2) Target BS (#3) MOB_NBR-ADV MOB_SCN-REQ MOB_SCN-RSP Scan Channel RNG_REQ RNG_REQ Scan Channel RNG_RSP MOB_ASC_REPORT RNG_RSP MOB_MSHO-REQ BS #2, BS #3 MOB_BSHO-RSP Handover to BS # 2 CONTEXT TRANSFER Service interruption duration MOB_HO-IND End Tx/Rx DL/UL MAP, DCD/UCD Shorter ranging cycle Multiple iterations to adjust local parameters RNG-REQ RNG-RSP … RNG-REQ Resume normal operation RNG-RSP AUTHENTICATION REG-REQ REG-RSP Resume normal operation

  12. Shared MS data BS1 BS2 BS3 Co-located Base Station Pool Faster Handovers with Co-operation • Handovers can be made faster by • Co-ordination between base stations for ranging • Transfer of static context (service flow, authentication & registration info) and dynamic context (ARQ states, pending data)

  13. Summary and Future Work • Co-operation between Base Stations can improve wireless system performance in various ways • Interference Avoidance and Interference Cancellation • Load Balancing via joint-scheduling • Macro-Diversity Schemes • Faster Handovers • Fine-grained co-operation becomes possible due to transparent information sharing in Base-Station Pools • So far, we have set the motivation for co-operation in BS pools through estimating potential cost-savings. Future work would be to demonstrate working schemes in a BS pool and solve associated issues.

More Related