4,845 research outputs found
On the MIMO Capacity with Multiple Linear Transmit Covariance Constraints
This paper presents an efficient approach to computing the capacity of multiple-input multiple-output (MIMO) channels under multiple linear transmit covariance constraints (LTCCs). LTCCs are general enough to include several special types of power constraints as special cases such as the sum power constraint (SPC), per-antenna power constraint (PAPC), or a combination thereof. Despite its importance and generality, most of the existing literature considers either SPC or PAPC independently. Efficient solutions to the computation of the MIMO capacity with a combination of SPC and PAPC have been recently reported, but were only dedicated to multipleinput single-output (MISO) systems. For the general case of LTCCs, we propose a low-complexity semi-closed-form approach tothecomputationoftheMIMOcapacity.Specifically,amodified minimax duality is first invoked to transform the considered problem in the broadcast channel into an equivalent minimax problem in the dual multiple access channel. Then alternating optimization and concave-convex procedure are utilized to derive water-filling-based algorithms to find a saddle point of the minimax problem. This is different from the state-of-the-art solutions to the considered problem, which are based on interiorpoint or subgradient methods. Analytical and numerical results are provided to demonstrate the effectiveness of the proposed low-complexity solution under various MIMO scenarios
Cooperative Multi-Cell Block Diagonalization with Per-Base-Station Power Constraints
Block diagonalization (BD) is a practical linear precoding technique that
eliminates the inter-user interference in downlink multiuser multiple-input
multiple-output (MIMO) systems. In this paper, we apply BD to the downlink
transmission in a cooperative multi-cell MIMO system, where the signals from
different base stations (BSs) to all the mobile stations (MSs) are jointly
designed with the perfect knowledge of the downlink channels and transmit
messages. Specifically, we study the optimal BD precoder design to maximize the
weighted sum-rate of all the MSs subject to a set of per-BS power constraints.
This design problem is formulated in an auxiliary MIMO broadcast channel (BC)
with a set of transmit power constraints corresponding to those for individual
BSs in the multi-cell system. By applying convex optimization techniques, this
paper develops an efficient algorithm to solve this problem, and derives the
closed-form expression for the optimal BD precoding matrix. It is revealed that
the optimal BD precoding vectors for each MS in the per-BS power constraint
case are in general non-orthogonal, which differs from the conventional
orthogonal BD precoder design for the MIMO-BC under one single sum-power
constraint. Moreover, for the special case of single-antenna BSs and MSs, the
proposed solution reduces to the optimal zero-forcing beamforming (ZF-BF)
precoder design for the weighted sum-rate maximization in the multiple-input
single-output (MISO) BC with per-antenna power constraints. Suboptimal and
low-complexity BD/ZF-BF precoding schemes are also presented, and their
achievable rates are compared against those with the optimal schemes.Comment: accepted in JSAC, special issue on cooperative communications on
cellular networks, June 201
Linear Precoding in Cooperative MIMO Cellular Networks with Limited Coordination Clusters
In a cooperative multiple-antenna downlink cellular network, maximization of
a concave function of user rates is considered. A new linear precoding
technique called soft interference nulling (SIN) is proposed, which performs at
least as well as zero-forcing (ZF) beamforming. All base stations share channel
state information, but each user's message is only routed to those that
participate in the user's coordination cluster. SIN precoding is particularly
useful when clusters of limited sizes overlap in the network, in which case
traditional techniques such as dirty paper coding or ZF do not directly apply.
The SIN precoder is computed by solving a sequence of convex optimization
problems. SIN under partial network coordination can outperform ZF under full
network coordination at moderate SNRs. Under overlapping coordination clusters,
SIN precoding achieves considerably higher throughput compared to myopic ZF,
especially when the clusters are large.Comment: 13 pages, 5 figure
Multiple-Input Multiple-Output Gaussian Broadcast Channels with Common and Confidential Messages
This paper considers the problem of the multiple-input multiple-output (MIMO)
Gaussian broadcast channel with two receivers (receivers 1 and 2) and two
messages: a common message intended for both receivers and a confidential
message intended only for receiver 1 but needing to be kept asymptotically
perfectly secure from receiver 2. A matrix characterization of the secrecy
capacity region is established via a channel enhancement argument. The enhanced
channel is constructed by first splitting receiver 1 into two virtual receivers
and then enhancing only the virtual receiver that decodes the confidential
message. The secrecy capacity region of the enhanced channel is characterized
using an extremal entropy inequality previously established for characterizing
the capacity region of a degraded compound MIMO Gaussian broadcast channel.Comment: Submitted to the IEEE Transactions on Information Theory, July 200
Linear Precoding and Equalization for Network MIMO with Partial Cooperation
A cellular multiple-input multiple-output (MIMO) downlink system is studied
in which each base station (BS) transmits to some of the users, so that each
user receives its intended signal from a subset of the BSs. This scenario is
referred to as network MIMO with partial cooperation, since only a subset of
the BSs are able to coordinate their transmission towards any user. The focus
of this paper is on the optimization of linear beamforming strategies at the
BSs and at the users for network MIMO with partial cooperation. Individual
power constraints at the BSs are enforced, along with constraints on the number
of streams per user. It is first shown that the system is equivalent to a MIMO
interference channel with generalized linear constraints (MIMO-IFC-GC). The
problems of maximizing the sum-rate(SR) and minimizing the weighted sum mean
square error (WSMSE) of the data estimates are non-convex, and suboptimal
solutions with reasonable complexity need to be devised. Based on this,
suboptimal techniques that aim at maximizing the sum-rate for the MIMO-IFC-GC
are reviewed from recent literature and extended to the MIMO-IFC-GC where
necessary. Novel designs that aim at minimizing the WSMSE are then proposed.
Extensive numerical simulations are provided to compare the performance of the
considered schemes for realistic cellular systems.Comment: 13 pages, 5 figures, published in IEEE Transactions on Vehicular
Technology, June 201
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