Cooperative MIMO
While MIMO has become an essential element of wireless communication standards, including IEEE 802.11n (Wi-Fi), IEEE 802.11ac (Wi-Fi), HSPA+ (3G), WiMAX (4G), and Long-Term Evolution (4G), many wireless devices cannot support multiple antennas due to size, cost, and/or hardware limitations.
More importantly, the separation between antennas on a mobile device and even on fixed radio platforms is often insufficient to allow meaningful performance gains.
Furthermore, as the number of antennas is increased, the actual MIMO performance falls farther behind the theoretical gains.
The advantages of cooperative MIMO, on the other hand, are its capability to improve the capacity, cell edge throughput, coverage, and group mobility of a wireless network in a cost-effective manner.
In many practical applications, such as cellular mobile and wireless ad hoc networks, the advantages of deploying cooperative MIMO technology outweigh the disadvantages.
They store data received from the BS and forward to the mobile stations (MSs), and vice versa.
Fixed relay stations (RSs) typically have smaller transmission powers and coverage areas than a BS.
They can be deployed strategically and cost effectively in cellular networks to extend coverage, reduce total transmission power, enhance the capacity of a specific region with high traffic demands, and/or improve signal reception.
As one of the most mature cooperative MIMO technologies, fixed relay has attracted significant support in major cellular communication standards.
Mobile relays are therefore more flexible in accommodating varying traffic patterns and adapting to different propagation environments.
For example, when a target MS temporarily suffers from poor channel conditions or requires relatively high-rate service, its neighboring MSs can help to provide multi-hop coverage or increase the data rate by relaying information to the target MS.
This can be addressed by sending additional random linear combinations (such as by increasing the rank of the MIMO channel matrix or retransmitting at a later time that is greater than the channel coherence time) until the receiver obtains a sufficient number of coded packets to permit decoding.
Random linear network codes have a high overhead due to the large coefficient vectors attached to encoded blocks.
But in Cooperative-MIMO radio, the coefficient vectors can be measured from known training signals, which is already performed for channel estimation.
Finally, linear dependency among coding vectors reduces the number of innovative encoded blocks.
Before the introduction of cooperative MIMO, joint processing among cellular base stations was proposed to mitigate inter-cell interference,[15] and Cooperative diversity[16] offered increased diversity gain using relays, but at the cost of poorer spectral efficiency.
However, neither of these techniques exploits interference for spatial multiplexing gains, which can dramatically increase spectral efficiency.
In 2001, cooperative MIMO was introduced by Steve Shattil, a scientist at Idris Communications, in a provisional patent application,[17] which disclosed Coordinated Multipoint and Fixed Relays, followed by a paper in which S. Shamai and B.M.
[18] In 2002, Shattil introduced the Mobile Relay and Network Coding aspects of cooperative MIMO in US Pat.
[20] Implementations of software-defined radio (SDR) and distributed computing in cooperative MIMO were introduced in US Pat.
This allows processing resources to easily scale to meet network demand, and the distributed antennas could enable each user device to be served by the full spectral bandwidth of the system.
Despite the great potential for client-side cooperative MIMO, a user-based infrastructure is more difficult for service providers to monetize, and there are additional technical challenges.
[22] Various techniques have been developed for handling timing and frequency offsets, which is one of the most critical and challenging issues in cooperative MIMO.
A group of BSs employs an aggregate M transmit antennas to communicate with K users simultaneously.
, represents interference received by user k. The network channel is defined as H = [H1T,…, HKT]T, and the corresponding set of signals received by all users is expressed by where H = [H1T,…, HKT]T, y = [y1T,…, yKT]T, W = [W1T,…, WKT]T, s = [s1T,…, sKT]T, and n = [n1T,…, nKT]T. The precoding matrix W is designed based on channel information in order to improve performance of the Cooperative-MIMO system.
Common types of spatial demultiplexing include ZF, MMSE combining, and successive interference cancellation.
