Results 1  10
of
50
Degrees of freedom region of the MIMO X Channel
, 2007
"... hop, is especially interesting, as the intermediate hop takes place over an interference channel with single antenna nodes. While the two user interference channel with single antenna nodes has only one degree of freedom by itself, it is able to deliver degrees of freedom when used as an intermediat ..."
Abstract

Cited by 92 (28 self)
 Add to MetaCart
hop, is especially interesting, as the intermediate hop takes place over an interference channel with single antenna nodes. While the two user interference channel with single antenna nodes has only one degree of freedom by itself, it is able to deliver degrees of freedom when used as an intermediate stage between a antenna source and a antenna destination [5]. The key is an amplify and forward scheme where the relay nodes, instead of trying to decode the messages, simply scale and forward their received signals. [1]–[3] consider end to end channel orthogonalization with distributed sources, relays and destination nodes and determine the capacity scaling behavior with the number of relay nodes. It is shown that distributed orthogonalization can be obtained even with synchronization errors if a minimum amount of coherence at the relays can be sustained. Degrees of freedom for linear interference networks with local sideinformation are explored in [22] and cognitive message sharing is found to improve the degrees of freedom for certain structured channel matrices. The MIMO MAC and BC channels show that there is no loss in degrees of freedom even if antennas are distributed among users at one end (either transmitters or receivers) making joint signal processing infeasible, as long as joint signal processing is possible at the other end of the communication link. The multiple hop example of [5], described above, shows that there is no loss of degrees of freedom even with distributed antennas at both ends of a communication hop (an interference channel) as long as the distributed antenna stages are only intermediate
Interference alignment for cellular networks
 in Communication, Control, and Computing, 2008 46th Annual Allerton Conference
, 2008
"... Abstract — In this paper, we propose a new way of interference management for cellular networks. We develop the scheme that approaches to interferencefree degreeoffreedom (dof) as the number K of users in each cell increases. Also we find the corresponding bandwidth scaling conditions for typical ..."
Abstract

Cited by 78 (5 self)
 Add to MetaCart
Abstract — In this paper, we propose a new way of interference management for cellular networks. We develop the scheme that approaches to interferencefree degreeoffreedom (dof) as the number K of users in each cell increases. Also we find the corresponding bandwidth scaling conditions for typical wireless channels: multipath channels and singlepath channels with propagation delay. The scheme is based on interference alignment. Especially for morethantwocell cases where there are multiple nonintended BSs, we propose a new version of interference alignment, namely subspace interference alignment. The idea is to align interferences into multidimensional subspace (instead of one dimension) for simultaneous alignments at multiple nonintended BSs. The proposed scheme requires finite dimensions growing linearly with K, i.e., ∼ O(K). I.
The case for structured random codes in network capacity theorems
 in Proceedings of the IEEE Information Theory Workshop (ITW 2007), (Lake Tahoe, CA
, 2007
"... Random coding arguments are the backbone of most channel capacity achievability proofs. In this paper, we show that in their standard form, such arguments are insufficient for proving some network capacity theorems: structured coding arguments, such as random linear or lattice codes, attain higher r ..."
Abstract

Cited by 52 (10 self)
 Add to MetaCart
Random coding arguments are the backbone of most channel capacity achievability proofs. In this paper, we show that in their standard form, such arguments are insufficient for proving some network capacity theorems: structured coding arguments, such as random linear or lattice codes, attain higher rates. Historically, structured codes have been studied as a stepping stone to practical constructions. However, Körner and Marton demonstrated their usefulness for capacity theorems through the derivation of the optimal rate region of a distributed functional source coding problem. Here, we use multicasting over finite field and Gaussian multipleaccess networks as canonical examples to demonstrate that even if we want to send bits over a network, structured codes succeed where simple random codes fail. Beyond network coding, we also consider distributed computation over noisy channels and a special relaytype problem. I.
Cellular Interference Alignment with Imperfect Channel Knowledge
"... Abstract—Interference alignment is evaluated as a technique to mitigate intercell interference in the downlink of a cellular network using OFDMA. The sum mutual information achieved by interference alignment together with a zeroforcing receiver is considered, and upper and lower bounds are derived ..."
Abstract

Cited by 39 (5 self)
 Add to MetaCart
(Show Context)
Abstract—Interference alignment is evaluated as a technique to mitigate intercell interference in the downlink of a cellular network using OFDMA. The sum mutual information achieved by interference alignment together with a zeroforcing receiver is considered, and upper and lower bounds are derived for the case of imperfect channel knowledge. The sum mutual information achieved by interference alignment when the base stations share their information about the channels is shown to compare favorably to the achievable sumrate of methods where the base stations do not cooperate, even under moderately accurate knowledge of the channel state. I.
On the achievability of interference alignment in the Kuser constant MIMO interference channel
 In Proc. IEEE Workshop on Statistical Signal Processing (SSP
, 2009
"... Interference alignment in the Kuser MIMO interference channel with constant channel coefficients is considered. A novel constructive method for finding the interference alignment solution is proposed for the case where the number of transmit antennas equals the number of receive antennas (NT = NR = ..."
Abstract

Cited by 33 (6 self)
 Add to MetaCart
(Show Context)
Interference alignment in the Kuser MIMO interference channel with constant channel coefficients is considered. A novel constructive method for finding the interference alignment solution is proposed for the case where the number of transmit antennas equals the number of receive antennas (NT = NR = N), the number of transmitterreceiver pairs equals K = N + 1, and all interference alignment multiplexing gains are one. The core of the method consists of solving an eigenvalue problem that incorporates the channel matrices of all interfering links. This procedure provides insight into the feasibility of signal vector spaces alignment schemes in finite dimensional MIMO interference channels.
Interference alignment and spatial degrees of freedom for the K user interference channel
 In Proc. IEEE International Conference on Communications
, 2008
"... While the best known outerbound for the K user interference channel states that there cannot be more than K/2 degrees of freedom, it has been conjectured that in general the constant interference channel with any number of users has only one degree of freedom. In this paper, we explore the spatial d ..."
Abstract

Cited by 31 (0 self)
 Add to MetaCart
(Show Context)
While the best known outerbound for the K user interference channel states that there cannot be more than K/2 degrees of freedom, it has been conjectured that in general the constant interference channel with any number of users has only one degree of freedom. In this paper, we explore the spatial degrees of freedom per orthogonal time and frequency dimension for the K user wireless interference channel where the channel coefficients take distinct values across frequency slots but are fixed in time. We answer five closely related questions. First, we show that K/2 degrees of freedom can be achieved by channel design, i.e. if the nodes are allowed to choose the best constant, finite and nonzero channel coefficient values. Second, we show that if channel coefficients can not be controlled by the nodes but are selected by nature, i.e., randomly drawn from a continuous distribution, the total number of spatial degrees of freedom for the K user interference channel is almost surely K/2 per orthogonal time and frequency dimension. Thus, only half the spatial degrees of freedom are lost due to distributed processing of transmitted and received signals on the interference channel. Third, we show that interference alignment and zero forcing suffice to achieve all the degrees of freedom in all cases. Fourth, we show that the degrees of freedom D directly lead to an O(1) capacity characterization of the form C(SNR) = D log(1 + SNR) +O(1) for the multiple access channel, the broadcast channel, the 2 user interference channel, the 2 user MIMO X channel and the 3 user interference
On the secure degrees of freedom of wireless X networks
 In 46th Annual Allerton Conference on Communication, Control and Computing
, 2008
"... Abstract — Previous work showed that the X network with M transmitters, N receivers has MN degrees of freedom. In this ..."
Abstract

Cited by 30 (4 self)
 Add to MetaCart
(Show Context)
Abstract — Previous work showed that the X network with M transmitters, N receivers has MN degrees of freedom. In this
The Feasibility of Interference Alignment Over Measured MIMOOFDM Channels
 IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
, 2010
"... Interference alignment (IA) has been shown to achieve the maximum achievable degrees of freedom in the interference channel. This results in sum rate scaling linearly with the number of users in the high signaltonoiseratio (SNR) regime. Linear scaling is achieved by precoding transmitted signals ..."
Abstract

Cited by 29 (7 self)
 Add to MetaCart
Interference alignment (IA) has been shown to achieve the maximum achievable degrees of freedom in the interference channel. This results in sum rate scaling linearly with the number of users in the high signaltonoiseratio (SNR) regime. Linear scaling is achieved by precoding transmitted signals to align interference subspaces at the receivers, given channel knowledge of all transmitreceive pairs, effectively reducing the number of discernible interferers. The theory of IA was derived under assumptions about the richness of scattering in the propagation channel; practical channels do not guarantee such ideal characteristics. This paper presents the first experimental study of IA in measured multipleinput multipleoutput orthogonal frequencydivision multiplexing (MIMOOFDM) interference channels. Our measurement campaign includes a variety of indoor and outdoor measurement scenarios at The University of Texas at Austin. We show that IA achieves the claimed scaling factors, or degrees of freedom, in several measured channel settings for a 3 user, 2 antennas per node setup. In addition to verifying the claimed performance, we characterize the effect of Kronecker spatial correlation on sum rate and present two other correlation measures, which we show are more tightly related to the achieved sum rate.
Interference alignment as a rank constrained rank minimization
 in Proc. of IEEE Global Telecommunications Conference (GLOBECOM
, 2010
"... Abstract—We show that the maximization of the sum degreesoffreedom for the static flatfading multipleinput multipleoutput (MIMO) interference channel is equivalent to a rank constrained rank minimization problem, when the signal spaces span all available dimensions. The rank minimization correspo ..."
Abstract

Cited by 29 (2 self)
 Add to MetaCart
(Show Context)
Abstract—We show that the maximization of the sum degreesoffreedom for the static flatfading multipleinput multipleoutput (MIMO) interference channel is equivalent to a rank constrained rank minimization problem, when the signal spaces span all available dimensions. The rank minimization corresponds to maximizing interference alignment (IA) such that interference spans the lowest dimensional subspace possible. The rank constraints account for the useful signal spaces spanning all available spatial dimensions. That way, we reformulate all IA requirements to requirements involving ranks. Then, we present a convex relaxation of the RCRM problem inspired by recent results in compressed sensing and lowrank matrix completion theory that rely on approximating rank with the nuclear norm. We show that the convex envelope of the sum of ranks of the interference matrices is the sum of their corresponding nuclear norms and introduce tractable constraints that are asymptotically equivalent to the rank constraints for the initial problem. We also show that our heuristic relaxation can be also tuned to the multicell interference channel. Furthermore, we experimentally show that the proposed algorithm outperforms previous approaches for finding precoding and zeroforcing matrices for interference alignment. I.
The Multiplexing Gain of MIMO XChannels with Partial Transmit SideInformation
"... In this paper, we obtain the scaling laws of the sumrate capacity of a MIMO Xchannel, a 2 independent sender, 2 independent receiver channel with messages from each transmitter to each receiver, at high signal to noise ratios (SNR). The Xchannel has sparked recent interest in the context of coop ..."
Abstract

Cited by 26 (3 self)
 Add to MetaCart
In this paper, we obtain the scaling laws of the sumrate capacity of a MIMO Xchannel, a 2 independent sender, 2 independent receiver channel with messages from each transmitter to each receiver, at high signal to noise ratios (SNR). The Xchannel has sparked recent interest in the context of cooperative networks and it encompasses the interference, multiple access, and broadcast channels as special cases. Here, we consider the case with partially cooperative transmitters in which asymmetric sideinformation (in the form of a codeword) is available at one of the transmitters. It is proved that when there are M antennas at all four nodes, the sumrate scales like 2M log SNR which is in sharp contrast to ˆ ⌊ 4M