In multi-user multiple input multiple output orthogonal frequency division multiplexing (MU- MIMO-OFDM) downlink system, it is important to have accurate channel state information (CSI) at the base station (BS) in order to minimize the inter-user interference (IUI). However when the channel changes rapidly, the available CSI at the BS can be outdated and this leads to the performance loss. The channel prediction algorithms can be used to predict the future CSI and compensate for the discrepancy between the estimated channel and the actual channel, and improve the performance of the overall system. In this paper, we propose to use recursive least square (RLS) algorithm to predict the CSI for the upcoming frame in MU-MIMO-OFDM downlink system. The simulation results show that RLS algorithm outperforms the other prediction algorithms such as normalized least mean square (NLMS) and the conventional linear extrapolation method. The simulation results are further validated with the measured channel results.

Distributed transmit beamforming (DTBF) is an efficient technique to increase the transmission coverage in a situation where distributed nodes have limited hardware capabilities such as transmit power or the number of equipped antennas. This scenario is quite typical in tactical communication systems. The DTBF precodes transmit signals based on feedback information. In practice, continuous channel phase information is quantized based on codebook to reduce feedback overhead. In this paper, we will analyze the effect of channel quantization in the DTBF and show a small number of bits are enough to encode channel feedback information to achieve comparable performance compared to the DTBF without channel quantization. We will also derive approximated probability density function of the received SNR, which is very useful to further derive the performance of the DTBF in terms of channel capacity, outage probability, and error rates.

The performance of cooperative localization using received signal strength (RSS) benefits from accurate radio channel modeling. While log-normal shadowing is commonly used to model the relationship between RSS and range, the RSS error distribution in indoor environments has been observed to be neither normal nor symmetric. In this paper, we propose a skew log-normal channel model, which includes the standard log-normal model as a special case. We further propose an algorithm for using this model for RSS based cooperative localization. The algorithm was evaluated using data from an electro-magnetic simulation of an aircraft cabin, and was shown to generate more accurate node locations compared to the use of log-normal shadowing in the same localization algorithm.

Carrier-frequency offset (CFO) estimation for uplink orthogonal frequency-division multiplexing access (OFDMA) systems is very challenging as it requires the estimation of multiple CFOs. In this paper, we propose a new framework referred to as sparse blind CFO estimation for interleaved uplink OFDMA. The proposed framework first discretizes the potential frequency offset ranges into discrete grid points, and formulates the original CFO estimation into a sparse signal recovery problem. Then, a novel two-stage matrix Bayesian compressive sensing-based CFO estimation method is proposed to solve the formulated problem. In the first stage, we employ a relatively large grid interval, and iteratively reconstruct a hyperparameter vector to generate a coarse estimation of multiple CFOs. The second stage reduces the grid interval, and the refined CFOs are estimated one by one through a novel low-complexity one-dimension searching algorithm. Numerical results show that the proposed method significantly outperforms conventional ones in terms of estimation accuracy, especially in the scenarios, such as low signal-to-noise ratios, large CFOs, and a large number of users.

Interference from unknown devices makes time-of-arrival (ToA) estimation using conventional signal processing methods unreliable. In this paper, we propose new ToA estimation techniques based on Bayesian compressive sensing (BCS) to improve the accuracy of the ToA estimation under the interference scenario. Our proposed BCS based ToA estimation schemes maximize the posterior probability of the channel impulse response (CIR) with given frequency domain received signals. Simulation results show that proposed BCS based ToA estimations exhibit significantly improved ToA detection accuracy and mean-squared error performance in interference scenarios. We also demonstrate a practical example of the ToA estimation using real measured indoor channels.

The availability of channel state information (CSI) at the transmitter is crucial to multiple-input multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM) systems to suppress interference with precoding techniques. In a MIMO-OFDM frequency division duplex (FDD) system, the amount of CSI required by the transmitter increases linear with the number of subcarriers, and therefore becomes prohibitive for a large bandwidth configuration. Conventional schemes based on broadband analog CSI feedback can reduce the overhead, but has limitations to achieve higher spectral efficiency and feedback robustness. In this paper we first propose a novel broadband analog CSI feedback framework for MIMO-OFDM systems based on the distributed compressive sensing (DCS) theory. Then, we further optimize this framework to maximize its feedback benefits. By exploiting the sparse common support (joint sparsity) inherent in MIMO channels and taking advantage of the high efficiency of DCS in encoding original MIMO channels, the proposed framework is capable of significantly reducing the overhead and enhancing the robustness. The numerical results validate the effectiveness of the proposed scheme.

With recent advances in energy harvesting technology, practical wireless nano-sensor networks (WNSNs) are coming within reach. An important aspect of these WNSNs is that the charge time is significantly longer than each sensor mote can reliably transmit its data-leading to sparse transmission requests in the time-domain. In this paper, we propose a compressed sensing-based approach for efficient request handling. We show that our scheme can achieve near contention free transmission while ensuring that each sensor mote's queue is stable. This sharply contrasts with the unstable sensor mote queues obtained using the standard round- robin approach. To guide design, we also derive closed-form expressions for the average energy consumption, which show that the average energy state of the battery increases exponentially with the transmit power.

In the cooperative multiple-input/multiple-output (MIMO), multiple access point (AP)-user links form a cluster to increase achievable throughput by cooperatively mitigating inter-user interference within the cluster. In this paper, we propose a clustered cooperative MIMO endowing a constraint on the cluster size such that the clustered MIMO can be implemented as a distributed version of downlink multiuser MIMO in existing standards with minimal modifications. New greedy algorithm and coalition formation algorithm are proposed using matching theory for establishing clusters and allocating frequencies. Simulation results shows that the proposed algorithms achieve almost 20% higher throughput than the fixed cell planning scheme, with very low searching complexity.

In this paper, we propose clustered quantization techniques for multiuser multi-input/multi-output (MIMO) orthogonal frequency division multiplexing (OFDM) using constellation based codebooks. Constellation based codebooks provide scalability and efficient codeword search capability, which are key features for practical multiuser MIMO-OFDM systems with a large number of antennas. The proposed clustered quantization scheme quantizes consecutive subcarriers into a single codeword that minimizes aggregated quantization errors. We base our new clustering techniques on two constellation based quantization methods, namely equal-magnitude angular quantization (EMAQ) and squared-lattice angular quantization. New efficient codebook search algorithms are proposed for the clustered quantization. In addition, we propose new constellations to guarantee different users quantize channels into distinct codewords. One is a rotated M-PSK constellation suitable for randomly-distributed user scenarios, and the other is a random phase equal-magnitude (RPEM) constellation suitable for ill-conditioned user scenarios. Thus, full spatial multiplexing gain can be achievable even with small number of users. Finally, a near-sphere codeword search algorithm is proposed for the RPEM. In simulations, the proposed clustered quantization shows up to 50% higher throughput compared to conventional fixed-pilot channel quantization. Also, we show our new constellations for EMAQ improve throughput almost 35% compared to the standard EMAQ.

A key component of multiuser MIMO using zero-forcing precoding is the feedback of quantized channel state information to the base station. A problem arises when each user has a common codebook as the quantized channels can form a singular matrix that results in a reduced sum-rate. In this paper, we propose two new structured constructions to generate different codebooks at each user via transformations of a base codebook. The first construction is based on the Householder transform, which is used to construct a different codebook at each user for most types of base codebooks, with no storage in addition to the base codebook. A feature of our first construction is that the transformed codebook using the Fourier base codebook has a search complexity reduction of up to 50% compared to the standard approach, although only one additional unique codebook can be constructed with this type of base codebook. To construct multiple different codebooks using the Fourier base codebook, we propose a second construction that is based on the representation theory of groups. We show that both our constructions significantly reduce storage requirements compared with the intuitive but impractical random construction, while obtaining the same sum-rate performance. In particular, we only require elements generated directly from the base codebook or from finite fields, instead of random complex numbers.

Cooperative multiplexing has the potential to double the achievable throughput by allowing the base station (BS) and the relay station (RS) to transmit to different users at the same time in the second time slot of the half time division duplexed (TDD) relay transmission. This throughput improvement comes at a cost of performance degradation due to inter-user interference between the BS and the RS. To overcome this degradation, we propose cooperative multiplexing optimum combining (CMOC) for the relay-link users. The proposed CMOC receiver combines the signals in the first and second time slot of the half TDD transmission such that the output signal-to-interference-plus-noise ratio (SINR) is maximized. New insights are drawn from our exact closed-form expressions of SINR distributions. Based on these, we present new analytical expressions for the outage probability, symbol error rate, and achievable throughput. Our results show a 3.5 times improvement in the achievable throughput relative to the standard single-channel receiver in the high interference regime.

In this paper, we consider a channel quantization problem that selects the best codeword representing a group of consecutive subcarriers (cluster) in multiuser MIMO-OFDM systems. The clustering is required to reduce the amount of feedback information in practical OFDM systems. Based on M-PSK based codebooks, we propose a new codeword search algorithm for the clustered MIMO-OFDM with complexity of O(2NT) compared to the complexity of O(MNT) for conventional exhaustive search, where NTdenotes the number of transmit antennas. Through simulations, the proposed clustered codeword selection scheme for the multiuser MIMO-OFDM shows up to 54% performance improvement in throughput compared with the conventional MIMO-OFDM system that performs the channel quantization only on predetermined pilot subcarriers.

We present a novel codebook design criterion for the limited feedback MIMO broadcast channel with zero-forcing precoding. To reduce system implementation complexity, each user has the same codebook. We derive a new sum-rate bound, show the bound maximization problem is invex and explicitly solve it using the Karush-Kuhn-Tucker (KKT) conditions. We show that the solution has the same structure as Grassmannian frames. We also derive a new lower bound on the outage probability. We show via simulations that our codebooks designed using Grassmannian frames outperform random vector quantization with different codebooks at each user for codebook sizes greater than 9 bits.

In this paper, we present a novel user selection scheme for the broadcast channel (BC) using zero-forcing (ZF) precoding with optimal power control. Our scheme is based on a threshold that sets a minimum acceptable sum-rate. In order to design the threshold, we develop a new approximation of the sum-rate and derive simple design rules for the threshold with complexity constraints. We also extend the scheme so that users with differing quality of service (QoS) demands can be accommodated. Simulations show our scheme performs close to the exhaustive search algorithm, but with significantly reduced complexity. Moreover, it significantly reduces the outages compared to non-threshold based low complexity scheduling schemes.

In order to employ adaptive modulation and coding, it is important to determine the post-detected signal-to-noise ratio (SNR) of the system. In this paper, we propose a new method to predict the link performance for multiple-input multiple-output (MIMO) systems with maximum likelihood detector (MLD) which adopt a single channel encoder. By using the relation between the upper and the lower bound systems of the MIMO system with MLD and defining the dynamic SNR penalty, we show that it is possible to represent the link performance of MIMO systems with MLD as a single scalar value with high accuracy. By adopting the proposed method to adaptive modulation and coding systems, we confirm the efficiency of the proposed method for frequency selective channels through simulations.

We present new statistical properties of the end-to-end signal-to-noise ratio (SNR) in multiple-input multiple-output (MIMO) relaying with distributed transmit antenna selection and receiver maximal-ratio combining (TAS/MRC). In particular, we derive new expressions for the exact and the first order expansions of the cumulative distribution function (cdf). Based on these, new closed-form expressions are derived for the exact and the first order expansions of the moment generating function (mgf). We then present a new concise expression for the symbol error rate (SER) with M-ary phase-shift keying in the high SNR regime. Our asymptotic SER expression explicitly reveals the diversity order and the array gain of the MIMO relay network.

This paper considers multiple-input/multiple-output(MIMO) cooperative communication systems with a decode-and-forward (DF) relay. We propose novel rate maximization schemes which utilizes uniform channel decompositions (UCD)to decompose the MIMO channel between a base station (BS) and a relay station (RS). In the proposed scheme, the MIMO channel is decomposed into multiple independent subchannels with equal gains by the UCD and the power allocation will be performed over the subchannels to maximize achievable throughput. We consider not only a simple individual power constraint but also a sum power constraint as well which is useful for the DF based relay systems. It will be shown through simulations that proposed schemes enhance the achievable throughput significantly by distributing the transmit power of the BS and the RS efficiently.

In this paper, we consider complementary sensing nodes to increase the spectrum sensing efficiency of cognitive radio (CR) systems. As the CR system has no prior knowledge about the operation of the licensed network, it is difficult to employ efficient diversity techniques such as the selection diversity. In this paper, by jointly designed with a medium access layer protocol, we propose a sensor node selection technique on the channel where the primary user is active. Collaborated with the mode of operation defined for CR nodes, the proposed scheme selects the dedicated sensing node for the channel with the best sensing performance. Numerical results show that the performance of the proposed scheme is almost the same as the cooperative spectrum sensing while our proposed scheme requires only one sensing node for the spectrum sensing.

Distributed multi-antenna relaying is analyzed in nonregenerative cooperative networks with unbalanced hops and independent but not necessarily identically distributed Rayleigh fading. Exact closed-form expressions are derived for the moment generating function of the end-to-end signal-to-noise ratio (SNR) at the destination. Our analysis applies to arbitrary N number of antennas at the relay and arbitrary average received SNRs. Based on the derived results, we determine the symbol error rate with M-ary phase-shift keying. In addition, we carry out an asymptotic analysis in the high SNR regime. Our results provide fundamental insights into the impact of the number of antennas on the performance of nonregenerative relaying. We explicitly reveal that the diversity order is equal to N + 1. Theoretical analysis is verified by simulation.

Performance of a decode-and-forward (DF) based two-hop multiuser cooperative transmission system is usually restricted by the link quality between a base station (BS) and a relay station (RS) as the number of users grows. Thus the BS-RS link quality should be improved to enhance the performance. In this paper, we propose two-hop cooperative communication systems with multiple receive antennas at the RS to boost the quality of the BS-RS link. With M-QAM signal constellations, our proposed optimize the system performance with a symbol error constraint. By evaluating the performance via simulations, we will show that the proposed schemes improve the performance of the DF based cooperative transmission systems significantly by exploiting advantages of multiple antennas at the RS efficiently.

In this paper, we consider multiuser cooperative multiplexing schemes in cellular networks with fixed relays and address the problem of inter-user interference. We propose two schemes to mitigate the effect of the interference: an interference cancellation scheme and a cooperative precoding scheme. The interference cancellation scheme utilizes the broadcast nature of the signal transmitted to the fixed relay, and is based on a decode-and-forward relaying protocol. We provide a capacity bound for the proposed scheme, and also propose a power reduction scheme for the fixed relay. For the cooperative precoding scheme, we employ a cooperative linear filter at the transmitter to eliminate interference. Simulation results show that both proposed schemes enhance the achievable throughput significantly.

In this paper, we consider multiuser cooperative multiplexing schemes in cellular networks with fixed relays and address the problem of inter-user interference. We propose two schemes to mitigate the effect of the interference: an interference cancellation scheme and a cooperative preceding scheme. The interference cancellation scheme utilizes the broadcast nature of the signal transmitted to the fixed relay, and is based on a decode-and-forward relaying protocol. The second scheme we propose is a cooperative preceding technique which employs a cooperative linear filter at the transmitter to eliminate interference. Simulation results show that both proposed schemes enhance the achievable throughput significantly.

In this paper, we consider efficient power control scheme for a multiuser cooperative multiplexing system with fixed relays employing decode-and-forward relaying schemes. We take two power control problems into account and propose efficient schemes for each problem: transmission power control for the fixed relay and power allocation problem between a base station and the fixed relay. Proposed schemes utilize only reported channel state information required to perform multiuser scheduling. Simulation results show that the power control scheme for the fixed relay significantly reduces unnecessary power consumption and the power allocation scheme for the base station and the fixed relay enhances the system performance by optimizing transmit power to maximize sum capacity.

We consider joint optimization problems among link, MAC, and routing layers for wireless sensor networks with hetrogeneous nodes where battery lifetime maximization for a certain portion of nodes is more important than the maximization across all nodes in the network. For such applications, we propose two battery lifetime maximization schemes: a sequential joint link, MAC, and routing optimization, and a receive antenna selection for burst data applications. Numerical results show that proposed schemes greatly increase the battery lifetime for the node group regarded as more critical while the total energy consumption degradation maintains in a reasonable amount.

Adaptive modulation and coding (AMC) is a powerful technique to enhance the link performance by adjusting the transmission power, channel coding rates and modulation levels according to channel state information. In order to efficiently utilize the AMC scheme, an accurate signal-to-noise ratio (SNR) value is normally required for determining the AMC level. In this paper, we propose a simple method to represent the SNR values for maximum likelihood (ML) detector in multi-input multi-output (MIMO) systems. By analyzing the relation between the upper bound and the lower bound of the ML detector performance, we introduce an efficient way to determine the SNR for the ML receiver. Based on the proposed SNR representation, an AMC scheme for single antenna systems can be extended to MIMO systems with ML detector. From computer simulations, we confirm that the proposed SNR representation allows us to achieve almost the same system throughput as the optimum AMC systems in frequency selective channels with reduced complexity.

In this paper, we consider multiuser multi-input/multi-output antenna systems with zero-forcing receivers in downlink. In this case, to exploit multiuser diversity, spatial-division multiple access (SDMA) system allows to assign different users to a part of transmit antennas at the base station whereas spatial-division multiplexing (SDM) system assigns all antennas to single user's data stream. In this paper, we present analytical frameworks to evaluate the performance of these systems. We first analyze the performance of these two systems by deriving closed-form expressions of achievable throughput. Numerical results show that the derived expressions are very tight. In addition, we approximate the capacity expression of SDM and SDMA systems and compare the SDM with the optimal case.

For space-time bit-interleaved coded modulation (ST-BICM) systems with iterative decoding, the overall performance is affected by the chosen mapping. In bit-error rate (BER) curves, one mapping reaches an error floor (EF) at a low signal-to-noise ratio (SNR), while other mappings result in a lower EF at a higher SNR. The constellation mappings are divided into groups where each group exhibits a distinctive BER curve. We show that the convergence abscissa of the system depends on the average total bit errors and the harmonic mean of the minimum squared Euclidean distance. In this letter, we characterize all mapping groups for ST-BICM with 8-phase-shift keying and present the optimal selection for each mapping group over independent fading channels

If the channel is static and is perfectly known to both the transmitter and the receiver, the water-filling technique with adaptive modulation is known to be optimal (Gallager, 1968). However, for orthogonal frequency-division multiplexing (OFDM) systems, this requires intensive traffic overheads for reporting channel state information on all subcarriers to the transmitter. In this paper, we consider an adaptive modulation and coding scheme for bit-interleaved coded OFDM with reduced feedback information satisfying a specified quality of service level. We propose a rate adaptation scheme, which utilizes the estimated bit error rate for supportable transmission rates. In this scheme, a user equipment chooses a modulation and coding scheme (MCS) level, which can provide the maximum spectral efficiency based on one OFDM symbol rather than on all subchannels. Then the user needs to send back only the selected MCS level index. The proposed scheme does not require the water-filling procedure, and the amount of the feedback information reduces to a single integer value irrespective of the number of subcarriers. Simulation results show that the proposed scheme can significantly reduce the system complexity while minimizing the performance loss compared to the optimum water-filling scheme.

In wireless access systems, there has been much interest in enhancing the performance of orthogonal frequency division multiplexing (OFDM) in a frequency selective fading channel. If the channel is static and is perfectly known to both the transmitter and the receiver, the water-filling technique with adaptive modulation is known to be optimal [1]. However, this requires intensive traffic overheads for reporting channel state information on all subcarriers. In this paper, we consider an adaptive modulation and coding scheme for bit-interleaved coded OFDM with reduced feedback information satisfying a specified quality of service level. We propose a rate adaptation scheme which utilizes the estimated bit error rate for supportable transmission rates. In this scheme, a user equipment chooses the best modulation and coding scheme (MCS) level based on one OFDM symbol rather than on the whole subcarriers, then the user needs to send back only the selected MCS level index. The proposed scheme does not require the water-filling procedure and the amount of the feedback reduces to a single integer value irrespective of the number of subcarriers. Simulation results show that the proposed scheme can significantly reduce the system complexity while minimizing the performance loss compared to the optimum water-filling scheme.

When channel state information (CSI) is available at the transmitter, the system throughput can be enhanced by adaptive transmissions and opportunistic multiuser scheduling. In this paper, we consider multi-input multi-output (MIMO) systems employing bit-interleaved coded orthogonal frequency division multiplexing (BIC-OFDM). We first propose a bit- loading algorithm based on the Levin-Campello algorithm for the BIC-OFDM. Then we will apply this algorithm to the MIMO system with a finite set of constellations, by reassigning residual power on each stream. Simulation results show that proposed bit-loading scheme which takes the residual power into account improves the system performance especially at high signal-to- noise ratio (SNR) range.

When adapting the transmitter to the channel state information (CSI), improved transmission is possible compared to the open loop system where no CSI is provided at the transmitter. However, since the perfect channel information is rarely available at the transmitter, the system design based on the partial CSI becomes an important factor. Especially, in mobile environments, the consideration for the outdated CSI should be applied for mitigating the performance degradation. In this paper, we propose a robust adaptive modulation and coding scheme for bit-interleaved coded orthogonal frequency division multiplexing over time-varying channels. With reasonable feedback overhead, the proposed scheme shows the enhanced performance by compensating for the outdated CSI due to Doppler spread. Simulation results confirm that the performance gain is achieved by applying an accurate BER estimation method