Results 11  20
of
341
Pilot contamination and precoding in multicell TDD systems
 LU et al.: OVERVIEW OF MASSIVE MIMO: BENEFITS AND CHALLENGES 757
, 2011
"... Abstract—This paper considers a multicell multiple antenna system with precoding used at the base stations for downlink transmission. Channel state information (CSI) is essential for precoding at the base stations. An effective technique for obtaining this CSI is timedivision duplex (TDD) operati ..."
Abstract

Cited by 76 (6 self)
 Add to MetaCart
Abstract—This paper considers a multicell multiple antenna system with precoding used at the base stations for downlink transmission. Channel state information (CSI) is essential for precoding at the base stations. An effective technique for obtaining this CSI is timedivision duplex (TDD) operation where uplink training in conjunction with reciprocity simultaneously provides the base stations with downlink as well as uplink channel estimates. This paper mathematically characterizes the impact that uplink training has on the performance of such multicell multiple antenna systems. When nonorthogonal training sequences are used for uplink training, the paper shows that the precoding matrix used by the base station in one cell becomes corrupted by the channel between that base station and the users in other cells in an undesirable manner. This paper analyzes this fundamental problem of pilot contamination in multicell systems. Furthermore, it develops a new multicell MMSEbased precoding method that mitigates this problem. In addition to being linear, this precoding method has a simple closedform expression that results from an intuitive optimization. Numerical results show significant performance gains compared to certain popular singlecell precoding methods. Index Terms—Timedivision duplex systems, uplink training, pilot contamination, MMSE precoding. I.
Capacity of a Class of Cognitive Radio Channels: Interference Channels with Degraded Message Sets
"... This paper is motivated by two different scenarios. The first is a cognitive radio system where a cognitive radio knows a “dumb ” radio’s message and the second is a sensor network in a correlated field where sensors possessing a nested message structure assist one another’s in information transmis ..."
Abstract

Cited by 73 (3 self)
 Add to MetaCart
This paper is motivated by two different scenarios. The first is a cognitive radio system where a cognitive radio knows a “dumb ” radio’s message and the second is a sensor network in a correlated field where sensors possessing a nested message structure assist one another’s in information transmission. Both scenarios are modeled using the framework of discrete memoryless interference channels with degraded message sets (IFCDMS), a setting where one of the two transmitters in an interference channel knows both the messages to be conveyed to the receivers. Both inner and outer bounds are provided in this paper for a class of IFCDMS channels. The case of the Gaussian interference channels with degraded message sets is also investigated. In this case, achievability and converse arguments are presented for a class of “weak” interference channels, resulting in a characterization of this class’ capacity region.
Sum Rate Characterization of Joint Multiple CellSite Processing
, 2005
"... The sumrate capacity of a cellular system model is analyzed, considering the uplink and downlink channels, while addressing both nonfading and flatfading channels. The focus is on a simple Wynerlike multicell model, where the system cells are arranged on a circle, assuming the cellsites are lo ..."
Abstract

Cited by 68 (11 self)
 Add to MetaCart
The sumrate capacity of a cellular system model is analyzed, considering the uplink and downlink channels, while addressing both nonfading and flatfading channels. The focus is on a simple Wynerlike multicell model, where the system cells are arranged on a circle, assuming the cellsites are located at the boundaries of the cells. For the uplink channel, analytical expressions of the sumrate capacities are derived for intracell TDMA scheduling, and a “WideBand ” (WB) scheme (where all users are active simultaneously utilizing all bandwidth for coding). Assuming individual percell power constraints, and using the Lagrangian uplinkdownlink duality principle, an analytical expression for the sumrate capacity of the downlink channel is derived for nonfading channels, and shown to coincide with the corresponding uplink result. Introducing flatfading, lower and upper bounds on the average percell sumrate capacity are derived. The bounds exhibit an O(loge K) multiuser diversity factor for a number of users percell K ≫ 1, in addition to the array diversity gain. Joint multicell processing is shown to eliminate outofcell interference, which is traditionally considered to be a limiting factor in highrate reliable communications. This paper was presented in part at the 9
Low complexity user selection algorithms for multiuser MIMO systems with block diagonalization,”
 IEEE Trans. Signal Process,
, 2006
"... ..."
(Show Context)
Multicell downlink capacity with coordinated processing
 In Information Theory and Applications Workshop
, 2007
"... Abstract — This paper considers the application of cooperative basestation (BS) transmission schemes to the downlink of multicell networks. Based on a simplified Wynertype network model with users clustered at the celledges, closed form sum rate expressions for nonfading channels are derived for ..."
Abstract

Cited by 66 (0 self)
 Add to MetaCart
(Show Context)
Abstract — This paper considers the application of cooperative basestation (BS) transmission schemes to the downlink of multicell networks. Based on a simplified Wynertype network model with users clustered at the celledges, closed form sum rate expressions for nonfading channels are derived for dirtypaper coding (DPC), linear zeroforcing (ZF) precoding, and cophasing with reuse. By extending the model to include cellinterior users, the capacity region for various transmission strategies is determined for the rate pairs achievable by the two classes of users. In addition to the upper bound of DPC across the whole network and the simple approach of having adjacent BSs alternate between serving celledge and cellinterior users, we consider several hybrid approaches to serve cellinterior users in each cell but celledge users in alternating cells. These hybrid approaches allow the cooperation to be localized among adjacent BSs based on either DPC or superposition coding (SPC). The resulting capacity regions show the tradeoff for improving performance based on techniques that have differing levels of BS cooperation and processing complexity and differing requirements for channel state information at the transmitter. I.
MIMO Transceiver Design via Majorization Theory
, 2007
"... and unified representation of different physical communication systems, ranging from multiantenna wireless channels to wireless digital subscriber line systems. They have the key property that several data streams can be simultaneously established. In general, the design of communication systems f ..."
Abstract

Cited by 66 (1 self)
 Add to MetaCart
(Show Context)
and unified representation of different physical communication systems, ranging from multiantenna wireless channels to wireless digital subscriber line systems. They have the key property that several data streams can be simultaneously established. In general, the design of communication systems for MIMO channels is quite involved (if one can assume the use of sufficiently long and good codes, then the problem formulation simplifies drastically). The first difficulty lies on how to measure the global performance of such systems given the tradeoff on the performance among the different data streams. Once the problem formulation is defined, the resulting mathematical problem is typically too complicated to be optimally solved as it is a matrixvalued nonconvex optimization problem. This design problem has been studied for the past three decades (the first papers
Uplinkdownlink duality via minimax duality
 in Canadian Workshop on Info. Theory
, 2003
"... Abstract—The sum capacity of a Gaussian vector broadcast channel is the saddle point of a minimax Gaussian mutual information expression where the maximization is over the set of transmit covariance matrices subject to a power constraint and the minimization is over the set of noise covariance matri ..."
Abstract

Cited by 63 (6 self)
 Add to MetaCart
(Show Context)
Abstract—The sum capacity of a Gaussian vector broadcast channel is the saddle point of a minimax Gaussian mutual information expression where the maximization is over the set of transmit covariance matrices subject to a power constraint and the minimization is over the set of noise covariance matrices subject to a diagonal constraint. This sum capacity result has been proved using two different methods, one based on decisionfeedback equalization and the other based on a duality between uplink and downlink channels. This paper illustrates the connection between the two approaches by establishing that uplink–downlink duality is equivalent to Lagrangian duality in minimax optimization. This minimax Lagrangian duality relation allows the optimal transmit covariance and the leastfavorablenoise covariance matrices in a Gaussian vector broadcast channel to be characterized in terms of the dual variables. In particular, it reveals that the least favorable noise is not unique. Further, the new Lagrangian interpretation of uplink–downlink duality allows the duality relation to be generalized to Gaussian vector broadcast channels with arbitrary linear constraints. However, duality depends critically on the linearity of input constraints. Duality breaks down when the input constraint is an arbitrary convex constraint. This shows that the minimax representation of the broadcast channel sum capacity is more general than the uplink–downlink duality representation. Index Terms—Broadcast channel, Lagrangian duality, minimax optimization, multipleinput multipleoutput (MIMO), multipleaccess
Broadcast Channels with Cooperating Decoders
, 2006
"... We consider the problem of communicating over the general discrete memoryless broadcast channel (BC) with partially cooperating receivers. In our setup, receivers are able to exchange messages over noiseless conference links of finite capacities, prior to decoding the messages sent from the transmi ..."
Abstract

Cited by 59 (4 self)
 Add to MetaCart
We consider the problem of communicating over the general discrete memoryless broadcast channel (BC) with partially cooperating receivers. In our setup, receivers are able to exchange messages over noiseless conference links of finite capacities, prior to decoding the messages sent from the transmitter. In this paper we formulate the general problem of broadcast with cooperation. We first find the capacity region for the case where the BC is physically degraded. Then, we give achievability results for the general broadcast channel, for both the two independent messages case and the single common message case.
On the capacity of fading MIMO broadcast channels with imperfect transmitter sideinformation
 in Annual Allerton Conference on Communication, Control, and Computing
, 2005
"... A fading broadcast channel is considered where the transmitter employs two antennas and each of the two receivers employs a single receive antenna. It is demonstrated that even if the realization of the fading is precisely known to the receivers, the high signaltonoise (SNR) throughput is greatly ..."
Abstract

Cited by 57 (3 self)
 Add to MetaCart
(Show Context)
A fading broadcast channel is considered where the transmitter employs two antennas and each of the two receivers employs a single receive antenna. It is demonstrated that even if the realization of the fading is precisely known to the receivers, the high signaltonoise (SNR) throughput is greatly reduced if, rather than knowing the fading realization precisely, the trasmitter only knows the fading realization approximately. The results are general and are not limited to memoryless Gaussian fading. 1
An introduction to convex optimization for communications and signal processing
 IEEE J. SEL. AREAS COMMUN
, 2006
"... Convex optimization methods are widely used in the ..."
Abstract

Cited by 56 (2 self)
 Add to MetaCart
Convex optimization methods are widely used in the