In this paper, we derive the Cramer-Rao lower bound (CRLB) for a newly developed approach for direction of arrival (DoA) estimation in hybrid analog/digital (HAD) receivers under snapshot constraints. In such cases, the inherent structure of the received signals can be exploited for reliable DoA estimation rather than using statistical averaging techniques. One approach to exploit this structure is the matrix pencil method (MPM). Unfortunately, existing HAD receivers tangle the signals at the output of the HAD receiver, hindering the direct use of the MPM. To address this difficulty, an approach developed in [1] enables the MPM to expose the structure of the output signal of the analog combiner by leveraging periodic, potentially unknown signals to disentangle the output of the HAD receiver. We derive the CRLB for this approach and show that it yields output signals resembling those of a fully-digital receiver, albeit with a snapshot penalty. Numerical simulations show that the developed approach achieves performance within a small gap of the corresponding CRLB and outperforms existing counterparts.

We take a fresh look at the non-coherent communication scenario in which the coherence time of the channel spans one time slot. We show that, in agreement with established information-theoretic results, the symbol error rate (SER) achieved with scalar signalling constellations saturates beyond a rate-dependent signal-to-noise ratio. More interestingly, we show that the SER saturation value is monotonically decreasing with the number of receive antennas, establishing that practical efficient communication over this, traditionally unpopular, channel is possible. In addition to additive Gaussian noise, we assume that the received signal is also contaminated by an additive non-Gaussian jamming signal. The impact of such jamming is considered under various channel conditions and detection techniques, which include optimal maximum likelihood and detection based on Gaussian approximations. Analytical findings are supported by numerical simulations.

We propose a low-complexity downlink channel covariance matrix estimation for massive multiple-input multiple-output systems in which the base station (BS) is equipped with a uniform rectangular antenna array (URA). This scheme can be expressed in the form of an affine transformation which depends only on the uplink and downlink carrier frequencies, and the BS array configurations. An upper bound on the estimation error is derived, which shows that the accuracy of the proposed scheme increases with the number of URA antennas and the compactness and differentiability class of the periodic extension of a non-linearly transformed version of the angular power spread. The performance superiority of the proposed scheme over its existing counterparts is confirmed through simulations.

This paper considers channel-unaware two-way relay networks (TWRNs) with a single-antenna decode-and-forward (DF) relay. These networks typically rely on differential signalling which results in error propagation and error floors. To overcome this drawback, we develop a novel differential signalling scheme, which, in contrast with existing ones, does not cause detection errors to propagate from one block to the next. We obtain a union bound on the block error rate (BLER), which shows that the proposed differential method exhibits superior performance to existing schemes, eliminates error floors and results in a BLER that decays as the inverse of the signal-to-noise ratio. Furthermore, the proposed scheme removes the unitarity restriction on the transmitted symbols in conventional differential signalling. Therefore, non-unitary symbols can be used in the differential signalling which provides more degrees of design freedom and better performance. Our numerical results confirm our analytical findings.

We consider a scenario in which a transmitter sends complex multidimensional symbols to a receiver in the presence of a proactive continuous jammer emitting a zero-mean complex Gaussian signal over an unknown complex Gaussian channel. The complex Gaussian signal transmitted over the unknown complex Gaussian channel induces a non-Gaussian signal at the receiver. We develop the optimal maximum likelihood (ML) detector for cases in which the receiver has full channel state information (CSI), full channel distribution information (CDI), or partial CDI about the transmitter channel. The jammer CDI is either partially or fully available at the receiver. We identify cases in which the non-Gaussian signals resulting from the jammer's transmission can be approximated by Gaussian signals to reduce the computational cost without compromising optimality of detection. Furthermore, we identify cases in which the Gaussian approximation ML detector is not equivalent to the exact ML detector. In these cases, we show that the advantage of the exact ML detector over the Gaussian approximation one can be significant.

One of the key weaknesses of low-density parity-check (LDPC) codes is the error floor that they typically exhibit at high signal-to-noise ratios (SNRs). Such an error floor is usually attributed to problematic structures known as trapping sets (TSs). The overwhelming majority of existing error floor estimation schemes consider the case of binary phase shift keying (BPSK) signalling. Unfortunately, these schemes are not readily extensible to estimate the error floor of high order LDPC coded modulation systems considered herein. To provide such a scheme, in this work, we use mean-shift importance sampling (MS-IS) to develop a novel error floor estimation methodology for high-order pulse amplitude modulation (PAM) and quadrature amplitude modulation (QAM) LDPC coded systems. First, a computationally efficient graphical-based approach is used to identify the TSs of a given LDPC code. Subsequently, a novel analytical approach is devised to identify the TSs that are likely to have a higher contribution in the error floor. These TSs are referred to as potentially dominant TSs (PDTSs). Finally, a new methodology for categorizing the PDTSs into equivalence classes is developed. A representative PDTS of each equivalence class is chosen and an MS-IS framework is devised to obtain the error rate corresponding to each equivalence class. To arrive at the desired MS-IS scheme, we develop an algorithm that invokes the geometry of the constellation to determine the MS value. In contrast with the conventional MS-IS method used in BPSK signalling, in the proposed MS-IS scheme, the MS value is a variable that is determined based on the TS and the transmitted codeword. The computational complexity of the three main steps of our methodology, viz. extracting the PDTSs, determining the MS values, and applying the MS-IS scheme, depends merely on the size of the constellation and the structure of the code, but not on the SNR. Numerical simulations confirm the efficacy and accuracy of the proposed technique at different SNRs.

Designing multidimensional constellations (MdCs) is an integral part of sparse code multiple access (SCMA). Since the optimal maximum a posteriori (MAP) receiver for SCMA is too complex in most applications, one highly popular technique is the near-optimal message passing algorithm (MPA), where its performance improves with increasing the signal-to-noise ratio (SNR) and the number of iterations. When the number of MPA iterations has to be limited (e.g., low-latency and/or low-complexity and/or energy-sensitive applications), the performance gap between MAP and MPA becomes significant, especially at low-to-medium SNRs. Inspired by the promising features of hypercubes when used along with bit-interleaved coded modulation, we construct novel MdCs which are based on a unitary rotation of a hypercube by a rotation angle that aims to achieve the minimum frame-error-rate (FER). By exploiting special properties of hypercubes, we fit a second-order rational polynomial to a few measured FER samples, and find a close-to-optimal rotation angle at each SNR and MPA iteration. Our proposed MdCs provide substantial performance gains (as much as 2 dB) in comparison to the best known SCMA MdCs in the literature, especially in low-to-medium SNR regions when the number of MPA iterations has to be low, and in the presence of 5G-compliant LDPC codes.

Sparse code multiple access (SCMA) is a potential non-orthogonal multiple access candidate for future wireless systems. The key performance indicators (KPIs) of uplink SCMA multidimensional constellations (MdCs) that should be considered in their design process have recently been identified in conjunction with the LTE turbo code for different channel scenarios. However, it is questionable whether the same KPIs are applicable to designing MdCs when a different error correcting code is employed. In this paper, we investigate the effect of the high-rate and low-rate 5G low density parity check (LDPC) codes on determining KPIs in designing MdCs for uplink SCMA systems under various channel scenarios. Through simulations, we show that similar results to the LTE turbo coded case occur in the presence of 5G LDPC code, with one notable exception over one specific scenario. The exception is in the performance of one MdC, which has a low number of distinct points; its performance is significantly worse than predicted by the KPIs when the low-rate 5G LDPC code is employed. This phenomenon happens due to the inherent structure of the 5G LDPC code, in which we propose a pseudorandom interleaver to rectify the problem.

We consider channel-unaware two-way relay networks in which two single-antenna nodes exchange information through multiple single-antenna half-duplex amplify-and-forward relays. For these networks, we develop a novel self-interference (SI) cancellation scheme that does not invoke channel information neither at the relays nor at the nodes. Unlike its previous counterparts, the scheme proposed herein enables perfect SI cancellation even when the relays have a single antenna each. This was not possible previously without employing an even number of antennas at each relay. In the first phase of the proposed scheme, the network is operated in a one-way relaying mode, whereas in the second phase it is operated in a two-way relaying mode. In the latter mode, the vectors received by the nodes at the end of the first phase are used to completely eliminate SI, without estimating the channel as in existing methodologies. To analyze the effectiveness of the proposed scheme, we derive upper bounds on the pairwise error probability for the Alamouti and SP(2) codes. Using these bounds, we investigate the dependence of the system performance on the data rate. In addition, we show that for all data rates, the proposed scheme outperforms previously proposed ones. Theoretical results are confirmed by simulations.

Due to the battery-limited nature of mobile devices, improving energy efficiency (EE) of individual users and ensuring EE fairness among those users are one of the key design issues in uplink transmission of cellular networks. In this paper, we consider the joint optimization of discrete power and resource blocks allocations to maximize the minimum EE among users subject to individual power budget constraints. The optimization problem is combinatorial. Thus, we propose an efficient algorithm, based on semidefinite relaxation with Gaussian randomization, to solve the resultant non-convex problem in polynomial time complexity. The numerical results show how well the proposed algorithm performs against the optimal one and indicate the impact of discrete power levels on the fairness-oriented EE optimization.

This paper considers channel-unaware two-way relay networks in which two single-antenna nodes exchange information via multiple non-regenerative relays, each with multiple antennas. A novel self-interference cancellation scheme for distributed differential space-time signalling is developed. Despite the absence of channel-state information, this scheme enables self-interference to be completely eliminated, thereby maximizing the signal-to-interference-plus-noise-ratio of the nodes. First, we obtain a lower bound on the pairwise error probability (PEP) under residual self-interference and we show that this bound approaches a non-zero constant at high signal-to-noise ratios (SNRs), indicating a zero diversity order and an asymptotic error floor. Second, we derive a necessary and sufficient condition for the proposed scheme to eliminate self-interference perfectly. Proper operation of this scheme requires the relays to have an even number of active antennas and for relays with odd number of active antennas, such a scheme does not exist. Third, we show that, when self-interference is cancelled perfectly, the error floor vanishes and an upper bound on the PEP approaches zero at high SNRs. In this case, it is shown that the diversity gain is equal to the number of relays and is independent of the number of antennas per relay. Finally, it is shown that the coding gain increases with increasing the number of antennas per relay and converges to a constant as the number of relay antennas becomes large.

Terminal relays are expected to play a key role in facilitating the communication between base stations and low-cost power-constrained cellular Internet of Things (IoT) devices. However, these mobile relays require a mechanism by which they can autonomously assign the available resource blocks (RBs) to their assisted IoT devices in the absence of channel state information (CSI) and with minimal assignment conflicts. To address this problem, in this paper, we develop an autonomous sequence-based RB assignment scheme that dispenses with CSI. The sequences underlying the proposed scheme are designed using the Chinese remainder theorem (CRT). In particular, the CRT is used to combine the cyclic sequences generated by simple cyclic group structures into longer ones. The combining process introduces additional degrees of freedom in sequence generation, thereby enriching the set of RB assignment sequences. Simulation results show that the sequences generated by the proposed CRT-based scheme outperform those generated by currently available autonomous ones.

In this paper, we consider the design of multi-resolution non-coherent multiple-input multiple-output (MIMO) systems that enable Unequal Error Protection (UEP). A method for designing multi-layer non-coherent Grassmannian constellations is introduced. Specifically, the proposed method yields multi-layer constellations that are amenable to a natural set partitioning strategy. The resulting subsets from such partitioning are used to encode the more protected symbols. On the other hand, the less protected symbols are mapped to points within these subsets. Furthermore, we present two methods to establish the link between the gain of the more protected layer and the design parameters of this construction. Finally, we exploit the underlying structure to develop a sequential decoding approach. Numerical results suggest that employing such decoding scheme leads to computational savings with respect to the optimal decoding while maintaining comparable performance.

In this paper, we design and compare multilevel polar coding (MLPC) and bit-interleaved polar coded modulation (BIPCM) for uplink sparse code multiple access (SCMA) systems that operate over fast and block fading channels. Both successive cancellation (SC) and successive cancellation list (SCL) decoding algorithms are considered. Simulation results show that, with either decoder, BIPCM performs better than its MLPC counterpart. Also, both BIPCM and MLPC exhibit a performance advantage over LTE turbo-coded and WiMAX LDPC SCMA systems when the SCL technique is used for decoding.

We consider the design of space-time codes for the multiple-input multiple-output multicast communication systems with two classes of receivers. The first class comprises high-resolution (HR) receivers which have access to reliable channel state information (CSI) and can perform coherent detection, and the second class comprises low-resolution (LR) receivers which do not have access to CSI and can only perform non-coherent detection. We propose a layered encoding structure in which LR information available to both classes of receivers is encoded using Grassmannian constellations, and an incremental component, which is available only to the HR receivers, is encoded in the particular bases of the transmitted Grassmannian constellation points, thereby giving rise to constellations on the Stiefel manifold. The proposed structure enables reliable coherent communication of the HR information without compromising the reliability with which the basic LR information is non-coherently communicated. To effect rate-efficient communication of the incremental, HR layer, we use optimization methods on the Stiefel manifold to develop a novel technique for designing the unitary constellations directly. This approach alleviates the restriction imposed by the traditional techniques in which unitary space-time codes are constructed from scalar constellations. As such, this approach enables better control of the distance spectrum of the developed constellations and more effective utilization of the degrees of freedom that underlie the Stiefel manifold. For the LR receivers, we use maximum likelihood detection, whereas for the HR receivers, we develop a computationally-efficient two-step sequential detector which detects the LR information prior to detecting the incremental component superimposed on it. The detectors and the layered structure with the aforementioned constellations enable full diversity and maximum degrees of freedom to be achieved on the Grassmann and Stiefel manifolds.

We consider joint optimization of user-to-base-station (BS) association, and time-frequency resource block (RB) and power allocation in heterogeneous networks (HetNets). The objective is to develop a design: 1) that maximizes the number of users accommodated in the network while satisfying their quality of service demands and 2) that minimizes usage of the resources required to meet these demands. We investigate two novel instances of HetNets with opportunistic RB-reuse. In the first instance, user-to-BS associations and power allocations can be time-shared, and the RBs can be reused during the signaling interval. For this instance, it is shown that the design problem can be approximated by a problem that yields tight convex upper and lower bounds on the objective. In contrast, the second instance represents a case in which the RBs can be reused, but the user-to-BS associations and power allocations are not time-shared, and hence, fixed throughout the signaling interval. The latter case gives rise to a combinatorial optimization problem, which we provide an approximate solution for by using a polynomial-complexity two-phase approach based on semidefinite relaxation with randomization.

Polar codes, ever since their introduction, have been shown to be very effective for various wireless communication channels. This together with their relatively low implementation complexity has made polar codes an attractive coding scheme for wireless communications. On the other hand, within the realm of non-coherent wireless MIMO communication, Grassmannian signalling has been shown to approach the ergodic capacity of frequency-flat block fading channels. In this paper, a novel methodology for designing polar codes that works effectively with Grassmannian signalling and a novel set partitioning algorithm for Grassmannian constellations are proposed. We compare the error rate performance of our design with that of existing schemes and show that a gain of over 1 dB over the previously known best technique, which is based on turbo codes, is possible, at much lower decoding complexity.

In this paper, we develop a geometry-inspired methodology for generating systematic and structured Grassmannian constellations with large cardinalities. In the proposed methodology we begin with a small close-to-optimal “parent” Grassmann constellation. Each point in this constellation is augmented with a number of “children” points, which are generated along a set of geodesics emanating from that point. These geodesics are chosen to ensure close-to-maximal spacing. In particular, the directions of the geodesics and the distance that each “children” point is moved are chosen to maximize the pairwise Frobenius distance between the resulting constellation points. Although finding these directions directly seems difficult, by embedding the Grassmann manifold on a sphere of larger dimension, we were able to develop structures that are not only simple to generate but that also yield constellations that, under certain conditions, satisfy the maximum distance criterion and lie within a decaying gap from a tight upper bound. Numerical results suggest that the performance of the new constellations is comparable to that of the ones generated directly and significantly better than the performance of the ones generated using the exponential map.

In this paper, proactive resource allocation based on user location for point-to-point communication over fading channels is introduced, whereby the source must transmit a packet when the user requests it within a deadline of a single time slot. We introduce a prediction model in which the source predicts the request arrival Tpslots ahead, where Tpdenotes the prediction window (PW) size. The source allocates energy to transmit some bits proactively for each time slot of the PW with the objective of reducing the transmission energy over the non-predictive case. The requests are predicted based on the user location utilizing the prior statistics about the user requests at each location. We also assume that the prediction is not perfect. We propose proactive scheduling policies to minimize the expected energy consumption required to transmit the requested packets under two different assumptions on the channel state information at the source. In the first scenario, offline scheduling, we assume the channel states are known a-priori at the source at the beginning of the PW. In the second scenario, online scheduling, it is assumed that the source has causal knowledge of the channel state. Numerical results are presented showing the gains achieved by using proactive scheduling policies compared with classical (reactive) networks. Simulation results also show that increasing the PW size leads to a significant reduction in the consumed transmission energy even with imperfect prediction.

This paper studies the optimal points in the capacity region of Gaussian multiple access channels (GMACs) with constant fading, multiple antennas, and various power constraints. The points of interest maximize general rate objectives that arise in practical communication scenarios. Achieving these points constitutes the task of jointly optimizing the time-sharing parameters, the input covariance matrices, and the order of decoding used by the successive interference cancellation receiver. To approach this problem, Carathéodory's theorem is invoked to represent time-sharing and decoding orders jointly as a finite-dimensional matrix variable. This variable enables us to use variational inequalities to extend results pertaining to problems with linear rate objectives to more general, potentially nonconvex, problems, and to obtain a necessary and sufficient condition for the optimality of the transmission parameters in a wide range of problems. Using the insights gained from this condition, we develop and analyze the convergence of an algorithm for solving, otherwise daunting, GMAC-based optimization problems.

In this paper, a forward decoding procedure is developed for the compress-and-forward (CF) relaying scheme. This procedure uses a layered framework and is based on exploiting a feature of the N -to-1 mapping inherent in the underlying Wyner-Ziv binning. It is shown that exploiting this mapping enables the relaxation of the constraint on the rate of the relay codewords representing the bin indices. For the cooperative multimessage network, the proposed procedure achieves the same rate region as the short-message noisy network coding (SNNC) scheme. However, this procedure is more advantageous for other networks including the two networks presented herein. The first network is a relay chain one with two destinations, whereas the second network is a partially cooperative multimessage one with three destinations. In both networks, side information is available to a subset of the decoding nodes, but not to the rest of the nodes, and in both cases, the network benefits from the relaxation of the rate of the CF bin indices. This relaxation results in rate regions larger than those achieved by the conventional CF and SNNC.

In this paper, joint designs of data routes and resource allocations are developed for generic half-duplex multicarrier wireless networks in which each subcarrier can be reused by multiple links. Two instances are considered. The first instance pertains to the general case in which each subcarrier can be time-shared by multiple links, whereas the second instance pertains to a special case in which time-sharing is not allowed and a subcarrier, once assigned to a set of links, is used by those links throughout the signalling interval. Novel frameworks are developed to optimize the joint design of data routes, subcarrier schedules, and power allocations. These design problems are nonconvex and hence difficult to solve. To circumvent this difficulty, efficient techniques based on geometric programming are developed to obtain locally optimal solutions. Numerical results show that the designs developed in both instances yield performance that is superior to that of their counterparts in which frequency-reuse is not allowed.

In this paper, we develop two approaches for designing unitarily-constrained space-time block codes, which are suitable for communicating high-resolution information in layered multiple-input multiple-output broadcast channels. Unlike existing space-time codes, which are usually synthesized from standard phase-shift keying (PSK) or quadrature amplitude modulation constellations, the space-time codes proposed herein are designed using direct optimization over the unitary group. In comparison with conventional unitary space-time block codes, including Alamouti code with PSK constellations, the space-time codes generated by the proposed approaches exhibit significantly better performance, more favorable distance spectra and more effective utilization of the degrees of freedom that underlie the unitary group.

This paper considers a multicarrier communication system assisted by multiple relays, one for each subcarrier. The total power emitted by the source and the total power emitted by the relays are constrained to be less than the respective power budgets. The relays are assumed to operate in the full-duplex decode-and-forward mode, and the objective is to design the codebooks of the source and the relays jointly with the power allocations that maximize the total data rate that can be reliably decoded at the destination. To approach this goal, the design problem is cast as an optimization problem, which is unfortunately nonconvex and difficult to solve. The Karush-Kuhn-Tucker (KKT) system corresponding to this problem is analyzed, and despite the nonconvexity of the problem, we were able to use the KKT system to develop an efficient technique for solving it optimally.

The objective of this work is to determine a close-to-optimal user-to-base station (BS) association that maximizes the number of users served by the downlink of a heterogeneous network (HetNet). Such an association must not only ensure that the number of accommodated users is maximized but also that the network resources are efficiently utilized and the users' quality of service (QoS) demands are met. In its simplest form, the optimization problem that underlies this association is combinatorial NP-hard and the difficulty of solving it is aggravated in HetNets by the disparity of transmit powers and BS computational capabilities. To find close-to- optimal user-to-BS associations, we develop a two- phase method based on semidefinite relaxation (SDR) with randomization, which is a powerful method for solving a class of combinatorial problems. Unlike the majority of other polynomial complexity techniques, SDR has a provable approximation accuracy. Numerical examples show that, in comparison with other user-to-BS association approaches, the one proposed herein enables more efficient utilization of resources and a significantly higher number of users to be accommodated.

One approach for analyzing the high signal-to-noise ratio (SNR) capacity of non-coherent wireless communication systems is to ignore the noise component of the received signal in the computation of its differential entropy. In this paper we consider the error incurred by this approximation when the transmitter and the receiver have one antenna each, and the noise has a Gaussian distribution. For a general instance of this case, we show that the approximation error decays as 1/SNR. In addition, we consider the special instance in which the received signal corresponds to a signal transmitted over a channel with additive Gaussian noise and a Gaussian fading coefficient. For that case, we provide an explicit expression for the second order term of the Taylor series expansion of the differential entropy. To circumvent the difficulty that arises in the direct computation of that term, we invoke Schwartz's inequality to obtain an efficiently computable bound on it, and we provide examples that illustrate the utility of this bound.

We consider the design of space-time codes for multi-resolution multiple-input multiple-output (MIMO) broadcast communication systems. Two classes of receivers are considered: high-resolution (HR) receivers, which have access to reliable channel state information (CSI) and can perform coherent detection, and low-resolution (LR) receivers which do not have access to CSI and can only perform non-coherent detection. We propose a layered encoding structure, whereby, for the LR receivers, the transmitted codewords are chosen to be points on the Grassmann manifold whereas, for the HR receivers, incremental information is encoded in the particular bases of the transmitted codewords, thereby representing points on the Stiefel manifold. For the HR receivers, we develop a computationally-efficient two-step detector. Using this detector, we show that the proposed structure enables reliable coherent communication of the incremental HR information without compromising the reliability with which the basic LR information is non-coherently communicated. We also show that this structure enables full diversity to be achieved for both LR and HR receivers. Finally, we show that this structure achieves the maximum number of degrees of freedom for non-coherent LR channels and coherent HR channels with unitarily-constrained input signals.

This paper considers the ergodic noncoherent capacity of a multiple-input multiple-output frequency-flat block Rayleigh fading full duplex relay channel at high signal-to-noise ratios (SNRs). It is shown that, for these SNRs, restricting the input distribution to be isotropic on a compact Grassmann manifold maximizes an upper bound on the cut-set bound. Furthermore, it is shown that, from a degrees of freedom point of view, no relaying is necessary and Grassmannian signalling at the source achieves the upper bound within an SNR-independent gap. When the source-relay channel is sufficiently stronger than the source-destination and relay-destination channels, it is shown that, with the number of relay transmit antennas appropriately chosen, a Grassmannian decode-and-forward scheme, which is devised herein, achieves the ergodic noncoherent capacity of the relay channel within an approximation gap that goes to zero as the SNR goes to infinity. Closed-form expressions for the optimal number of relay transmit antennas indicate that this number decreases monotonically with the source transmit power.

The goal of this paper is to determine the data routes, subchannel schedules, and power allocations that maximize a weighted-sum rate of the data communicated over a generic OFDMA wireless network in which the nodes are capable of simultaneously transmitting, receiving and relaying data. Two instances are considered. In the first instance, subchannels are allowed to be time-shared by multiple links, whereas in the second instance, each subchannel is exclusively used by one of the links. Using a change of variables, the first problem is transformed into a convex form. In contrast, the second problem is not amenable to such a transformation and results in a complex mixed integer optimization problem. To develop insight into this problem, we utilize the first instance to obtain efficiently computable lower and upper bounds on the weighted-sum rate that can be achieved in the absence of time-sharing. Another lower bound is obtained by enforcing the scheduling constraints through additional power constraints and a monomial approximation technique to formulate the design problem as a geometric program. Numerical investigations show that the obtained rates are higher when time-sharing is allowed, and that the lower bounds on rates in the absence of time-sharing are relatively tight.

This paper considers a joint design that incorporates the physical, medium access and network layers of a generic OFDMA-based wireless network with an ad hoc topology. The network employs channel reuse, whereby a frequency subchannel might be used simultaneously by multiple nodes. In addition to being a source and/or a destination, each node can act as a half-duplex relay to assist other nodes. The design objective is to determine the jointly optimal data routes and subchannel power allocations that maximize a weighted sum of the rates that can be reliably communicated over the network. Assuming that the signals transmitted by the nodes are Gaussian, the joint cross layer design of routing and power allocation is cast as an optimization problem. Unfortunately, this problem is non-convex, and hence difficult to solve. To circumvent this difficulty, an efficient technique based on geometric programming is developed to obtain a local solution that satisfies the Karush-Kuhn-Tucker necessary optimality conditions. Numerical results show that, despite the potential suboptimality of the obtained solution, for some network scenarios, it offers significant gains over optimal scheduling-based schemes in which a frequency band is allowed to be used by one node only at any time instant.

In this paper we study the optimality of particular points in the capacity region of Gaussian multiple access channels (GMACs) with various power constraints. The points of interest maximize general rate objectives that arise in practical communication scenarios. Achieving these points constitutes the task of jointly optimizing time-sharing parameters, input covariance matrices and the order of decoding used by the successive interference cancellation receiver. To approach this problem Carathéodory's theorem is invoked to represent time-sharing and decoding orders jointly as a finite-dimensional matrix variable. This variable enables us to use variational inequalities to extend results pertaining to problems with linear objectives to more general, potentially nonconvex, problems. In particular, it is shown that for arbitrary objectives, if the power constraints are convex, it suffices for each user to use only one covariance matrix in all its allocated time slots. On the other hand, for arbitrary power constraints, if the objective is linear no time-sharing is necessary. These results significantly reduce the design complexity and render optimal signalling over GMAC more amenable to implementation in various practical scenarios.

The downlink of a coordinated multi-cell distributed antenna system is considered. In [1], coordinated port selection was shown to achieve significant performance gains. However, in the system considered therein, the ports in each cell were constrained to transmit only to user terminals (UTs) in that cell. In this work, we consider a generalization of the problem considered in [1] by alleviating this constraint. We formulate the problem of determining the ports that maximize the minimum signal to interference plus noise ratio observed by the UTs as a binary-constrained optimization problem. Observing that it is NP-hard, we propose a semidefinite relaxation and Gaussian randomization based technique to obtain close-to-optimal solutions. Our simulation results show that the performance achieved by the proposed technique approaches that of the optimal solution. It is also shown that the proposed technique outperforms the one in [1], particularly for cell-edge UTs, albeit with an increased computational complexity.

We consider a three-node quasi-static communication system with a full-duplex relay. The goal is to determine the relaying mode that enables rate-efficient communication under given channel conditions. To achieve this goal, we consider a generalized scheme that subsumes the decode-and-forward (DF) and compress-and-forward (CF) schemes as special cases. The generalized scheme is considered when the source and relay signals are synthesized from commonly-used Gaussian codebooks, which are shown to be capacity achieving in two asymptotic cases: perfect relay-destination link and broken source-destination link. Studying the generalized DF-CF scheme, it is shown that, for two non-asymptotic cases in which the signal-to-noise ratios (SNRs) of the links satisfy certain conditions, this scheme reduces to either DF or CF. For another set of non-asymptotic SNRs, the generalized scheme is shown to yield strictly higher rates than both DF and CF. Despite the complexity of the generalized scheme, its rate advantage over DF and CF is shown to be upper bounded by 0.5 bits per channel use. This indicates that the practical benefit of the analysis of this scheme is to enable selecting the relaying mode that suits a given channel realization. Numerical results show that, under Rayleigh fading conditions, this selection yields significant gains over fixed DF and CF.

This paper considers a Gaussian broadcast channel with two unmatched degraded components, three individual messages, and a common message that is intended for all three receivers. It is shown that for this channel, superposition coding with Gaussian signalling is sufficient to achieve every point in the capacity region.

The maximum data rate that can be achieved by the strictly causal full-duplex amplify-and-forward (AF) scheme in general Gaussian relay channels is achieved by Gaussian codebooks and can be cast as the solution of an optimization problem of the input transmit covariance and relay precoder. This problem possesses an intricate nonconvex structure and is hence difficult to solve. To circumvent this difficulty, the relay precoder is assumed to be given and then the Karush-Kuhn-Tucker conditions are used to obtain closed form expressions for the optimal input covariance corresponding to that precoder. These expressions are used to show that subdiagonal precoders suffice to attain the maximum achievable rate of the AF scheme at any source transmit power. In addition to significantly reducing the effort expended in searching for the optimal relay precoder, this observation enables us to find the optimal precoders at low and high source transmit powers. For asymptotically low transmit powers, the optimal relaying mechanism is shown to possess an interlacing structure, thereby resembling half-duplex operation. In contrast, for asymptotically high transmit powers, it is optimal for the relay to be silent. The asymptotic analysis enables us to develop an explicit formulation for a suboptimal precoder that, at intermediate source transmit powers, are shown numerically to outperform asymptotically optimal precoders.

The focus of this paper is on studying the tradeoff between the sum efficiency and Jain's fairness index in general resource allocation problems. Such problems are frequently encountered in wireless communication systems with M users. Among the commonly-used methods to approach these problems is the one based on the α-fair policy. Analyzing this policy, it is shown that it does not necessarily achieve the optimal Efficiency-Jain tradeoff (EJT) except for the case of M=2 users. When the number of users M>2, it is shown that the gap between the efficiency achieved by the α-fair policy and that achieved by the optimal EJT policy for the same Jain's index can be unbounded. Finding the optimal EJT corresponds to solving a family of potentially difficult non-convex optimization problems. To alleviate this difficulty, we derive sufficient conditions which are shown to be sharp and naturally satisfied in various radio resource allocation problems. These conditions provide us with a means for identifying cases in which finding the optimal EJT and the rate vectors that achieve it can be reformulated as convex optimization problems. The new formulations are used to devise computationally-efficient resource schedulers that enable the optimal EJT to be achieved for both quasi-static and ergodic time-varying communication scenarios. Analytical findings are confirmed by numerical examples.

We consider the downlink of a cellular system in which each base station (BS) has multiple distributed antenna ports that are geographically dispersed over the cell. The goal of the BSs is to improve cell-edge performance by selecting the subset of ports that maximizes the minimum signal-to-interference-plus-noise ratio of the user terminals in a coordinated manner. This problem is cast as a binary-constrained optimization problem, which is known to be NP-hard. To circumvent this difficulty, the semidefinite relaxation technique is used to efficiently generate Gaussian distributed vectors. Simulation results show that rounding a relatively small number of these vectors yields close-to-optimal solutions of the original problem.

In this paper an OFDMA-based wireless ad hoc network is considered. In addition to the potential of being a source and/or a destination, each node is assumed to be capable of decoding and forwarding its received packets to other nodes in the network. The goal is to determine the optimal data routes, subchannel schedules, and power allocations that maximize a weighted sum rate of the data communicated over the network. Two instances of this problem are considered. In the first instance, each subchannel is exclusively used on one of the links, whereas in the second instance subchannels are allowed to be time shared by multiple links. The first problem gives rise to an NP-hard mixed integer optimization problem that is difficult to solve. In contrast, using a change of variables, the second problem is cast in a convex form, which is amenable to highly efficient interior point solvers. Simulation results suggest that the gain in the weighted sum rate achieved by the relaxation in the second problem over that achieved by the original mixed integer problem is negligible for small networks, and increases with the size of the network.

The capacity of the amplify-and-forward (AF) scheme in general full-duplex Gaussian relay channels is achieved by Gaussian codebooks and can be cast as the solution of an optimization problem of the input transmit covariance and the relay precoder. This problem is non-convex. To circumvent this difficulty, the Karush-Kuhn-Tucker (KKT) conditions are used to obtain closed form expressions of the optimal input covariance that corresponds to an arbitrary relay precoder. Using these expressions, it is shown the maximum rate of the AF scheme is achieved by subdiagonal precoders. This observation is used to facilitate the search for the optimal relay precoder, and to show that at high transmit powers, it is optimal for the relay to remain silent and, at low transmit powers, it is optimal to operate in a mode that resembles half-duplex operation.

This paper considers the ergodic non-coherent capacity of a multiple-input multiple-output frequency-flat block Rayleigh fading relay channel. It is shown that for this channel restricting the input distribution to be isotropic on a compact Grassmann manifold maximizes an upper bound on the cut-set bound at high signal-to-noise ratios (SNRs). Furthermore, Grassmannian signalling at the source achieves this bound within an SNR-independent gap. For moderate-to-high SNRs, a Grassmannian decode-and-forward (DF) relaying scheme is devised, and the optimal signalling dimensionality that minimizes the gap to the upper bound is obtained.

In this paper, we study tradeoff policies between efficiency and the Jain's fairness index of the benefits received by M users in general resource allocation scenarios. Analyzing the commonly-used α-fair tradeoff policy, it is shown that, except for the case of M =2 users, this policy does not necessarily achieve the optimal Efficiency-Jain tradeoff. In particular, it is shown that, when the number of users M >;2, the gap between the efficiency achieved by the α-fair and the optimal Efficiency-Jain tradeoff policy can be unbounded, for the same Jain's index. Finding the optimal Efficiency-Jain tradeoff for arbitrary set of admissible benefits is generally difficult. To alleviate this difficulty, we derive sufficient conditions, which, when satisfied by the set of admissible benefits, lead to efficiently computable optimal tradeoff and benefit vectors. Numerical results for a typical communication network scenario are provided to confirm analytical findings.

In this paper, we consider coordinated downlink transmission in a cellular system wherein each base station (BS) has multiple geographically dispersed antenna ports. Each port uses a fixed transmit power and the goal of the BSs is to collectively determine the subset of ports and the corresponding beam steering coefficients that maximize the minimum signal-to-interference-plus-noise ratio observed by the user terminals. This problem is NP-hard. To circumvent this difficulty, a two-stage polynomial-complexity technique that relies on semidefinite relaxation and Gaussian randomization is developed. It is shown that, for the considered scenarios, the port state vectors and beam steering coefficients generated by the proposed technique yield a performance comparable to that yielded by exhaustive search, but with a significantly less computational complexity. It is also shown that the proposed technique results in significant power savings when compared with other transmission strategies proposed in the literature.

Terminal relaying offers an effective means for improving the performance of OFDMA-based wireless networks. However, \revrr{with the increase in the number of relaying terminals (RTs), their coordination becomes a cumbersome task.} To address this drawback, in this paper an autonomous scheme is proposed whereby the RTs assign resource blocks (RBs) to incoming wireless terminals (WTs) in a way that minimizes the number of hit occurrences at which the same RB is assigned to multiple WTs. The proposed scheme uses cyclic group generators to determine the sequence of RBs to be assigned by each RT. This scheme is particularly beneficial in terminal relaying systems in which the distribution of the WTs is nonuniform and the channel quality indicators are not available. Simulation results show that the proposed scheme performs significantly better than currently available autonomous assignment schemes.

In this paper, the generalization of the decode-and-forward (DF) and compress-and-forward (CF) relaying schemes is studied for the case in which Gaussian codebooks are used for signalling over scalar Gaussian memoryless channels. Three SNR regions are identified wherein the generalized DF-CF scheme reduces to either the DF or the CF scheme. In addition, it is shown that there is an SNR region in which the generalized DF-CF scheme can be more advantageous than both schemes.

Effective coordination between terminal relays in OFDMA-based systems becomes a cumbersome task with the increase in the number of these relays. To address this drawback, in this paper an autonomous scheme is proposed whereby the relays assign resource blocks to incoming wireless terminals (WTs) in a way that minimizes the number of hit occurrences at which the same resource block is assigned to other WTs. The proposed scheme uses cyclic group generators to determine the sequence of resource blocks to be assigned by each relay. This scheme is particularly beneficial in self-organizing terminal relaying systems in which the distribution of the wireless terminals is nonuniform. Simulation results show that the performance of the proposed scheme is significantly better than that of assignment schemes proposed in the literature.

This paper presents two polynomial-complexity techniques for assigning Gray-like binary labels to arbitrary Grassmannian constellations. In the first technique, the constellation of interest, C, is matched directly to an auxiliary constellation that can be readily Gray labelled. The optimal matching in this technique can be obtained efficiently, but its application is limited to cases in which an auxiliary constellation with a geometric structure that resembles that of C can be identified. In the second technique no auxiliary constellation is required and the labels are generated by matching the distance spectrum of C with that of a hypothetical constellation that is assumed to be perfectly Gray labelled. Optimal matching in this case is computationally prohibitive. Instead, an efficient suboptimal matching algorithm is proposed. When compared with several existing schemes, the proposed labellings provide better performance in both uncoded and BICM-based non-coherent MIMO systems with iterative demapping and decoding (IDD). Furthermore, with the proposed labels, the Grassmannian-based BICM-IDD scheme performs better than a training-based counterpart that employs the Golden code and optimal demapping.

This paper considers a Gaussian broadcast channel with two unmatched degraded components, three particular messages, and a common message that is intended for all three receivers. It is shown that for this channel superposition coding and Gaussian signalling is sufficient to achieve every point in the capacity region.

Consider a scenario in which K users and a jammer have a limited power budget and share a common spectrum of N orthogonal tones. The goal of each user is to allocate its power across the N tones in such a way that maximizes the total sum rate that he/she can achieve, while treating the interference of other users and the jammer's signal as additive Gaussian noise. The jammer, on the other hand, wishes to allocate its power in such a way that minimizes the utility of the whole system; that being the total sum of the rates communicated over the network. For this non-cooperative game, we propose a generalized version of the existing iterative water-filling algorithm whereby the users and the jammer update their power allocations in a greedy manner. We study conditions under which the generalized iterative water-filling algorithm converges to a Nash equilibrium of the game. The conditions that we derive in this paper depend only on the system parameters, and hence can be checked a priori.

In this paper we consider a Digital Subscriber Line (DSL) system with N orthogonal narrowband tones. Each user has a limited power budget, and our goal is to determine the power allocation of each user that enables the ‘user capacity’ of the system to be approached. In this paper, we use ‘user capacity’ to denote the maximum number of users that can be supported by the system, provided that each user is guaranteed to have a data rate that lies within a prescribed range. Finding a power allocation that enables this capacity to be approached directly can be quite cumbersome because it involves solving a (non-convex) integer-program. In order to circumvent this difficulty, in this paper we propose an alternate approach that is based on exploiting the fairness and per-tone convexity of the harmonic mean-rate objective. Using these features, we devise a computationally-efficient power allocation technique that enables the user capacity of the DSL system to be approached more closely than power allocation techniques that are more computationally demanding.

Finding the power allocations that maximize the sum-rate of a K-user N-tone Digital Subscriber Line (DSL) system is known to be NP-hard. In this paper we devise a polynomial-time algorithm to approximate the maximum sum rate of the system. The development of this algorithm is guided by the fact that, to approach the sumrate maximum, the users should operate in an FDMA-mode over frequency tones where the crosstalk coefficients exceed a certain threshold, and should share the tones for which the crosstalk coefficients are sufficiently small. Drawing on this insight, the algorithm partitions the N tones into three sections and imposes an appropriate signalling structure on each section. The first section contains those tones for which the crosstalk coefficients are small and uses an iterative water-filling technique to determine the power allocations. The second section contains the tones with intermediate crosstalk coefficients and uses a primal-dual algorithm, and the third section contains the tones with large crosstalk coefficients and uses a dual FDMA algorithm. To decouple the overall optimization of power allocation across the three sections, we use tools from Lagrangian duality and sensitivity analysis to devise an iterative scheme that can optimally allocate each user's power budget to the three sections. Our numerical simulations, show that the sum-rate of the proposed algorithm is very close to that of the ‘optimal’ spectrum balancing algorithm, but requires considerably less computational effort.

This paper considers the design of both a transmitter and a receiver for noncoherent communication over a frequency-flat, richly scattered multiple-input multiple-output (MIMO) channel. The design is guided by the fact that at high signal-to-noise ratios (SNRs), the ergodic capacity of the channel can be achieved by input signals that are isotropically distributed on the (compact) Grassmann manifold. The first part of the paper considers the design of Grassmannian constellations that MIMIC the isotropic distribution. A subspace perturbation analysis is used to determine an appropriate metric for the distance between Grassmannian constellation points, and using this metric, greedy, direct and rotation-based techniques for designing constellations are proposed. These techniques offer different tradeoffs between the minimum distance of the constellation and the design complexity. In addition, the rotation-based technique results in constellations that have lower storage requirements and admit a natural ldquoquasi-set-partitioningrdquo binary labeling.

A bit-interleaved coded modulation (BICM) scheme with iterative (soft) demapping and decoding (IDD) is developed for non-coherent communication over a multiple-input multiple-output (MIMO) channel. The scheme exploits the underlying Grassmannian geometry of the signalling scheme that approaches the ergodic capacity of the non-coherent model at high signal-to-noise ratios. In particular, this geometry guides the construction of the constellation and the mapper at the transmitter, and gives rise to a computationally-efficient list-based demapping algorithm. The incorporation of a scheme that enables the decoder to augment the demapping list virtually eliminates the mild performance degradation of the efficient demapper. Simulation results demonstrate that at high data rates the proposed scheme can provide significantly better performance than several training-based BICM-IDD schemes.

We consider a single-user multiple-input multiple-output (MIMO) communication system in which the transmitter has access to both the channel covariance and the channel mean. For this scenario, we provide an explicit second-order approximation of the ergodic capacity of the channel, and we use this approximation to show that when the channel has a non-zero mean, the basis of the optimal input covariance matrix depends on the input signal power. (This basis is independent of the signal power in the zero-mean case.) The second-order approximation also provides insight into the way in which the low-signal-to-noise-ratio (SNR) optimal input covariance matrix is related to the optimal input covariance matrix at arbitrary SNRs. Furthermore, we show that the design of the input covariance matrix that optimizes the second-order approximation can be cast as a convex optimization problem for which the Karush-Kuhn-Tucker (KKT) conditions completely characterize the optimal solution. Using these conditions, we provide an efficient algorithm for obtaining second-order optimal input covariance matrices. The resulting covariances confirm our theoretical observation that, in general, the low-SNR optimal signal basis does not coincide with the optimal basis at higher SNRs. Finally, we show how our second-order design algorithm can be used to efficiently obtain input covariance matrices that provide ergodic rates that approach the ergodic capacity of the system.

We consider a multiple-input multiple-output (MIMO) wireless communication scenario in which the channel follows a general spatially-correlated complex Gaussian distribution with non-zero mean. We derive an explicit characterization of the optimal input covariance from an ergodic rate perspective for systems that operate at low SNRs. This characterization is in terms of the eigen decomposition of a matrix that depends on the mean and the covariance of the channel, and typically results in a beamforming strategy along the principal eigenvector of that matrix. Simulation results show the potential impact of (jointly) exploiting the mean and the covariance of the channel on the ergodic achievable rate at both low and moderate- to-high SNRs.

We consider a broadcast scenario in which a single transmitter wishes to send common, partially common and particular messages to several receivers over the product of unmatched parallel scalar Gaussian subchannels with a total power constraint. This scenario is a generalization of the 2-user 2-subchannel scenario that was studied earlier in the literature. In order to expose the signal structure and the difficulties that arise in generalizing the results on the 2-user 2-subchannel case to the case of K users and N subchannels, we consider a representative scenario with 3 users and 2 subchannels. For this case, we characterize the achievable rate region, and express the boundary points thereof as the solution of an optimization problem. This problem is not convex in the general case, but it provides insight that leads to tight inner and outer bounds on the capacity region that can be obtained efficiently via the solution of a convex Geometric Program (GP). (The GP also generates the corresponding power loads and partitions.) In addition to these bounds, we provide a (precise) GP formulation for the optimal power allocation problem for the 2-user 2-subchannel case.

A bit-interleaved coded modulation (BICM) scheme with iterative demapping and decoding (ID) is developed for non-coherent communication over a multiple-input multiple-output (MIMO) channel. The scheme exploits the underlying Grassmannian geometry of the signalling scheme that achieves the ergodic capacity of the non-coherent model at high signal-to-noise ratios. In particular, this geometry gives rise to an efficient list-based demapping algorithm that substantially reduces the computational complexity of the receiver. By allowing the decoder to augment the demapping list, the performance degradation of this efficient algorithm can be rendered insignificant. We compare the performance of the proposed scheme with that of a training-based BICM-ID scheme and show, via simulation, that the proposed scheme can provide significantly better performance at high data rates.

In this paper we derive analytic expressions for the boundaries of the search region of a recently proposed efficient detector for non-coherent reception of Grassmannian constellations. These boundaries are designed to ensure that the restriction of the detection search space to the enclosed region does not incur significant performance degradation. In addition, we show that the cardinality of the set of candidate constellation points, that is the number of constellation points that lie within the search space, approaches one at least as fast as the inverse of the square root of the received signal SNR

In this paper, a design method is developed for the class of linear-dispersion (LD) codes - a diverse set of space-time codes that subsumes several standard designs. The development begins by showing that for systems that employ a large number of transmit antennas, LD codes constructed from unitary coding matrices are asymptotically optimum from different design perspectives, viz., minimum mean square error (MMSE), mutual information, and average pairwise error probability (PEP). Those measures have a direct impact on the detection complexity, data rate, and error performance that a space-time code can achieve. Using the insight generated by the asymptotic result, a structured design technique for the LD coding matrices, that suits a broad class of configurations is provided. The resulting codes can support high data rates and provide performance advantages over current designs when decoded with a standard detector. Based on the asymptotic results, a row interleaving scheme is proposed, and it is shown to result in significant performance enhancement.

We propose a greedy algorithm for designing Grassmannian constellations that mimic the distribution that achieves the high SNR capacity of a noncoherent MIMO fading channel. We also introduce a reduced complexity suboptimum detector whose performance is comparable to that of the optimal detector.

We consider the problem of determining an optimal transmission scheme for broadcasting a common message over vector channels, given (perfect) channel knowledge at both the receive and transmit ends. We provide an efficient method for jointly designing a linear transmitter and and a set of linear receivers so as to minimize a weighted mean square error (WMSE) of the data estimates. The computational efficiency follows from the convex formulations that we develop. These formulations enable utilization of highly efficient interior point methods. For diagonal channel matrices, which appear in multicarrier systems that employ cyclic prefixing, we show that the optimal transmitter is obtained by subcarrier allocation and power loading. The set of minimum MSE transceivers for a vector broadcast system is parametrized by a unitary matrix degree of freedom. For the case of diagonal systems, we show how this unitary matrix can be chosen so that the symbol error rate is minimized (over the given set). This optimal unitary matrix ensures that, for each receiver, the subcarrier signal-to-noise ratios (SNRs) are all the same. Simulations indicate that our designs can provide significantly improved performance over standard designs.

Abstract In this paper, we propose and analyse parallel CCI multistage cancellation by combining RAKE and selection diversity. In order to account for channel variations, adaptive implementation of decision thresholds at the RAKE output is suggested. It is shown to provide significant improvement over either hard or soft decision techniques especially in the near‐far situation. Investigation of the system robustness to imperfect channel parameter estimation is also presented. The communication channel is modelled as slowly varying Rayleigh fading discrete multipath channel. Copyright © 2002 John Wiley & Sons, Ltd.

A parallel interference cancellation system employing an adaptive LMS technique is introduced. Simulation results for the proposed system show appreciable improvements over the parallel interference cancellation techniques that employ either hard decision or partial cancellation.