186 Pages

## TitlesEdit

### MU-MIMOEdit

• MIMO Broadcast Channels With Finite-Rate Feedback[1]
• Limited Feedback-based Block Diagonalization for the MIMO Broadcast Channel[2]
• Antenna Combing for the MIMO Downlink Channel[3]

### User CooperationEdit

• On the achievable diversity-multiplexing tradeoff in half-duplex cooperative channels[4]
• On the performance of cooperative amplify-and-forward relay networks[5]
• On the performance of multiuser MIMO systems in WCDMA/HSDPA: beamforming, feedback and user diversity[6]
• Compress-and-forwad Cooperative Relaying in MIMO-OFDM Systems[7]
• Capacity bounds and power allocation for wireless relay channel[8]

• Rethinking Information Theory for Mobile Ad Hoc Networks[9]

## Abstract Collections of Interesting MIMO PapersEdit

Main article: Abstract Collections of Interesting MIMO Papers

### Paper of Jinal MIMO BC Nov06[1]Edit

Multiple transmit antennas in a downlink channel can provide tremendous capacity (i.e., multiplexing) gains, even when receivers have only single antenna. However, receiver and transmitter channel status information is generally required. In this correspondence, a system where each receiver has perfect channel knowledge, but the transmitter only receives quantized information regarding the channel instantiation is analyzed. The well-known zero-forcing transmission technique is considered and simple expressions for the throughput degradation due to finite-rate feedback are derived. A key finding is that the feedback rate per mobile must be increased linearly with the signal-to-noise ratio (SNR) (in decibels) in order to achieve the full multiplexing gain. This is in sharp contrast to point-to-point multiple-input multiple-output (MIMO) systems, in which it is not necessary to increase the feedback rate as a function of the SNR.

This paper provides the required number of feedback bits to achieve the optimal multiplexing gain. The required number of feedback bits should be proportional to the average SNR of the downlink channel, as follows:

$B = (M-1) \log_2(P)$

where $M$ is the number of transmit antennas and $P$ is the average SNR.

Futher study issues in this paper is that we consider the feedback resource requirement in addition to the feedback bits requirement. Althought the number of the required feedback bits is needed to be increase, the amount of the required feedback resource can be adjusted according to the uplink quality. Moreover, we remind that the overall system performance can be reduced if the feedback signaling uses large wireless resources including transmit power, frequency and time resources.

### Paper[9]Edit

The subject of this paper is the long-standing open problem of developing a general capacity theory for wireless networks, particularly a theory capable of describing the fundamental performance limits of mobile ad hoc networks (MANETs). A MANET is a peer-to-peer network with no pre-existing infrastructure. MANET's the most general wireless networks, with single-hop, relay, interference, mesh, and start networks comprising special cases. The lack of a MANET capacity theory has stunted the developement and commercialization of many types of wireless networks, including emergency, military, sensor, and community mesh networks. Information theory, which as been vital for links and ceteralized networks, has not been sucessfullly applied to decentralized wireless networks. Even if this was accomplished, for such a theory to truly chracterize the limits of deployed MANETs it must overcome three key roadblocks. First, most current capacity results rely on the allowance of unbounded delay and reliability. Second, sptial and timescale decompositions have not yet been developed for optimally modeling the spatial and temporal dynamics of wireless networks. Third, a useful network capacity theory must integrate rather than ignore the important role of overhead messaging and feedback. This paper describes some of the shifts in thinking that may be needed to overcome these roadblocks and develop a more general theory that we refer to as non-equilibrium information theory.

### Paper[7]Edit

Tags: cooperation, relay, compress-and-forward

In this paper, we investigate the capacity of Compress-and-Forward (C&F) cooperative relaying scheme when the C&F relay operates in Time Division Duplex (TDD). In our evaluation, we consider MIMO-OFDM transmission. An achievable rate was previously dereived in [6] assuming sclar channel. We extend this Wyner-Ziv bound to MIMO-OFDM, by applying results from Bayesian vector estimation and rate-distortion coding theory. Then we derive the mutual information of a sub-optimum relaying scheme in which the relay applies K-L transform to the signal received from the source before quantizing it and forwarding it to the destination as a new codeword. Finally, we illustrate by simulations (in an environment similar to IEEE802.16) the fact that for some scenarios, the C&F approach other known relaying techniques. This remains true even if the C&F sub-optimal scheme is considered.

### Paper[8]Edit

This paper studies upper bounds and lower bounds on the outage capacity and ergodic capacity of a three-node wireless relay channel in a Rayleigh fading environment. We also take into accout practical constraints on the transmission duplexing at the relay node and on the synchronization between the source node and the relay node. We find that the gap between the upper bounds and lower bounds is typically negligible. Compared to direct transmission and tranditional multihop protocols, out results reveal that optimum relay channel signaling can significantly outperform multi-hop protocols, and that power allocation has a significant impact on performance.

### Paper: The Capacity of Wireless Networks Edit

• P. Gupta and P. R. Kumar. The Capacity of Wireless Networks. IEEE Transactions on Information Theory, 46(2):388-404, March 2000.

#### AbstractEdit

When identical randomly located nodes, each capable of transmitting at bits per second and using a fixed range, form a wireless network, the throughput $\lambda(n)$ obtainable by each node for a randomly chosen destination is $\theta\left( \frac{W}{\sqrt{n \log n}} \right)$ log bits per second under a noninterference protocol.

If the nodes are optimally placed in a disk of unit area, traffic patterns are optimally assigned, and each transmission’s range is optimally chosen, the bit–distance product that can be transported by the network per second is $\theta\left( W\sqrt{An} \right)$ bit-meters per second. Thus even under optimal circumstances, the throughput is only bits per second for each node for a destination nonvanishingly far away.

Similar results also hold under an alternate physical model where a required signal-to-interference ratio is specified for successful receptions.

Fundamentally, it is the need for every node all over the domain to share whatever portion of the channel it is utilizing with nodes in its local neighborhood that is the reason for the constriction in capacity.

Splitting the channel into several subchannels does not change any of the results.

Some implications may be worth considering by designers. Since the throughput furnished to each user diminishes to zero as the number of users is increased, perhaps networks connecting smaller numbers of users, or featuring connections mostly with nearby neighbors, may be more likely to be find acceptance.

## TechnologyEdit

### Cooperative DiversityEdit

• A set of terminals relay their received signals resuting in virtual antenna array. There is trade-off between the costs in power, bandwidth, and complexity and the greater benefits gained by exploiting spatial diversity in the channel. By contrast, classical network architectures only employ point-to-point transmission and thus, forego these bebefits. (Cooperative Diversity in Wireless Networks: Algorithms and...)
• Roughly speaking, several terminals, each with one or more antennas, form a kind of "coalition" to cooperatively act as a large transmit or receive array. The channel therefore share characteristics with the MIMO channel. (Paper[8])

## ReferenceEdit

1. 1.0 1.1 N. Jindal, MIMO Broadcast Channels with Finite Rate Feedback, IEEE Trans. Information Theory, Vol. 52, No. 11, pp. 5045-5059, Nov. 2006.
2. N. Ravindran and N. Jindal, Limited Feedback-based Block Diagonalization for the MIMO Broadcast Channel, Submitted to IEEE Journal Sel. Areas in Communications, Nov. 2007 (Revised April 2008).
3. N. Jindal, Antenna Combining for the MIMO Downlink Channel, To Appear: IEEE Trans. Wireless Communications.
4. K. Azarian, H. El Gamal and P. Schniter, "On the Achievable Diversity-Multiplexing Tradeoff in Half-Duplex Cooperative Channels," IEEE Trans. Info. Theory, vol. 51, no. 12, Dec. 2005, pp. 4152-4172.
5. P. Herhold, E. Zimmermann, and G. Fettewis, "On the performance of cooperative amplify-and-forward relay networks," Proc. ITG Conf. on Source and Channel Coding (SCC), pp.451–458, Erlangen, Germany, Jan. 2004.
6. S. J. Kim, H. J. Kim, C. S. Park, and K. B. Lee, "On the Performance of Multiuser MIMO Systems in WCDMA/HSDPA: Beamforming, Feedback and User Diversity," IEICE Transactions on Communications , vol. E98-B, no. 8, pp. 2161-2169, Aug. 2006.
7. 7.0 7.1 Sébastien Simoens, Josep Vidal, Olga Muñoz,"Compress-and-forwad Cooperative Relaying in MIMO-OFDM Systems,"
8. 8.0 8.1 8.2 A. Høst-Madsen and J. Zhang, "Capacity bounds and power allocation for wireless relay channel," IEEE Trans. Inf. Theory, submitted for publication.
9. 9.0 9.1 J. G. Andrews, N. Jindal, M. Haenggi, R. Berry, S. Jafar, D. Guo, S. Shakkottai, R. Heath, M. Neely, S. Weber, and A. Yener, “Rethinking Information Theory for Mobile Ad Hoc Networks,” IEEE Communications Magazine, 2008. Under revision.